2026-08-26T18:44:35Z https://ijarai.com/index.php/files/oai

oai:ojs.ijarai.com:article/4 2026-04-27T10:29:07Z files:ART

THE ROLE OF CORPORATE TAX POLICIES IN BUSINESS STRATEGY DELOITTE’S ANALYSIS Ms. A. Jyothsna Nakkalapally Shiva Prasad Corporate tax policies, Financial strategies, Operational efficiency, Market competitiveness, Tax reforms, Profitability, Investment decisions, Tax planning strategies Deloitte is one of the largest professional services organizations in the world, and this research examines the effects of corporation tax policy on them. It analyzes the impact of tax law changes on Deloitte's business strategy, operational efficiency, and market competitiveness. This research analysis examines the impact of tax changes on company earnings and investment choices from both a direct and indirect perspective. In light of recent developments in international taxation, such as the BEPS policies put in place by the OECD, the research examines the ways in which Deloitte's tax planning methods have evolved. Furthermore, it examines the impact of recent tax legislation on Deloitte's internal tax processes and consumer advice. How corporation tax rates impact Deloitte's global expansion and CSR initiatives is the focus of this paper. Based on the findings, Deloitte's capacity to satisfy regulatory obligations and achieve financial success will be significantly impacted. It lays out in plain sight the strategies that multinational corporations employ to adapt to shifting tax landscapes. International Journal of Advanced Research and Innovations 2025-01-07 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/4 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 1, January 2025; 1-9 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/4/67 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/5 2026-04-27T10:28:20Z files:ART

A FINANCIAL PERSPECTIVE: PROFITABILITY ANALYSIS OF RELIANCE JIO IN THE TELECOM INDUSTRY Dr. Aitha Cheralu kadala Ganesh Profitability Analysis, Reliance Jio, Telecom Industry Pricing Strategies Studying what makes a telecom company, and Reliance Jio in particular, profitable in a cutthroat market is a primary goal of profitability researchers. This study aims to investigate Reliance Jio's revenue, expenses, and position in the industry because of the company's huge market share and distinctive pricing tactics. Profitability is examined in relation to technological investment, operational efficiency, and client acquisition strategies. The study also delves into the ways in which regulatory issues, customer behavior, and the dynamics of competition impact financial outcomes. Maintaining a profit in the dynamic telecoms industry requires an understanding of these elements. International Journal of Advanced Research and Innovations 2025-01-07 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/5 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 1, January 2025; 10-16 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/5/2 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/6 2026-04-29T04:51:58Z files:ART

THE STAFFING SELECTION PROCESS AT HYUNDAI MOTORS: INSIGHTS INTO EFFICIENT HIRING Mrs. M. A. Sharmila Gannavaram Rajesh Kumar Staffing Selection, Human Resources, Organizational Goals, Structured Interviews, Productivity, and Global Market Dynamics. Aligning talent with the organization's objectives is crucial, and the recruitment process plays a big role in that at Hyundai Motors. It is critical to identify, analyze, and attract persons in a systematic manner in order to address labor demands successfully. Hyundai uses cutting-edge tools and techniques to find people who would fit in with its dynamic work environment. Diversity, equity, and conformity to industry norms are the technique's guiding principles. The approach as a whole includes crucial steps including competency mapping, job analysis, and structured interviews. In doing so, we ensure that we are hiring the right people to boost productivity and drive expansion. When hiring new employees, Hyundai uses a strategy that has proven to be quite effective in increasing both engagement and retention rates. This approach helps businesses achieve long-term success by adjusting to changes in the global market. International Journal of Advanced Research and Innovations 2025-01-07 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/6 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 1, January 2025; 17-24 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/6/3 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/8 2026-04-27T10:31:50Z files:ART

DIGITAL HIRING PRACTICES: ANALYSIS OF MINDWAVE INFORMATICS E-RECRUITMENT PROCESS Dr. Danda Udaya Shekha Goundla Shirisha Digital Hiring, E-recruitment, AI-driven Tools, Applicant Tracking Systems and Candidate Experience. The hiring process has changed significantly with the introduction of digital hiring solutions. Employers can use these tools to find and assess candidates more efficiently. This research looks at Mindwave Informatics' online hiring procedure, focusing on how the business uses social network integration, application tracking tools, and artificial intelligence. It examines the ways in which data-driven decision-making might expedite the hiring process, enhance candidate experiences, and save time. Aspects that are now being reviewed include protection of personal information, openness to user participation, and minimization of computer biases. The results show how ongoing development can boost effectiveness and efficiency. They also stress the importance of digital hiring's capacity to connect hiring practices with corporate goals. International Journal of Advanced Research and Innovations 2025-02-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/8 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 2, February 2025; 1-9 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/8/35 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/9 2026-04-27T10:33:41Z files:ART

OPTIMIZING WORKFORCE HIRING: A RESEARCH ON HCL TECHNOLOGIES RECRUITMENT PRACTICES Mrs. P. Nivedita Guggilapu Pravalika Recruitment Practices, Talent Acquisition, Workforce Optimization, Diversity and Inclusion and Organizational Culture. The purpose of this research is to examine the hiring process at HCL Technologies with an eye toward enhancing it. Investigating the company's methods for locating and recruiting top talent can help ensure that its hiring policies are consistent with its mission and values. Examining HCL's use of technology, evaluation tools, and strategies to communicate with job prospects, the research looks at how well their hiring procedures perform. The research also delves into how HCL has evolved to suit shifting workforce demands and industry trends. It examines the significance of diversity and inclusion in recruiting as well. In light of the current job climate, the findings provide guidance on how to streamline and improve the hiring process. They also provide crucial details regarding the most effective methods. International Journal of Advanced Research and Innovations 2025-02-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/9 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 2, February 2025; 10-18 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/9/7 https://creativecommons.org/licenses/by/4.0

oai:ojs2.ijllcs.com:article/10 2026-01-10T11:25:06Z files:ART

oai:ojs.ijarai.com:article/11 2026-04-27T10:44:44Z files:ART

CORPORATE GOVERNANCE AND FINANCIAL MANAGEMENT AT INFOSYS: BALANCING GROWTH AND RESPONSIBILITY Mr. A. Kiran Vanganti Sravani Corporate Finance, Corporate Governance, Financial Decision-Making, Governance Practices, Capital Structure, Investment Strategies, Risk Management This research examines Infosys, a globally recognized technology business, with a focus on problems related to financial management and corporate governance. A significant portion of this article is devoted to researching the effects of governance procedures and financial choices on relationships between shareholders and the strategic course of the organization. Along with the effectiveness of the board of directors' oversight and the company's compliance with regulatory requirements, key considerations include the capital structure, investment strategies, and risk management of the organization. Reducing financial risks and enhancing transparency are two key components of excellent governance systems that contribute to long-term, sustainable growth, according to the report. This report provides valuable insight for enhancing tech companies' financial performance and corporate governance by highlighting issues and promising solutions. International Journal of Advanced Research and Innovations 2025-03-13 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/11 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 3, March 2025; 1-10 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/11/9 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/12 2026-04-27T10:46:57Z files:ART

THE ROLE OF EMPLOYEE COMMITMENT IN DRIVING EXCELLENCE AT PHILIPS ELECTRONICS Dr. Danda Udaya Shekhar Mogulothu Madhurya Employee Commitment, Organizational Excellence, Innovation, Employee Engagement, Workforce Motivation and Competitive Advantage Employee engagement is essential in industries characterized by rapid transformation, such as electronics, where innovative concepts frequently emerge. Philips Electronics possesses the requisite resources to achieve its long-term growth and sustainability objectives, attributable to the diligent efforts of its employees. This study examines the correlation between enhanced quality, efficiency, and customer satisfaction, and the presence of motivated, engaged, and loyal employees. It underscores the paramount importance of robust human resources, effective leadership, and transparent communication channels in cultivating employee loyalty. The study elucidates the vital importance of a dedicated workforce for Philips Electronics in securing a competitive advantage and ensuring sustained success, through an examination of particular strategies and results. International Journal of Advanced Research and Innovations 2025-03-13 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/12 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 3, March 2025; 11-19 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/12/10 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/13 2026-04-29T04:57:49Z files:ART

UNDERSTANDING THE FUNDAMENTALS OF ADITYA BIRLA CEMENT'S PERFORMANCE Ms. A. Jyothsna paltya santosh Performance, Operational Efficiency, Market Share and Sustainability Market development, operational excellence, and strategic efforts have contributed to Aditya Birla Cement's success as a key organization within the Indian cement business. The abstract delves into the core aspects of the company's success, which include cutting-edge items, eco-friendly methods, efficient pricing, and a strong distribution network. By taking use of new technologies while also being environmentally responsible, Aditya Birla Cement has been able to stay ahead of the competition. The company's capacity to react to market changes and its emphasis on customer-centric solutions have allowed it to steadily expand its operations and establish itself as a major player in both local and international markets. This report provides significant insights into the basic variables impacting Aditya Birla Cement's performance and highlights the effectiveness of the company's measures to achieve long-term sustainability and market leadership. International Journal of Advanced Research and Innovations 2025-03-13 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/13 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 3, March 2025; 20-27 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/13/11 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/14 2026-04-27T11:03:03Z files:ART

NOVEL EMBEDDED SYSTEM FOR REAL TIME FAULT DIAGNOSIS OF PHOTOVOLTAIC MODULES Soure Anjinayulu Dr. J. Maheshwar Reddy Dr. B.D Venkataramana Reddy Photovoltaic modules, fault diagnosis, embedded system, real-time monitoring, machine learning, signal processing, solar energy, predictive maintenance. The innovative embedded technology described here can improve the efficiency and dependability of solar energy systems by detecting problems with photovoltaic (PV) modules in real time. To detect, categorize, and foresee problems like discoloration, degeneration, and hotspots, the system employs sophisticated machine learning algorithms and data processing techniques. The suggested technique leads to more efficient power generation, fewer power outages, and lower maintenance costs. An important resource for green power administration, the system has been experimentally validated to prove its rightness and efficacy in actual photovoltaic configurations. International Journal of Advanced Research and Innovations 2025-04-24 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/14 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 4, April 2025; 1-9 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/14/12 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/15 2026-04-27T11:15:36Z files:ART

SMART ENERGY EFFICIENT HOME AUTOMATION SYSTEM USING IOT R. Bhargav Dr. S. Nanda Kishore Dr. B.D Venkataramana Reddy Smart Home, IoT, Energy Efficiency, Home Automation, Machine Learning, Smart Sensors, Renewable Energy, Cybersecurity, Smart Meters, Remote Access. A smart, energy-efficient home control system that saves energy and simplifies user life is the focus of this study, which is based on the Internet of Things (IoT). The technology makes use of cloud computing, smart sensors, and actuators to monitor and control household appliances in real-time. Automation, made possible by the Internet of Things, reduces energy waste by responding to human behavior and environmental factors. Machine learning systems aim to predict future energy consumption in an effort to improve efficiency. By enabling remote access through mobile apps, the solution provides you freedom and security. People can save energy with the help of smart meters because they provide immediate feedback. If we want to stay alive, we have to start using renewable energy. The home's automated lighting, climate control, and security systems all contribute to its lower energy consumption. The study examines the cost, scalability, and interoperability of IoT devices. The goal of implementing cybersecurity measures is to prevent unauthorized individuals from gaining access. By adjusting to various living conditions, the proposed approach enhances energy consumption. Energy savings and an improved user experience are demonstrated through case studies. In order to enhance optimization, future research will concentrate on new AI advancements. The findings demonstrate the impact of the Internet of Things on sustainable smart home solutions. International Journal of Advanced Research and Innovations 2025-04-24 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/15 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 4, April 2025; 10-18 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/15/13 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/16 2026-04-27T11:16:45Z files:ART

DEEP LEARNING-BASED DETECTION OF FRAUD IN ONLINE RECRUITMENT K.S.Asif Mohiddin Dr. Gopinathan P.Viswanatha Reddy Deep learning, fraud detection, online recruitment, job scams, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs). Concerned citizens might be reassured that this research examines a deep learning method for identifying fraud in online recruitment. Traditional systems for spotting rising fraud trends predominantly depend on rule-based screening, rendering them susceptible. In comparison to alternative methods, deep learning models, especially CNNs and RNNs, exhibit superior performance in identifying bogus job advertisements. The research utilizes a database of real and fabricated job adverts to derive pertinent textual and metadata for categorization objectives. A hybrid deep learning model integrates an attention mechanism with LSTM techniques to improve detection accuracy. The experimental findings indicate that the proposed model surpasses existing machine learning techniques in accuracy and reliability. The model's stability and generalizability are assessed through multiple datasets. Researchers may explore explainable AI systems for fraud detection in the future. This research significantly aids in the development of dependable online job boards. International Journal of Advanced Research and Innovations 2025-04-24 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/16 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 4, April 2025; 19-26 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/16/14 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/17 2026-04-28T11:06:28Z files:ART

IMPROVING SOFTWARE FAULT PREDICTION THROUGH CROSS-PROJECT ANALYSIS A FOCUS ON IMBALANCED DATA AND GENERALIZATION Subbalakshmamma Thaadi P.Viswanatha Reddy Dr. V. Hemasree Software Fault Prediction, Cross-Project Analysis, Imbalanced Data, Machine Learning, Generalization, Feature Selection, Data Resampling, Software Quality Assurance. This paper delves into the challenges of generalizing models and dealing with contradicting evidence. Additionally, it delves into the potential for enhancing software failure prediction by integrating several research endeavors. Conventional methods of failure prediction could not be highly task-specific due to the fact that not all tasks had access to the same data. To overcome these challenges and achieve better prediction accuracy, you can employ feature selection techniques, data resampling tactics, and machine learning procedures. The project involves exploring the usage of various datasets and enhancing model training to expedite problem detection and ensure that solutions are compatible with different software configurations. Software quality assurance methods can be improved and made more adaptable as a direct consequence of the findings. International Journal of Advanced Research and Innovations 2025-05-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/17 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 5, May 2025; 1-8 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/17/15 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/18 2026-04-28T11:06:28Z files:ART

SATELLITE IMAGERY-BASED LANDSLIDE PREDICTION USING AI TECHNIQUES S.Vaibhava Sindhu Mr. A. Srinivasan P.Viswanatha Reddy Landslide Prediction, Satellite Imagery, Artificial Intelligence, Machine Learning, Deep Learning, Disaster Management, Geospatial Analysis. This research explores the potential of artificial intelligence (AI) for flood forecasting through the use of satellite imagery. Landslides and other natural calamities have the potential to wreak havoc on communities and their infrastructure. The limits of typical prediction algorithms are caused by the difficult geographic conditions and the lack of data. Using CNNs and other deep learning and machine learning models, this research analyzes high-resolution satellite pictures for patterns and identifies potentially hazardous regions. Using AI improves prediction accuracy, streamlines real-time monitoring, and fortifies early warning systems. This research's findings show that landslide prevention and disaster response could benefit from geospatial analysis supplemented with artificial intelligence. International Journal of Advanced Research and Innovations 2025-05-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/18 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 5, May 2025; 9-16 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/18/16 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/19 2026-04-29T05:01:57Z files:ART

COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS FOR DETECTING EVASIVE SMS SPAM BP.Lakshmipriya K.Chandraprasad A. Ravi Sankar Machine Learning, SMS Spam Detection, Evasive Spam, Text Classification, Naïve Bayes, Support Vector Machines, Decision Trees, Deep Learning, Feature Engineering, Spam Filtering. This article analyzes different machine learning methods to identify deceptive SMS spam. Evasive spam communications are famously challenging to identify due to their use of obfuscation to circumvent conventional filters. A variety of models are assessed, including Deep Learning, Naïve Bayes, Decision Trees, and Support Vector Machines. The collection comprises preprocessed spam and ham messages derived from real-world sources. The evaluative metrics employed for comparison are F1-score, recall, accuracy, and precision. The experiment's results demonstrate the advantages and disadvantages of each paradigm. In the presence of intricate patterns, deep learning models surpass conventional methods.To enhance detection, feature engineering and data augmentation are necessary. The article offers various tips to enhance spam detection models. In response to the evolving tactics of spam, models will be enhanced in the future. International Journal of Advanced Research and Innovations 2025-05-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/19 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 5, May 2025; 17-22 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/19/17 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/21 2026-04-29T07:11:10Z files:ART

COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS FOR DETECTING EVASIVE SMS SPAM G. Himavanth T.V.N Radha Parameswari A. Ravi Sankar Genetic Algorithm (GA), Convolutional Neural Network (CNN), Hyperparameter Optimization, Driver Drowsiness Detection, Real-Time Monitoring. Convolutional neural networks are enhanced by genetic algorithms to detect drowsy drivers. Detection enhances model performance by locating the optimal hyperparameters. In this approach, Convolutional Neural Networks (CNNs) and Genetic Algorithms (GAs) are employed to modify learning rates, filter dimension, and layer layout. By repeatedly modifying CNN parameters through mutation, selection, and crossover, genetic algorithms enhance detection performance. Eye closing and yawning patterns are crucial facial signals that the upgraded CNN records for real-time fatigue measurement. In addition to reducing handling costs and overfitting, this strategy makes items more durable. The results demonstrate that, compared to conventional CNNs, the convergence process is quicker and more accurate. This system improves traffic safety by consistently monitoring drivers. Researchers will primarily focus on real-world applications and ways to integrate many sensors in the future. International Journal of Advanced Research and Innovations 2025-06-10 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/21 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 6, June 2025; 1-6 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/21/18 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/22 2026-04-29T07:11:10Z files:ART

LEVERAGING VALUE-AT-RISK AND MACHINE LEARNING FOR FINANCIAL FRAUD DETECTION IN IMBALANCED DATA P.Guru Harshitha K.Chandraprasad A. Ravi Sankar Financial Fraud Detection, Value-at-Risk (VaR), Machine Learning, Imbalanced Data, Anomaly Detection, CostSensitive Learning, Risk Management, Supervised Learning, Ensemble Methods It is difficult to detect financial crime due to the extremely skewed character of datasets including illicit transactions. When there are few instances, rule-based and statistical approaches may not be able to detect fraud tendencies. Improved fraud detection is the goal of this study, which integrates ML methods with the popular risk management metric Value-at-Risk (VaR). Value at Risk (VaR) provides a numerical assessment of a company's financial risk, which aids in the detection of scams. Sorting deals into groups and fixing class imbalances using cost-sensitive learning methods and resampling tactics are both accomplished by a multitude of machine learning algorithms. Among these, you can find anomaly detection algorithms, ensemble techniques, and supervised learning models. To demonstrate that the proposed strategy may enhance the precision and recall of fraud detection, it is tested on real-world financial datasets. The findings highlight the potential of financial risk assessment and data driven by artificial intelligence to combat financial crime. International Journal of Advanced Research and Innovations 2025-06-10 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/22 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 6, June 2025; 7-16 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/22/19 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/23 2026-04-29T07:11:10Z files:ART

