International Journal of Advanced Research and Innovations
Elevating Global Research. Empowering Scientific Innovation. The International Journal of Advanced Research and Innovations (IJARI) is a premier international, peer-reviewed, open-access journal dedicated to advancing scientific excellence, fostering multidisciplinary innovation, and supporting high-impact research across the globe. International Journal of Advanced Research and Innovations (IJARI) provides a trusted publishing environment where scholars, researchers, and practitioners share groundbreaking discoveries that shape the future of science, technology, and society. About International Journal of Advanced Research and Innovations (IJARI) International Journal of Advanced Research and Innovations (IJARI) is committed to publishing original, ethical, and impactful research that contributes meaningfully to global knowledge systems. The journal welcomes high-quality manuscripts across multiple domains, including cutting-edge experimental work, theoretical advancements, applied innovations, interdisciplinary studies, and critical scientific reviews. With a rigorous double-blind peer-review process, International Journal of Advanced Research and Innovations (IJARI) ensures that every article meets the highest standards of academic integrity, credibility, and scientific rigor. As an open-access platform, the journal offers free and immediate access to all published content, enabling broad dissemination and maximum research visibility worldwide. Why Choose International Journal of Advanced Research and Innovations (IJARI)? Global Reach & Maximum Visibility: All articles are freely accessible, ensuring wide readership, increased citations, and strong international impact. High-Quality Peer Review: Manuscripts undergo an unbiased, double-blind peer-review process conducted by expert reviewers from diverse academic disciplines. Fast & Transparent Editorial Processing: International Journal of Advanced Research and Innovations (IJARI) provides an efficient submission workflow, clear communication, and timely editorial decisions without compromising quality. Ethical & Trusted Publishing Standards: The journal adheres to internationally recognized ethical guidelines, ensuring originality, proper citation, and research integrity. Broad Interdisciplinary Scope: IJARI accepts innovative research from a wide range of fields, including: Life & Biological Sciences Physical & Chemical Sciences Engineering & Technology Computer & Information Sciences Medicine & Health Sciences Environmental & Earth Sciences Social & Interdisciplinary Research Emerging Technologies & Applied Innovations Editorial Excellence The International Journal of Advanced Research and Innovations (IJARI) Editorial Board consists of globally recognized scholars, domain specialists, and experienced reviewers dedicated to maintaining the highest standards of scholarly publishing. The editorial team ensures: Fair and objective evaluation Constructive and actionable feedback Strict confidentiality and conflict-free assessments Commitment to transparency and ethical research practices What International Journal of Advanced Research and Innovations (IJARI) Publishes The journal accepts a wide range of scholarly contributions, including: Original Research Articles Review Papers Short Communications Case Studies Technical Notes Perspectives & Commentaries Interdisciplinary and Emerging Research Each manuscript is carefully assessed for originality, significance, methodology, and contribution to the field. Mission and Vision Our Mission: To advance global scientific knowledge by publishing high-quality, accessible, and ethically responsible research that promotes innovation, collaboration, and societal progress. Our Vision: To become a leading international hub for interdisciplinary research, fostering a research culture that values excellence, transparency, and global scientific exchange. Submit Your Manuscript International Journal of Advanced Research and Innovations (IJARI) invites researchers to submit high-quality manuscripts that contribute new knowledge, challenge existing paradigms, or offer meaningful solutions to scientific and societal challenges. Authors benefit from: Easy online submission Transparent manuscript tracking Professional editorial support Wide international readership Share your research with the world. Submit your manuscript to International Journal of Advanced Research and Innovations (IJARI) and be part of a global community of innovation. Join International Journal of Advanced Research and Innovations (IJARI) – Where Research Meets Innovation By choosing International Journal of Advanced Research and Innovations (IJARI), you join an international network of scholars committed to ethical publishing, scientific leadership, and multidisciplinary advancement.
FEDERATED LEARNING FOR PRIVACY-PRESERVING ONSCREEN ACTIVITY RECOGNITION IN E-LEARNING
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.
