Journal of Science Engineering Technology and Management Science

Manufacturers today are exposed to a widening range of shocks, from collapsing supply chains and cyberattacks to unexpected machine failures, geopolitical tension, and natural disasters. Most factories, however, are still organised mainly around throughput and efficiency, and they rarely build in the kind of forward-looking mechanisms needed to cope with uncertainty. Digital Twin (DT) technology, one of the defining developments of Industry 4.0, offers a promising response: it can mirror a physical system as a live, virtual counterpart. Yet the existing body of work has concentrated heavily on tuning processes and lifting efficiency, and comparatively little of it asks how these virtual models can be put to work for resilience and risk mitigation. To help close that gap, this study puts forward a resilience-oriented Digital Twin framework built to play out “What-If” disaster scenarios inside manufacturing ecosystems. We look at how a virtual replica of a factory can stand in for events such as supplier breakdowns, equipment faults, transport delays, labour shortages, and cyber threats, so that contingency planning can begin before trouble actually arrives. The work draws on a systematic review of 45 high-quality papers, which we used to map out what Digital Twins can currently do, where the open questions lie, and which technical limits still hold them back in manufacturing risk management. The framework we propose brings together the Internet of Things (IoT), Artificial Intelligence (AI), predictive analytics, and simulation modelling inside a multi-layered Digital Twin architecture that supports both real-time monitoring and scenario analysis. To test it, we built a hypothetical dataset standing in for typical manufacturing operating variables and used it to compare how the system handled disruptions before and after the Digital Twin was introduced. Our results suggest that Digital Twins make a real difference to manufacturing resilience: they cut downtime, sharpen response times, widen visibility across the supply chain, and let decisions be made ahead of events rather than after them. The contribution here is a fresh, resilience-centred framework that moves the Digital Twin away from being merely an efficiency tool and toward a strategic instrument for managing risk. For manufacturers chasing adaptive, sustainable operations in an unsettled industrial climate, the practical takeaways are clear.

Keywords :

Author : Srikanth Kummari, K. Sai jeetesh, Police Machendarao, B.S.V.Ramanna Chowdary

Title : DIGITAL TWINS FOR RISK MITIGATION IN MANUFACTURING: A RESILIENCE-ORIENTED FRAMEWORK FOR PREDICTIVE SCENARIO SIMULATION AND CONTINGENCY PLANNING

Volume/Issue : 2026;03(07)

Page No : 483-499

Investment is the collection and accumulation of cash or resources with the goal of producing wealth and earning returns in the future. Individuals can invest through a variety of channels, including bank deposits, post office schemes, mutual funds, stocks and shares, gold and silver, real estate, and other financial instruments. The investment preferences of individuals differ due to various factors like income, purpose of investment , risk bearing capacity, age etc. This study aims to analyse and compare the investment preferences of two major age groups: Generation X (The older generation, born between 1965-1980) and Millennials (The younger generation, born between 1981-1996). To achieve this objective, the Garrett Ranking Technique has been used to rank the investment options preferred by respondents in each group. Subsequently, a comparative analysis has been conducted to identify differences and similarities in their investment choices, leading to meaningful conclusions regarding the investment behaviour of these generations. The study shows that the primary difference between Generation X & Millennials is that respondents from Generation X, who are comparatively older, prefer risk-free investments, while Millennials, who are comparatively younger, are more interested in taking on moderate market risks. As a result, Millennials place Mutual Funds over traditional banking , but invest more in mutual funds than in the stock market directly. Nonetheless, both age groups share similar preference for real estate and post office-oriented investments. The report also shows that Millennials do not think Life insurance as a wise financial choice.

Keywords : Investment preferences, Generation X, Millennials, Garrett Ranking

Author : Dr. Kallol Saha* & Abhirup Midday

Title : RANKING & COMPARATIVE ANALYSIS OF INVESTMENT PREFERENCES OF GENERATION X & MILLENNIAL INVESTORS: A GARRETT RANKING APPROACH

Volume/Issue : 2026;03(07)