FINANCIAL DISTRESS PREDICTION USING A HYBRID MACHINE LEARNING AND NETWORK ANALYSIS FRAMEWORK Pula Blessy A. Ravi Sankar Financial distress prediction, machine learning, network analysis, hybrid model, financial risk assessment Financial companies need to be able to predict financial crises in order to reduce risk and act quickly. Traditional models often run into problems because of how complicated it is for a lot of different financial factors to interact with unusual data. This research offers a method that uses both network analysis and machine learning to make predictions more accurate. To begin, we can find the most important financial signs using feature selection methods. After that, machine learning models such as XGBoost, Random Forest, and Neural Networks are used to sort the data into groups. Network analysis can be used for many things, such as modeling financial connections and finding trends in how problems spread. When it comes to figuring out what will happen, the hybrid method does a better job than individual machine learning models on real-world financial information. The results show that combining statistical learning with network knowledge can make predictions of financial trouble more accurate. International Journal of Advanced Research and Innovations 2025-06-10 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/23 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 6, June 2025; 17-25 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/23/20 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/24 2026-04-29T08:46:07Z files:ART

ENHANCING VIDEO FORGERY DETECTION WITH DEEP CONVOLUTIONAL NEURAL NETWORKS Shaik Roshini A. Ravi Sankar Video forgery detection, Deep Convolutional Neural Networks, Deep learning, Frame tampering, Digital forensics, Deepfake detection. A lot of people are worried about video forgeries and how they could affect digital forensics, media integrity, and security because of how sophisticated editing tools are getting. Because subtle changes are so hard to spot using conventional detection methods, it is a demanding undertaking. The primary goal of this paper is to examine how Deep Convolutional Neural Networks (DCNN) can improve video counterfeit detection. With the help of deep learning techniques, the proposed model can detect cases of deepfake changes, splicing, and frame tampering with a remarkable degree of accuracy. Results from the experiments indicate that deep convolutional neural networks (DCNN) outperform more traditional approaches, which could make them valuable in forensic investigations. International Journal of Advanced Research and Innovations 2025-07-16 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/24 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 7, July 2025; 1-9 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/24/21 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/25 2026-04-29T08:46:07Z files:ART

THE DISTRIBUTED KNOWLEDGE DISTILLATION FRAMEWORK FOR FINANCIAL FRAUD PREVENTION BASED ON TRANSFORMER Chintakunta Chennakesava Reddy H. Madhusudhana Rao Transformer, knowledge distillation, financial fraud detection. Financial fraud cases causing serious damage to the interests of investors are not uncommon. As a result, a wide range of intelligent detection techniques are put forth to support financial institutions’ decision-making. Currently, existing methods have problems such as poor detection accuracy, slow inference speed, and weak generalization ability. Therefore, we suggest a distributed knowledge distillation architecture for financial fraud detection based on Transformer. Firstly, the multi-attention mechanism is used to give weights to the features, followed by feed-forward neural networks to extract high-level features that include relevant information, and finally neural networks are used to categorize financial fraud. Secondly, for the problem of inconsistent financial data indicators and unbalanced data distribution focused on different industries, a distributed knowledge distillation algorithm is proposed. This algorithm combines the detection knowledge of the multi-teacher network and migrates the knowledge to the student network, which detects the financial data of different industries. The final experimental results show that the proposed method outperforms other methods in terms of F1 score (92.87%), accuracy (98.98%), precision (81.48%), recall (95.45%), and AUC score (96.73%) when compared to the traditional detection methods. International Journal of Advanced Research and Innovations 2025-07-16 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/25 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 7, July 2025; 10-27 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/25/25 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/26 2026-04-29T08:46:07Z files:ART

TOTAL QUALITY MANAGEMENT AT HERITAGE FOODS: IMPROVING PROCESSES FOR SUSTAINED GROWTH Dr. Danda Udaya Shekhar M. Archana Total Quality Management (Tqm), Continuous Improvement, Employee Involvement, Business Performance, Sustainable Practices, Quality Assurance. Heritage Foods employs Total Quality Management (TQM) as a means to enhance both the efficiency and excellence of their products and services. They place a premium on staff participation, customer satisfaction, and a dedication to continuous improvement. Every step of the process, from sourcing raw materials to shipping completed goods, is qualitychecked according to established protocols. In order to establish a culture of excellence, TQM principles emphasize the significance of collaboration, leadership, and continuous evaluation. By including all employees in decision-making and problem-solving, Heritage Foods fosters a culture of accountability and pride. The company equips its employees with the knowledge and abilities to maintain high standards and adjust to industry changes through comprehensive training programs. Improved products that are in tune with market demand are the result of TQM's use of customer feedback systems. By adhering to stringent quality control protocols, Heritage Foods increases operational efficiency, decreases error rates, and decreases waste. Heritage Foods is able to outperform its competitors and maintain client loyalty thanks to its dedication to Total Quality Management. By implementing this all-encompassing strategy, the company solidifies its position as a frontrunner in the dairy industry, demonstrating its dedication to rigorous quality control and advocating for eco-friendly practices. International Journal of Advanced Research and Innovations 2025-07-16 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/26 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 7, July 2025; 28-37 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/26/24 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/27 2026-04-30T07:46:24Z files:ART

TRANSFORMING INVESTMENT STRATEGIES THROUGH ONLINE TRADING: INDIA INFOLINE Dr. Aitha Cheralu Choppari Karthik Online Trading, Investment Strategies, Financial Markets, Portfolio Optimization and Technological Advancements. Investors may now access financial markets considerably more easily and make decisions more quickly thanks to the development of online trading. By creating user-friendly platforms, comprehensive research tools, and customized financial solutions using cutting-edge technology, India Infoline (IIFL) has significantly contributed to this change. This research explores how owners have improved their portfolios, decreased risks, and improved their understanding of money thanks to IIFL's digital innovations. This illustrates how important internet trading is for increasing market accessibility, fostering openness in the investment process, and helping institutional and individual investors create strategies. The results emphasize how important it is to continuously come up with new ideas in order to succeed in the fast-paced finance sector. International Journal of Advanced Research and Innovations 2025-07-13 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/27 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 8, August 2025; 1-8 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/27/26 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/28 2026-04-30T07:47:24Z files:ART

CAPITAL MARKET INSIGHTS: A BAJAJ CAPITAL APPROACH Ms. A. Jyothsna Prathikantam Sravya Capital Markets, Investment Advisory, Risk Management, Wealth Creation and Financial Services The ability to transmit and receive money is facilitated by capital markets, which are an integral aspect of the financial system. Famous financial services provider Bajaj Capital has devised a novel strategy to weather the current economic storm. This research examines Bajaj Capital's techniques for dealing with the capital markets. Profitability, risk management, and investment advising are its primary areas of emphasis. The business has consistently provided individualized solutions for its clients by combining time-tested methods with innovative financial technology. The research evaluates Bajaj Capital's strategy in light of customer demands, market shifts, and regulatory developments. Those who work in the financial sector will find this knowledge very useful. Bajaj Capital's approach demonstrates how to attain sustainable development and maximize profits for investors by utilizing expert knowledge and modern technologies. International Journal of Advanced Research and Innovations 2025-08-13 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/28 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 8, August 2025; 9-14 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/28/27 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/29 2026-04-30T07:49:12Z files:ART

SUCCESSION PLANNING AT MOTHER DAIRY: PREPARING THE NEXT GENERATION OF LEADERS Dr. Danda Udaya Shekhar Mamidi Sruthi Succession Planning, Leadership Development, Talent Management, Organizational Growth, Internal Talent and Leadership Transition. The company has implemented a strategy program called "succession planning" to train and educate future executives in order to guarantee the success and stability of Mother Dairy's leadership. Mother Dairy, a well-known Indian dairy firm, upholds operational excellence and stays true to its values by assisting the prosperity of its own people. This research looks at the benefits of a systematic succession plan for identifying, training, and retaining future leaders. By examining current practices and expected expectations, the paper lays out crucial steps for developing future leaders who can take on high-level roles and encourage sustainability, creativity, and adaptation in the face of fierce market competition. Programs for leadership development, mentoring, and performance reviews are put in place to make sure the company is ready for any changes in leadership. International Journal of Advanced Research and Innovations 2025-08-13 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/29 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 8, August 2025; 15-23 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/29/30 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/30 2026-04-30T08:39:43Z files:ART

ENHANCING BRAND VISIBILITY: ADVERTISING AND SALES PROMOTION STRATEGIES AT IDEA Mr. D. Pradeep Kumar Tadimalla Pushya Mitra Advertising, Sales Promotion, Brand Visibility, Consumer Engagement, Marketing Strategies, Promotional Techniques, Consumer Behavior, Brand Positioning. Advertising and sales promotions are the focus of this research because of their potential to increase brand awareness, consumer interest, and ultimately, sales. Examining various forms of advertising and promotion, this research aims to discover ways in which organizations might enhance their marketing strategies. The research's primary objectives are to determine the efficacy of various advertising strategies, their effects on consumer behavior, and the brand's positioning as a result. The findings will be useful for businesses who are looking to improve their ROI and maintain a competitive edge in a dynamic market. International Journal of Advanced Research and Innovations 2025-09-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/30 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 9, September 2025; 1-8 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/30/31 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/31 2026-04-30T08:40:55Z files:ART

EMOTIONAL INTELLIGENCE: A VITAL HR ASSET FOR EMPOWERING LEADERS AND EMPLOYEES Ms. L.Saraswathi Mrs. P.Shoba Rani Mrs. T. Subbalakshamamma Human Resources, Emotional Intelligence, Organizations, Leadership, Employees,Team, Performance, Effectiveness. Emotional intelligence (EI) is the comprehension and processing of emotions and emotional data. Researchers and Human Resource (HR) practitioners are particularly interested in this topic because of its implications for leaders, employees, and organizational effectiveness. In this post, we will discuss the theories of Emotional Intelligence (EI) and review study data that show strong links between EI, leaders, and employees. Finally, we identify prospective areas for future research on the impact of Emotional Intelligence (EI) in business. International Journal of Advanced Research and Innovations 2025-09-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/31 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 9, September 2025; 9-13 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/31/32 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/32 2026-04-30T08:42:20Z files:ART

A BLOCKCHAIN-ENABLED FRAMEWORK FOR DETECTING AND PREVENTING COUNTERFEIT PRODUCTS Mrs. T. Subbalakshamamma Mr. P.Viswanatha Redd Blockchain, Decentralized, Ethereum, Smart Contract, Counterfeited Product, QR Code. Consumers and businesses alike are becoming increasingly concerned about the prevalence of counterfeit goods. The presence of counterfeit products on the market jeopardizes consumer safety as well as business finances and reputations. Because blockchain technology is secure and decentralized, users will be able to recognize and avoid counterfeit products more easily. This study looks into whether blockchain technology can be used to detect counterfeit items. This research looks at the core concepts underpinning blockchain technology and how they can be used to create a reliable and accessible system for product identification. We present a novel approach for certifying the validity of an object using blockchain technology and smart contracts. Businesses can utilize this notion to assign unique digital identities to physical objects and record them in a distributed ledger. Consumers can check the validity of a product by scanning a QR code. Furthermore, we assess the potential benefits of employing blockchain technology for product identification, such as enhanced transparency, lower fraud rates, and higher customer confidence. Finally, we look into the drawbacks of employing blockchain-based systems for product identification, such as interoperability and scalability issues. Our findings suggest that employing blockchain technology to identify and eliminate counterfeit products from the market could be a viable strategy. Consumers and businesses are protected from the negative consequences of counterfeit products due to the system's decentralized structure and high level of security. It also gives a simple and dependable framework for establishing the validity of an item or notion. International Journal of Advanced Research and Innovations 2025-09-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/32 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 9, September 2025; 14-20 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/32/33 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/33 2026-04-30T08:44:08Z files:ART

AI-DRIVEN INAPPROPRIATE CONTENT DETECTION AND CLASSIFICATION IN YOUTUBE VIDEOS USING DEEP LEARNING Dr. Kishor Kumar Gajula Jilla Swathi Deep Learning, Content Filtering, Neural Networks, Convolution Neural Networks (CNN). Video information is one of the most convenient and contemporary methods of staying informed about current events. The popularity of video content on the internet is on the rise, and it is having a significant impact on various aspects of our lives, such as education, entertainment, and communication. Video content is one of the most captivating forms of information, as it not only captivates viewers with its visuals but also facilitates the acquisition of knowledge and comprehension. The primary resource for the development and categorization of the text that we intend to use for the endeavor is YouTube. YouTube is considered to be one of the most pleasurable methods of acquiring global information. The primary objective of our endeavor is to generate and organize video content into distinct categories. We consider YouTube videos that include translations. The primary objective is to extract and categorize data from videos. The process involves the extraction of text that may contain undesirable letters or symbols through the use of natural language processing (NLP), which necessitates text cleaning. In essence, NLP is employed to evaluate pertinent data. Special text processing methods, such as tokenization and stemming, may be necessary to derive meaningful information from text. A replica of the YouTube URL has been incorporated into the front-end web page. The entire process commences upon the URL's upload. The connected dataset is employed to produce text that includes subtitles and natural language processing (NLP), which eliminates stop words and generates keywords. The preprocessed records and keywords generated are contained in the CSV file. A summary is generated after pre-processing, and the extracted text is arranged according to synonyms and keywords. The entire process is transmitted into the LSTM model to train and validate it for precise output. The system will generate a categorized summary based on the URLs that users submit. The Flask framework is employed to produce an interactive webbased output for the project. International Journal of Advanced Research and Innovations 2025-09-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/33 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 9, September 2025; 21-28 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/33/34 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/34 2026-04-27T10:28:20Z files:ART

PRIVACY-PRESERVING PHOTO SHARING FRAMEWORK FOR SOCIAL MEDIA reddy Akhila Solleti Tejashwini Privacy-Preserving, Photo Sharing, Social Media, Cryptography, Homomorphic Encryption, Secure MultiParty Computation (SMC), Data Security, Context-Aware Privacy, User Control, Personal Data Protection. Social media is a big part of our daily lives now that it's so easy to share special moments with friends and family. There is a sizable portion of the user base that is unaware of the potential privacy issues that can arise from the careless sharing of personal images. It is challenging to ensure the security of private information while also making sharing easy, because existing privacy solutions, such as encryption and access restrictions, have their limitations. A more robust method for protecting shared images is developed in this paper by employing more advanced cryptographic techniques such as secure multi-party computing and homomorphic encryption. Users are able to control who can view and utilize their images using these ways, all while keeping the full picture hidden. Encrypted data and intricate access rules allow only authorized users to view specific portions of an image. While preserving privacy and practicality, this strategy makes social media interactions safer and more trustworthy. The approach may be a solution to the current issues with digital privacy since empirical investigations have demonstrated its efficacy and scalability. Giving users greater agency over their data in an era where it's pervasive, this research alters the meaning of securely sharing images on social media platforms. International Journal of Advanced Research and Innovations 2025-01-07 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/34 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 1, January 2025; 25-36 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/34/36 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/35 2026-04-27T10:28:20Z files:ART

VALUE-AT-RISK DRIVEN FRAUD DETECTION FRAMEWORK WITH MACHINE LEARNING UNDER DATA IMBALANCE Pokala Lavanya Dr. T. Ravi Kumar Value-at-Risk (VaR), Fraud Detection, Machine Learning, Data Imbalance, Cost-Sensitive Learning and Financial Risk Analysis. The minimal presence of illicit operations within the overall data creates a significant challenge in detecting financial crime due to data imbalance. This paper delineates a revolutionary methodology for fraud detection. The integration of XGBoost, Random Forest, and neural networks enhances the accuracy of Value-at-Risk (VaR). Techniques such as costsensitive learning and SMOTE are utilized to address the imbalance and ensure the identification of fraudulent cases. This technique assists financial organizations in mitigating potential losses by concentrating on high-risk scam scenarios and utilizing Value at Risk theories. This technology has proven in practical studies its capacity to mitigate financial risk and accelerate scam detection. It provides an innovative, risk-aware methodology for transaction security. International Journal of Advanced Research and Innovations 2025-01-07 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/35 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 1, January 2025; 37-44 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/35/37 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/36 2026-04-27T10:34:57Z files:ART

MOTIVATION AT HERO MOTORS: FOSTERING A CULTURE OF EXCELLENCE Mr. K. Manoj Kumar Saipriya Ganjai Motivation, Employee Engagement, Performance, Culture of Excellence, Leadership Styles and Organizational Success Motivation at Hero Motors: Fostering a Culture of Excellence" examines the relationship between intrinsic motivation and organizational outcomes including employee engagement, output, and profitability. A highly engaged workforce is crucial for establishing a culture of excellence in any firm. The goal of Hero Motors' use of both extrinsic and intrinsic motivating strategies is to increase employee engagement, output, and satisfaction. More specifically, this research delves into the connection between leadership styles and motivational tactics. Additionally, it verifies if these strategies are in line with the company's objectives. Finding ways to engage and inspire Hero Motors' personnel is critical to the company's long-term success. International Journal of Advanced Research and Innovations 2025-02-22 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/36 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 2, February 2025; 19-25 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/36/38 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/37 2026-04-27T10:36:57Z files:ART

HIERARCHICAL ML MODEL FOR DISTRIBUTED DDOS ATTACK CLASSIFICATION AND HYPERPARAMETER TUNING Nida Afnan Bandari Swarnalatha Hierarchical Machine Learning, DDoS Attack Classification Hyperparameter Tuning, Cybersecurity and Anomaly Detection. The security of networks is seriously threatened by sophisticated DDoS attacks. In order to increase the precision of distant detection and classification, this article describes a hierarchical machine learning methodology. The system starts with a core layer that detects fundamental issues, then expands classifications into multiple DDoS assault categories. By eliminating extraneous data and increasing efficiency, the approach uses mutual information and correlationbased filters to identify the most relevant qualities. Bayesian optimization for hyperparameters enables improved detection compared to conventional techniques like grid and random search. Experiments using datasets like CIC-DDoS2019 show that this approach lowers false positives while significantly improving accuracy, recall, and F1-score. This cutting-edge and adaptable technology works in real time and can defend modern network systems against dynamic DDoS attacks. International Journal of Advanced Research and Innovations 2025-02-22 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/37 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 2, February 2025; 26-35 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/37/39 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/38 2026-04-27T10:38:25Z files:ART

