About the Advanced Research & Development Journal The Advanced Research & Development Journal is a peer-reviewed, national-level scholarly journal committed to publishing high-quality research in science, engineering, technology, and allied disciplines. The journal serves as a platform for Indian researchers, academicians, industry professionals, and students to disseminate innovative ideas, experimental results, case studies, and technological advancements that contribute to India’s academic and industrial growth. ARD Journal emphasizes original research conducted within India and encourages submissions that address real-world challenges, emerging technologies, and practical applications relevant to the national research ecosystem. A structured editorial and peer-review process ensures academic quality, transparency, and integrity. Aim To promote and disseminate national-level research in science, engineering, and technology. To encourage innovation, applied research, and problem-solving approaches. To provide a credible and constructive platform for Indian researchers. To strengthen research culture through multidisciplinary and interdisciplinary studies. To foster collaboration among scholars, educators, and research institutions across India. Scope The scope of the Advanced Research & Development Journal includes, but is not limited to: Engineering & Technology Computer Science & Information Technology Electronics, Communication & Electrical Engineering Mechanical, Civil & Industrial Engineering Artificial Intelligence, Machine Learning & Data Science Internet of Things (IoT), Embedded Systems & Robotics Renewable Energy & Smart Technologies Science & Applied Sciences Physics, Chemistry & Mathematics Environmental Science & Sustainable Development Biotechnology, Life Sciences & Agriculture Management & Humanities (where applicable) Business Management, Commerce & Economics Education, Social Sciences & Interdisciplinary Studies Article Types Research Articles Review Articles Case Studies Technical Notes Short Communications Journal Details Journal Title Advanced Research & Development Journal Frequency Quarterly (January, April, July, October) ISSN 3139-1281 Publisher NOVARA PUBLISHERS Publisher Address #605, Jasmine Block, RDB Coconut Grove, Dynamics Colony, Mayuri Nagar, Hyderabad, Telangana – 500049, India Editor-in-Chief Dr. Kishor Kumar Gajula Starting Year 2025 Subject Area Multidisciplinary Subjects Language English Publication Mode Online Phone +91 9030161092 Email editorardjournal@gmail.com| info@novarapublishers.com | novarapublishers@gmail.com Journal Website ardjournal.com Publisher Website novarapublishers.com Submissions & Peer Review ARD Journal accepts original manuscripts that have not been published or submitted elsewhere. All submissions undergo a peer-review process to ensure academic quality and relevance. Manuscripts must follow the journal’s Author Guidelines. Accepted articles are published online according to the quarterly schedule. Why Publish with ARD Journal? National-level visibility for Indian research. Peer-reviewed and quality-driven editorial process. Wide multidisciplinary scope. Timely online publication and academic accessibility. Contact Advanced Research & Development Journal Phone: +91 9030161092 Email: info@novarapublishers.com | novarapublishers@gmail.com Publisher: NOVARA PUBLISHERS, Hyderabad, Telangana, India
All articles published in the Journal of Engineering Excellence (JEE) are licensed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). Under this license, authors retain full copyright of their work while granting permission for anyone to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from the publisher or author — provided that the original work is properly cited. This open-access license ensures maximum dissemination and impact of the published research by allowing free and immediate access to scholarly work. For more details, please refer to the official license page: ???? https://creativecommons.org/licenses/by/4.0/
Design and Optimization of a Quaternary Signed-Digit Adder for VLSI Applications
The time required for addition is constrained by the carry propagation from the least significant bit to the most significant bit in the context of conventional binary adders. This lower bound increases in proportion to the operand's width. The quaternary signed-digit (QSD) number system, a redundant representation of radix-4 using {-3,...,3}, is used to overcome this constraint. Because each digit can be represented in several ways, addition can be done in a consistent amount of steps regardless of the number of digits. A carry-free QSD adder for VLSI applications is designed and optimized in this study. A compact three-bit two's complement digit encoding and two-step implementation are used in the adder. From each pair of digits, an intermediate carry and sum are generated. This prevents subsequent digit additions from generating a carry. All digit slices are reduced to tiny two-level networks, and the adder is a parallel array of identical slices. Due to generating logic enhancement, it has been decreased to this level. The design is compared to ripple-carry, carry-lookahead, carry-select, and redundant-binary adders in power, transistor count, and latency using a transistor-level approximation in a 45 nm CMOS flow. The normal data shown here show that worst-case addition latency is consistent from 8 to 64 digits. In contrast to binary adders' growing delay, the transistor's cost is linearly related to a ripple-carry adder. The numerical values provided are for illustration only; the reader should duplicate them using SPICE data and synthesis.
