Journal of Science Engineering Technology and Management Science

This paper reviews the growing importance of energy storage technologies in modern power systems driven by the rapid expansion of renewable energy sources such as solar PV and wind. The inherent variability and intermittency of renewables create challenges in maintaining grid stability, power quality, and reliability. To address these issues, several energy storage elements—batteries, ultra-capacitors, flywheels, and fuel cells—are increasingly integrated into grid-connected and islanded systems. The review covers their operating principles, performance characteristics, and suitability for AC and DC microgrid environments. Special attention is given to battery charging and discharging strategies, as they significantly influence system efficiency and lifetime. The survey further examines the impacts of renewable energy integration on the main grid and highlights how microgrids and Building-to-Grid (B2G) concepts enhance flexibility, controllability, and energy management. Key findings indicate that hybrid storage systems and coordinated control methods are critical for future smart grids, while gaps remain in multi-storage optimization and standardized frameworks for AC/DC microgrids and B2G interaction.

Keywords : Energy storage, batteries, ultra-capacitors, flywheels, microgrids, building-togrid, fuel cells.

Author : Katkuri Rajendar,Dr. B. Sathyavani,Dr. Ch Hussaian Basha

Title : A COMPREHENSIVE REVIEW ON ENERGY STORAGE TECHNOLOGIES FOR GRID INTERACTIVE

Volume/Issue : 2025;02(12)

Page No : 1-8

Active distribution networks (ADNs) are transforming conventional passive grids into intelligent, consumer-interactive energy systems. The rapid integration of renewable energy sources, smart appliances, low-voltage DC (LVDC) distribution, and home energy management systems (HEMS) is enabling fine-grained control over energy consumption at residential, commercial, and industrial levels. Parallel developments in prosumer technologies such as Building-to-Grid (B2G), Vehicle-toGrid (V2G), and Solar-to-Grid (S2G) are accelerating consumer participation in energy markets. Additionally, distributed power generation using distributed energy resources (DERs), energy storage, and microgrids significantly improves reliability, resilience, and efficiency. This paper provides a comprehensive IEEE-style review of smart consumption strategies, distributed generation, net metering policies, and demand response mechanisms that shape the future of active distribution networks. Emerging trends, challenges, and opportunities for grid–consumer interaction are also discussed.

Keywords : Active distribution networks, LVDC, HEMS, B2G, V2G, Solar-to-Grid, Demand Response, DER, Microgrids.

Author : Perike olive,Dr. B. Sathyavani

Title : A REVIEW ON SMART CONSUMPTION AND DISTRIBUTED POWER GENERATION IN ACTIVE DISTRIBUTION NETWORKS

Volume/Issue : 2025;02(12)

Page No : 9-16

The purpose of this study was to develop and scientifically authenticate newly constructed Kabaddi offensive skill tests for high school girls aged fourteen to sixteen years. Twelve offensive skill tests— Riding Footwork, Simple Hand Touch, Running Hand Touch, Stooping Hand Touch, Turn & Hand Touch, Hopping Hand Touch, Toe Touch, Double Attack in Toe Touch, Drag Back and Toe Touch, Running Toe Touch, Back Kick and Bonus—were administered to 100 high school girls selected from five schools. Validity, reliability and objectivity were established using Karl Pearson’s coefficient of correlation. Results showed significant negative correlations between offensive skills and expert-rated playing ability, indicating high validity. Reliability scores (test–retest) and objectivity scores (inter-tester correlation) were above 0.90 for all tests. Thus, the constructed offensive skill tests were found to be valid, reliable and objective. These tests can be used as standardized tools for evaluating offensive Kabaddi skills among high school girls.