EXPOSING ONLINE RECRUITMENT FRAUD WITH DEEP LEARNING ALGORITHMS Mohammad Shoib Mr. S. Sateesh Reddy Online Recruitment Fraud, Deep Learning, Natural Language Processing (NLP), Job Scam Detection and Cybersecurity in Hiring. The problem of fraudulent recruitment is a significant problem with the internet job market. others engage in this practice when they make an effort to deceive others who are looking for work by presenting them with phony job offers and money transactions. With the help of deep learning algorithms, our research intends to put an end to these fraudulent practices by evaluating job advertisements, emails, and application procedures. Through the utilization of Natural Language Processing (NLP) in conjunction with more sophisticated models like LSTM and BERT, the system is able to identify irregularities and alert users to the possibility of being taken advantage of. The findings indicate that solutions that are powered by artificial intelligence have the potential to lessen the risks that are associated with the recruitment process, protect job seekers, and preserve the integrity of online applicant tracking systems. International Journal of Advanced Research and Innovations 2025-02-22 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/38 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 2, February 2025; 36-43 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/38/40 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/39 2026-04-27T10:51:54Z files:ART

INTELLIGENT DRIVER DROWSINESS DETECTION USING GAOPTIMIZED CNN ARCHITECTURE Shagufa Anjum Dr. N. Chandramouli Drowsiness Detection, Convolutional Neural Network (CNN), Genetic Algorithm (GA), Driver Safety and Real-time Monitoring The implementation of real-time detection technology is essential, as the danger of driving while fatigued is a significant global concern that results in a significant number of accidents. This article delineates a more advanced model for the detection of fatigued drivers by utilizing a genetic algorithm (GA) to improve a convolutional neural network (CNN). In order to enhance accuracy and reduce false alarms, the GA modifies critical CNN parameters, including the learning rate, filter size, and number of layers. The system is capable of accurately identifying when a motorist is fatigued by observing their facial expressions and tracing their eye and head movements. Our method is more precise and responsive than traditional CNN models, as evidenced by experiments. The landscape of transportation safety could be completely transformed by this CNN model modified for GA, which could reduce the occurrence of accidents and save lives. This is a result of its reliability and scalability. International Journal of Advanced Research and Innovations 2025-03-13 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/39 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 3, March 2025; 28-37 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/39/41 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/40 2026-04-27T10:54:11Z files:ART

MITIGATING WEB-BASED VULNERABILITIES: MAN-IN-THEMIDDLE AND SESSION HIJACKING IN FOCUS Undinti Sai Kiran Dr. E. Srikanth Reddy Man-in-the-Middle (MitM), Session Hijacking, Web Security, Encryption and Secure Authentication. Modern online interactions and user sessions are vulnerable to cyber risks including session hijacking and Manin-the-Middle attacks. During these assaults, malicious actors can take over online chats or steal private information by taking advantage of weaknesses in session management and identification. Modern intrusion detection systems, private coding techniques, authentication using tokens, and SSL/TLS encryption are some of the ways that businesses mitigate these dangers. Looking at real-life attack scenarios and finding out how to avoid them can help firms strengthen their defenses and keep user data safe, according to this research. Ongoing education and awareness are necessary to reduce exposure and create a safer internet for everyone. International Journal of Advanced Research and Innovations 2025-03-13 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/40 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 3, March 2025; 38-46 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/40/42 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/41 2026-04-27T11:18:06Z files:ART

ENERGY ECONOMY PREDICTION FOR ELECTRIC CITY BUSES USING MACHINE LEARNING: A DATA-DRIVEN METHODOLOGY Sk. Yakoob Ch. Kavya P. Gokul A. Ravindra Sk. Ansar N. Vinay Kumar Machine Learning, Energy Economy, Electric City Buses, Data Analytics, Smart Grid. Electric city buses have several potential applications as one of several evolving forms of electric transportation. Automobile design and fleet management require an in-depth understanding of real transportation data. The effective functioning of alternative powertrains requires a thorough analysis of certain technological challenges. Designers tend to exercise prudence when the energy consumption is ambiguous, resulting in designs that are both expensive and insufficient. Organizations and scholars are incapable of formulating analytical answers to this problem owing to the intricacy and interrelation of the criteria. Optimizing processes and accurately estimating energy use can yield significant cost reductions. The main aim of the study is to provide an in-depth analysis of the energy usage of BEBs. To accomplish this, we utilize new explanatory components and advanced machine learning techniques to develop performance profiles. Five unique programs are developed to ensure their reliability, precision, and functionality in the realm of prediction generation. Our models exhibited outstanding performance due to the careful selection of characteristics, with an average accuracy above 94% in their predictions. The proposed concept might revolutionize transportation and create a basis for sustainable public transit if executed by manufacturers, fleet administrators, and governments. International Journal of Advanced Research and Innovations 2025-04-24 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/41 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 4, April 2025; 27-32 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/41/43 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/42 2026-04-27T11:19:47Z files:ART

ENHANCING SPAM COMMENT DETECTION ON SOCIAL MEDIA WITH EMOJI FEATURE AND POST-COMMENT PAIRS APPROACH USING ENSEMBLE METHODS OF MACHINE LEARNING CH. Balakrishna A. Durga Bhavani G. Pallavi I. Giridhar N. Vinay Kumar Spam Detection, Social Media, Emoji Features, Post-Comment Pairs, Machine Learning, Ensemble Methods, Content Moderation, Natural Language Processing, Sentiment Analysis, Contextual Spam Filtering. This research improves the detection of social media abuse by assessing post-comment combinations and integrating emoji traits. Traditional approaches ignore the post-comment context and emoji semantics. We are able to detect subtle signs of spam that text-only methods miss because of these features. Combining various machine learning models, such as Support Vector Machines, Random Forest, and XGBoost, improves the classification accuracy. In order to examine actual conversations taking place on social media, this dataset makes use of spam labels. The engineering of features includes emoji sentiment, frequency, and context. Ensemble approaches seem to be superior to single-model baselines time and time again. Significant improvements in recall and precision were shown by the results. This system is capable of scaling content moderation. Future developments that require deep learning and multimodal data will likewise be made easier by this. International Journal of Advanced Research and Innovations 2025-04-24 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/42 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 4, April 2025; 33-39 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/42/44 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/43 2026-04-28T11:06:28Z files:ART

A REVIEW OF EXPLAINABLE AI APPLICATIONS IN PHARMACOVIGILANCE FOR IMPROVED PATIENT SAFETY J.Rajakala P. Gayathri Priya G. Gayathri P. Manohar K. Jayanth Artificial Intelligence, Drugs, Predictive Models, Data Models, Safety, Machine Learning. An alternative paradigm to classical AI's "black box" approach, explainable artificial intelligence (XAI) has recently garnered a lot of attention for its potential usefulness. This study aims to identify pharmacovigilance studies that have utilized XAI. By helping pharmacovigilance teams assess patient conditions like diabetic retinopathy and chronic diseases, as well as increase the speed and accuracy of signal detection, AI can solve major safety concerns. Therefore, experts and clinicians must continually evaluate the possible advantages and disadvantages of AI in pharmacovigilance as technology advances if it is to have the greatest possible effect on patient safety. Applying XAI in pharmacovigilance was incredibly challenging, as shown by the study's many obstacles. The fields of patient safety and pharmacovigilance make extensive use of AI for data collection on adverse pharmacological responses, analysis of medication interactions, and impact prediction; however, XAI is hardly employed in these fields. International Journal of Advanced Research and Innovations 2025-05-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/43 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 5, May 2025; 23-30 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/43/45 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/44 2026-04-28T11:06:28Z files:ART

KNOWLEDGE MANAGEMENT IMPROVES ORGANIZATIONAL LEARNING AND PERFORMANCE Dr.D.N.V. Krishna Reddy Aakoju Hema Lath Koleti Rakesh Venkateswarlu Vajja Patheparapu Madhavi Knowledge Management (KM), Organizational Learning (OL), Performance Enhancement, Behavioral Routines, Information Flow, Knowledge Formation Philosophers, scientists, and educated people have long been fascinated by the idea of increasing knowledge formation, acquisition, transmission, and application. This alchemy may be ancient. The academic subject of "knowledge management" (KM) is just 15–20 years old. Most firms don't optimize data consumption for knowledge management (KM). This reveals human intelligence's limits. Knowledge management (KM) helps firms maximize resources by ensuring the right people get the correct knowledge at the right time. We all know that a company's bottom line will suffer if it can't leverage its data better. When implemented company-wide, OL and KM can enhance performance. In 1988, Levitt and March defined OL as "...encoding inferences from history into routines that guide behavior." Data added from the box will be impacted. International Journal of Advanced Research and Innovations 2025-05-20 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/44 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 5, May 2025; 31-36 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/44/46 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/45 2026-04-29T07:11:10Z files:ART

INTERNAL COMMUNICATION AND ITS IMPORTANCE IN CORPORATE MANAGEMENT Mr.V.Rambabu Banka Mareswari Kavisetti Siva Nandini Korukonda Mounika Nagulavancha Divya Internal Communication, Corporate Management, Organizational Communication, Employee Engagement, Information Flow, Corporate Culture, Change Management, Communication Strategies, Organizational Effectiveness, Workplace Transparency. The smooth flow of information throughout a company's many levels and departments is essential for effective corporate administration. Consequently, this calls for first-rate communication throughout the organization. Organizations utilize a range of strategies to enhance internal communication. There are more formal channels, such as meetings and email, and there are also more casual channels, such as conversations. Not including all of these different kinds of communication makes it insufficient. The ability of a corporation to make decisions and achieve its goals can be greatly improved with an honest, open, and impartial system of internal communication. Not only that, but it can help with change management, boost morale, and lessen misunderstandings and conflicts inside the firm. With the complexity of modern enterprises comes a greater awareness of the need of good internal communication in sustaining consistency and accomplishing goals. International Journal of Advanced Research and Innovations 2025-06-10 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/45 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 6, June 2025; 26-35 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/45/47 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/46 2026-04-30T08:34:42Z files:ART

A PORTAL FOR EMPLOYEE SELF-SERVICE Mr.G.Narendra Babu Marakala Yaswanth Kumar Pallapothu Tejaswi Shaik Riyaz Pasha Mohammed Fazil Employee Self-Service (ESS), Efficiency, Streamline, Processes, Centralized Digital Platform, Autonomy, Administrative Tasks. Organizations simplify operations to improve efficiency in a competitive market. A gateway for employee selfservice is essential. This brief highlights the portal's main benefits. Employee Self-Service provides a simple, online platform to empower employees. Their own administrative tasks will be simplified. Due to strong security and user-friendly interfaces, employees can change their contact, tax, and direct deposit information. The site's simplified communication channels enable regulatory verification, worker self-evaluation, and vacation requests. By freeing HR of onerous administrative tasks, the ESS interface boosts operational efficiency and cuts expenses. Basic methods are simpler, more accurate, and less detailed. Continuous access to financial and personal data promotes accountability and openness. This abstract shows how employee self-service portals may affect organizational dynamics. Using technology to streamline administrative processes boosts morale, responsiveness, and production. International Journal of Advanced Research and Innovations 2025-06-10 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/46 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 6, June 2025; 36-40 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/46/48 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/47 2026-04-29T08:46:07Z files:ART

INVESTOR SENTIMENT-DRIVEN STOCK PRICE PREDICTION USING OPTIMIZED DEEP LEARNING MODELS VV Siva Prasad P. Abhinaya K. Nissi Mahitha T.Rohith Kumar M. Siva Kesava Stock Market; Sentiment Analysis; Deep Learning; Artificial Neural Network (ANN). The technique of estimating the future value of a company's shares, or any other financial instrument listed on an exchange, is known as shares market prediction. Investors face a significant challenge when attempting to forecast future events in the stock market. Investors will aim to maximize their profits if they can accurately predict a company's future price. Social media users' opinions are having a greater impact on the performance of the stock market. To create a prediction model, this study examines a variety of prediction techniques. According to the approach, actions should be taken in two stages. Sentiment analysis and historical data are used in the first stage. The second stage places a strong emphasis on deep learning. A helpful technique for comprehending the tone of comments on social media platforms is sentiment analysis. Understanding how emotions impact stock prices is crucial. Using the Deep Learning module, we create a forecast model based on correlation. The outcomes demonstrated that the suggested method regularly produced more accurate forecasts. International Journal of Advanced Research and Innovations 2025-07-16 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/47 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 7, July 2025; 38-44 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/47/49 https://creativecommons.org/licenses/by/4.0

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FAST TEXT EMBEDDINGS AND DEEP LEARNING FOR ROBUST DETECTION OF DEEPFAKES IN SOCIAL MEDIA TWEETS Dr.T.Veeranna Sd.Iliyaz Ali Ch.Udaykoteswara Rao D.Harsha Vardhan Reddy M. Gayathri Deep fake detection, deep learning, Fast Text embeddings, machine-generated tweets. The increasing prevalence of deep fake technology has prompted apprehension regarding the dissemination of inaccurate information on social media. This paper illustrates a deep learning-based approach to identifying deep fake tweets, particularly those generated by machines. This will mitigate the detrimental effects of false information on the internet. Our approach categorizes tweets into categories by employing Fast Text embeddings and deep learning models. We employ Fast Text embeddings to generate dense vector models after preprocessing the tweet text. The distinction between genuine and fraudulent tweets is determined by the semantic information regarding tweet topics that these embeddings accumulate. We incorporate these embeddings into a deep learning model, such as a CNN or a Long Short-Term Memory (LSTM) network, to determine whether the tweets are genuine or fabricated. Machine-generated tweets are generated using contemporary text generation algorithms that have been instructed on a collection of tagged tweets. Research conducted on a real-world tweet collection demonstrates that our methodology is effective in identifying tweets that were generated by algorithms. Our approach is significantly more precise than other methods for identifying social media deep fakes. In general, our proposed approach is a dependable and efficient approach to identify tweets that were generated by machines and to halt the dissemination of inaccurate information on social media. International Journal of Advanced Research and Innovations 2025-07-16 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/48 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 7, July 2025; 45-52 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/48/50 https://creativecommons.org/licenses/by/4.0

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A DATA-DRIVEN APPROACH TO CROP YIELD PREDICTION USING ADVANCED MACHINE LEARNING TECHNIQUES Dr.T.Veeranna B.Yashwanth J.Sivanagaraju S.Chaitanya Reddy D.Reshma Priya J.Praveen Crop yield prediction, ML Half or more of India's population relies on agriculture for their livelihood, making it an essential sector of the Indian economy. The future of agriculture is in jeopardy due to the growing threat posed by climate change and other environmental factors. Improving decision-making about agricultural cultivation and growing practices, machine learning (ML) offers a tool for crop yield prediction (CYP). Several approaches have been devised to analyze AI-based crop yield prediction algorithms; this study centers on a systematic review that extracts and synthesizes CYP features. Less relative inaccuracy and less capacity to predict crop yield are the primary drawbacks of neural networks. Supervised learning algorithms had a hard time selecting, sorting, or rating fruits due to the nonlinear connection between input and output variables. Many agricultural development research proposals sought to build an accurate and efficient model for crop classification, which would allow for the prediction of crop yields in response to weather and disease conditions, the categorization of crops according to their developmental stage, and so on. An extensive evaluation of the accuracy of various machine learning models used to estimate agricultural productivity is presented in this article. International Journal of Advanced Research and Innovations 2025-08-13 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/49 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 8, August 2025; 24-32 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/49/51 https://creativecommons.org/licenses/by/4.0

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TRUST-BASED DETECTION OF MALICIOUS NODES IN WIRELESS SENSOR NETWORKS Hafsa Kouser Dr. Madana Srinivas Wireless Sensor Networks (WSNs), Trust-Based Detection, Malicious Nodes, Network Security and Node Behavior Analysis. Despite their extensive value, wireless sensor networks (WSNs) are vulnerable to many security concerns, particularly from rogue nodes. Owing to their constrained resources, sensor nodes often fail to adhere to established security rules. This study presents a trust-based methodology for recognizing and differentiating malicious nodes in wireless sensor networks (WSNs). The system continuously modifies trust scores by assessing the reliability of each node based on its operational attributes, such as packet forwarding efficiency, data accuracy, and communication consistency. A node is classified as malignant when its score is below a designated threshold. The proposed strategy enhances data accuracy, reduces false positives, and improves network stability. The simulation results indicate that the trust-based method effectively reduces processing power consumption while ensuring secure and uninterrupted network operations. International Journal of Advanced Research and Innovations 2025-08-13 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/50 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 8, August 2025; 33-41 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/50/52 https://creativecommons.org/licenses/by/4.0

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MACHINE LEARNING-BASED FACE MASK DETECTION FOR PUBLIC HEALTH SAFETY Mr.A.Srinivasrao D.Aakanksha J.Navya K.Kavya V.Bharath COVID-19, Tensorflow, OpenCV, FaceMask, ImageProcessing, ComputerVision. It is advisable to utilize a face mask if you are concerned about your health and wish to prevent the transmission of respiratory diseases. One approach that may assist in guaranteeing that you are adhering to the appropriate safety protocols is the identification of face coverings. This technique can be employed to verify that the masks are being utilized accurately and to identify which ones are being used. The name of our initiative is "Face Mask Detection." The utilization of masks is a critical measure in the prevention of the COVID-19 virus; deep learning can be employed to ascertain whether an individual is donning one. Masks are an indispensable component of the daily lives of all individuals in this world. Wearing coverings enhances our ability to communicate and conduct business. International Journal of Advanced Research and Innovations 2025-09-18 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/51 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 9, September 2025; 29-33 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/51/53 https://creativecommons.org/licenses/by/4.0

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EXPLORING THE ROLE OF BLOCKCHAIN TECHNOLOGY IN FARMER’S PORTALS FOR AGRICULTURAL INNOVATION Mr.A.Srinivas Rao B.Varshitha Reddy B.Babyshalini Sk.Farhana R.Varshith Blockchain Technology, Farmer’s Portal, Supply Chain Transparency, Smart Contracts. The block chain method allows you to record proof of a bitcoin transaction. A peer-to-peer network links numerous computers together so that records can be kept on both ends. Any of these words can be used to describe the economic system of a country. All of these things are written down in contracts, deals, and papers. At every step, they set limits and make sure the assets are safe. This study use a farmer's website that tracks crop sales and purchases to showcase the real-world uses of blockchain technology. Among the many benefits of blockchain technology that this show emphasizes is its immutability and security of financial transaction records. Python and blockchain technology are combined in this idea. Everyone involved stands to gain by keeping the trade arrangement in place. This includes dealers as well as farmers. An interface that incorporates blockchain technology was created for the farmers using the programming language Python. This system keeps tabs on the buyer, seller, item, and total amount of money that is traded. International Journal of Advanced Research and Innovations 2025-10-22 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/52 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 10, October 2025; 1-8 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/52/54 https://creativecommons.org/licenses/by/4.0