Page No : 500-511

Artificial Intelligence (AI) has become a game-changer in the realm of Human Resource Management (HRM), revolutionizing the way organizations manage their resources, boost productivity, and make informed decisions. From traditional HR to digital HR systems, organizations can now use the advanced technologies to handle talent acquisition, employee engagement, performance management, and workforce analytics (Bondarouk & Brewster, 2016). AI-powered tools that utilize predictive analytics, natural language processing, machine learning, chat bots and virtual assistants have revolutionized the recruitment process, enhancing its effectiveness, employee experience and operational efficiency ( Vrontis et al., 2022). The study explores the role of Artificial Intelligence in the HRM of the present era and its effect on different HR functions. This study has a descriptive and analytical research design with both primary and secondary data. Data was obtained from HR professionals and employees using a structured questionnaire and a sample of 200 respondents. The results indicate that AI-powered recruitment solutions improve the screening of resumes, match candidates, and automate hiring procedures, ultimately saving time in recruitment and ensuring that the best candidates are chosen (Upadhyay & Khandelwal, 2018). Additionally, AI-driven HR analytics can aid in workforce forecasting, retention projections, and data-informed decision-making, which can lead to better organizational performance (Marler & Boudreau, 2017). The research also brings to light the benefits of AI for employee engagement, such as customised learning, ongoing feedback processes, and intelligent assistance systems (Jarrahi, 2018). Even with these advantages, there are several issues of algorithmic bias, employee privacy, and transparency that must be addressed in a responsible and ethical manner (Strohmeier, 2020). The study reveals that AI is a valuable strategic ally for HRM and will be pivotal in helping to create an Industry 5.0 workplace, characterized by innovation, adaptability in the workforce, and sustainable competitiveness in the digital world. AI in HRM encompasses a wide range of applications, such as automated scheduling, candidate screening, predictive hiring algorithms, and performance analytics, among others.AI in HRM includes various applications, including automated scheduling, candidate screening, predictive hiring algorithms, performance analytics, and more.

Keywords : Game-Changer, Acquisition, Forecasting, Pivotal, Workforce, Automated

Author : Mrs.Ankita Tiwari, Mrs.Seetha Pallavi

Title : ROLE OF ARTIFICIAL INTELLIGENCE IN MODERN HUMAN RESOURCE MANAGEMENT

Volume/Issue : 2026;03(07)

Page No : 512-519

The increasing adoption of Electric Vehicles (EVs) has created new challenges for power systems, particularly in managing charging demand and maintaining the stability of smart grids. Since EV charging patterns vary significantly across locations and time, accurate load forecasting has become essential for effective energy distribution and grid operation. Traditional centralized forecasting methods often require transferring charging data from multiple stations to a central server, which raises concerns related to user privacy, communication costs, scalability, and security vulnerabilities To address these challenges, this study presents a privacy-aware EV load forecasting framework that combines Gated Recurrent Unit (GRU) networks, Federated Learning (FL), Blockchain technology, KMeans clustering, and Anomaly Detection techniques. The GRU model is used to learn temporal characteristics from historical charging records and generate future load predictions. Federated Learning allows charging stations to collaboratively train forecasting models without sharing their raw data, thereby ensuring data confidentiality and reducing privacy risks A blockchain-based validation mechanism is incorporated to verify local model updates before aggregation, improving transparency and protecting the system from malicious or unreliable contributions. In addition, K-Means clustering is applied to categorize charging stations according to their charging behavior, enabling better pattern analysis and resource management. Anomaly detection methods are employed to identify unusual charging activities that may negatively affect forecasting accuracy and operational efficiency. The effectiveness of the proposed framework is assessed using widely adopted forecasting metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). By integrating deep learning, decentralized model training, and secure blockchain validation, the framework offers a robust solution for intelligent EV charging environments

Keywords : Electric Vehicle (EV), Load Forecasting, Federated Learning, Deep Learning, Gated Recurrent Unit, Blockchain Technology, Smart Grid, Energy Management, Privacy Preservation, Distributed Machine Learning.

Author : B Swapna, Dr. U. Sri Lakshmi, Dr.V.Anantha Krishna, Dr. U. Sri Lakshmi

Title : ELECTRIC VEHICLE LOAD FORECASTING WITH PRIVACY PROTECTION USING FEDERATED LEARNING AND BLOCKCHAIN

Volume/Issue : 2026;03(07)

Page No : 520-528

Important medical information has to be protected from unwanted access. Healthcare systems make substantial use of SDNs to optimize resource use, security, network administration, and control. SDNs provide many advantages, but because patient data is sensitive, they are susceptible to several threats. These attacks compromise networks and may result in fatal malfunctions. We adapt a layer three (L3) learning switch application to collect normal and abnormal traffic and apply it to the Ryu controller in order to propose a machine learning-based cyber-attack detector (MCAD) for healthcare systems. Our findings enhance the security of healthcare applications and lessen assaults. In order to demonstrate MCAD's advantages and disadvantages, this paper evaluates it using a variety of ML methods and attacks. Excellent reliability for the MCAD is indicated by an exceptional F1-score on both attack and normal classes. The complexity-optimized real-time system of MCAD generates 5,709,692 samples per second.