Copyright (c) 2026 Advanced Research & Development Journal https://creativecommons.org/licenses/by-nc-nd/4.0
DIFFERENTIAL PRIVACY-BASED PROTECTION OF USER SHOPPING PREFERENCES IN E-COMMERCE SYSTEMS
This study investigates the potential of differentiated privacy to safeguard the purchase preferences of online consumers while simultaneously enabling data analysis and personalized services. Protecting user privacy is a challenging endeavor due to the extensive collection of customer data by online purchasing websites. This study examines the potential of differential privacy techniques to conceal customers' private purchasing habits by introducing controlled noise to data without affecting the accuracy of the analysis. It also maintains a balance between data privacy and service quality to guarantee that enterprises can acquire critical knowledge while safeguarding customer data. The results indicate that differential privacy is a dependable and efficient method for fostering user confidence, alleviating privacy concerns, and facilitating secure data exchange in contemporary e-commerce.
DEVELOPMENT OF A SMART BIDIRECTIONAL CONVERSION INTERFACE FOR RENEWABLE STORAGE SYSTEMS
This initiative aims to enhance the reliability, efficiency, and energy management capabilities of existing power applications by developing a clever two-way conversion interface for renewable storage systems. The proposed system incorporates sophisticated power electronic converters and intelligent control techniques. This facilitates the easy transfer of energy between the grid, energy storage devices, and renewable sources. The interface ensures that the energy saved is utilized as efficiently as possible while maintaining a steady voltage and power quality with both charging and discharging modes. When adaptive control techniques and real-time tracking are employed, the system reacts more effectively to variations in load and generation. Additionally, the design reduces conversion losses, harmonic distortion, and overall system efficiency. Microgrids, solar photovoltaic systems, and electric car energy management all benefit from the new smart interface. Finding adaptable and durable energy solutions is made simpler as a result.
OPTIMIZATION OF VOLTAGE STABILITY IN LOW-VOLTAGE DISTRIBUTION SYSTEMS USING INTELLIGENT TECHNIQUES
This paper explores the utilization of innovative methodologies to optimize voltage stability in low-voltage distribution systems, thereby improving operational efficiency, reliability, and power quality. Modern distribution networks are confronted with the formidable challenge of stabilizing voltage profiles in response to the growing demand from electric vehicles, variable loads, and distributed energy resources. The research investigates intricate computational methods, including metaheuristic optimization algorithms, artificial neural networks, and fuzzy logic systems, to predict, monitor, and regulate voltage fluctuations in real time. In dynamic contexts, these astute techniques enable the mitigation of voltage deviations, efficient reactive power management, and adaptive decision-making. The proposed method is a strong contender for use in next-generation smart grid applications, as it outperforms conventional control strategies in terms of system stability, power loss, and resilience.
PERFORMANCE ANALYSIS OF A SINGLE-STAGE SOLAR INVERTER WITH INTEGRATED BOOSTING TECHNIQUE
The intention of this study is to examine a single-stage solar inverter that integrates boosting to improve the efficiency of energy conversion and the compactness of the system. The concept that has been proposed eliminates the necessity for a DC–DC boost converter by enhancing the voltage at the inverter stage. In addition, the overall system cost, switching inefficiencies, and the number of components are all reduced. The conversion efficiency, power quality, total harmonic distortion (THD), and voltage gain are all subjected to rigorous testing under a diverse range of solar irradiation and load conditions. The integrated control system not only improves dynamic responsiveness and MPPT efficiency, but also improves stability. Simulators and experiments have demonstrated that the inverter that has been introduced is superior to two-stage systems in terms of energy losses, voltage management, and efficiency, rendering it appropriate for use in modern solar applications.