Keywords : Kabaddi, offensive skills, test development, validity, reliability, objectivity

Author : Ch Mahesh,T. Hanumanthu

Title : DEVELOPMENT OF KABADDI OFFENSIVE SKILLS TEST FOR HIGH SCHOOL GIRLS

Volume/Issue : 2025;02(12)

Page No : 17-20

The rapid evolution of artificial intelligence is redefining the skills required for modern web development, pushing developers beyond traditional programming competencies toward intelligent automation, data-driven design, and adaptive learning capabilities. This study proposes an AIaugmented competency development framework designed to equip next-generation web developers with an integrated blend of technical, cognitive, and collaborative skills. The framework incorporates machine learning–assisted coding, intelligent UI/UX generation, automated testing workflows, APIdriven AI services, and ethical considerations related to responsible AI use. By combining empirical insights from industry practices with structured competency mapping, the proposed model establishes a clear pathway for developers to transition from conventional workflows to AI-enriched development ecosystems. Experimental validation through developer surveys and prototype evaluation demonstrates improved productivity, enhanced problem-solving efficiency, and higher adoption readiness for AI-driven web technologies. This work provides a comprehensive foundation for educational institutions, training programs, and software organizations aiming to modernize web development competencies in the era of artificial intelligence.

Keywords :

Author : Dr. Kishor Kumar Gajula

Title : AI-AUGMENTED WEB DEVELOPMENT: A COMPETENCY FRAMEWORK FOR THE NEXT GENERATION WORKFORCE

Volume/Issue : 2025;02(12)

Page No : 21-29

Financial institutions in developing nations are under increasing pressure to modernize their digital payment systems to meet growing demand for speed, reliability, and flexibility. Building upon the framework proposed by Paruchuri (2023), this paper explores the implementation of a real-time modular architecture that addresses common limitations of legacy payment systems — including inflexibility, poor scalability, and integration challenges. We present a novel design that decouples core payment services into independent, modular components enabling real-time transaction processing, easier integration with legacy systems, and adaptability to emerging payment methods. Through a simulated implementation, we evaluate system performance in terms of throughput, latency, fault tolerance, and scalability, and compare it with traditional monolithic payment infrastructures. Results confirm that the modular approach significantly reduces latency, improves throughput, and enhances operational flexibility — making it a viable modernization strategy for financial institutions.

Keywords : Digital Payments, Modular Architecture, Real-Time Processing, Microservices, Financial Technology, Payment Systems Modernization, Event-Driven Architecture

Author : P. Madhuri

Title : REAL-TIME MODULAR ARCHITECTURE FOR DIGITAL PAYMENTS: ENHANCING EFFICIENCY, SCALABILITY AND FLEXIBILITY IN FINANCIAL INSTITUTIONS

Volume/Issue : 2025;02(12)

Page No : 30-39

An energy administration system that takes into account both ecological with technological economic considerations is presented in this study for a hybrid solar with biogas-powered battery charging station (EVCS). In order to monitor immediate charging costs and green energy usage, the proposed software, designed to manage a 20-kW EVCS, improves the use of an inference system using fuzzy logic using MATLAB SIMULINK for controlling power generation, Electric battery demand, recharging time frames, the current charged rates. The outcomes show the suitability of the recommended algorithm gives lower charge prices for weekdays and weekends and saves energy costs by 74.67% in contrast to the present flat rate tariffs. Because the use of hybrid renewable energy also considerably lowers greenhouse gas emissions, the concept is feasible with comparatively quick payback times for charging station owners.

Keywords :

Author : Palapati Divya,Mr.T.Lingaiah

Title : ENERGY MANAGEMENT SYSTEM FOR THE ELECTRIC VEHICLE CHARGING UNIT POWERED BY HYBRID RENEWABLE ENERGY

Volume/Issue : 2025;02(12)

Page No : 40-60

Private wireless networks are undergoing a revolution that takes them from a static deployment of 5G networks across the board to a stage where autonomous, AI-driven private 6G networks will be operational. To facilitate the migration of enterprises and thus the smooth transition while at the same time controlling the growing complexity of network operations, essential design patterns and migration strategies are in place. The collaboration co-management models that include the operative, the system integrators, and the IT/OT teams of the enterprise provide the basis for shared responsibility regarding the performance, security, and service assurance of the networks. Architectural principles such as distributed intelligence, dynamic spectrum management, and zero-trust security frameworks are considered the very basics in the case of autonomous private 6G networks. Migration pathways steer towards phased adoption in order to secure interoperability with the current 5G infrastructure and integration of AI-driven monitoring and orchestration tools. This strategy presents a complete roadmap for companies that wish to utilize the benefits of the private 6G technology, such as ultra-low latency, improved reliability, and flexible service delivery. The merging of tech innovations and organizational changes will empower the telecom sector to be more agile, productive, and digitally resilient with the next-gen private networks.