Keywords : Network resilience, network management, intrusion detection system (IDS), software defined networking, healthcare, machine learning.

Author : J. Kumari 1 , B. Madhu Krishna 2

Title : AN ENSEMBLE LEARNING EXTENSION OF MCAD FOR CYBERATTACK DETECTION IN SDN- BASED HEALTHCARE SYSTEMS

Volume/Issue : 2026;03(07)

Page No : 529-542

Brain tumor detection is a critical task in the field of medical imaging, as early diagnosis can significantly improve patient survival rates. Magnetic Resonance Imaging (MRI) is widely used for identifying brain abnormalities due to its highresolution imaging capability. However, manual analysis of MRI scans is timeconsuming and may lead to human errors. This project presents an automated approach for brain tumor detection using image processing techniques. The proposed system involves preprocessing of MRI images to remove noise and enhance quality, followed by segmentation methods such as thresholding and clustering to isolate the tumor region. Morphological operations are applied to refine the detected area, and feature extraction techniques are used to analyze the size and shape of the tumor. The system is implemented using MATLAB, providing efficient processing and visualization tools. The results demonstrate accurate detection and clear identification of tumor boundaries. This method offers a reliable, cost-effective, and non-invasive solution to assist medical professionals in diagnosing brain tumors.

Keywords : Magnetic Resonance Imaging (MRI),MATLAB GUI,Tumor detection, Morphological operations

Author : Bodhankar Pooja1 , Animalla Sindu2 , Syeda Qathija Marriya3 , Ms. Shafia Tasneem4

Title : BRAIN TUMOR DETECTION USING IMAGE PROCESSING

Volume/Issue : 2026;03(07)

Page No : 543-550

The rapid growth of electric vehicles (EVs) has increased the demand for efficient thermal management systems to ensure the reliable operation of in-wheel electric motors. During continuous operation, the motor generates a significant amount of heat due to copper losses, iron losses, eddy current losses, and mechanical friction. Excessive temperature adversely affects motor efficiency, permanent magnet performance, insulation life, and overall system reliability. Therefore, an effective cooling system is essential to maintain the motor within its permissible operating temperature. This research presents a numerical investigation of heat transfer characteristics of an in-wheel electric motor housing with different cooling fin arrangements using ANSYS Workbench. Four innovative cooling fin configurations, namely Straight Fin (baseline), Wave Fin, ZigZag Fin, and Rounded Layer Fin, are designed using SolidWorks while maintaining identical housing dimensions, fin height, fin thickness, and fin spacing to ensure a fair comparison. Aluminium Alloy 6061, Aluminium Alloy 7075, and Magnesium Alloy AZ91D are selected as candidate materials due to their excellent thermal conductivity, lightweight characteristics, and suitability for electric vehicle applications. Steady-state thermal analysis, and Static Structural analysis are performed under identical operating conditions by applying a uniform heat source and forced air cooling. The performance of each fin arrangement is evaluated based on maximum temperature, minimum temperature, temperature distribution, heat flux, deformation, equivalent stress, and factor of safety. Unlike previous studies that focused only on conventional straight, slanting, and transverse fin arrangements, this research extends the existing work by introducing advanced fin geometries capable of enhancing convective heat transfer while minimizing weight and maintaining structural integrity. The comparative investigation identifies the optimum cooling fin arrangement that provides maximum heat dissipation, lower operating temperature, reduced thermal resistance, and improved thermal efficiency. The findings of this work provide practical design guidelines for developing high-performance air-cooled inwheel electric motor housings for nextgeneration electric vehicles.

Keywords : The increasing demand for sustainable transportation has accelerated the development of electric vehicles (EVs) worldwide.