A NOVEL TRANSFORMERLESS PROTECTION SCHEME FOR VOLTAGE REGULATION IN DISTRIBUTION GRIDS
This work introduces a groundbreaking transformerless protection approach to improve voltage regulation in current distribution circuits. In order to maintain voltage stability regardless of changes in loads or defects, the proposed method uses sophisticated power electronic converters and astute control procedures, doing away with the necessity for conventional transformers. Adaptive compensatory mechanisms, real-time monitoring, and quick problem detection enable the system to reduce system size, cost, and energy losses, resulting in dependable operation. The proposed method successfully mitigates voltage fluctuations, increases power quality, and provides robust protection against overvoltage and undervoltage scenarios, according to both experimental and theoretical findings. The transformerless design is a great fit for next-gen smart distribution networks since it can increase system efficiency and facilitate the integration of renewable energy sources.
DESIGN AND DEVELOPMENT OF AN INTEGRATED DRIVE AND CHARGING SYSTEM FOR ELECTRIC VEHICLES
The objective of this project is to construct and refine an integrated drive and charging system for electric vehicles, thereby integrating propulsion and charging operations into a single power electronics architecture. The proposed solution reduces costs, saves space, and streamlines the system by utilizing shared components such as the inverter and motor windings to manage both on-board charging and traction. The system is capable of transitioning between driving and charging modes with minimal degradation in power quality, reliability, or efficiency through the use of advanced control algorithms. The integration also enables bidirectional power transfer in vehicle-to-grid (V2G) applications, which improves energy management and reduces the necessity for supplementary hardware. The system that has been developed is a viable alternative for next-generation electric vehicles, as it outperforms conventional separate propulsion and charging systems while consuming less energy, as demonstrated by experimental and simulation results. Additionally, it is more compact and diminutive.
A STRATEGIC FRAMEWORK FOR SOCIAL ENGINEERING AND HUMAN-CENTRIC CYBER THREATS
Social engineering attacks are a common cybersecurity problem. Despite their prevalence and importance, few concepts and techniques fully explain social engineering assaults. The six steps of social engineering in this paper use different methods. This provides a complete foundation for examining various methods. Understanding the internal mechanisms of these attacks may help us design better defenses. According to the research, the framework is straightforward, comprehensible, and practical, similar to the MITRE ATT&CK, although it has more benefits. To demonstrate the actual applicability of this paper's strategy, the comprehensive framework is used to rigorously paper and examine a number of real-world social engineering instances.
FEATURE LEARNING AND PRICE ESTIMATION USING DEEP LEARNING IN REAL ESTATE
Taiwanese houses, flats, and other types of urban homes should be looked at very carefully. Real estate researchers and forecasters frequently implement surveys and statistical instruments to accumulate data. Nevertheless, when confronted with multidimensional data, these methodologies become inefficient and time-consuming. The objective of this initiative was to develop a real estate forecasting model that could adapt to the current environment and circumstances. The data was standardized to ensure that it was categorized correctly after being extracted from public government records. In order to guarantee that the appropriate clustering method was implemented, cross-statistical analysis was implemented. In order to enhance the precision of classification, an automatic encoder for deep learning was implemented. Double-bottom map ppaper swarm optimization (DBM-PSO) was implemented to identify the optimal clustering solution. We sought to determine the factors that contributed to the increase in property prices in Taiwan over the past decade by employing cluster analysis and deep learning on publicly available data. The findings indicate that the average unit price, the frequency of real estate transactions, and the building material and construction index all significantly influence the prices of property in Taiwan. These discoveries can assist policymakers and researchers in addressing specific aspects of real estate development and improving market regulation to prevent excessive growth. The framework that was proposed is a novel approach to analyzing and forecasting real estate trends.