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HEALTH PREDICTION SYSTEM POWERED BY MACHINE LEARNING AND IBM CLOUD PAAS N.Sudharani B.Kavya K.Harikasatya M.Ravi Teja Patient Care System, Naïve Bayes, Logistic Regression, Ensemble Methods, IBM Cloud. Create a system that can change and grow with the healthcare system to solve current problems. Superior treatment for critically ill patients will improve the quality of hospital care. Make regular use of PaaS and machine learning technology to keep an eye on important employees. Enhancingthe healthcare industry's ability to be vigilant and make decisions is the primary objective. The IBM Cloud component is locally built in order to meet financial issues. Among the ensemble learning components used in this model are Naïve Bayes, Logistic Regression, and Decision Tree Classification. The plan's goal is to create a complex system that can foresee important health problems. Rapid remote patient condition evaluation is made possible by the "Critical Patient Management System" (CPMS) software. The program gives doctors access to healthcare management tools that allow them to remotely monitor patients who arein severe condition. International Journal of Advanced Research and Innovations 2025-10-22 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/53 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 10, October 2025; 9-15 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/53/55 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/54 2026-04-30T08:59:03Z files:ART

POPULARITY PREDICTION OF SHORT VIDEOS USING INTERNET OF THINGS AND NEURAL NETWORKS Kathroj Snehalatha Mr. S. Sateesh Reddy Popularity Prediction, Short Videos, Internet of Things (IoT), Neural Networks, Deep Learning, User Engagement, Video Analytics, Social Media, Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), Predictive Modeling, Viral Content Detection. This research focuses on the popularity prediction of short videos by leveraging the capabilities of the Internet of Things (IoT) and Neural Networks. With the rapid rise of short video platforms such as TikTok, Instagram Reels, and YouTube Shorts, understanding and predicting what content becomes viral has become a valuable challenge. The Internet of Things enables real-time data collection from various user interactions and environmental contexts, including view counts, likes, shares, location, device type, and viewing time. This data is processed and analyzed using advanced neural network architectures, such as Convolutional Neural Networks (CNNs) for visual content analysis and Recurrent Neural Networks (RNNs) for temporal behavior prediction. The proposed model integrates these data streams to forecast the potential popularity of a video shortly after upload. Experimental results on benchmark short video datasets show significant improvement in prediction accuracy compared to traditional machine learning methods. The findings of this study can assist content creators, marketers, and platform developers in optimizing content strategies and enhancing user engagement. International Journal of Advanced Research and Innovations 2025-10-22 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/54 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 10, October 2025; 16-22 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/54/56 https://creativecommons.org/licenses/by/4.0

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DYNAMIC TRAFFIC FLOW MANAGEMENT SYSTEM Mr. B Narendar K. Chandrika MD. Javiya G. Karthikeyani N. Manikanta RF Transmitter and Receiver, LCD, PIC16F877A, Piezoelectric buzzer, ambulance vehicle, traffic junction. This paper presents an efficient priority control for ambulance clearance. Each ambulance is equipped with radio frequency transmitter (RF Tx). We use RF receiver, PIC16F877A, liquid crystal display (LCD), piezo electric buzzer were attached to the traffic signals. It detects ambulance while arriving at 100 meters before reaching the signal. In addition, when an ambulance is approaching the junction it will communicate to the traffic signal in the junction to turn ON the green light. This module uses radio frequency (RF) transmitter, receiver and PIC16F877A for wireless communication between the ambulance and traffic signal. International Journal of Advanced Research and Innovations 2025-10-22 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/55 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 10, October 2025; 23-29 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/55/57 https://creativecommons.org/licenses/by/4.0

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SOLAR BASED MOBILE CHARGING ON COIN INSERTION Mrs.G Rani G. Mohna Sri J. Mounika G.Ganesh Kumar N. Ajay Solar Power, Mobile Charging, Arduino, Coin Insertion, Relay, Solar Panel, Rechargeable Battery, Multi-Pin Charger, LCD Display, Renewable Energy, Eco-friendly, Public Charging Stations, Coin Operated Mechanism, Charging Circuit, Sustainable Technology. The growing reliance on mobile devices has created an increasing demand for public charging stations, especially in areas where access to conventional power grids is limited. Traditional charging stations often depend on grid electricity, which may not be sustainable or cost-effective in remote locations. To address this issue, this paper introduces a solar-based mobile charging station that operates on coin insertion using Arduino, a solar panel, a rechargeable battery, a relay, a multi-pin charger, a coin box, and a 20x4 LCD display.The system is designed to be energy-efficient, cost-effective, and eco-friendly by harnessing solar power to charge mobile devices. The solar panel generates electricity during the day, which is stored in a rechargeable battery. When a user inserts a coin into the coin box, the system activates the charging circuit by controlling a relay via the Arduino microcontroller.The Arduino also communicates with the LCD display, which provides real-time feedback to the user regarding the charging status, remaining time, and battery health. The system offers a solution that is not only self-sustaining but also promotes the use of renewable energy, reducing the need for grid electricity. The coin mechanism ensures that the user pays for the service, making it suitable for deployment in public areas such as parks, bus stations, and shopping malls. The paper discusses the system's design, working principle, and advantages over traditional charging solutions. Additionally, it highlights the challenges of maintaining consistent power availability due to weather dependencies and the initial setup costs. The solar-based charging station with coin insertion offers a promising alternative to conventional charging infrastructure and encourages the use of green energy in urban and semi-urban environments. International Journal of Advanced Research and Innovations 2025-10-22 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/56 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 10, October 2025; 30-36 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/56/58 https://creativecommons.org/licenses/by/4.0

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SMART VIDEO SURVEILLANCE WITH WIRELESS NOTICE ANNOUNCEMENT VEHICLE FOR COLLEGE Mr .P.Naga Sekhar G. Manusha D. Anil Kumar S. Sailaja M. Lingaswami Smart Video Surveillance, Wireless Notice Announcement, Arduino Controller, Solar Panel, 12V Battery, Camera, HC-05 Bluetooth Module, Voice IC, Speaker, L293D Motor Driver, Gear Motor, Autonomous Vehicle In this paper, we present the design and implementation of a Smart Video Surveillance System integrated with a Wireless Notice Announcement Vehicle specifically developed for a college campus environment. The system utilizes a low-cost, efficient approach leveraging Arduino microcontrollers, solar energy, and wireless communication technologies to enhance security and campus communication. The vehicle, powered by a solar panel and a 12V battery, is designed to move autonomously while providing live video surveillance via an onboard camera. Real-time video data can be transmitted to monitoring stations or stored for future reference. HC-05 Bluetooth module is used for wireless communication between the vehicle and a smartphone or PC, allowing users to control the vehicle remotely. Additionally, the system features a Voice IC and speaker, enabling the vehicle to broadcast pre-recorded announcements to various parts of the campus. The L293D motor driver with a gear motor controls the movement of the vehicle, ensuring precise navigation and movement. The integration of a Radio Positioning System (RPS) allows for autonomous navigation, providing increased mobility and flexibility for the vehicle to cover large areas without manual control. The primary goal of this project is to create a self-sustaining, eco-friendly solution for continuous surveillance and communication that reduces reliance on traditional power sources. By combining these technologies, the system offers enhanced campus security while simultaneously serving as a tool for efficient notice dissemination. The results of this system can be scaled for large educational institutions, and with further enhancements, it could integrate more advanced technologies such as GPS, cloud storage, and AI-based object recognition for further automation. International Journal of Advanced Research and Innovations 2025-11-12 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/58 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 11, November 2025; 1-7 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/58/59 https://creativecommons.org/licenses/by/4.0

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REWARD SYSTEM AS A STRATEGY TO ENHANCE EMPLOYEES PERFORMANCE INANORGANIZATION Dr.M. Swetha Reward System, Employee Motivation, Organizational Performance, Human Resource Strategy, Job Satisfaction, Incentives, Productivity Improvement, Workforce Engagement. Employee performance is a crucial factor that has a direct impact on the success and viability of any organization. Of all the strategic tools employed by management, reward systems have always been effective in increasing employees' motivation levels, job satisfaction, and ultimately, their performance outcomes. This paper explores the function of reward systems—both intrinsic (recognition, career growth, flexible work arrangements) and extrinsic (bonuses, salary increases, incentives)—as a performance improvement tactic in organizational contexts. Using established motivational theories like Maslow's Hierarchy of Needs and Herzberg's Two-Factor Theory, the research analyzes the psychological and behavioral effects of rewards on employees. Applying a mixed-methods research strategy, data were gathered using structured questionnaires and performance records in various organizational industries. The findings reveal a significant positive relationship between well-designed reward systems and worker productivity, commitment, and retention. In addition, the study outlines best practices for designing fair and goal-focused reward systems that link employee ambitions with organizational goals. This study provides significant insights to human resource practitioners and organizational managers aiming to build a high-performance work culture using proper reward schemes. International Journal of Advanced Research and Innovations 2025-11-12 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/59 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 11, November 2025; 8-13 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/59/60 https://creativecommons.org/licenses/by/4.0

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SECURE ENERGY DEMAND PREDICTION FOR ELECTRIC VEHICLES USING FEDERATED LEARNING ON BLOCKCHAIN Bandipelly Harish Kumar Dr. D. Srinivas Reddy Electric Vehicles, Energy Demand Prediction, Federated Learning, Blockchain Technology, Data Privacy, Smart Grid, Decentralized Learning, Secure Energy Management, Smart Contracts, Cybersecurity This investigation employs blockchain technology and federated learning to develop a novel approach to accurately forecast the energy requirements of electric vehicles (EVs). It has become increasingly challenging to accurately forecast the amount of energy required while simultaneously safeguarding data privacy and security as more individuals adopt electric vehicles. Federated learning safeguards user privacy by enabling energy providers and electric car manufacturers to collaborate in the development of predictive models without transmitting raw data to one another. The decentralized ledger and smart contracts of blockchain technology simultaneously safeguard data security, transparency, and trust. The comprehensive approach effectively anticipates fluctuations in energy consumption while simultaneously addressing significant challenges related to scalability, security, and privacy. The results of the experiments indicate that this architecture is effective in safeguarding private data from intruders and enabling smart grids to manage their energy in a reliable and long-lasting manner. This research offers a robust approach to enhance the safety and utility of EV energy demand forecasting. International Journal of Advanced Research and Innovations 2025-11-12 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/60 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 11, November 2025; 14-22 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/60/61 https://creativecommons.org/licenses/by/4.0

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MULTIMODAL FOOD TEXTURE PREDICTION USING TEMPLATE MATCHING TECHNIQUES sana Naaz Dr. D. Srinivas Reddy Food texture prediction, multimodal sensing, template matching, visual analysis, haptic data, food quality, machine perception. Accurate texture forecasts are essential for a number of industries, including food quality assurance, automated cooking systems, and consumer satisfaction surveys. This study presents a novel, all-encompassing method for predicting food texture by integrating visual and tactile data using advanced template matching algorithms. Crunchy, soft, chewy, and crispy are among the textures that the system correctly detects and labels by combining information from RGB pictures, depth maps, and force-feedback signals. By integrating real-time sensory input with a meticulously maintained library of known texture patterns, we can create incredibly accurate texture estimations using a process known as template matching. Extensive experiments have shown that the suggested approach performs better than traditional one-dimensional texture analysis methods under various lighting, occlusion, and surface contamination conditions. By allowing robots to evaluate food similarly to people, this study advances human-computer interaction in culinary robotics and smart food processing systems. International Journal of Advanced Research and Innovations 2025-11-12 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/61 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 11, November 2025; 23-31 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/61/62 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/62 2026-04-30T09:23:41Z files:ART

EKYC-DF: A REALISTIC DEEPFAKE CORPUS FOR TESTING AND TRAINING EKYC VERIFICATION MODELS Sania Mirza Mrs. Y. Susheela Deepfake, eKYC Verification, Facial Recognition, Synthetic Dataset and Identity Fraud Prevention Digital registration methods, such as electronic Know Your Customer (eKYC) checks, have become increasingly difficult to validate as a result of the widespread availability of deepfake computer technology. This has made the task of validating digital registration procedures more challenging. Within the context of deepfake attacks, the eKYCDF corpus is a particular dataset that has the potential to be employed for the purpose of evaluating and strengthening facial recognition systems. There are opportunities for both of these uses. This sample contains a considerable number of phony facial recordings, which are included in the collection. These phony recordings bear a strong resemblance to the ones that were actually available. The lighting, editing, and racial composition of the recordings are all notably different from one another, which makes it simple to discern amongst the recordings. By developing more effective methods of identity verification, researchers and developers have the power to safeguard the trust that users have in the internet and stop persons from gaining access to systems without authorization. This will allow them to enhance the security of electronic know-yourcustomer (eKYC) systems, which will allow them to better protect their customers. International Journal of Advanced Research and Innovations 2025-11-12 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/62 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 11, November 2025; 32-39 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/62/63 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/64 2026-04-30T09:38:46Z files:ART

IMPROVING CROP YIELD FORECASTING WITH AGRICULTURAL ENVIRONMENT FEATURES: FEATURE SELECTION AND CLASSIFIER-BASED APPROACHES B. Veera Prathap R. Kavya Sri P. Snehalatha Sk. Davud Baba B. Sai Kiran Agriculture, classification, crop prediction, feature selection.. The study of agriculture as an academic discipline is very recent. Soil and climatic variables, such as rainfall, temperature, and humidity, significantly impact crop yields, making crop yield prediction a crucial part of agriculture. Farmers used to have greater leeway in terms of when and what crops they could grow when I was a kid. The rate of global change is making it impossible for farmers to maintain their traditional methods. Since machine learning approaches can now estimate, this study used many sorts of these algorithms to predict the productivity of farms. If you want to be sure that a specific ML model is doing its job, you need to employ excellent feature selection methods to transform raw data into an ML dataset. It is critical to add only data points that are relevant to the model's output in order to maintain the accuracy of the machine learning model and prevent redundancy. Careful feature selection is required to ensure that the model contains just the most crucial attributes. An overly complex model would result from adding all raw data traits without first determining if they were beneficial for developing the model. The accuracy of the output would also be affected by adding qualities that simplify the ML model in terms of space and time. According to the research, the current classification system is not as effective as an ensemble strategy when it comes to producing predictions. International Journal of Advanced Research and Innovations 2025-12-17 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/64 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 12, December 2025; 1-9 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/64/64 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/65 2026-04-30T09:38:46Z files:ART

DRIVING SHOPPING MALL REVENUE GROWTH WITH PERSONALIZED REAL-TIME DIGITAL COUPON ISSUANCE CH. Siva Prakash P. Mohini Satya V. Navya S. Harsha Vardhan S. Vara Prasad Shopping Mall Revenue, Digital Couponing, Real-Time Marketing, Customer Personalization. The marketing department has long been seen as an outgrowth of the accounting department. The marketing industry shifted to make use of new technologies like big data and deep learning. Monitoring customer churn is an essential part of any marketing strategy. This research details a data-driven, real-time system that employs personalized discount coupons to increase spending and engagement from returning customers. In order to classify the customers, we employed two-dimensional segmentation. Live estimates of each group's attrition rate were obtained using click stream data. The next step was to provide each customer a unique voucher. Lastly, we examined the increase in sales and the rate of conversion. When combined with a two-dimensional cluster analysis churn rate estimate, a suggestion system outperformed the basic models by a significant margin. By adopting this method, online malls can automatically determine a customer's likelihood of leaving and the items they purchase, which can boost revenue. International Journal of Advanced Research and Innovations 2025-12-17 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/65 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 12, December 2025; 10-16 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/65/65 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/67 2026-04-30T09:38:46Z files:ART

FEDERATED LEARNING FOR PRIVACY-PRESERVING ONSCREEN ACTIVITY RECOGNITION IN E-LEARNING Fareha Tabassum Dr. B. Anvesh Kumar Federated Learning, Privacy Preservation, On-Screen Activity Recognition, E-Learning and User Engagement The digital classroom of today enables teachers to monitor the screen activity of their students, which enables them to assess their level of engagement and pinpoint areas where their learning could be enhanced. There are additional privacy concerns that have been raised by centralized behavior monitoring technology. Federated Learning (FL) is the primary focus of this article due to its secure, screen-aware approach to online learning. FL guarantees the security of its customers' personal information by training models locally on their devices and storing model updates instead of raw data. Our federated architecture ensures user privacy by precisely monitoring student actions through deep learning, which encompasses reading, viewing, and engagement. Experimental data suggests that federated learning (FL) is a promising alternative for large-scale e-learning systems, as it has a high recognition success rate and minimal privacy concerns. The findings of this study underscore the urgent need to identify methods to protect the privacy of students in the rapidly developing field of personalized digital education. International Journal of Advanced Research and Innovations 2025-12-17 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/67 International Journal of Advanced Research and Innovations; IJARAI: Vol.15, Issue 12, December 2025; 17-25 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/67/66 https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/69 2026-07-30T12:26:28Z files:ART

LARGE LANGUAGE MODELS IN PRECISION ONCOLOGY: TRANSFORMING CLINICAL DECISION SUPPORT AND CANCER CARE Dr. Anoop Krishnan Large language models Precision oncology Clinical decision support Artificial intelligence Foundation models Computational oncology Personalized medicine Digital pathology Multi-omics Cancer care Large language models (LLMs) are rapidly transforming precision oncology by enabling intelligent clinical reasoning, biomedical knowledge synthesis, multimodal data interpretation, and personalized clinical decision support across the cancer care continuum. Unlike conventional artificial intelligence (AI) systems designed for isolated predictive tasks, LLMs integrate natural language understanding with multimodal foundation models capable of processing radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, electronic health records, biomedical literature, clinical guidelines, and longitudinal patient data. Recent advances in transformer architectures, self-supervised learning, retrieval-augmented generation, graph neural networks, reinforcement learning, and generative AI have enabled comprehensive biomedical reasoning supporting cancer diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and multidisciplinary clinical decision-making. Emerging technologies including multimodal large language models, federated learning, explainable artificial intelligence, agentic AI, cloud-native healthcare platforms, and digital twins further enhance precision oncology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive computational intelligence. Despite substantial technological advances, important scientific, technical, ethical, regulatory, and implementation challenges remain regarding hallucination, interpretability, multimodal integration, computational scalability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of large language models in precision oncology, emphasizing their transformative role in clinical decision support and personalized cancer care. International Journal of Advanced Research and Innovations 2026-07-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/69 International Journal of Advanced Research and Innovations; Vol. 13 No. 12 (2023): IJARAI: Vol.13, Issue 12, December 2023 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/69/68 Copyright (c) 2023 Dr. Anoop Krishnan (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/70 2026-07-30T12:38:52Z files:ART