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DRIVING SHOPPING MALL REVENUE GROWTH WITH PERSONALIZED REAL-TIME DIGITAL COUPON ISSUANCE
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.
IMPROVING CROP YIELD FORECASTING WITH AGRICULTURAL ENVIRONMENT FEATURES: FEATURE SELECTION AND CLASSIFIER-BASED APPROACHES
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.
EKYC-DF: A REALISTIC DEEPFAKE CORPUS FOR TESTING AND TRAINING EKYC VERIFICATION MODELS
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.
MULTIMODAL FOOD TEXTURE PREDICTION USING TEMPLATE MATCHING TECHNIQUES
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.
SECURE ENERGY DEMAND PREDICTION FOR ELECTRIC VEHICLES USING FEDERATED LEARNING ON BLOCKCHAIN
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.
REWARD SYSTEM AS A STRATEGY TO ENHANCE EMPLOYEES PERFORMANCE INANORGANIZATION
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.
SMART VIDEO SURVEILLANCE WITH WIRELESS NOTICE ANNOUNCEMENT VEHICLE FOR COLLEGE
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.
SOLAR BASED MOBILE CHARGING ON COIN INSERTION
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.
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.
POPULARITY PREDICTION OF SHORT VIDEOS USING INTERNET OF THINGS AND NEURAL NETWORKS
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.
HEALTH PREDICTION SYSTEM POWERED BY MACHINE LEARNING AND IBM CLOUD PAAS
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.
EXPLORING THE ROLE OF BLOCKCHAIN TECHNOLOGY IN FARMER’S PORTALS FOR AGRICULTURAL INNOVATION
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.
MACHINE LEARNING-BASED FACE MASK DETECTION FOR PUBLIC HEALTH SAFETY
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.
TRUST-BASED DETECTION OF MALICIOUS NODES IN WIRELESS SENSOR NETWORKS
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.
A DATA-DRIVEN APPROACH TO CROP YIELD PREDICTION USING ADVANCED MACHINE LEARNING TECHNIQUES
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.
FAST TEXT EMBEDDINGS AND DEEP LEARNING FOR ROBUST DETECTION OF DEEPFAKES IN SOCIAL MEDIA 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.
INVESTOR SENTIMENT-DRIVEN STOCK PRICE PREDICTION USING OPTIMIZED DEEP LEARNING MODELS
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.
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.
INTERNAL COMMUNICATION AND ITS IMPORTANCE IN CORPORATE MANAGEMENT
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.
KNOWLEDGE MANAGEMENT IMPROVES ORGANIZATIONAL LEARNING AND PERFORMANCE
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.
A REVIEW OF EXPLAINABLE AI APPLICATIONS IN PHARMACOVIGILANCE FOR IMPROVED PATIENT SAFETY
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.
ENHANCING SPAM COMMENT DETECTION ON SOCIAL MEDIA WITH EMOJI FEATURE AND POST-COMMENT PAIRS APPROACH USING ENSEMBLE METHODS OF MACHINE LEARNING
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.
ENERGY ECONOMY PREDICTION FOR ELECTRIC CITY BUSES USING MACHINE LEARNING: A DATA-DRIVEN METHODOLOGY
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.
MITIGATING WEB-BASED VULNERABILITIES: MAN-IN-THEMIDDLE AND SESSION HIJACKING IN FOCUS
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.
INTELLIGENT DRIVER DROWSINESS DETECTION USING GAOPTIMIZED CNN ARCHITECTURE
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.
EXPOSING ONLINE RECRUITMENT FRAUD WITH DEEP LEARNING ALGORITHMS
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.
HIERARCHICAL ML MODEL FOR DISTRIBUTED DDOS ATTACK CLASSIFICATION AND HYPERPARAMETER TUNING
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.
MOTIVATION AT HERO MOTORS: FOSTERING A CULTURE OF EXCELLENCE
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.
VALUE-AT-RISK DRIVEN FRAUD DETECTION FRAMEWORK WITH MACHINE LEARNING UNDER DATA IMBALANCE
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.