SCALABLE AND SECURE E-LEARNING PLATFORMS USING CLOUD COMPUTING TECHNOLOGIES
Combining cloud computing and e-learning could have positive effects, which are looked into in this Paper. This Paper aims to uncover the intricate interaction between cloud computing and online learning. The Paper investigates the intricacies of this integration through the analysis of 154 scholarly Papers, focusing on the trends and topics that have piqued interest. Architecture (27% of all courses), software (19%), and performance (18%) are just a few of the many areas of online education that cloud computing has an impact on. In virtual settings, network and storage challenges are prioritised over security concerns. The three most common categories of cloud computing services are platforms (10%), software (18%), and hardware (17%). In total, 74% are public clouds, 3% are mixed clouds, and 11% are alternate models. The Paper investigates the potential association between cloud computing and online learning. It highlights the restrictions imposed by specialised infrastructure, the challenges with private and hybrid clouds, and the scarcity of other platform and infrastructure alternatives.
HEART RATE VARIABILITY-BASED STRESS DETECTION USING DEEP NEURAL NETWORK MODELS
This research makes use of Heart Rate Variability (HRV) to gauge people's levels of stress by analyzing physiological data through models trained on deep neural networks. Readings of heart rate variability (HRV) obtained from an electrocardiogram (ECG) are employed in the investigation due to their significance as indicators of autonomic nervous system activity that are associated with stress responses. In order to enhance the accuracy and utility of stress recognition, advanced deep neural network techniques, such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models, are implemented in lieu of conventional machine learning methodologies. By means of feature extraction, classification, and data preparation, the proposed system is capable of distinguishing between stressful and non-stressful scenarios in real time. For instance, researchers have demonstrated that deep learning models are more adept at learning new features, making predictions, and managing intricate physiological trends. This investigation enhances healthcare monitoring systems that can identify early indicators of stress and mental illness, thereby facilitating the maintenance of one's own health in daily life.
ADVANCED CREDIT CARD FRAUD DETECTION USING ENSEMBLE LEARNING ALGORITHMS
The objective of this research is to improve the reliability and accuracy of financial system fraud detection through the use of advanced credit card fraud detection algorithms powered by ensemble learning algorithms. Credit card fraud has emerged as a significant concern for banks and other financial organizations due to the rapid expansion of online banking and digital payments. In order to more accurately evaluate transaction patterns and identify suspicious activity, the proposed paper investigates the use of ensemble methodologies, such as Random Forest, Gradient Boosting, AdaBoost, and XGBoost, in contrast to traditional machine learning models. Ensemble learning enhances the accuracy of predictions, reduces the number of false positives, and enhances the system's resilience to imbalanced datasets, all of which are common issues in fraud detection. In order to facilitate successful fraud detection, the paper prioritizes data preparation, feature selection, and model evaluation measures such as accuracy, precision, recall, and F1-score. The ultimate objective is to develop a fraud detection system that is both intelligent and scalable, thereby enabling institutions to mitigate losses and enhance the security of transactions in real time.
PERSONALIZED DIGITAL COUPON ISSUANCE FOR ENHANCING RETAIL REVENUE IN SHOPPING MALLS
Big data and deep learning have recently gained prominence in a variety of industries, such as business administration and marketing, and have found new applications. Customer attrition management is a critical component of marketing that directly influences the efficiency of a company. This research demonstrated that real-time big data analysis may result in the provision of personalised discount coupons to clients with a high turnover rate. Consequently, consumer attrition decreased, while buy conversion rates increased. Initially, cluster analysis is employed to investigate two-dimensional consumer categories. Afterwards, we evaluate the clickstream data for each cohort in order to construct a real-time attrition prediction model. Sales that are customised to the preferences of each customer are generated and distributed through the use of insights such as these. The strategy's effectiveness was evaluated by factoring in revenue growth and conversion rates. The findings indicate that a hybrid model that integrates attrition estimation and recommendation systems outperforms conventional individual models in terms of consumer behaviour forecasting and engagement. By employing Support Vector Machine to objectively assess churn probability and purchase trends, online businesses can optimise their marketing expenditures, retain consumers, and boost sales.