MULTIMODAL GENERATIVE ARTIFICIAL INTELLIGENCE FOR PRECISION ONCOLOGY: FROM CANCER DIAGNOSIS TO THERAPEUTIC DISCOVERY Dr. Vishal Arora Generative artificial intelligence Multimodal learning Foundation models Precision oncology Computational oncology Digital pathology Radiomics Drug discovery Clinical decision support Personalized medicine Multimodal generative artificial intelligence (AI) is redefining precision oncology by enabling intelligent computational systems capable of synthesizing heterogeneous biomedical information, generating clinically actionable knowledge, and supporting personalized cancer diagnosis, prognostic prediction, therapeutic optimization, and drug discovery. Unlike conventional artificial intelligence models designed for isolated prediction tasks, multimodal generative AI integrates radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, electronic health records, biomedical literature, and longitudinal clinical outcomes into unified generative foundation models capable of multimodal reasoning and knowledge generation. Recent advances in transformer architectures, self-supervised learning, graph neural networks, diffusion models, variationalautoencoders, generative adversarial networks, retrieval-augmented generation, and multimodal large language models have enabled comprehensive biomedical representation learning supporting cancer diagnosis, biomarker discovery, molecular characterization, immunotherapy selection, digital twin simulation, therapeutic optimization, adaptive disease monitoring, and intelligent clinical decision support. Emerging technologies including federated learning, explainable artificial intelligence, reinforcement learning, and agentic AI further strengthen multimodal oncology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, significant scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, model interpretability, computational scalability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of multimodal generative artificial intelligence in precision oncology, emphasizing its transformative role from cancer diagnosis to therapeutic discovery. International Journal of Advanced Research and Innovations 2026-07-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/70 International Journal of Advanced Research and Innovations; Vol. 13 No. 12 (2023): IJARAI: Vol.13, Issue 12, December 2023 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/70/69 Copyright (c) 2023 Dr. Vishal Arora (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/71 2026-07-30T12:45:10Z files:ART

FOUNDATION AI AGENTS IN ONCOLOGY: AUTONOMOUS CLINICAL INTELLIGENCE FOR PERSONALIZED CANCER MANAGEMENT Dr. Zahir Khan Dr. LavanyaPrabhu Foundation AI agents Precision oncology Autonomous artificial intelligence Clinical intelligence Digital twins Computational oncology Personalized medicine Multimodal learning Clinical decision support Foundation models. Foundation artificial intelligence (AI) agents are emerging as a transformative paradigm in precision oncology by enabling autonomous clinical intelligence capable of perceiving, reasoning, planning, executing, and continuously learning across the entire cancer care continuum. Unlike conventional AI systems that perform isolated prediction tasks, foundation AI agents integrate multimodal foundation models, large language models, digital twins, reinforcement learning, graph neural networks, and autonomous reasoning into intelligent computational ecosystems capable of coordinating diagnostic workflows, therapeutic planning, longitudinal disease monitoring, biomarker discovery, and personalized clinical decision-making. Recent advances in multimodal transformer architectures, self-supervised learning, generative artificial intelligence, retrieval-augmented generation, and agentic AI have enabled comprehensive integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, biomedical literature, and longitudinal clinical outcomes into unified computational frameworks. These intelligent systems support early cancer detection, molecular characterization, prognostic prediction, immunotherapy selection, digital twin simulation, adaptive disease monitoring, therapeutic optimization, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, explainable artificial intelligence, cloud-native healthcare platforms, and Internet of Medical Things (IoMT) technologies further strengthen autonomous oncology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, regulatory, and implementation challenges remain regarding multimodal data harmonization, computational scalability, interpretability, cybersecurity, clinical validation, human oversight, and equitable implementation. This review provides a comprehensive overview of foundation AI agents in oncology, emphasizing autonomous clinical intelligence for personalized cancer management. International Journal of Advanced Research and Innovations 2026-07-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/71 International Journal of Advanced Research and Innovations; Vol. 13 No. 12 (2023): IJARAI: Vol.13, Issue 12, December 2023 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/71/70 Copyright (c) 2023 Dr. Zahir Khan, Dr. LavanyaPrabhu (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/72 2026-07-30T12:53:51Z files:ART

DIGITAL BIOMARKERS AND ARTIFICIAL INTELLIGENCE: THE FUTURE OF PRECISION ONCOLOGY AND PERSONALIZED THERAPEUTICS Dr. Joel Varghese Digital biomarkers Artificial intelligence Precision oncology Personalized therapeutics Foundation models Computational oncology Digital health Clinical decision support Multi-omics Wearable technologies Digital biomarkers have emerged as a transformative component of precision oncology by enabling continuous, objective, and multidimensional assessment of cancer biology through integration of artificial intelligence (AI), multimodal biomedical data, wearable technologies, imaging, and molecular profiling. Unlike conventional biomarkers that are often measured at isolated time points, digital biomarkers provide dynamic longitudinal information capable of capturing disease evolution, therapeutic response, treatment-related toxicity, and patient physiology throughout the cancer care continuum. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative artificial intelligence have enabled comprehensive integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, patient-reported outcomes, and real-world clinical evidence into unified computational frameworks. These intelligent systems support early cancer detection, biomarker discovery, molecular characterization, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and intelligent clinical decision support. Emerging technologies including multimodal large language models, federated learning, retrieval-augmented generation, explainable artificial intelligence, agentic AI, cloud-native healthcare platforms, and Internet of Medical Things (IoMT) technologies further strengthen digital biomarker ecosystems by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, explainability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of digital biomarkers and artificial intelligence, emphasizing their transformative role in precision oncology and personalized therapeutics. International Journal of Advanced Research and Innovations 2026-07-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/72 International Journal of Advanced Research and Innovations; Vol. 13 No. 12 (2023): IJARAI: Vol.13, Issue 12, December 2023 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/72/71 Copyright (c) 2023 Dr. Joel Varghese (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/73 2026-07-30T16:45:43Z files:ART

EDGE ARTIFICIAL INTELLIGENCE IN ONCOLOGY: REAL-TIME CANCER DIAGNOSTICS AND INTELLIGENT CLINICAL MONITORING Dr. Pavan Shetty Edge artificial intelligence Precision oncology Foundation models Computational oncology Digital pathology Real-time diagnostics Intelligent monitoring Wearable technologies Clinical decision support Personalized medicine Edge artificial intelligence (Edge AI) is emerging as a transformative technology in precision oncology by enabling real-time processing of biomedical data directly at or near the point of care, thereby reducing latency, enhancing data privacy, and supporting intelligent clinical monitoring throughout the cancer care continuum. Unlike conventional cloud-dependent artificial intelligence systems, Edge AI integrates computational intelligence within medical imaging devices, digital pathology platforms, wearable biosensors, mobile health technologies, and Internet of Medical Things (IoMT) infrastructure to provide immediate clinical insights without requiring continuous remote computation. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative artificial intelligence have enabled comprehensive integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes into intelligent edge computing ecosystems. These systems support early cancer detection, biomarker discovery, molecular characterization, prognostic prediction, therapeutic optimization, immunotherapy selection, adaptive disease monitoring, digital twin simulation, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, explainable artificial intelligence, agentic AI, cloud-edge hybrid architectures, and 6G-enabled healthcare networks further strengthen Edge AI by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding computational resource constraints, multimodal data harmonization, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of Edge AI in oncology, emphasizing real-time cancer diagnostics and intelligent clinical monitoring as transformative technologies for next-generation precision cancer care. International Journal of Advanced Research and Innovations 2026-07-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/73 International Journal of Advanced Research and Innovations; Vol. 13 No. 12 (2023): IJARAI: Vol.13, Issue 12, December 2023 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/73/72 Copyright (c) 2023 Dr. Pavan Shetty (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/74 2026-07-30T16:52:17Z files:ART

AI-DRIVEN PRECISION CLINICAL TRIALS: FOUNDATION MODELS FOR ADAPTIVE ONCOLOGY RESEARCH AND PERSONALIZED THERAPEUTICS Dr. Nikhil Varma Dr. Asha Krishnan Precision clinical trials Foundation models Artificial intelligence Adaptive oncology Personalized therapeutics Computational oncology Clinical research Digital biomarkers Clinical decision support Precision medicine Artificial intelligence (AI) is fundamentally transforming oncology clinical research through the emergence of foundation models capable of enabling adaptive, data-driven, and patient-centered clinical trials. Conventional oncology trials frequently face substantial challenges including prolonged patient recruitment, heterogeneous populations, limited biomarker stratification, high operational costs, delayed endpoint evaluation, and restricted generalizability. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative artificial intelligence have enabled comprehensive integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, biomedical literature, and longitudinal clinical outcomes into unified computational frameworks. These intelligent systems support patient identification, biomarker discovery, adaptive trial design, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, real-time safety monitoring, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, retrieval-augmented generation, explainable artificial intelligence, agentic AI, cloud-native clinical research platforms, and digital biomarkers further strengthen precision clinical trials by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological advances, important scientific, technical, ethical, regulatory, and implementation challenges remain regarding multimodal data harmonization, computational scalability, interoperability, cybersecurity, clinical validation, regulatory acceptance, and equitable implementation. This review provides a comprehensive overview of AI-driven precision clinical trials, emphasizing foundation models for adaptive oncology research and personalized therapeutics. International Journal of Advanced Research and Innovations 2026-07-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/74 International Journal of Advanced Research and Innovations; Vol. 13 No. 12 (2023): IJARAI: Vol.13, Issue 12, December 2023 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/74/73 Copyright (c) 2023 Dr. Nikhil Varma, Dr. Asha Krishnan (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/75 2026-07-30T16:58:21Z files:ART

INTEGRATIVE CANCER INTELLIGENCE: FOUNDATION MODELS FOR RADIOMICS, PATHOMICS, MULTI-OMICS, AND DIGITAL HEALTH Dr. Preetha Nair Dr. Danish Ahmed Integrative cancer intelligence Foundation models Radiomics Pathomics Multi-omics Digital health Precision oncology Computational oncology Artificial intelligence Personalized medicine Integrative cancer intelligence is emerging as a transformative paradigm in precision oncology by combining foundation artificial intelligence (AI) models with multimodal biomedical data derived from radiomics, pathomics, multi-omics, and digital health technologies. Traditional oncology workflows often analyze radiological imaging, histopathology, molecular biomarkers, laboratory investigations, and clinical records independently, limiting comprehensive characterization of tumor biology and individualized therapeutic decision-making. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative artificial intelligence have enabled comprehensive integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, digital biomarkers, and longitudinal clinical outcomes into unified computational representations of cancer biology. These intelligent systems support early diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, retrieval-augmented generation, explainable artificial intelligence, agentic AI, cloud-native healthcare platforms, and digital health ecosystems further strengthen computational oncology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, cybersecurity, clinical validation, regulatory acceptance, and equitable implementation. This review provides a comprehensive overview of integrative cancer intelligence, emphasizing foundation models for radiomics, pathomics, multi-omics, and digital health as transformative technologies for next-generation precision oncology. International Journal of Advanced Research and Innovations 2026-07-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/75 International Journal of Advanced Research and Innovations; Vol. 13 No. 12 (2023): IJARAI: Vol.13, Issue 12, December 2023 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/75/74 Copyright (c) 2023 Dr. Preetha Nair, Dr. Danish Ahmed (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/76 2026-07-30T17:04:49Z files:ART

ARTIFICIAL INTELLIGENCE FOR PRECISION CANCER PREVENTION: PREDICTIVE ANALYTICS, RISK STRATIFICATION, AND EARLY INTERVENTION Dr. Keshav Prasad Dr. Sonia Kapoor Dr. Abhinav Reddy Precision cancer prevention Artificial intelligence Foundation models Predictive analytics Risk stratification Early intervention Precision oncology Digital health Computational oncology Personalized medicine Artificial intelligence (AI) is transforming cancer prevention by enabling precision risk prediction, individualized screening, early disease detection, and personalized preventive interventions through comprehensive integration of multimodal biomedical data. Conventional cancer prevention strategies primarily rely on population-level risk estimates based on age, family history, environmental exposures, and lifestyle factors, often failing to capture the complex biological heterogeneity underlying cancer susceptibility and disease initiation. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative artificial intelligence have enabled integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, microbiomics, spatial biology, circulating tumor DNA, laboratory biomarkers, wearable physiological monitoring, environmental exposures, electronic health records, digital biomarkers, and longitudinal clinical outcomes into unified computational representations of cancer risk. These intelligent systems support individualized risk stratification, biomarker discovery, personalized screening, early cancer detection, behavioral intervention, chemoprevention, lifestyle optimization, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, retrieval-augmented generation, explainable artificial intelligence, agentic AI, digital twins, cloud-native healthcare platforms, and digital health ecosystems further strengthen preventive oncology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, cybersecurity, clinical validation, regulatory acceptance, and equitable implementation. This review provides a comprehensive overview of artificial intelligence for precision cancer prevention, emphasizing predictive analytics, risk stratification, and early intervention as transformative approaches for next-generation preventive oncology. International Journal of Advanced Research and Innovations 2026-07-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/76 International Journal of Advanced Research and Innovations; Vol. 13 No. 12 (2023): IJARAI: Vol.13, Issue 12, December 2023 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/76/75 Copyright (c) 2023 Dr. Keshav Prasad, Dr. Sonia Kapoor, Dr. Abhinav Reddy (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/77 2026-07-30T17:09:39Z files:ART

HUMAN–ARTIFICIAL INTELLIGENCE COLLABORATION IN ONCOLOGY: BUILDING TRUSTWORTHY CLINICAL INTELLIGENCE SYSTEMS Dr. Sandeep Chatterjee Human–AI collaboration Trustworthy artificial intelligence Precision oncology Foundation models Clinical intelligence Explainable AI Clinical decision support Digital health Computational oncology Personalized medicine Human–artificial intelligence (AI) collaboration is redefining precision oncology by establishing trustworthy clinical intelligence systems that combine computational power with clinician expertise to improve cancer diagnosis, therapeutic decision-making, disease monitoring, and personalized patient care. Rather than replacing healthcare professionals, modern AI systems function as collaborative partners that augment multidisciplinary oncology teams through integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, biomedical literature, and longitudinal clinical outcomes. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative artificial intelligence have enabled comprehensive multimodal reasoning across heterogeneous biomedical data while supporting explainable, adaptive, and continuously learning clinical decision support. Emerging technologies including multimodal large language models, federated learning, retrieval-augmented generation, agentic AI, digital twins, cloud-native healthcare platforms, and digital health ecosystems further strengthen collaborative intelligence by enabling transparent, privacy-preserving, evidence-based, and human-centered oncology practice. Despite remarkable technological progress, successful implementation requires overcoming challenges related to explainability, clinical validation, interoperability, algorithmic bias, cybersecurity, regulatory governance, clinician trust, patient acceptance, and ethical deployment. This review provides a comprehensive overview of human–AI collaboration in oncology, emphasizing the development of trustworthy clinical intelligence systems capable of supporting adaptive precision medicine and personalized therapeutics. International Journal of Advanced Research and Innovations 2026-07-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/77 International Journal of Advanced Research and Innovations; Vol. 13 No. 12 (2023): IJARAI: Vol.13, Issue 12, December 2023 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/77/76 Copyright (c) 2023 Dr. Sandeep Chatterjee (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/78 2026-07-30T17:15:33Z files:ART

THE FUTURE OF PRECISION ONCOLOGY: CONVERGING FOUNDATION MODELS, AGENTIC AI, DIGITAL TWINS, AND MULTIMODAL CLINICAL INTELLIGENCE Dr. Anusha Pillai Precision oncology Foundation models Agentic AI Digital twins Multimodal clinical intelligence Artificial intelligence Computational oncology Personalized therapeutics Digital health Precision medicine Precision oncology is entering a transformative era driven by the convergence of foundation artificial intelligence (AI) models, agentic AI, digital twins, multimodal clinical intelligence, and continuously learning healthcare ecosystems. Traditional precision oncology has significantly improved diagnosis and treatment by incorporating molecular profiling and targeted therapies; however, fragmented biomedical data, limited interoperability, delayed clinical decision-making, and insufficient integration of heterogeneous patient information continue to constrain personalized cancer care. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative artificial intelligence have enabled comprehensive integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, biomedical literature, digital biomarkers, and longitudinal clinical outcomes into unified computational representations of cancer biology. Agentic AI systems extend these capabilities by autonomously coordinating diagnostic workflows, synthesizing biomedical evidence, optimizing therapeutic recommendations, monitoring disease progression, and supporting multidisciplinary clinical decision-making under human supervision. Simultaneously, digital twin technology enables continuously evolving virtual representations of individual patients capable of simulating disease progression, therapeutic response, toxicity, recurrence, and personalized treatment strategies. Emerging technologies including multimodal large language models, federated learning, retrieval-augmented generation, explainable artificial intelligence, cloud-native healthcare platforms, quantum computing, robotics, and digital health ecosystems further strengthen computational oncology by enabling collaborative, transparent, privacy-preserving, and adaptive clinical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, cybersecurity, clinical validation, regulatory acceptance, and equitable implementation. This review provides a comprehensive overview of the future of precision oncology, emphasizing the convergence of foundation models, agentic AI, digital twins, and multimodal clinical intelligence as transformative technologies for next-generation cancer medicine. International Journal of Advanced Research and Innovations 2026-07-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/78 International Journal of Advanced Research and Innovations; Vol. 13 No. 12 (2023): IJARAI: Vol.13, Issue 12, December 2023 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/78/77 Copyright (c) 2023 Dr. Anusha Pillai (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/79 2026-07-30T17:30:25Z files:ART