Author : DASI RAVI TEJA1 , B.JITHENDER2

Title : HEAT TRANSFER ANALYSIS OF IN-WHEEL ELECTRIC MOTOR HOUSING WITH DIFFERENT COOLING FIN ARRANGEMENT USING ANSYS

Volume/Issue : 2026;03(07)

Page No : 551-561

This study investigates the relationship between dividend policy and its impact on share prices, focusing on Infosys Limited, a leading Indian multinational IT services company. Dividend policy remains a critical financial decision that influences investor perception and company valuation. The research aims to analyze whether changes in dividend payout ratios, dividend yield, and consistency in dividend distribution affect Infosys’s stock prices over time. Primary and secondary data were utilized, including five years of financial statements, dividend declarations, and stock market trends from 2019 to 2024. Quantitative techniques such as regression analysis and correlation were employed using MS Excel and SPSS to evaluate the relationship between dividend metrics and stock performance. Descriptive statistics further support the data interpretation. The findings suggest a moderate positive correlation between dividend announcements and short-term share price movements. However, the effect diminishes in the long term, with earnings growth and market conditions playing a more dominant role. Investors tend to value consistent and predictable dividend policies, which enhance investor confidence and potentially reduce stock volatility. The study concludes that while dividend policy does influence share prices to a certain extent, it is not the sole determinant of stock valuation. Strategic dividend planning, aligned with long-term growth and stability, is essential for maximizing shareholder wealth. This study examines the relationship between dividend policy and share price movements at Infosys. It analyzes how dividend decisions impact investor perception and stock valuation, using historical data and financial metrics. The findings provide insights into optimal dividend strategies and their influence on market performance and shareholder value.

Keywords : Dividend Policy, Share Prices, Stock Market Performance, Dividend Payout Ratio, Shareholder Wealth, Infosys Limited.

Author : 1Ms. BADDAM AKSHAYA, 2A. BALA KRISHNA

Title : A STUDY ON DIVIDEND POLICY AND ITS EFFECT ON SHARE PRICES AT INFOSYS

Volume/Issue : 2026;03(07)

Page No : 562-567

Online banking has emerged as a significant component of the banking sector, enabling financial institutions to deliver efficient, secure, and convenient services to users through digital platforms. The rapid advancement of information technology and increasing internet penetration have accelerated the adoption of online banking services across India. This study examines the role of online banking in enhancing digital banking performance, with special reference to HDFC Bank. The primary objective of the research is to analyze how online banking services contribute to the effectiveness, accessibility, and overall performance of digital banking operations. The study focuses on various online banking services such as fund transfers, account management, bill payments, mobile banking integration, and digital payment solutions. Data were collected through structured questionnaires and secondary sources, including annual reports, banking publications, and relevant academic literature. The findings indicate that online banking has significantly improved transaction speed, operational efficiency, service accessibility, and user engagement. The adoption of advanced technologies, including mobile applications, real-time payment systems, and enhanced cybersecurity measures, has strengthened digital banking performance and increased the utilization of banking services through digital channels. The study further reveals that online banking reduces operational costs, minimizes dependence on physical branches, and supports sustainable banking practices. Additionally, the availability of round-the-clock services has enhanced convenience and encouraged greater adoption of digital banking solutions. Despite challenges such as cybersecurity risks, technological barriers, and digital literacy concerns, HDFC Bank has implemented effective strategies to maintain service quality and digital reliability. The research concludes that online banking plays a vital role in improving digital banking performance and contributes significantly to the operational growth and technological advancement of HDFC Bank. The findings provide valuable insights for banking professionals, policymakers, and researchers seeking to understand the evolving landscape of digital banking in India.

Keywords : Online Banking, Digital Banking Performance, Banking Technology, E-Banking, HDFC Bank, Financial Services.

Author : 1Mr. C. ARAVIND GOUD, 2 T. RAKESH

Title : ROLE OF ONLINE BANKING SYSTEM AT HDFC BANKS

Volume/Issue : 2026;03(07)

Page No : 568-573

This project focuses on understanding the process of financing in charge by resolving the connection 'tween risk and return in a notebook. The study analyzes the cost or loan judgment process, evaluates the risk and return traits of picked sample bonds, and climaxes the significance of active risk administration in money preparation. Various fiscal study methods are used to measure risk, equate anticipated returns, and label appropriate expense hope. The project likewise aims to assemble an optimum grant case that determines the maximum likely return for a likely level of risk, or the minimum likely risk for a requested level of return. Portfolio variety and risk administration law are used to gain a equalized grant approach. The judgments of the study help financiers form conversant economic conclusions by selecting an adept case that joins accompanying their asset aims while underrating potential misfortunes. This study illustrates the useful request of bag administration ideas and stresses the importance of compare risk and resume obtain unending fiscal aims.