Keywords : Autonomous private 6G, Private 5G migration, AI-driven networks, Co-management models, Design patterns

Author : Bhaskara Raju Rallabandi

Title : EVOLVING PRIVATE NETWORK : FROM 5G TO AUTONOMOUS 6G WITH CO - MANAGEMENT

Volume/Issue : 2025;02(12)

Page No : 61-66

The rapid development in power electronic systems, driven by the use of power semiconductor devices, has led to their application across a wide range of sectors including residential, commercial, aerospace, and more. However, power converters are a major source of harmonic distortion and low power factor in power systems, which remains a key concern. The conventional single-phase AC–DC conversion method using a diode bridge and bulk capacitor presents several drawbacks, such as injection of low-order harmonics into the AC supply, poor power factor, high peak current, distortion of line voltage, increased electromagnetic interference, and additional power losses. This study focuses on evaluating two control techniques to mitigate these issues: Proportional-Integral (PI) controller with hysteresis current control, and Fuzzy Logic Controller (FLC) with hysteresis. Both methods are implemented in a single-phase Zeta converter. Simulation results highlight that the fuzzy logic-based controller delivers better performance than the PI controller, demonstrating improved power quality, reduced harmonics, and more efficient voltage regulation.

Keywords : Single-Phase Zeta Converter, Power Factor Improvement, Total Harmonic Distortion (THD), Proportional-Integral (PI) Controller, Hysteresis Current Control, Fuzzy Logic Control.

Author : S.T.Siddharthan

Title : A FUZZY LOGIC APPROACH TO IMPROVE POWER QUALITY IN SINGLE-PHASE AC–DC ZETA

Volume/Issue : 2025;02(12)

Page No : 67-78

The rapid evolution of 5G and future wireless systems demands reliable, high-throughput communication techniques. Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is a key enabler due to its spectral efficiency and resilience to multipath fading, yet it suffers from high PAPR and complexity in channel estimation and equalization. This thesis proposes an optimized MIMO-OFDM framework that integrates pilotassisted Least Squares (LS) and Minimum Mean Square Error (MMSE) channel estimation with a two-stage MMSE equalization scheme and reduced-complexity modulation recognition. Simulations for an 8×8 MIMO-OFDM system with 64 subcarriers over frequency-selective Rayleigh fading channels show notable gains. The proposed approach achieves a BER of 2.5×10?? at 15 dB SNR, outperforming conventional LS methods achieving 1.2×10?³. Throughput improves by 12%, while computational complexity is reduced by 30%, enabling real-time implementation. Results demonstrate strong robustness under 120-Hz Doppler conditions, highlighting the method’s suitability for next-generation wireless networks.

Keywords : MIMO-OFDM; Hybrid Channel Estimation; MMSE Equalization; LS–MMSE Estimator; Massive MIMO; XL-MIMO; ISAC (Integrated Sensing and Communication); THz Communication; Optical Wireless Communication; Beamforming; Precoding

Author : Dr.Kanne Naveen, Farha khanam

Title : HARDWARE-EFFICIENT MIMO-OFDM SYSTEM WITH VARIABLECAPACITANCE ANTENNA ARRAYS FOR REAL-TIME WIRELESS COMMUNICATION

Volume/Issue : 2025;02(12)

Page No : 79-90

The Open and Distance Learning (ODL) paradigm, characterized by flexibility and geographical dispersion, is uniquely positioned to benefit from the transformative power of Artificial Intelligence (AI).1 This article investigates the role of AI technologies such as Intelligent Tutoring Systems (ITS), learning analytics, and generative AI in revolutionizing ODL by enhancing personalized learning, automating administrative tasks, and boosting student engagement. Drawing upon contemporary literature, this paper outlines the significant advantages, including adaptive content delivery and proactive student support, while critically addressing associated challenges like data privacy, ethical considerations, and the risk of diminished critical thinking. The study concludes by proposing a structured framework for the ethical, equitable, and effective integration of AI in ODL to ensure a human-centred and pedagogically sound educational future.