AGENTIC FOUNDATION MODELS FOR PRECISION ONCOLOGY: AUTONOMOUS CLINICAL INTELLIGENCE ACROSS THE CANCER CARE CONTINUUM Dr. Raghav Menon Dr. Divya Krishnan Agentic AI Foundation models Precision oncology Autonomous clinical intelligence Digital twins Multimodal artificial intelligence Computational oncology Clinical decision support Personalized therapeutics Precision medicine Agentic foundation models represent the next evolutionary stage of artificial intelligence (AI) in precision oncology by integrating autonomous reasoning, multimodal clinical intelligence, adaptive decision support, and continuous learning across the cancer care continuum. Unlike conventional AI systems that perform isolated predictive tasks, agentic foundation models coordinate complex clinical workflows through autonomous planning, evidence synthesis, multimodal reasoning, and intelligent collaboration with healthcare professionals under human supervision. Recent advances in multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, retrieval-augmented generation, and generative artificial intelligence have enabled comprehensive integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, biomedical literature, and longitudinal clinical outcomes into unified computational representations of cancer biology. These intelligent systems support precision diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, adaptive clinical trials, digital twin simulation, survivorship management, and evidence-based multidisciplinary decision-making. Emerging technologies including multimodal large language models, federated learning, explainable artificial intelligence, cloud-native healthcare platforms, digital health ecosystems, robotics, and autonomous clinical orchestration further strengthen agentic oncology systems by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, regulatory, and implementation challenges remain regarding multimodal data harmonization, computational scalability, interoperability, cybersecurity, clinical validation, governance, and equitable implementation. This review provides a comprehensive overview of agentic foundation models for precision oncology, emphasizing autonomous clinical intelligence across the entire cancer care continuum International Journal of Advanced Research and Innovations 2026-07-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/79 International Journal of Advanced Research and Innovations; Vol. 13 No. 12 (2023): IJARAI: Vol.13, Issue 12, December 2023 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/79/78 Copyright (c) 2023 Dr. Raghav Menon, Dr. Divya Krishnan (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/80 2026-07-30T18:41:25Z files:ART

INTELLIGENT MULTIMODAL ONCOLOGY: INTEGRATING RADIOLOGY, PATHOLOGY, MULTI-OMICS, AND WEARABLE HEALTH DATA Dr. Mohammed Asif Multimodal oncology Foundation models Radiology Pathology Multi-omics Wearable health Artificial intelligence Computational oncology Precision medicine Personalized therapeutics Intelligent multimodal oncology is emerging as a transformative paradigm in precision cancer medicine by integrating radiology, pathology, multi-omics, wearable health technologies, and foundation artificial intelligence (AI) models into unified computational ecosystems. Conventional oncology workflows frequently analyze radiological imaging, histopathology, molecular biomarkers, laboratory investigations, and physiological monitoring independently, limiting comprehensive characterization of tumor biology and personalized therapeutic decision-making. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative artificial intelligence have enabled seamless integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, biomedical literature, and longitudinal clinical outcomes into unified computational representations of cancer biology. These intelligent systems support early diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, adaptive disease monitoring, immunotherapy selection, digital twin simulation, survivorship management, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, retrieval-augmented generation, explainable artificial intelligence, agentic AI, cloud-native healthcare platforms, and digital health ecosystems further strengthen multimodal oncology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, cybersecurity, clinical validation, regulatory acceptance, and equitable implementation. This review provides a comprehensive overview of intelligent multimodal oncology, emphasizing integration of radiology, pathology, multi-omics, and wearable health data as transformative technologies for next-generation precision oncology. International Journal of Advanced Research and Innovations 2026-07-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/80 International Journal of Advanced Research and Innovations; Vol. 13 No. 12 (2023): IJARAI: Vol.13, Issue 12, December 2023 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/80/79 Copyright (c) 2023 Dr. Mohammed Asif (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/81 2026-07-30T18:46:00Z files:ART

ARTIFICIAL INTELLIGENCE–DRIVEN CANCER KNOWLEDGE GRAPHS FOR PRECISION DIAGNOSIS AND THERAPEUTIC DISCOVERY Dr. LavanyaIyer Cancer knowledge graphs Foundation models Graph neural networks Artificial intelligence Precision oncology Therapeutic discovery Computational oncology Clinical decision support Precision medicine Biomedical knowledge Artificial intelligence (AI)-driven cancer knowledge graphs are emerging as a transformative computational paradigm for precision oncology by integrating heterogeneous biomedical information into structured, interconnected representations of cancer biology. Conventional oncology workflows frequently analyze radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, laboratory investigations, biomedical literature, electronic health records, and clinical guidelines independently, limiting comprehensive understanding of molecular mechanisms and therapeutic opportunities. Recent advances in foundation AI models, graph neural networks, multimodal transformer architectures, self-supervised learning, reinforcement learning, retrieval-augmented generation, and generative artificial intelligence have enabled construction of dynamic cancer knowledge graphs capable of integrating genes, proteins, signaling pathways, biomarkers, imaging phenotypes, histopathological characteristics, therapeutic agents, clinical trials, digital biomarkers, patient outcomes, and biomedical evidence into unified computational ecosystems. These intelligent systems support precision diagnosis, biomarker discovery, molecular pathway analysis, drug target identification, therapeutic optimization, drug repurposing, adaptive clinical trials, digital twin simulation, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, explainable artificial intelligence, agentic AI, cloud-native healthcare platforms, and digital health ecosystems further strengthen cancer knowledge graphs by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding knowledge harmonization, computational scalability, interoperability, data quality, cybersecurity, clinical validation, regulatory acceptance, and equitable implementation. This review provides a comprehensive overview of AI-driven cancer knowledge graphs, emphasizing their role in precision diagnosis and therapeutic discovery for next-generation oncology. International Journal of Advanced Research and Innovations 2026-07-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/81 International Journal of Advanced Research and Innovations; Vol. 13 No. 12 (2023): IJARAI: Vol.13, Issue 12, December 2023 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/81/80 Copyright (c) 2023 Dr. LavanyaIyer (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/82 2026-07-30T18:50:32Z files:ART

DIGITAL TWIN INTELLIGENCE IN ONCOLOGY: AI-POWERED VIRTUAL PATIENTS FOR PERSONALIZED TREATMENT OPTIMIZATION Dr. Anand Pillai Digital twins, Precision oncology Foundation models Artificial intelligence Virtual patients Personalized therapeutics Computational oncology Digital biomarkers Clinical decision support Precision medicine Digital twin intelligence is emerging as one of the most transformative innovations in precision oncology by creating continuously evolving virtual representations of individual cancer patients that integrate multimodal biomedical information to support personalized diagnosis, therapeutic optimization, disease monitoring, and clinical decision-making. Unlike conventional predictive models, digital twins continuously synchronize with real-world patient data, enabling dynamic simulation of disease progression, treatment response, toxicity, recurrence, and survivorship. Recent advances in foundation artificial intelligence (AI) models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative artificial intelligence have enabled comprehensive integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, biomedical literature, and longitudinal clinical outcomes into adaptive computational patient models. These intelligent virtual patients support biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, adaptive clinical trials, digital biomarker development, and evidence-based multidisciplinary decision support. Emerging technologies including multimodal large language models, agentic AI, federated learning, explainable artificial intelligence, retrieval-augmented generation, cloud-native healthcare infrastructure, and digital health ecosystems further strengthen digital twin intelligence by enabling collaborative, privacy-preserving, transparent, and continuously adaptive computational oncology. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, cybersecurity, clinical validation, governance, and equitable implementation. This review provides a comprehensive overview of digital twin intelligence in oncology, emphasizing AI-powered virtual patients for personalized treatment optimization across the cancer care continuum. International Journal of Advanced Research and Innovations 2026-07-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/82 International Journal of Advanced Research and Innovations; Vol. 13 No. 12 (2023): IJARAI: Vol.13, Issue 12, December 2023 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/82/81 Copyright (c) 2023 Dr. Anand Pillai (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/83 2026-07-30T19:03:12Z files:ART

FOUNDATION MODELS FOR COMPUTATIONAL CANCER BIOLOGY: BRIDGING MOLECULAR MECHANISMS AND CLINICAL TRANSLATION Dr. Gautham Reddy Foundation models Computational cancer biology Artificial intelligence Precision oncology Multi-omics Molecular mechanisms Clinical translation Digital pathology Computational oncology Personalized medicine Foundation artificial intelligence (AI) models are transforming computational cancer biology by bridging molecular mechanisms with clinical translation through large-scale multimodal representation learning and adaptive biomedical reasoning. Traditional computational oncology has generated substantial biological insights from genomics, transcriptomics, proteomics, metabolomics, and medical imaging; however, fragmented analysis of heterogeneous biomedical data has limited comprehensive understanding of tumor evolution, therapeutic resistance, biomarker discovery, and precision medicine. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative artificial intelligence have enabled integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, electronic health records, biomedical literature, and longitudinal clinical outcomes into unified computational representations of cancer biology. These intelligent systems support molecular characterization, biomarker discovery, pathway analysis, prognostic prediction, therapeutic optimization, drug target identification, adaptive clinical trials, digital twin simulation, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, agentic AI, federated learning, retrieval-augmented generation, explainable artificial intelligence, cancer knowledge graphs, cloud-native healthcare platforms, and digital health ecosystems further strengthen computational cancer biology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, cybersecurity, clinical validation, regulatory acceptance, and equitable implementation. This review provides a comprehensive overview of foundation models for computational cancer biology, emphasizing their role in bridging molecular mechanisms with clinical translation for next-generation precision oncology. International Journal of Advanced Research and Innovations 2026-07-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/83 International Journal of Advanced Research and Innovations; Vol. 13 No. 12 (2023): IJARAI: Vol.13, Issue 12, December 2023 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/83/82 Copyright (c) 2023 Dr. Gautham Reddy (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/84 2026-07-30T19:18:18Z files:ART

EXPLAINABLE MULTIMODAL ARTIFICIAL INTELLIGENCE IN PRECISION ONCOLOGY: FROM BIOMARKER DISCOVERY TO CLINICAL ADOPTION Dr. Suresh Narayanan Dr. Aishwarya Menon Dr. Rohit Chandra Explainable artificial intelligence Multimodal AI Precision oncology Foundation models Biomarker discovery Clinical decision support Computational oncology Digital pathology Radiomics Personalized medicine Explainable multimodal artificial intelligence (XAI) is emerging as a transformative paradigm in precision oncology by combining transparent computational reasoning with comprehensive integration of heterogeneous biomedical data. Although deep learning and foundation artificial intelligence (AI) models have achieved remarkable diagnostic and prognostic performance, their limited interpretability has constrained widespread clinical adoption due to concerns regarding transparency, trust, accountability, and regulatory acceptance. Recent advances in multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, foundation AI models, and generative artificial intelligence have enabled seamless integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, biomedical literature, and longitudinal clinical outcomes into unified computational representations of cancer biology. Explainable AI techniques including attention visualization, saliency mapping, SHAP (Shapley Additive Explanations), integrated gradients, counterfactual reasoning, uncertainty estimation, concept attribution, and causal inference enable transparent interpretation of multimodal predictions while supporting biomarker discovery, therapeutic optimization, adaptive disease monitoring, digital twin simulation, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, agentic AI, federated learning, retrieval-augmented generation, cancer knowledge graphs, cloud-native healthcare platforms, and digital health ecosystems further strengthen explainable oncology by enabling collaborative, privacy-preserving, trustworthy, and continuously adaptive biomedical intelligence. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, algorithmic bias, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of explainable multimodal artificial intelligence in precision oncology, emphasizing its role from biomarker discovery to routine clinical adoption. International Journal of Advanced Research and Innovations 2026-07-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/84 International Journal of Advanced Research and Innovations; Vol. 13 No. 12 (2023): IJARAI: Vol.13, Issue 12, December 2023 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/84/83 Copyright (c) 2023 Dr. Suresh Narayanan, Dr. Aishwarya Menon, Dr. Rohit Chandra (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/85 2026-07-31T04:35:45Z files:ART

AI-ENABLED PRECISION RADIOPATHOMICS: INTEGRATING IMAGING, HISTOPATHOLOGY, AND GENOMICS FOR PERSONALIZED CANCER CARE Dr. Vishnu Prasad Radiopathomics Foundation models Artificial intelligence Precision oncology Digital pathology Radiomics Genomics Computational oncology Personalized medicine Multimodal learning Artificial intelligence (AI)-enabled precision radiopathomics has emerged as a transformative paradigm in precision oncology by integrating radiological imaging, digital histopathology, genomics, and multimodal biomedical data into unified computational frameworks for personalized cancer care. Conventional oncology frequently analyzes imaging, pathology, and molecular biomarkers independently, limiting comprehensive understanding of tumor heterogeneity, molecular evolution, therapeutic responsiveness, and disease progression. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative artificial intelligence have enabled seamless integration of computed tomography, magnetic resonance imaging, positron emission tomography, ultrasound, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, biomedical literature, and longitudinal clinical outcomes into unified computational representations of cancer biology. These intelligent systems support precision diagnosis, biomarker discovery, molecular characterization, prognostic prediction, therapeutic optimization, digital twin simulation, adaptive disease monitoring, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, retrieval-augmented generation, explainable artificial intelligence, agentic AI, cloud-native healthcare platforms, cancer knowledge graphs, and digital health ecosystems further strengthen radiopathomics by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, cybersecurity, clinical validation, regulatory acceptance, and equitable implementation. This review provides a comprehensive overview of AI-enabled precision radiopathomics, emphasizing integration of imaging, histopathology, and genomics for personalized cancer care. International Journal of Advanced Research and Innovations 2026-07-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/85 International Journal of Advanced Research and Innovations; Vol. 13 No. 12 (2023): IJARAI: Vol.13, Issue 12, December 2023 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/85/84 Copyright (c) 2023 Dr. Vishnu Prasad (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/86 2026-07-31T04:41:20Z files:ART

CANCER INTELLIGENCE NETWORKS: FEDERATED FOUNDATION MODELS FOR SECURE AND COLLABORATIVE PRECISION ONCOLOGY Dr. Aparna Krishnan Federated learning Foundation models Cancer intelligence networks Precision oncology Artificial intelligence Computational oncology Digital pathology Radiomics Privacy-preserving AI Personalized medicine Cancer intelligence networks are emerging as a transformative paradigm in precision oncology by combining federated foundation artificial intelligence (AI) models, distributed biomedical intelligence, and privacy-preserving computational infrastructures to enable secure collaboration across healthcare institutions. Conventional AI development in oncology is frequently constrained by fragmented biomedical datasets, institutional data silos, regulatory restrictions, patient privacy concerns, and limited generalizability of locally trained algorithms. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, federated learning, reinforcement learning, and generative artificial intelligence have enabled decentralized integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, biomedical literature, and longitudinal clinical outcomes without direct exchange of sensitive patient information. These intelligent systems support precision diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, digital twin simulation, adaptive clinical trials, knowledge graph construction, and evidence-based clinical decision support while preserving institutional autonomy and patient confidentiality. Emerging technologies including multimodal large language models, agentic AI, retrieval-augmented generation, explainable artificial intelligence, cloud-native healthcare platforms, differential privacy, secure multi-party computation, and digital health ecosystems further strengthen collaborative precision oncology by enabling trustworthy, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, regulatory, and implementation challenges remain regarding interoperability, computational scalability, cybersecurity, model harmonization, governance, fairness, and equitable deployment. This review provides a comprehensive overview of cancer intelligence networks, emphasizing federated foundation models for secure and collaborative precision oncology. International Journal of Advanced Research and Innovations 2026-07-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/86 International Journal of Advanced Research and Innovations; Vol. 13 No. 12 (2023): IJARAI: Vol.13, Issue 12, December 2023 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/86/85 Copyright (c) 2023 Dr. Aparna Krishnan (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/87 2026-07-31T04:50:34Z files:ART

PREDICTIVE ONCOLOGY THROUGH FOUNDATION AI: INTEGRATING LONGITUDINAL CLINICAL DATA, MULTI-OMICS, AND DIGITAL TWINS Dr. Shreya Kulkarni Predictive oncology Foundation models Artificial intelligence Digital twins Longitudinal clinical data Multi-omics Precision oncology Computational oncology Personalized medicine Clinical decision support Predictive oncology is undergoing a transformative evolution through the integration of foundation artificial intelligence (AI) models, longitudinal clinical data, multi-omics technologies, and digital twin systems into comprehensive computational ecosystems capable of anticipating disease progression, therapeutic response, recurrence, and survival. Conventional oncology frequently relies on static clinical assessments and isolated molecular analyses, limiting accurate prediction of evolving tumor biology and individualized treatment outcomes. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative artificial intelligence have enabled seamless integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, biomedical literature, and longitudinal clinical outcomes into unified computational representations of cancer biology. These intelligent systems support precision diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, adaptive disease monitoring, digital twin simulation, intelligent clinical decision support, and personalized cancer care. Emerging technologies including multimodal large language models, agentic AI, federated learning, retrieval-augmented generation, explainable artificial intelligence, cancer knowledge graphs, cloud-native healthcare platforms, and digital health ecosystems further strengthen predictive oncology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, cybersecurity, clinical validation, regulatory acceptance, and equitable implementation. This review provides a comprehensive overview of predictive oncology through foundation AI, emphasizing integration of longitudinal clinical data, multi-omics, and digital twins for next-generation precision oncology. International Journal of Advanced Research and Innovations 2026-07-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/87 International Journal of Advanced Research and Innovations; Vol. 13 No. 12 (2023): IJARAI: Vol.13, Issue 12, December 2023 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/87/86 Copyright (c) 2023 Dr. Shreya Kulkarni (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/88 2026-07-31T04:55:52Z files:ART

NEXT-GENERATION INTELLIGENT ONCOLOGY: CONVERGING AGENTIC AI, FOUNDATION MODELS, AND PRECISION MEDICINE FOR PERSONALIZED CANCER CARE Dr. Ritu Sharma Agentic AI Foundation models Precision oncology Intelligent oncology Personalized medicine Digital twins Computational oncology Clinical decision support Artificial intelligence Multimodal learning Next-generation intelligent oncology is emerging as a transformative paradigm in precision medicine through the convergence of agentic artificial intelligence (AI), foundation models, multimodal biomedical intelligence, and adaptive clinical decision support. Conventional precision oncology has significantly improved diagnosis and targeted therapy through molecular characterization; however, fragmented biomedical data, limited interoperability, static predictive models, and increasing clinical complexity continue to challenge personalized cancer management. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, agentic AI, and generative artificial intelligence have enabled seamless integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, biomedical literature, and longitudinal clinical outcomes into unified computational representations of cancer biology. Agentic AI extends these capabilities through autonomous planning, workflow orchestration, adaptive reasoning, evidence synthesis, and intelligent collaboration with multidisciplinary oncology teams under continuous human supervision. These intelligent systems support precision diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, digital twin simulation, adaptive clinical trials, survivorship management, and evidence-based personalized cancer care. Emerging technologies including multimodal large language models, federated learning, retrieval-augmented generation, explainable artificial intelligence, cancer knowledge graphs, cloud-native healthcare platforms, robotics, and digital health ecosystems further strengthen intelligent oncology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, cybersecurity, clinical validation, governance, and equitable implementation. This review provides a comprehensive overview of next-generation intelligent oncology, emphasizing convergence of agentic AI, foundation models, and precision medicine for personalized cancer care. International Journal of Advanced Research and Innovations 2026-07-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/88 International Journal of Advanced Research and Innovations; Vol. 13 No. 12 (2023): IJARAI: Vol.13, Issue 12, December 2023 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/88/87 Copyright (c) 2023 Dr. Ritu Sharma (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/89 2026-07-31T06:44:00Z files:ART