EVALUATING ENERGY-EFFICIENT DRIVING PATTERNS OF ELECTRIC BUSES AT STOPS
Eco-driving techniques can drastically lower the energy consumption and operating costs of electric buses, especially when approaching and departing stops that call for frequent acceleration and deceleration. In this Paper, we provide a methodology for evaluating the eco-driving capabilities of electric buses by gathering data from real-world scenarios and analysing driving behaviour. The collected data underwent pre-processing, which included data cleaning, parameter addition, and sample unification. By analysing a range of energy-related driving events, including acceleration patterns, braking behaviour, pedal operation, and the duration of economical speed, we were able to identify critical driving behaviour parameters that impact energy consumption. A multiple regression model and a machine learning recognition framework were developed to evaluate eco-driving efficiency and categorise driving behaviours as either eco-driving or non-eco-driving. Implementing eco-driving techniques, which enhanced energy recovery and decreased power consumption at bus stops, was made possible by optimising NSGA-II and driving modes. Significant energy savings, increased assessment and recognition accuracy, and robust support for fleet management and driver assistance systems were all demonstrated by the results. The proposed model promotes sustainable urban transportation by lowering operating costs, boosting energy efficiency, and improving intelligent electric bus driving management.
FEATURE EXTRACTION AND CLASSIFICATION FOR SPAM DETECTION USING SUPERVISED ML
The objective of this research is to enhance the efficacy and precision of spam detection in digital communication systems by utilising supervised machine learning techniques for feature extraction and classification procedures. This paper examines large email and text message datasets for feature extraction using preprocessing techniques such as tokenization, stop-word deletion, stemming, and vectorization. A number of supervised machine learning techniques, including as Naïve Bayes, Support Vector Machine (SVM), Decision Tree, Random Forest, and Logistic Regression, are compared in order to distinguish between spam and real messages. To measure how well these models work, we use metrics like F1-scores, recall, accuracy, and precision. In order to enhance classification accuracy and decrease false positives, the Paper stresses the significance of effective feature extraction methods as TF-IDF and Bag-of-Words. The proposed method allows trustworthy and secure communication by use of an adaptive spam detection framework that adjusts to actual changes in spam trends.
DEEP LEARNING MODELS FOR ANALYZING PUPILLARY DYNAMICS IN GENETIC DISORDER DETECTION
The primary objective of this initiative is to develop deep learning models that will investigate pupillary dynamics in the diagnosis of genetic diseases. Variations in pupil size, reflex speed, and light adaptation patterns, among other pupillary responses, are significant neurological and physiological markers that may be associated with underlying genetic disorders. The objective of this research is to analyze and comprehend pupillary behavior as captured by eye-tracking and imaging sensors by employing cutting-edge deep learning techniques, including CNNs, RNNs, and LSTM models. The proposed method seeks to improve the accuracy, efficiency, and early diagnosis of genetic abnormalities in comparison to current clinical assessment procedures by extracting complex spatial and temporal information from pupillary movement data. A non-invasive, cost-effective, and reliable diagnostic framework that integrates AI with neurological and ocular indicators can enable healthcare providers to benefit from predictive genetic analysis and individualized treatment plans.
ENHANCING PUBLIC SAFETY IN SMART CITIES USING DEEP LEARNING-BASED VIOLENCE DETECTION
This work illustrates the utilization of deep learning to detect violent incidents in smart cities, thereby improving public safety. Automated threat identification and real-time surveillance research are implemented by the technology. The proposed method employs sophisticated convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to analyze video feeds from public surveillance cameras, accurately detecting violent incidents such as altercations, assaults, vandalism, and mass violence. The system's proficiency in distinguishing between violent and non-aggressive human behaviors across a variety of external conditions is attributed to its implementation of spatial and temporal feature extraction techniques. The model is trained using a vast array of annotated datasets. The goals are to improve the effectiveness of detection, decrease the number of false alarms, and facilitate the rapid response of law enforcement. The proposed method improves the efficiency of emergency response, reduces the need for human supervision, and enhances smart city monitoring by providing continuous updates on current events. The intelligence, safety, and security of urban areas can be substantially improved by this violence detection system, which employs deep learning.