Keywords : Artificial Intelligence, Open and Distance Learning (ODL), Personalized Learning, Intelligent Tutoring Systems, Learning Analytics, Educational Technology, Academic Integrity, Ethical AI

Author : Dr. Kasturi S. Pesala, Dr. Hemamalini H.C., Dr. Latha Shettappanavar

Title : ARTIFICIAL INTELLIGENCE AS A TRANSFORMATIVE TOOL IN OPEN AND DISTANCE LEARNING (ODL)

Volume/Issue : 2025;02(12)

Page No : 91-95

The rapid expansion of e-commerce has intensified the need for businesses to understand consumer engagement behaviours and leverage data-driven strategies to improve decision-making and marketing effectiveness. This combined research integrates insights from two prior studies—one focused on analysing user engagement metrics through machine learning and another emphasizing predictive analytics for marketing optimization—to present a unified framework for enhancing ecommerce performance. Using comprehensive transaction logs and interaction datasets, the study investigates key user engagement metrics such as reading time, session duration, clicks, and page views, and evaluates their influence on purchasing decisions. Advanced machine learning models, particularly the Random Forest Classifier, are employed to predict consumer behaviour with high accuracy, outperforming traditional models like Logistic Regression and Decision Trees. Findings demonstrate strong correlations between prolonged engagement and higher conversion likelihood, emphasizing the importance of optimizing product content and user experience. The research further highlights how predictive analytics can significantly improve marketing strategies by enabling precise segmentation, personalized recommendations, and targeted campaigns. Businesses adopting these insights observed notable improvements in conversion rates, customer satisfaction, and marketing ROI. By integrating engagement analysis with predictive modelling, this study offers a holistic approach for e-commerce firms seeking data-driven competitive advantage. The unified framework supports informed decision-making across content optimization, consumer targeting, and strategic marketing interventions. The work also provides a foundation for future enhancements using deep learning, realtime analytics, and multi-channel consumer behaviour integration.

Keywords : Consumer Engagement Analytics, Predictive Modelling, E-Commerce Strategy, Machine Learning, Conversion Optimization.

Author : Manish Gupta,Dr.Thota Siva Ratna Sai

Title : INTEGRATING CONSUMER ENGAGEMENT ANALYTICS AND PREDICTIVE MODELLING TO ENHANCE E-COMMERCE DECISION-MAKING AND MARKETING STRATEGIES

Volume/Issue : 2025;02(12)

Page No : 96-107

The rapid migration of critical workloads to cloud environments has amplified the need for robust, scalable, and manageable security mechanisms. This paper presents a unified research contribution that integrates Policy-Based Access Control (PBAC) with advanced data encryption techniques within Microsoft Azure. By combining fine-grained, attribute-driven access policies with AES-256 encryption for data at rest, TLS 1.3 for data in transit, and centralized key management via Azure Key Vault, the proposed framework establishes a multi-layered defense-in-depth model without compromising operational efficiency. Empirical evaluation in enterprise-grade test deployments demonstrates that the integrated framework successfully prevented 99% of simulated unauthorized access attempts, ensuring no data breaches or integrity violations during extended testing periods. Encryption operations added minimal latency (3– 5 ms for both transit and at-rest scenarios), while overall system response time increased by only ~7%, remaining well within acceptable service-level thresholds even under scaled workloads. Automated key rotation, stringent key-access policies, and continuous auditing further enhanced resilience against external attacks and insider threats. The results highlight PBAC and encryption as complementary pillars of modern cloud security, offering organizations a practical and scalable blueprint that balances stringent protection with minimal performance overhead. This study provides actionable guidance for implementing zero-trust principles in multi-tenant cloud environments and lays a foundation for future extensions, including machine learning-driven policy adaptation and blockchain-enhanced key governance.