MULTIMODAL DIGITAL BIOMARKERS IN ONCOLOGY: ARTIFICIAL INTELLIGENCE FOR EARLY DETECTION AND PERSONALIZED TREATMENT Dr. Nandita Roy Dr. Praveen Nair Digital biomarkers Artificial intelligence Precision oncology Foundation models Early cancer detection Personalized medicine Multimodal learning Computational oncology Clinical decision support Digital health Digital biomarkers have emerged as one of the most transformative innovations in precision oncology by enabling continuous, objective, and quantitative assessment of cancer biology through integration of multimodal biomedical data. Unlike conventional biomarkers that often rely on isolated molecular or laboratory measurements, multimodal digital biomarkers combine radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, wearable physiological monitoring, laboratory investigations, electronic health records, patient-reported outcomes, and real-world clinical data into comprehensive computational representations of disease. Recent advances in artificial intelligence (AI), particularly foundation models, multimodal transformers, graph neural networks, self-supervised learning, and generative AI, have fundamentally transformed digital biomarker discovery by enabling generalized biomedical representation learning across heterogeneous data modalities. These intelligent systems facilitate early cancer detection, molecular characterization, prognostic prediction, therapeutic response assessment, immunotherapy optimization, adaptive disease monitoring, digital twin simulation, and personalized clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen multimodal biomarker ecosystems by enabling collaborative, privacy-preserving, and continuously adaptive computational oncology. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, interpretability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of multimodal digital biomarkers in oncology, emphasizing artificial intelligence as the driving force for early cancer detection and personalized treatment. International Journal of Advanced Research and Innovations 2026-07-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/89 International Journal of Advanced Research and Innovations; Vol. 14 No. 12 (2024): IJARAI: Vol.14, Issue 12, December 2024 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/89/88 Copyright (c) 2024 Dr. Nandita Roy, Dr. Praveen Nair (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/90 2026-07-31T07:00:16Z files:ART

CROSS-MODAL ARTIFICIAL INTELLIGENCE FOR PRECISION ONCOLOGY: BRIDGING RADIOLOGY, HISTOPATHOLOGY, GENOMICS, AND CLINICAL RECORDS Dr. Ritesh Kulkarni Cross-modal artificial intelligence Precision oncology Foundation models Radiology Histopathology Genomics Clinical records Multimodal learning Computational oncology Clinical decision support. Precision oncology has entered an era in which artificial intelligence (AI) is no longer confined to individual diagnostic tasks but instead functions as an integrative computational framework capable of unifying heterogeneous biomedical information across the entire cancer care continuum. Conventional machine learning approaches have achieved remarkable success in isolated domains such as radiology, digital pathology, genomics, or electronic health records; however, their modality-specific design frequently limits comprehensive understanding of tumor biology and clinical decision-making. Recent advances in cross-modal artificial intelligence, including foundation models, multimodal transformers, graph neural networks, self-supervised learning, and generative AI, have enabled seamless integration of radiological imaging, histopathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, laboratory biomarkers, electronic health records, wearable technologies, and longitudinal clinical outcomes into unified patient-centered computational representations. These intelligent systems facilitate molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and clinical decision support while accelerating precision medicine. Emerging technologies such as multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen cross-modal computational oncology by enabling collaborative, privacy-preserving, and continuously adaptive learning across diverse healthcare environments. Despite extraordinary technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of cross-modal artificial intelligence for precision oncology, emphasizing integration of radiology, histopathology, genomics, and clinical records as the foundation for future intelligent cancer medicine. International Journal of Advanced Research and Innovations 2026-07-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/90 International Journal of Advanced Research and Innovations; Vol. 14 No. 12 (2024): IJARAI: Vol.14, Issue 12, December 2024 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/90/89 Copyright (c) 2024 Dr. Ritesh Kulkarni (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/91 2026-07-31T07:12:07Z files:ART

ADAPTIVE FOUNDATION MODELS IN ONCOLOGY: CONTINUOUS LEARNING FOR PERSONALIZED CANCER MANAGEMENT Dr. Aparna Ghosh Adaptive foundation models Artificial intelligence Precision oncology Continuous learning Personalized medicine Computational oncology Digital twins Multimodal learning Clinical decision support Cancer intelligence Artificial intelligence (AI) is transforming oncology from a static decision-support technology into a continuously adaptive computational ecosystem capable of learning from evolving biomedical knowledge and patient-specific clinical trajectories. Conventional machine learning algorithms remain fixed after deployment and often struggle to accommodate the dynamic nature of tumor evolution, therapeutic resistance, changing clinical guidelines, and rapidly expanding biomedical evidence. Adaptive foundation models overcome these limitations through large-scale self-supervised learning, continual learning, multimodal transformer architectures, graph neural networks, reinforcement learning, and generative AI, enabling continuous refinement of predictive performance throughout the cancer care continuum. These intelligent computational systems integrate radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, liquid biopsy, wearable physiological monitoring, electronic health records, patient-reported outcomes, and longitudinal clinical data into unified patient-specific representations. Adaptive foundation models support precision diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, intelligent clinical decision support, and personalized survivorship management while continuously incorporating new biomedical evidence. Emerging technologies including multimodal large language models, federated learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen adaptive computational oncology by enabling collaborative, privacy-preserving, and continuously evolving precision medicine. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of adaptive foundation models in oncology, emphasizing continuous learning as a transformative paradigm for personalized cancer management. International Journal of Advanced Research and Innovations 2026-07-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/91 International Journal of Advanced Research and Innovations; Vol. 14 No. 12 (2024): IJARAI: Vol.14, Issue 12, December 2024 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/91/90 Copyright (c) 2024 Dr. Aparna Ghosh (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/92 2026-07-31T07:18:35Z files:ART

ARTIFICIAL INTELLIGENCE–ENABLED TUMOR MICROENVIRONMENT MODELING: INTEGRATING SPATIAL BIOLOGY, MULTI-OMICS, AND DIGITAL TWINS Dr. Aditya Menon Tumor microenvironment Artificial intelligence Spatial biology Digital twins Multi-omics Foundation models Precision oncology Computational oncology Systems oncology Clinical decision support. The tumor microenvironment (TME) has emerged as one of the most important determinants of cancer initiation, progression, metastasis, immune evasion, and therapeutic response. Rather than functioning as an isolated population of malignant cells, tumors exist within highly dynamic ecosystems comprising immune cells, stromal fibroblasts, endothelial cells, extracellular matrix components, vascular networks, signaling molecules, metabolites, and microbiota that collectively shape disease evolution. Conventional analytical approaches frequently investigate these biological components independently, limiting comprehensive understanding of tumor ecosystem dynamics. Recent advances in artificial intelligence (AI), particularly foundation models, multimodal transformers, graph neural networks, self-supervised learning, spatial computing, and generative AI, have enabled comprehensive computational modeling of the TME through integration of spatial biology, multi-omics, digital pathology, radiological imaging, laboratory biomarkers, liquid biopsy, electronic health records, and longitudinal clinical data. These intelligent systems support biomarker discovery, molecular characterization, prognostic prediction, immunotherapy optimization, adaptive treatment planning, digital twin simulation, and intelligent clinical decision support while advancing systems oncology and precision medicine. Emerging innovations including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen computational modeling of tumor ecosystems by enabling collaborative, privacy-preserving, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, interpretability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of AI-enabled tumor microenvironment modeling, emphasizing integration of spatial biology, multi-omics, and digital twins as a transformative paradigm for personalized oncology. International Journal of Advanced Research and Innovations 2026-07-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/92 International Journal of Advanced Research and Innovations; Vol. 14 No. 12 (2024): IJARAI: Vol.14, Issue 12, December 2024 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/92/91 Copyright (c) 2024 Dr. Aditya Menon (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/93 2026-07-31T07:27:34Z files:ART

FOUNDATION AI MODELS FOR PRECISION IMMUNOTHERAPY: PREDICTING RESPONSE, RESISTANCE, AND CLINICAL OUTCOMES Dr. Neeraj Khanna Mrs. Divya Joshi Precision immunotherapy Foundation models Artificial intelligence Immuno-oncology Tumor microenvironment Digital twins Multi-omics Clinical decision support Personalized medicine Computational oncology Immunotherapy has revolutionized modern oncology by enabling the immune system to recognize and eliminate malignant cells through immune checkpoint inhibitors, adoptive cellular therapies, cancer vaccines, bispecific antibodies, and emerging immunomodulatory strategies. Despite remarkable clinical success, therapeutic responses remain highly heterogeneous because of the extraordinary complexity of tumor–immune interactions, intratumoral heterogeneity, and dynamic evolution of the tumor microenvironment. Foundation artificial intelligence (AI) models have emerged as transformative computational frameworks capable of integrating heterogeneous biomedical information across radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, immunomics, spatial biology, laboratory biomarkers, liquid biopsy, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes into unified patient-specific representations. These intelligent systems enable comprehensive prediction of immunotherapy response, resistance mechanisms, toxicity, recurrence, and survival while supporting biomarker discovery, digital twin simulation, adaptive treatment planning, and intelligent clinical decision support. Recent advances in multimodal transformer architectures, graph neural networks, self-supervised learning, multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen precision immunotherapy by enabling collaborative, privacy-preserving, and continuously adaptive computational medicine. Despite remarkable technological progress, significant scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, interpretability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of foundation AI models for precision immunotherapy, emphasizing prediction of therapeutic response, resistance, and clinical outcomes as a transformative paradigm in personalized oncology. International Journal of Advanced Research and Innovations 2026-07-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/93 International Journal of Advanced Research and Innovations; Vol. 14 No. 12 (2024): IJARAI: Vol.14, Issue 12, December 2024 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/93/92 Copyright (c) 2024 Dr. Neeraj Khanna, Mrs. Divya Joshi (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/94 2026-07-31T08:51:23Z files:ART

THE NEXT GENERATION OF COMPUTATIONAL ONCOLOGY: EXPLAINABLE, MULTIMODAL, AND FEDERATED ARTIFICIAL INTELLIGENCE Dr. Prakash Nambiar Dr. Shweta Gupta Dr. Imran Khan Computational oncology Explainable artificial intelligence Federated learning Foundation models Multimodal learning Precision oncology Clinical decision support Digital pathology Radiomics Personalized medicine Computational oncology is undergoing a profound transformation driven by the convergence of explainable artificial intelligence (XAI), multimodal foundation models, federated learning, and precision medicine. Conventional machine learning approaches have substantially improved cancer diagnosis, prognostic prediction, and therapeutic planning but often remain constrained by limited interpretability, fragmented analysis of heterogeneous biomedical information, and restricted generalizability across healthcare systems. The next generation of computational oncology addresses these limitations through intelligent AI ecosystems capable of integrating radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, liquid biopsy, wearable physiological monitoring, electronic health records, patient-reported outcomes, and longitudinal clinical data into unified patient-specific computational representations. Explainable AI enhances transparency and clinician trust, multimodal learning enables systems-level biological understanding, and federated learning facilitates collaborative model development while preserving patient privacy. Together with graph neural networks, multimodal transformers, self-supervised learning, digital twins, reinforcement learning, multimodal large language models, retrieval-augmented generation, and agentic AI, these technologies establish intelligent computational ecosystems capable of supporting precision diagnosis, biomarker discovery, therapeutic optimization, adaptive disease monitoring, and evidence-based clinical decision support. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, cybersecurity, clinical validation, governance, and equitable implementation. This review provides a comprehensive overview of the next generation of computational oncology, emphasizing explainable, multimodal, and federated artificial intelligence as the computational foundation of future precision cancer medicine. International Journal of Advanced Research and Innovations 2026-07-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/94 International Journal of Advanced Research and Innovations; Vol. 14 No. 12 (2024): IJARAI: Vol.14, Issue 12, December 2024 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/94/93 Copyright (c) 2024 Dr. Prakash Nambiar, Dr. Shweta Gupta, Dr. Imran Khan (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/95 2026-07-31T09:07:58Z files:ART

INTELLIGENT CANCER FOUNDATION MODELS: INTEGRATING MOLECULAR, IMAGING, AND CLINICAL INTELLIGENCE FOR PRECISION MEDICINE Dr. ChetanMehra Foundation models Artificial intelligence Precision oncology Computational oncology Multi-omics Radiomics Digital pathology Clinical intelligence Personalized medicine Clinical decision support Artificial intelligence (AI) is transforming precision oncology through the emergence of intelligent foundation models capable of integrating heterogeneous biomedical information into unified computational frameworks supporting personalized cancer care. Conventional machine learning algorithms have demonstrated substantial success in isolated oncology applications; however, their dependence on task-specific training, fragmented analysis of multimodal datasets, and limited generalizability restrict their clinical scalability. Intelligent cancer foundation models overcome these limitations through large-scale self-supervised learning, multimodal transformer architectures, graph neural networks, and generative AI that learn generalized biomedical representations from radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, liquid biopsy, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational ecosystems support precision diagnosis, molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and intelligent clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further enhance foundation model ecosystems by enabling collaborative, privacy-preserving, and continuously adaptive computational oncology. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of intelligent cancer foundation models, emphasizing integration of molecular, imaging, and clinical intelligence as a transformative paradigm for precision medicine. International Journal of Advanced Research and Innovations 2026-07-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/95 International Journal of Advanced Research and Innovations; Vol. 14 No. 12 (2024): IJARAI: Vol.14, Issue 12, December 2024 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/95/94 Copyright (c) 2024 Dr. ChetanMehra (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/96 2026-07-31T09:14:24Z files:ART

AI-DRIVEN PRECISION CANCER ECOSYSTEMS: FROM LIQUID BIOPSY AND RADIOMICS TO DIGITAL TWINS Dr. Lakshmi Krishnan Precision oncology Artificial intelligence Liquid biopsy Radiomics Digital twins Foundation models Computational oncology Multi-omics Clinical decision support Personalized medicine Precision oncology is undergoing a profound transformation through the convergence of artificial intelligence (AI), liquid biopsy, radiomics, digital pathology, multi-omics, and digital twin technologies into intelligent computational ecosystems capable of continuously modeling cancer biology across the patient journey. Conventional oncology frequently relies on fragmented diagnostic modalities and episodic clinical assessments that inadequately capture the dynamic evolution of tumors and their interactions with the host environment. Recent advances in foundation AI models, multimodal transformers, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled comprehensive integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes into unified computational frameworks. These intelligent ecosystems support early cancer detection, biomarker discovery, molecular characterization, prognostic prediction, therapeutic optimization, immunotherapy selection, adaptive disease monitoring, and digital twin simulation while facilitating evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen computational oncology by enabling collaborative, privacy-preserving, and continuously adaptive biomedical intelligence. Despite remarkable technological advances, significant scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of AI-driven precision cancer ecosystems, emphasizing the integration of liquid biopsy, radiomics, and digital twins as a transformative paradigm for personalized cancer medicine. International Journal of Advanced Research and Innovations 2026-07-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/96 International Journal of Advanced Research and Innovations; Vol. 14 No. 12 (2024): IJARAI: Vol.14, Issue 12, December 2024 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/96/95 Copyright (c) 2024 Dr. Lakshmi Krishnan (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/97 2026-07-31T09:22:03Z files:ART

AUTONOMOUS ONCOLOGY SYSTEMS: FOUNDATION MODELS FOR REAL-TIME CLINICAL INTELLIGENCE AND PERSONALIZED CANCER CARE Dr. Akshay Jain Autonomous oncology Foundation models Artificial intelligence Precision oncology Clinical intelligence Digital twins Multimodal learning Computational oncology Personalized medicine Clinical decision support Artificial intelligence (AI) is rapidly transforming oncology from a reactive clinical discipline into an autonomous, continuously learning healthcare ecosystem capable of supporting real-time clinical intelligence and personalized cancer care. Conventional clinical decision-making often relies on episodic assessments, fragmented biomedical information, and static predictive models that inadequately reflect the dynamic biological evolution of cancer. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled the development of autonomous oncology systems capable of integrating radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, liquid biopsy, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes into unified patient-specific computational frameworks. These intelligent systems continuously learn from evolving biomedical evidence while supporting cancer diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and intelligent clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen autonomous oncology by enabling collaborative, privacy-preserving, and continuously adaptive computational medicine. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of autonomous oncology systems, emphasizing foundation AI models for real-time clinical intelligence and personalized cancer care as a transformative paradigm in precision oncology. International Journal of Advanced Research and Innovations 2026-07-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/97 International Journal of Advanced Research and Innovations; Vol. 14 No. 12 (2024): IJARAI: Vol.14, Issue 12, December 2024 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/97/96 Copyright (c) 2024 Dr. Akshay Jain (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/98 2026-07-31T09:33:51Z files:ART

FUTURE CANCER INTELLIGENCE: CONVERGING FOUNDATION AI, DIGITAL TWINS, MULTIMODAL LEARNING, AND PRECISION ONCOLOGY Dr. Yogesh Rao Mrs. Pooja Kulshrestha Dr. Karthik Bhat Foundation models Artificial intelligence Digital twins Precision oncology Multimodal learning Computational oncology Clinical intelligence Personalized medicine Digital pathology Multi-omics. Cancer care is entering a new era driven by the convergence of foundation artificial intelligence (AI), digital twins, multimodal learning, and precision oncology into intelligent computational ecosystems capable of continuously modeling disease biology and supporting personalized clinical decision-making. Conventional oncology frequently depends upon fragmented diagnostic workflows, isolated biomarker interpretation, and episodic therapeutic assessment that inadequately reflect the dynamic biological evolution of malignant disease. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled comprehensive integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes into unified patient-specific computational frameworks. These intelligent systems support early cancer detection, molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, adaptive disease monitoring, and digital twin simulation while continuously learning from evolving biomedical evidence. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further enhance computational oncology by enabling collaborative, privacy-preserving, and adaptive biomedical intelligence. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of future cancer intelligence, emphasizing the convergence of foundation AI, digital twins, multimodal learning, and precision oncology as a transformative paradigm for next-generation personalized cancer care. International Journal of Advanced Research and Innovations 2026-07-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/98 International Journal of Advanced Research and Innovations; Vol. 14 No. 12 (2024): IJARAI: Vol.14, Issue 12, December 2024 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/98/97 Copyright (c) 2024 Dr. Yogesh Rao, Mrs. Pooja Kulshrestha, Dr. Karthik Bhat (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/99 2026-07-31T09:47:16Z files:ART