Keywords : Financial Performance Analysis, HDFC Bank, ICICI Bank, ITC Limited, ColgatePalmolive, Statistical Analysis, Average, Standard Deviation, Comparative Analysis, Data Visualization, Bar Chart, Line Chart, Pie Chart, Financial Performance, Company Performance, Performance Evaluation, Banking Sector, FMCG Sector, Descriptive Statistics, Business Analytics

Author : 1 T. RAKESH, 2DHANAGALLA MAHESH BABU

Title : A PROJECT REPORT ON “RISK MANAGEMENT” AT “ICICI PRUDENTIAL AMC

Volume/Issue : 2026;03(07)

Page No : 574-579

Mutual funds have emerged as one of the most preferred investment avenues for retail investors seeking long-term wealth creation through professionally managed portfolios. Among the various investment approaches offered by mutual fund companies, the Systematic Investment Plan (SIP) and Lump Sum investment methods remain the most widely adopted strategies. This study examines the comparative investment outcomes of SIPs and Lump Sum investments over time with specific reference to HDFC Mutual Fund. The objective is to evaluate how both approaches perform under varying market conditions and to determine their effectiveness in generating long-term returns for investors. The study is based on historical performance data obtained from HDFC Mutual Fund schemes and industry information published by the Association of Mutual Funds in India (AMFI, 2025). SIP investments facilitate disciplined and periodic capital allocation, thereby reducing the impact of market volatility through rupee-cost averaging, as highlighted by AMFI (2025). In contrast, Lump Sum investments deploy the entire investment amount at a single point in time and may generate superior returns when markets experience sustained upward movements, according to HDFC Mutual Fund reports (2025).

Keywords : Strategic Cost Management, E-commerce, Cost Optimization, Activity-Based Costing, Supply Chain Management, Operational Efficiency, Inventory Management, Digital Process Automation, Competitive Advantage, Organizational Performance.

Author : 1 T. RAKESH , 2 Mr. J. RAJASHEKAR

Title : A PROJECT REPORT ON “COMPERATIVE INVESTMENT OUTCOMES OF SIPS VERSUS LUMEPSUM MUTUAL FUND STRATEGIES OVERTIME AT HDFC MUTUAL FUND

Volume/Issue : 2026;03(07)

Page No : 580-585

Corporate risk management has become an integral component of strategic financial decisionmaking as organizations operate in increasingly volatile global markets characterized by exchange rate fluctuations, interest rate movements, commodity price instability, and changing regulatory environments. Financial derivatives have emerged as effective instruments for mitigating these risks by enabling firms to hedge against potential financial losses while enhancing earnings stability and shareholder value. The research focuses on understanding the effectiveness of derivatives such as forward contracts, futures, options, and swaps in minimizing exposure to foreign exchange, interest rate, and market risks arising from the company's global operations. The study adopts a descriptive and analytical research approach using secondary data collected from annual reports, financial statements, investor presentations, published literature, regulatory disclosures, and industry reports. The analysis evaluates the company's derivative policies, hedging practices, accounting treatment, and risk management framework to determine how these instruments contribute to financial stability and operational efficiency. Particular attention is given to the alignment of derivative usage with corporate governance principles, regulatory compliance, and strategic financial objectives. The findings indicate that Dr. Reddy’s Laboratories effectively integrates derivative instruments into its enterprise risk management framework to reduce uncertainty associated with international business operations. The adoption of structured hedging strategies has strengthened cash flow predictability, protected profit margins from adverse market movements, and enhanced financial resilience. Furthermore, the study highlights that prudent derivative usage, supported by strong internal controls and continuous market monitoring, contributes significantly to sustainable corporate performance. The research concludes that financial derivatives, when employed responsibly and strategically, serve as valuable risk management tools that improve organizational stability, support long-term financial planning, and reinforce stakeholder confidence. The study also provides practical recommendations for strengthening derivative governance, improving hedging effectiveness, and enhancing financial risk management practices in multinational pharmaceutical organizations.

Keywords : Financial Derivatives, Corporate Risk Management, Hedging Strategies, Market Risk, Interest Rate Risk, Foreign Exchange Risk, Commodity Price Risk, Risk Mitigation, Financial Risk Assessment, Derivative Instruments, Futures Contracts, Options Contracts, Swaps, Treasury Management, Dr. Reddy’s Laboratories.

Author : 1Mr. KETHAVATH KARTHIK, 2 T. RAKESH

Title : EVALUATING FINANCIAL DERIVATIVES AS TOOLS FOR CORPORATE RISK MANAGEMENT AND HEDGING STRATEGIES AT DR. REDDY’S LABORATORIES

Volume/Issue : 2026;03(07)

Page No : 586-591