Keywords : Cloud Security, Policy-Based Access Control (PBAC), Data Encryption, Azure Key Vault, Zero-Trust Architecture

Author : VIBHARANI PRASAD,Prof. SUNIL BHUTADA

Title : A UNIFIED FRAMEWORK FOR ENHANCING CLOUD SECURITY THROUGH POLICY-BASED ACCESS CONTROLAND MULTI-LAYER DATA ENCRYPTION: INSIGHTS FROM AZURE IMPLEMENTATION AND EVALUATION

Volume/Issue : 2025;02(12)

Page No : 108-115

Accurate prediction of stock market movements remains a major challenge due to the highly dynamic nature of financial markets and the complex interaction of economic and behavioural factors. Traditional prediction approaches based solely on fundamental or technical analysis are often limited in capturing nonlinear relationships and large-scale data patterns. This consolidated research proposes an integrated framework that leverages Big Data analytics and machine learning techniques to enhance stock market prediction by combining both fundamental financial indicators and technical market signals. The study synthesizes insights from multiple datasets, including corporate financial statements (earnings per share, return on equity, revenue, net income, and price-to-earnings ratios) and technical indicators derived from historical trading activity (moving averages, relative strength index, and daily volume trends). Extensive data preprocessing and feature selection techniques are applied to ensure model robustness and consistency. Several predictive models are implemented and assessed, including Linear Regression, Neural Networks, and ensemble-based Random Forest algorithms. Performance evaluation using standard metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared demonstrates that advanced machine learning methods significantly outperform traditional statistical approaches. Among the tested models, the Random Forest algorithm consistently delivers the highest prediction accuracy, effectively capturing nonlinear relationships and interactions between market variables. Real-world case studies of selected equities further validate model effectiveness, showing minimal deviation between predicted and actual stock prices. These results confirm that integrating fundamental financial strength indicators with technical market dynamics, supported by Big Data-driven machine learning, substantially improves forecasting reliability. The findings highlight the practical value of the proposed framework for investors, portfolio managers, and financial institutions, offering improved tools for investment optimization, risk mitigation, and automated trading strategies. Future enhancements may incorporate alternative data sources such as social media sentiment, macroeconomic indicators, and realtime market feeds, potentially leading to even more accurate and adaptive prediction systems.

Keywords : Stock Market Prediction, Machine Learning, Big Data Analytics, Fundamental and Technical Analysis, Financial Modelling

Author : YOGESH KUMAR MODI,Dr. RAVINDRA SINGH YADAV

Title : AN INTEGRATED BIG DATA AND MACHINE LEARNING FRAMEWORK FOR ENHANCED STOCK MARKET PREDICTION USING FUNDAMENTAL AND TECHNICAL INDICATORS

Volume/Issue : 2025;02(12)

Page No : 116-131

Modern network infrastructures face increasingly sophisticated and high-volume cyberattacks, demanding intrusion detection systems (IDS) that are not only accurate but also computationally efficient for real-time deployment. This research presents a unified framework that synthesizes the strengths of two complementary approaches in advanced IDS research. Drawing from ensemble deep learning architectures—specifically Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN/LSTM), and Autoencoders—as well as optimization strategies such as hyperparameter tuning, pruning, quantization, and hardware acceleration, this study proposes a comprehensive model that maximizes both detection accuracy and processing efficiency. The first contribution incorporated in this unified model is the use of diverse deep learning base learners (CNN, RNN, Autoencoders) combined via stacking and voting techniques to reduce false positives and enhance robust threat detection across known and unknown attack types. This ensemble method significantly improves precision, recall, F1-score, and detection rates, outperforming standalone models when tested on benchmark datasets. Insights from prior research also highlight the pivotal role of data preprocessing, feature extraction, and handling of class imbalance in improving model reliability. The second contribution integrated into this framework focuses on computational optimization. Model pruning and quantization are employed to reduce memory consumption and accelerate inference, while GPU/TPU-based hardware acceleration ensures real-time responsiveness. These optimizations maintain high accuracy while substantially lowering computational cost, enabling scalable deployment in resource-constrained environments such as IoT and edge networks. By merging accuracy-driven ensemble deep learning design with efficiency-focused optimization methods, this research delivers a holistic IDS capable of real-time detection with superior performance metrics. The proposed framework provides a balanced, scalable, and future-ready approach to intrusion detection, overcoming key limitations of traditional IDS and standalone deep learning systems. The study establishes a strong foundation for next-generation IDS research involving hybrid AI models, adaptive learning, and decentralized security architectures.