FOUNDATION AI MODELS FOR SYSTEMS ONCOLOGY: INTEGRATING MULTI-OMICS, IMAGING, AND CLINICAL INTELLIGENCE FOR PRECISION CANCER CARE Dr. Harsha Menon Dr. Bhavana Rao Dr. Nilesh Patel Systems oncology Foundation models Artificial intelligence Precision oncology Multi-omics Digital pathology Radiomics Clinical intelligence Computational oncology Personalized medicine Systems oncology is emerging as a transformative paradigm in precision cancer medicine through the integration of artificial intelligence (AI), multi-omics, medical imaging, digital pathology, and longitudinal clinical intelligence into comprehensive computational ecosystems capable of modeling cancer as a dynamic biological system. Conventional oncology often relies on fragmented analysis of individual diagnostic modalities, limiting the understanding of complex interactions among molecular pathways, tumor microenvironments, host immunity, and clinical outcomes. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled unified analysis of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, liquid biopsy, wearable physiological monitoring, electronic health records, and real-world clinical evidence. These intelligent systems support precision diagnosis, molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen systems oncology by enabling collaborative, privacy-preserving, and continuously adaptive biomedical intelligence. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of foundation AI models for systems oncology, emphasizing the integration of multi-omics, imaging, and clinical intelligence as a transformative framework for precision cancer care. International Journal of Advanced Research and Innovations 2026-07-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/99 International Journal of Advanced Research and Innovations; Vol. 14 No. 12 (2024): IJARAI: Vol.14, Issue 12, December 2024 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/99/98 Copyright (c) 2024 Dr. Harsha Menon, Dr. Bhavana Rao, Dr. Nilesh Patel (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/100 2026-07-31T09:53:55Z files:ART

INTELLIGENT DIGITAL TWINS IN ONCOLOGY: REAL-TIME VIRTUAL PATIENT MODELING FOR PERSONALIZED THERAPEUTIC DECISION-MAKING Dr. Farhan Siddiqui Digital twins Precision oncology Artificial intelligence Foundation models Virtual patients Computational oncology Personalized medicine Clinical intelligence Multi-omics Clinical decision support Digital twin technology is emerging as one of the most transformative innovations in precision oncology by enabling the creation of continuously evolving virtual representations of individual cancer patients capable of supporting real-time therapeutic decision-making. Conventional oncology frequently relies on episodic clinical assessments and fragmented diagnostic information that inadequately capture the dynamic biological evolution of tumors throughout diagnosis, treatment, recurrence, metastasis, and survivorship. Recent advances in artificial intelligence (AI), foundation models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative AI have enabled integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes into adaptive virtual patient models. These intelligent digital twins continuously synchronize with evolving patient biology, supporting precision diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, toxicity prediction, adaptive disease monitoring, and personalized clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further enhance digital twin ecosystems by enabling collaborative, privacy-preserving, transparent, and continuously adaptive computational intelligence. Despite remarkable technological progress, significant scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, explainability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of intelligent digital twins in oncology, emphasizing real-time virtual patient modeling for personalized therapeutic decision-making as a transformative paradigm for next-generation precision cancer care. International Journal of Advanced Research and Innovations 2026-07-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/100 International Journal of Advanced Research and Innovations; Vol. 14 No. 12 (2024): IJARAI: Vol.14, Issue 12, December 2024 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/100/99 Copyright (c) 2024 Dr. Farhan Siddiqui (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/101 2026-07-31T10:00:55Z files:ART

MULTIMODAL FOUNDATION MODELS FOR PREDICTIVE ONCOLOGY: UNIFYING RADIOMICS, PATHOMICS, GENOMICS, AND ELECTRONIC HEALTH RECORDS Dr. Ishaan Kapoor Foundation models Predictive oncology Artificial intelligence Radiomics Pathomics Genomics Electronic health records Computational oncology Precision medicine Multimodal learning Predictive oncology is undergoing a paradigm shift through the emergence of multimodal foundation models capable of integrating radiomics, pathomics, genomics, electronic health records (EHRs), and other heterogeneous biomedical data into unified computational frameworks for precision cancer care. Conventional artificial intelligence (AI) models have achieved remarkable success in individual diagnostic or prognostic tasks; however, their dependence on task-specific training and isolated data modalities limits their ability to fully characterize the biological complexity of cancer. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled generalized representation learning across radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, liquid biopsy, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational systems support precision diagnosis, biomarker discovery, molecular characterization, prognostic prediction, therapeutic optimization, immunotherapy selection, adaptive disease monitoring, and personalized clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen predictive oncology by enabling collaborative, privacy-preserving, and continuously adaptive biomedical intelligence. Despite substantial technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of multimodal foundation models for predictive oncology, emphasizing the integration of radiomics, pathomics, genomics, and electronic health records as a transformative framework for personalized cancer medicine. International Journal of Advanced Research and Innovations 2026-07-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/101 International Journal of Advanced Research and Innovations; Vol. 14 No. 12 (2024): IJARAI: Vol.14, Issue 12, December 2024 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/101/100 Copyright (c) 2024 Dr. Ishaan Kapoor (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/102 2026-07-31T10:17:56Z files:ART

ARTIFICIAL INTELLIGENCE–DRIVEN CLINICAL ONCOLOGY INTELLIGENCE: FROM EARLY CANCER DETECTION TO PRECISION THERAPEUTICS Dr. Charitha Naidu Dr. Mohit Arora Mrs. Padmini Shetty Artificial intelligence Clinical oncology Precision oncology Foundation models Early cancer detection Digital pathology Radiomics Clinical decision support Multi-omics Personalized medicine Artificial intelligence (AI) is transforming modern oncology by enabling the development of intelligent clinical ecosystems capable of supporting the entire continuum of cancer care, from early detection and diagnosis to precision therapeutics and long-term survivorship. Conventional oncology frequently relies on fragmented diagnostic modalities, episodic clinical assessments, and static predictive models that inadequately capture the dynamic biological evolution of malignant disease. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled comprehensive integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes into unified patient-specific computational frameworks. These intelligent systems support early cancer detection, molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and intelligent clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen clinical oncology intelligence by enabling collaborative, privacy-preserving, and continuously adaptive biomedical reasoning. Despite remarkable technological progress, significant scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of AI-driven clinical oncology intelligence, emphasizing its role in advancing early cancer detection, precision therapeutics, and personalized cancer medicine. International Journal of Advanced Research and Innovations 2026-07-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/102 International Journal of Advanced Research and Innovations; Vol. 14 No. 12 (2024): IJARAI: Vol.14, Issue 12, December 2024 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/102/101 Copyright (c) 2024 Dr. Charitha Naidu, Dr. Mohit Arora, Mrs. Padmini Shetty (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/103 2026-07-31T10:28:17Z files:ART

AI-POWERED COMPUTATIONAL ONCOLOGY: ADVANCING PERSONALIZED CANCER MEDICINE THROUGH MULTIMODAL DATA FUSION Dr. Arjun Menon Dr. Roshni Sharma Mrs. Shilpa Rao Dr. Danish Khan Computational oncology Artificial intelligence Precision oncology Foundation models Multimodal learning Multi-omics Digital pathology Radiomics Clinical decision support Personalized medicine Computational oncology is undergoing a transformative evolution through the integration of artificial intelligence (AI), multimodal biomedical data fusion, and precision medicine into intelligent computational ecosystems capable of supporting personalized cancer care throughout the disease continuum. Conventional oncology frequently relies on fragmented interpretation of imaging, pathology, molecular diagnostics, and clinical information, thereby limiting comprehensive understanding of the complex biological mechanisms governing tumor evolution and therapeutic response. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled unified analysis of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational systems support early cancer detection, molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen computational oncology by enabling collaborative, privacy-preserving, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, significant scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of AI-powered computational oncology, emphasizing multimodal data fusion as a transformative framework for advancing personalized cancer medicine. International Journal of Advanced Research and Innovations 2026-07-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/103 International Journal of Advanced Research and Innovations; Vol. 14 No. 12 (2024): IJARAI: Vol.14, Issue 12, December 2024 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/103/102 Copyright (c) 2024 Dr. Arjun Menon, Dr. Roshni Sharma, Mrs. Shilpa Rao, Dr. Danish Khan (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/104 2026-07-31T11:20:35Z files:ART

PRECISION CANCER INTELLIGENCE: LEVERAGING FOUNDATION MODELS, DIGITAL TWINS, AND MULTIMODAL LEARNING FOR CLINICAL TRANSLATION Dr. MohanaIyer Dr. Pradeep Joshi Dr. Aditi Shah Precision oncology Foundation models Artificial intelligence Digital twins Multimodal learning Computational oncology Clinical intelligence Personalized medicine Multi-omics Clinical translation Precision cancer intelligence is emerging as a transformative paradigm in modern oncology through the convergence of foundation artificial intelligence (AI) models, digital twins, multimodal learning, and translational clinical intelligence. Conventional oncology frequently relies on fragmented diagnostic modalities and isolated interpretation of molecular, imaging, pathological, and clinical information, thereby limiting comprehensive understanding of the dynamic biological complexity of cancer. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled unified analysis of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational ecosystems support early cancer detection, biomarker discovery, molecular characterization, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen precision cancer intelligence by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical reasoning. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of precision cancer intelligence, emphasizing the convergence of foundation AI, digital twins, and multimodal learning for successful clinical translation and personalized cancer medicine. International Journal of Advanced Research and Innovations 2026-07-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/104 International Journal of Advanced Research and Innovations; Vol. 14 No. 12 (2024): IJARAI: Vol.14, Issue 12, December 2024 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/104/103 Copyright (c) 2024 Dr. MohanaIyer, Dr. Pradeep Joshi, Dr. Aditi Shah (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/105 2026-07-31T11:27:57Z files:ART

NEXT-GENERATION AI IN ONCOLOGY: EXPLAINABLE FOUNDATION MODELS FOR PRECISION DIAGNOSIS, PROGNOSIS, AND TREATMENT OPTIMIZATION Dr. Ravi Chaturvedi Explainable artificial intelligence Foundation models Precision oncology Computational oncology Digital pathology Radiomics Multi-omics Clinical decision support Personalized medicine Cancer diagnosis Artificial intelligence (AI) is reshaping modern oncology through the emergence of explainable foundation models capable of integrating heterogeneous biomedical information into intelligent computational ecosystems that support precision diagnosis, prognostic prediction, and individualized therapeutic optimization. Conventional machine learning algorithms have demonstrated remarkable performance in isolated oncology applications; however, their task-specific design, limited interpretability, and dependence on fragmented datasets restrict their widespread clinical implementation. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled generalized biomedical representation learning across radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational systems support early cancer detection, molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and evidence-based clinical decision support. Explainable artificial intelligence (XAI) techniques—including attention visualization, saliency mapping, feature attribution, SHAP (Shapley Additive Explanations), counterfactual reasoning, and uncertainty estimation—provide transparency necessary for clinician confidence, regulatory approval, and trustworthy deployment. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, and agentic AI further strengthen explainable computational oncology through collaborative, privacy-preserving, and continuously adaptive biomedical intelligence. Despite substantial technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, explainability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of next-generation explainable foundation AI models for precision diagnosis, prognosis, and treatment optimization in modern oncology. International Journal of Advanced Research and Innovations 2026-07-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/105 International Journal of Advanced Research and Innovations; Vol. 14 No. 12 (2024): IJARAI: Vol.14, Issue 12, December 2024 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/105/104 Copyright (c) 2024 Dr. Ravi Chaturvedi (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/106 2026-07-31T11:34:28Z files:ART

DIGITAL TWIN–ENABLED PRECISION ONCOLOGY: INTEGRATING LONGITUDINAL PATIENT DATA, MULTI-OMICS, AND ARTIFICIAL INTELLIGENCE Dr. Zubin Patel Digital twins Precision oncology Artificial intelligence Longitudinal patient data Multi-omics Foundation models Computational oncology Personalized medicine Clinical intelligence Virtual patients Digital twin technology is rapidly emerging as one of the most transformative innovations in precision oncology by enabling continuously evolving virtual representations of individual cancer patients capable of supporting predictive, personalized, and adaptive clinical decision-making. Traditional oncology frequently depends on fragmented interpretation of radiological imaging, molecular diagnostics, pathological findings, and episodic clinical assessments, limiting comprehensive understanding of the dynamic biological evolution of cancer. Recent advances in artificial intelligence (AI), foundation models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative AI have enabled seamless integration of longitudinal patient data, radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and real-world clinical outcomes into adaptive virtual patient ecosystems. These intelligent digital twins continuously synchronize with evolving patient biology to support precision diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, toxicity prediction, adaptive disease monitoring, and personalized clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen digital twin ecosystems by enabling collaborative, privacy-preserving, transparent, and continuously adaptive computational intelligence. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, explainability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of digital twin–enabled precision oncology, emphasizing integration of longitudinal patient data, multi-omics, and artificial intelligence as a transformative framework for next-generation personalized cancer medicine. International Journal of Advanced Research and Innovations 2026-07-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/106 International Journal of Advanced Research and Innovations; Vol. 14 No. 12 (2024): IJARAI: Vol.14, Issue 12, December 2024 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/106/105 Copyright (c) 2024 Dr. Zubin Patel (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/107 2026-07-31T11:50:19Z files:ART

TOWARDS GENERALIST ARTIFICIAL INTELLIGENCE IN ONCOLOGY: FOUNDATION MODELS FOR INTEGRATED CANCER DIAGNOSIS AND PERSONALIZED CARE Dr. Vivek Narayan Generalist artificial intelligence Foundation models Precision oncology Computational oncology Multimodal learning Personalized medicine Digital pathology Radiomics Clinical intelligence Clinical decision support The rapid evolution of artificial intelligence (AI) has accelerated the transition from task-specific algorithms toward generalist foundation models capable of performing diverse clinical, molecular, and imaging tasks using unified computational intelligence. In oncology, these next-generation systems have the potential to transform cancer diagnosis and personalized care by integrating heterogeneous biomedical information into continuously learning computational ecosystems. Conventional oncology frequently depends on fragmented interpretation of radiological imaging, digital pathology, molecular diagnostics, laboratory investigations, and clinical information, limiting comprehensive understanding of the dynamic biological complexity of cancer. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled generalized biomedical representation learning across radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational systems support early cancer detection, molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and intelligent clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen generalist oncology intelligence by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical reasoning. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of generalist artificial intelligence in oncology, emphasizing foundation models for integrated cancer diagnosis and personalized patient care. International Journal of Advanced Research and Innovations 2026-07-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/107 International Journal of Advanced Research and Innovations; Vol. 14 No. 12 (2024): IJARAI: Vol.14, Issue 12, December 2024 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/107/106 Copyright (c) 2024 Dr. Vivek Narayan (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/108 2026-07-31T12:30:16Z files:ART

AUTONOMOUS PRECISION ONCOLOGY: THE CONVERGENCE OF FOUNDATION AI MODELS, DIGITAL TWINS, AND COMPUTATIONAL CANCER INTELLIGENCE Dr. Nirmal Raj Autonomous oncology Foundation models Artificial intelligence Digital twins Computational oncology Precision medicine Clinical intelligence Multi-omics Personalized cancer care Clinical decision support Autonomous precision oncology represents the next evolutionary stage of computational cancer medicine, where foundation artificial intelligence (AI) models, digital twins, multimodal learning, and computational cancer intelligence converge to create continuously adaptive clinical ecosystems capable of supporting personalized diagnosis, prognostic prediction, therapeutic optimization, and longitudinal disease management. Conventional oncology frequently relies on fragmented diagnostic workflows, episodic clinical assessments, and isolated interpretation of imaging, pathology, molecular diagnostics, and clinical information, limiting comprehensive understanding of the dynamic biological evolution of cancer. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative artificial intelligence have enabled unified analysis of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational systems support early cancer detection, biomarker discovery, molecular characterization, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and intelligent clinical decision support. Emerging technologies including multimodal large language models, federated learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen autonomous oncology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical reasoning. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of autonomous precision oncology, emphasizing the convergence of foundation AI models, digital twins, and computational cancer intelligence as a transformative paradigm for next-generation personalized cancer medicine. International Journal of Advanced Research and Innovations 2026-07-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/108 International Journal of Advanced Research and Innovations; Vol. 14 No. 12 (2024): IJARAI: Vol.14, Issue 12, December 2024 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/108/107 Copyright (c) 2024 Dr. Nirmal Raj (Author) https://creativecommons.org/licenses/by/4.0

oai:ojs.ijarai.com:article/109 2026-07-31T12:35:56Z files:ART

AGENTIC ARTIFICIAL INTELLIGENCE IN PRECISION ONCOLOGY: AUTONOMOUS MULTIMODAL SYSTEMS FOR PERSONALIZED CANCER CARE Dr. Anirudh Nair Agentic artificial intelligence Precision oncology Foundation models Autonomous systems Multimodal learning Digital twins Computational oncology Personalized medicine Clinical intelligence Clinical decision support Agentic artificial intelligence (AI) represents the next frontier in precision oncology by enabling autonomous computational systems capable of perceiving, reasoning, planning, executing, and continuously learning across the entire cancer care continuum. Unlike conventional AI systems that perform isolated predictive tasks, agentic AI integrates foundation models, multimodal learning, digital twins, reinforcement learning, and clinical reasoning into intelligent ecosystems capable of autonomously coordinating diagnostic workflows, therapeutic planning, longitudinal disease monitoring, and personalized clinical decision-making. Recent advances in multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled comprehensive integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, biomedical literature, and real-world clinical outcomes into unified computational frameworks. These intelligent systems support early cancer detection, molecular characterization, biomarker discovery, prognostic prediction, treatment optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and intelligent clinical decision support. Emerging technologies including multimodal large language models, federated learning, retrieval-augmented generation, explainable artificial intelligence, and autonomous planning algorithms further strengthen agentic oncology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, regulatory, and implementation challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, human oversight, and equitable implementation. This review provides a comprehensive overview of agentic artificial intelligence in precision oncology, emphasizing autonomous multimodal systems for personalized cancer care and clinical translation. International Journal of Advanced Research and Innovations 2026-07-31 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://ijarai.com/index.php/files/article/view/109 International Journal of Advanced Research and Innovations; Vol. 14 No. 12 (2024): IJARAI: Vol.14, Issue 12, December 2024 2319-9253 2319-9245 en https://ijarai.com/index.php/files/article/view/109/108 Copyright (c) 2024 Dr. Anirudh Nair (Author) https://creativecommons.org/licenses/by/4.0 0427ce62ba8047446f9c5554cd662566