MULTI CLASS TRAFFIC OBJECT DETECTION AND SCENE UNDERSTANDING USING SUPERVISED MODELS
The utilization of trained models to identify a variety of traffic objects and comprehend scenes is a critical component of intelligent transportation systems and autonomous driving technologies. This approach employs labeled datasets to instruct machine learning and deep learning algorithms on how to precisely identify and classify various road objects, including automobiles, pedestrians, traffic signs, bicycles, and lane markings, even in the presence of numerous moving vehicles. Supervised models, such as CNNs and advanced object recognition frameworks like YOLO, Faster R-CNN, and SSD, are capable of accurately and reliability identifying objects in real time. Scene understanding techniques encompass more than merely object recognition. Additionally, they examine the movement of traffic, the condition of the roadways, and the spatial relationships between objects, which aids in the comprehension of one's environment and the formulation of more informed decisions. Combining scene interpretation and multi-class detection enhances self-driving, traffic tracking, and road safety by providing vehicles with a more accurate perception of a diverse array of environmental conditions.
NETWORK SECURITY ENHANCEMENT THROUGH MACHINE LEARNING-BASED CYBER ATTACK DETECTION
The objective of this investigation is to identify and prevent harmful activities in contemporary communication networks by employing machine learning-based techniques to enhance network security and detect intrusions. Using supervised and unsupervised machine learning algorithms, including Decision Trees, Random Forest, Support Vector Machines, and Neural Networks, the investigation examines network traffic patterns and identifies outliers that may be the result of cyber threats such as phishing, DOS attacks, malware intrusions, and unauthorized access attempts. The proposed method analyzes a variety of network metrics, including packet flow, connection duration, source and destination IP behavior, and protocol utilization, to facilitate the identification of both normal and anomalous activity. Traditional security systems that rely on signatures are significantly outperformed by machine learning-driven detection models in terms of real-time attack detection, false alarm rates, and threat detection speed. The paper underscores the critical nature of enhanced cyber security systems in order to ensure the security of private information, the reliability of networks, and the safety of online communication.
BLOOM FILTER-BASED CRYPTOGRAPHIC TECHNIQUES FOR CLOUD DATA SECURITY
This paper discusses Bloom Filter-based cryptographic algorithms that can be employed to securely store data and regulate access in cloud computing environments. These algorithms provide solutions that are effective, scalable, and privacy-preserving. The proposed approach minimizes the volume of data that must be stored and the work that must be performed on a computer by incorporating Bloom Filters and sophisticated cryptographic approaches. It also expedites the process of identifying unlawful data access, authenticating users, and verifying membership. In the cloud, bloom filters are an excellent tool for managing big data transactions, as they can rapidly process queries and provide a detailed description of large datasets. In order to safeguard data from cyber threats such as data breaches, hostile intrusions, and unauthorized modifications, the framework includes encryption algorithms, hash functions, and secure key management methods. The proposed method enhances security performance, reduces the number of false positives, and enhances operational efficiency in comparison to conventional cloud security practices, as demonstrated by tests. The paper demonstrated that Bloom Filter-based cryptographic models can enhance the dependability of data security in contemporary distributed computing settings and enhance the security of cloud storage systems.
IMPROVING TRUST IN SOCIAL MEDIA PLATFORMS THROUGH ADVANCED MALICIOUS PROFILE DETECTION TECHNIQUES
This paper aims to enhance trust in social media platforms by identifying detrimental profiles in a more sophisticated manner through intelligent data analysis and machine learning. The proliferation of social media platforms has increased the likelihood of the occurrence of spam profiles, bots, false accounts, and cybercriminal activities. The veracity of information, the safety of the internet, and the privacy of users are all negatively impacted by these issues. The objective of this investigation is to devise a method for identifying fraudulent profiles by examining user behavior, profile details, posting habits, network connections, and content-related aspects. Machine learning and deep learning techniques, including Random Forest, Support Vector Machine, Decision Tree, and Neural Networks, are employed to facilitate the identification of genuine and fraudulent accounts. The framework employs anomaly detection and feature extraction to identify suspicious activities in real time. When we employed social media datasets for empirical testing, we observed enhanced platform security, fewer false hits, and improved detection performance. This research contributes to the safety of social media platforms by facilitating the identification and prevention of user misconduct.