Keywords : Ensemble Deep Learning for IDS,Computational Optimization,Network Intrusion Detection Systems,Real-Time Cybersecurity,CNN–RNN–Autoencoder Hybrid Models

Author : Hemant Kumar Verma,Prof. Dr Monika Tripathi

Title : A UNIFIED ENSEMBLE DEEP LEARNING FRAMEWORK FOR HIGH-ACCURACY AND COMPUTATIONALLY EFFICIENT NETWORK INTRUSION DETECTION

Volume/Issue : 2025;02(12)

Page No : 132-145

The rapid growth of digital platforms has transformed traditional marketing practices, compelling organisations to adopt advanced technologies for effective customer engagement and decisionmaking. Artificial Intelligence (AI) has emerged as a powerful tool in digital marketing by enabling data-driven strategies, automation, and enhanced personalisation. This study examines the role of Artificial Intelligence in digital marketing and analyses its impact on marketing effectiveness and customer experience. AI technologies such as machine learning, natural language processing, predictive analytics, and chatbots enable marketers to process large volumes of consumer data, identify behavioural patterns, and deliver personalised content in real time. The study highlights how AI improves customer targeting, engagement, and conversion rates while optimising marketing resources and reducing operational costs. In addition, the research discusses key challenges associated with AI adoption, including data privacy concerns, algorithmic bias, ethical issues, high implementation costs, and skill shortages. By reviewing existing literature and analysing current practices, the study provides a comprehensive understanding of AI-driven digital marketing strategies. The findings suggest that while Artificial Intelligence significantly enhances marketing performance and competitiveness, its successful implementation requires responsible data governance, ethical considerations, and continuous human oversight. The study contributes to academic research and offers practical insights for marketers seeking to integrate AI into digital marketing strategies effectively.

Keywords : Artificial Intelligence, Digital Marketing, Machine Learning, Customer Personalisation, Predictive Analytics, Marketing Automation, Consumer Behaviour

Author : Bolla Sai Babu,Valla Venkata Satya Kumar,Mungara Veena Shakeena,Penta Sri Lakshmi

Title : ROLE OF ARTIFICIAL INTELLIGENCE IN DIGITAL MARKETING

Volume/Issue : 2025;02(12)

Page No : 146-153

The rapid growth of online banking has fundamentally transformed the delivery of financial services by enabling customers to perform transactions, manage accounts, and access banking facilities through digital platforms. Driven by increased internet penetration, smartphone usage, and the demand for convenience, online banking has become an essential component of modern financial systems. However, this digital transformation has also intensified cyber security concerns, as financial institutions handle large volumes of sensitive customer and transaction data. Ensuring cyber security is therefore critical to maintaining trust, financial stability, and regulatory compliance within the banking sector. This study examines the major cyber security risks associated with online banking, including phishing attacks, malware infections, identity theft, man-in-the-middle attacks, and data breaches. A descriptive and analytical research methodology is adopted, primarily based on secondary data collected from academic journals, regulatory reports, industry publications, and documented case studies. The analysis focuses on identifying common attack patterns, vulnerable system components, and the role of human factors in cyber incidents. The key findings reveal that phishing and malware attacks remain the most prevalent threats, largely due to user unawareness and weak security practices. The study highlights that a multi-layered security approach combining technical safeguards, organisational policies, regulatory compliance, and user education significantly reduces cyber risk exposure. The practical significance of this research lies in providing valuable insights for banks, regulators, and customers to strengthen cyber security strategies and promote safer online banking practices.

Keywords : Cyber security, Online banking, Phishing attacks, Malware, Financial fraud, Data privacy, Risk management

Author : A. Lavanya,K. Venkata Satya Sai Anil,M. Sree Jyothika,Ch. Neeladri

Title : A STUDY ON CYBER SECURITY RISKS IN ONLINE BANKING

Volume/Issue : 2025;02(12)

Page No : 154-162

The rapid expansion of internet access and mobile technologies has transformed the way consumers search for information, evaluate products, and make purchasing decisions. Online shopping platforms have emerged as dominant channels by offering convenience, wider product assortments, competitive pricing, and personalised experiences. This study examines consumer buying behaviour towards online shopping platforms with the aim of understanding the key factors that influence purchase intentions and decision-making processes. Particular attention is given to aspects such as trust, perceived risk, ease of use, payment security, product variety, delivery efficiency, and post-purchase services. The study also explores how demographic variables, including age, income, education, and digital literacy, shape consumer preferences and attitudes towards online shopping. By analysing behavioural patterns and motivational drivers, the research highlights the reasons behind platform selection, frequency of online purchases, and customer satisfaction levels. The findings are expected to provide valuable insights for e-commerce companies to improve platform design, enhance customer engagement, and build long-term loyalty. Furthermore, the study contributes to academic understanding by linking traditional consumer behaviour theories with contemporary digital purchasing environments. Overall, the research emphasises that understanding consumer behaviour is critical for sustaining competitiveness and growth in the rapidly evolving online retail ecosystem.

Keywords : Online shopping, Consumer buying behaviour, E-commerce platforms, Purchase intention, Customer satisfaction, Digital marketing, Trust and security

Author : V. Vijay Kumar,V.satya venkata pavan kumar,B Satya Durga vara prasad,P.tirumala venkata Uday

Title : A STUDY ON CONSUMER BUYING BEHAVIOUR TOWARDS ONLINE SHOPPING PLATFORMS

Volume/Issue : 2025;02(12)

Page No : 163-174

The integration of Generative Artificial Intelligence (AI) into financial reporting is transforming the accounting profession, offering unprecedented opportunities for accuracy, transparency, and ethical compliance. This study examines how generative AI can enhance corporate financial reporting by automating data processing, detecting anomalies, and providing predictive insights, while simultaneously addressing risks such as algorithmic bias, cybersecurity threats, and regulatory uncertainty. A conceptual framework is proposed to illustrate how AI adoption influences reporting quality, corporate governance, and ethical standards. The research emphasizes the importance of ethical oversight, robust governance mechanisms, and adherence to regulatory frameworks to ensure that AI-driven financial reporting strengthens accountability without compromising integrity. Practical implications for finance professionals, auditors, and regulators are discussed, focusing on strategies to leverage AI responsibly. This study contributes to the literature on AI in accounting by offering a structured perspective on the integration of advanced technologies in financial reporting and lays the groundwork for future empirical investigations.

Keywords : Generative AI; Financial Reporting; Accounting Ethics; Transparency; Accuracy; Corporate Governance; Risk Management

Author : K. Prakash , M. Mallesham

Title : THE FUTURE OF FINANCIAL REPORTING: INTEGRATING GENERATIVE AI FOR ACCURACY, TRANSPARENCY, AND ETHICAL STANDARDS

Volume/Issue : 2025;02(12)

Page No : 175-185

The growing pressure on manufacturing firms to minimize environmental impact has increased the strategic relevance of green cost accounting and sustainable production practices. Although many organizations invest in sustainability initiatives, there remains limited empirical evidence on whether these practices lead to measurable improvements in business performance. This study examines the relationship between green cost accounting, sustainable production efficiency, profitability, and competitive advantage in manufacturing firms. An empirical research design was employed using survey data collected from production and finance managers across selected manufacturing units. The analysis explores the extent to which firms integrate environmental costs into their costing systems and evaluates how these practices influence production decisions, resource utilization, and financial outcomes. The findings reveal that firms that systematically implement green cost accounting achieve higher production efficiency, stronger cost control, and enhanced profitability compared to firms operating under conventional costing and production systems. The results further indicate that sustainability-oriented firms are more likely to develop long-term competitive advantage through innovation capability, brand credibility, and regulatory preparedness. This study contributes to the emerging sustainability accounting literature by highlighting the strategic role of environmental cost integration in managerial decisionmaking. The paper concludes with practical and policy implications for manufacturing firms, industry regulators, and policymakers seeking to promote environmentally responsible and financially viable industrial growth.