MULTIMODAL ANALYSIS OF INTER-BRAIN AND BEHAVIORAL SYNCHRONY IN REMOTE LEARNING
This paper examines multimodal analysis of behavioral and inter-brain synchronization in remote learning environments to determine students' social and cognitive alignment when using digital platforms to participate. The paper integrates neurophysiological signals like EEG-based inter-brain coherence with behavioral indicators like eye movements, facial expressions, and interaction patterns to determine synchrony. Advanced signal processing and machine learning integrate diverse data sources. Next, learning outcomes, teamwork, and participation patterns are identified. Increased inter-brain synchrony improves academic performance, group attention, and communication even when people are absent. Synchrony-aware analytics improves virtual collaboration, adaptive training, and academic success, according to this paper. Additionally, it improves intelligent remote learning system design.
Inter-brain synchrony, Behavioral synchrony, Multimodal analysis, Remote learning, EEG, Social interaction, Machine learning, Collaborative learning
ATTENTION ENHANCED CONVOLUTIONAL NEURAL NETWORKS FOR TRAFFIC ANOMALY DETECTION
This paper investigates Attention Enhanced Convolutional Neural Networks (AE-CNNs) as a potential method for identifying traffic issues in scenarios where the highways are highly complex and are in a state of flux. By incorporating attention processes into conventional convolutional neural network topologies, the proposed approach prioritizes the most pertinent spatial and temporal data. This facilitates the identification of issues such as traffic congestion, unusual vehicle behavior, and collisions. By integrating channel and spatial attention modules, the model eliminates superfluous data and enhances feature representation. This renders it superior to conventional CNN-based methodologies. The system is more resilient, accurate, and sends fewer false warnings, as evidenced by experiments with various traffic datasets. The results indicate that AE-CNNs enhance real-time traffic monitoring and smart transportation, thereby enhancing the efficiency and safety of urban transportation.
DEEP NEURAL NETWORKS FOR SUSPICIOUS ACTIVITY RECOGNITION IN VIDEO SURVEILLANCE
The objective of this investigation is to enhance automated security monitoring systems by investigating the utilization of deep neural networks (DNNs) to identify suspicious activity in video surveillance. To accurately identify abnormal or potentially hazardous behaviors, the research employs advanced architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to extract temporal and spatial features from video streams. The proposed methodology effectively distinguishes between normal and suspicious behavior by incorporating pattern recognition, motion analysis, and feature learning. In order to address obstacles such as occlusion, variable lighting, and complex crowd dynamics, the investigation implements rigorous training methodologies and extensive, annotated datasets. Experimental results suggest that deep learning models outperform conventional machine learning methods in terms of adaptability, scalability, and accuracy. Subsequently, they are optimal for contemporary surveillance applications in public safety, transportation systems, and smart cities.
AUTOMATED ATTENDANCE MANAGEMENT SYSTEM USING FACIAL RECOGNITION TECHNIQUES
This investigation introduces an automated method for monitoring the attendance of students or employees through the use of facial recognition technology, which improves accuracy, speed, and safety. Traditional methods, including biometric technologies and human roll calls, are error-prone, burdensome, and susceptible to fraudulent attendance. The proposed method employs computer vision and deep learning algorithms to identify and recognize individuals in real time using a camera. Upon the identification of a match within a database of known images, attendance is automatically documented. Convolutional Neural Networks (CNNs) are frequently employed by the system for the purposes of face detection, feature extraction, and classification. It ensures that individuals are not obligated to take any action, reduces administrative duties, and provides a dependable, contactless response, which is particularly advantageous in regions that are impacted by a pandemic. The attendance records can be stored online for swift access and analysis. The research demonstrates that attendance systems are more reliable, scalable, and efficient when facial recognition technology is implemented.