Keywords : Green Cost Accounting; Sustainable Production; Environmental Costing; Profitability; Competitive Advantage; Manufacturing Firms; Sustainability Accounting; Resource Efficiency

Author : V. Prakash, Ch. Ravi

Title : GREEN COST ACCOUNTING AND SUSTAINABLE PRODUCTION: IMPLICATIONS FOR PROFITABILITY AND COMPETITIVE ADVANTAGE IN MANUFACTURING FIRMS

Volume/Issue : 2025;02(12)

Page No : 186-196

Employee experience (EX) is emerging as a critical factor influencing organizational performance. This study examines how EX affects employee engagement, retention, and productivity in Indian corporate organizations. A structured survey was conducted among 250 employees from IT, manufacturing, and service sectors. Data were analyzed using descriptive statistics, correlation, and regression analyses. Results indicate that positive employee experiences characterized by supportive leadership, career development opportunities, recognition, and work-life balance significantly improve engagement, reduce turnover intentions, and enhance productivity. The findings suggest that organizations prioritizing employee-centric policies are likely to achieve sustainable performance and competitive advantage. The study provides practical recommendations for HR professionals to implement strategies that enhance employee experience and organizational outcomes.

Keywords : Employee Experience, Engagement, Retention, Productivity, Organizational Performance, Indian Corporate Employees.

Author : Nalla Praveen Kumar, Rachaboina Kanaka Goud

Title : EMPLOYEE EXPERIENCE AND ORGANIZATIONAL PERFORMANCE: A STUDY ON ENGAGEMENT, RETENTION, AND PRODUCTIVITY AMONG INDIAN CORPORATE EMPLOYEES

Volume/Issue : 2025;02(12)

Page No : 197-204

India is one of the world’s largest suppliers of generic pharmaceuticals, with the United States being its primary export destination. The imposition of a 100 percent tariff on selected pharmaceutical imports under the Trump administration has introduced significant trade disruption. This study investigates the impact of this tariff policy on the Indian pharmaceutical supply chain, examining trade flows, supply-side adjustments, and firm-level strategic responses. Adopting a mixed-method approach, the research combines qualitative analysis of policy documents, industry reports, and trade white papers with quantitative analysis of export statistics, pricing trends, and shipment volumes. Descriptive statistics, correlation, and regression analyses reveal that tariff escalation has resulted in margin compression, operational delays, and strategic realignment of production and sourcing. The study identifies critical adaptation mechanisms, including market diversification, technological upgrading, localization, and riskbased supply-chain management. Policy and managerial implications are discussed to enhance resilience and maintain India’s competitive position in the global pharmaceutical ecosystem.

Keywords : Indian Pharmaceutical Industry, Tariff Policy, Supply Chain Disruption, Export Competitiveness, Strategic Response, Trade Policy, Mixed-Method Analysis

Author : K. Sandhya Sravanthi, B. Tarangini

Title : IMPACT OF TRUMP’S TARIFF POLICY ON THE INDIAN PHARMACEUTICAL SUPPLY CHAIN: TRADE DISRUPTION, SUPPLYSIDE RESPONSES, AND STRATEGIC IMPLICATIONS

Volume/Issue : 2025;02(12)

Page No : 205-213

Road signs are crucial for ensuring a safe and orderly flow of traffic. Negligence in failing to read traffic signs and misinterpreting them is a significant contributor to auto accidents. The suggested system assists in identifying traffic signs and alerting the driver through speaker so that he or she may make the appropriate selections. Convolutional Neural Network (CNN) training is used in the proposed system to aid in the detection and categorization of images of traffic signs. To increase the accuracy of a given dataset, a set of classes are created and trained. We used the German Traffic Sign Benchmarks Dataset, which includes 51,900 photos of traffic signs in around 43 categories. About 98.52 percent of the execution was accurate. A voice alarm is broadcast over the speaker when the system recognizes the sign to inform the driver. The proposed system also has a component where drivers of moving vehicles are informed of nearby traffic signs so they are aware of the laws they should observe. The system's goal is to protect the driver, passengers, and pedestrians from harm.

Keywords :

Author : Dr. Mohd Azeemullah, Dr. Vijay Pal Singh

Title : A DEEP NEURAL NETWORK APPROACH FOR TRAFFIC SIGN RECOGNITION

Volume/Issue : 2025;02(12)

Page No : 214-222