A HYBRID ML FRAMEWORK FOR PERSONALITY-BASED CUSTOMER SERVICE OPTIMIZATION
This research introduces a hybrid machine learning framework that optimizes customer service by incorporating personality. The goal is to enhance service efficiency and consumer satisfaction by meticulously examining their behavior. The framework employs machine learning algorithms, natural language processing, and personality prediction techniques to ascertain the personality characteristics of a client by analyzing their communication patterns, preferences, and previous interactions. Clients are categorized according to their personality traits in the recommended methodology. This allows businesses to offer them more personalized responses, suggestions, and methods of support. The hybrid approach combines predictive analytics and categorization to enhance consumer behavior prediction and facilitate decision-making. The framework enhances the overall quality of service in digital customer support environments, reduces response times, and increases customer engagement, as demonstrated by an experiment. The research also illustrates the importance of AI-based personalization in modern CRM systems and provides a practical solution for improving customer service in a variety of sectors.
INTEGRATING BIOINFORMATICS AND DEEP LEARNING FOR DRUG SIDE EFFECT PREDICTION
This research investigates the integration of deep learning techniques and bioinformatics to enhance the precision and efficacy of pharmacological adverse effect forecasts. The objective of this investigation is to identify intricate patterns and concealed connections that result in adverse drug reactions by integrating advanced deep learning algorithms with bioinformatics data, including genetic information, protein interactions, and drug-target networks. In contrast to conventional statistical methodologies, the methodology utilizes a wide range of datasets to improve the accuracy of its forecasts. The objective of this research is to enhance the precision and reliability of side effect predictions, reduce risks in clinical trials, and advance personalized medicine by enhancing early pharmaceutical safety evaluation.
PREDICTIVE MODELING OF CUSTOMER PURCHASE BEHAVIOR USING RETAIL DATA MINING METHODS
This research examines the process of predicting consumer behavior by utilizing retail data mining to evaluate a substantial quantity of transactional and customer data in order to identify patterns that can be used to anticipate future purchases. This project employs a variety of sophisticated machine learning techniques, such as clustering, classification, and association rule mining, to accurately classify clients, ascertain their preferences, and forecast purchasing trends. The research demonstrates the potential of behavioral, demographic, and historical sales data to enhance targeted marketing, assist in strategic decision-making, and enhance inventory management. By utilizing predictive and educated data, this method enables retailers to increase consumer engagement and generate additional revenue.
PROFITABILITY AND RETURN ANALYSIS THROUGH GROWTH STRATEGIES AT UNION BANK OF INDIA
This paper evaluates Union Bank of India's profitability and return by assessing recent expansion initiatives. It analyzes how strategy goals like credit portfolio expansion, digital banking, and cost reduction affect the bank's finances. The paper examines net profit margins, return on assets, and return on equity to assess the organization's efficiency and profitability. It investigates how market, competition, and legal changes affect bank profitability. Development activities linked to financial results improve performance in an environmentally sustainable way, according to the research. The paper's list of finance and business administration's pros and cons can help strategic decision-makers. The paper recommends developing new financial solutions and focusing on customer preferences to boost revenue.
COMPENSATION STRUCTURES AT HCL TECHNOLOGIES
This paper seeks to shed light on the impact of compensation policies on employee motivation, retention, and organizational performance by analyzing the pay structures at HCL Technologies, a prominent IT service provider in India. Examining components such as base pay, performance-based incentives, bonuses, benefits, and long-term rewards, the paper determines how effectively HCL's compensation structure complies with industry standards and meets employee expectations. In order to promote employee loyalty and increase productivity, the paper examines how competitive and transparent compensation systems work. Based on the results, it seems that a well-planned compensation plan is essential in the competitive IT sector for attracting and retaining top talent, maintaining employee engagement, and driving organizational growth. Researchers hope that HR experts and business leaders will use the paper's findings to better understand how to structure compensation packages to boost morale and productivity.