Journal of Science Engineering Technology and Management Science

This study, titled "Financial Performance Analysis of HFCL Limited in India," evaluates the segment revenues, operating margins, debt reductions, and financial feasibility of highcapacity telecommunication equipment manufacturing. HFCL Limited is India's premier manufacturer of optical fiber cables, FTTH systems, and Wi-Fi networks. A five-year project lifecycle (2021-2025) of a new manufacturing unit investment is analyzed using standard capital budgeting parameters: Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PBP), and Benefit-Cost Ratio (BCR). Quantitative analysis indicates that optical fiber and cable products represent 48% of total revenue. Operating profit margins (EBITDA) rose to 15.2% in 2025, while the debt-equity ratio was reduced to 0.32, improving the interest coverage ratio to 7.5 and supporting annual sales of 6,800 Crores. The financial model yields a positive NPV of 284.5 Crores and an IRR of 38.6%, far exceeding the 10% discount hurdle rate. The study concludes that investing in advanced manufacturing capacity is highly viable, enabling telecom hardware manufacturers to leverage PLI schemes, secure domestic market share, and increase shareholder value.

Keywords : Financial Performance, Optical Fiber Cables, EBITDA Margin, Solvency, PLI Scheme, Capital Budgeting, Financial Feasibility, HFCL Limited.

Author : Jetla saikumar, K. Shashidhar, B.Vijay

Title : Financial Performance Analysis Of HFCL Limited In India

Volume/Issue : 2026;03(09)

Page No : 1-8

Artificial Intelligence (AI) has become a transformative technology in Human Resource Management (HRM), revolutionizing recruitment and talent acquisition by improving the efficiency, accuracy, and quality of hiring decisions. Organizations are increasingly adopting AI-powered tools such as applicant tracking systems (ATS), resume screening software, chatbots, predictive analytics, and machine learning algorithms to automate recruitment tasks, identify suitable candidates, and enhance the overall hiring experience. This study examines the role of Artificial Intelligence in recruitment and talent acquisition, focusing on its impact on candidate sourcing, screening, selection, recruiter productivity, hiring quality, and organizational performance. The research also explores the benefits and challenges associated with AI adoption, including algorithmic bias, data privacy, ethical concerns, and the need for human oversight. A descriptive research design was employed using both primary and secondary data. Primary data were collected through structured questionnaires administered to HR professionals and employees, while secondary data were obtained from academic journals, books, industry reports, and credible online sources. The collected data were analyzed using percentage analysis, mean analysis, and graphical representations to evaluate the effectiveness of AI-driven recruitment practices. The findings indicate that AI significantly reduces recruitment time, improves candidate matching, enhances decision-making accuracy, and increases recruiter efficiency. However, successful implementation requires transparent AI systems, ethical governance, continuous monitoring, and the integration of human judgment with AI-driven decision support. The study concludes that Artificial Intelligence has the potential to transform recruitment and talent acquisition by enabling organizations to build a more efficient, data-driven, and strategic hiring process while improving overall organizational competitiveness.

Keywords :

Author : Kadari Jyothi, Katha Shiva Kesava Reddy, T. Meghana

Title : A Study on Artificial Intelligence in Recruitment and Talent Acquisition

Volume/Issue : 2026;03(09)

Page No : 9-16

Digital marketing has become one of the most influential tools for e-commerce businesses to attract customers, enhance brand awareness, and increase online sales. With the rapid growth of internet usage, smartphone adoption, and social media platforms, companies are increasingly relying on digital marketing strategies to engage consumers and remain competitive in the online marketplace. Myntra, one of India's leading fashion and lifestyle e-commerce platforms, has successfully utilized digital marketing through social media campaigns, influencer marketing, search engine optimization (SEO), search engine marketing (SEM), email marketing, mobile application promotions, personalized recommendations, and content marketing to strengthen customer engagement and improve purchasing behavior. The effectiveness of these strategies significantly impacts consumer awareness, website traffic, customer satisfaction, purchase intention, and brand loyalty. This study aims to evaluate the effectiveness of digital marketing strategies adopted by Myntra and analyze their influence on consumer buying behavior and business performance. The research also examines the role of personalized advertising, mobile marketing, promotional campaigns, customer reviews, and online shopping experiences in shaping consumer decisions. A descriptive research design was adopted for the study. Primary data were collected from 180 respondents using a structured questionnaire through convenience sampling, while secondary data were obtained from research journals, books, company reports, industry publications, and authentic online databases. Percentage analysis and graphical techniques such as bar charts and line graphs were employed to analyze the collected data. The findings reveal that social media advertising, influencer marketing, personalized recommendations, festive sales campaigns, and mobile app notifications significantly improve customer engagement and purchase intention. However, concerns related to excessive advertisements, data privacy, and promotional authenticity remain challenges for digital marketers. The study concludes that an integrated digital marketing strategy supported by data analytics, artificial intelligence, and customer-centric communication enables Myntra to strengthen its competitive advantage, improve customer satisfaction, and achieve sustainable business growth in India's rapidly expanding e-commerce sector.

Keywords :

Author : Amudala Vinay Reddy, Pavani Mudem, B.Vijay

Title : Effectiveness of Digital Marketing Strategies in E Commerce Businesses at Myntra

Volume/Issue : 2026;03(09)

Page No : 17-22

This study, titled "A Study of Financial Performance Evaluation of Telecom Service Providers in the 5G Era at Bharti Airtel Limited," evaluates segment revenue composition, Average Revenue Per User (ARPU) expansion, EBITDA margin trajectory, Return on Capital Employed (ROCE), and financial feasibility of 5G network infrastructure deployment at Bharti Airtel Limited. Telecom operators face massive capital expenditure requirements during 5G rollouts, where consumer mobility represents 45% and B2B enterprise services account for 28% of total revenue. A five-year project lifecycle (2021-2025) of 5G network rollout is evaluated using capital budgeting parameters: Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PBP), and Benefit-Cost Ratio (BCR). Quantitative analysis reveals that 5G migration elevates ARPU to ₹245/month compared to ₹155 under 4G LTE. High data efficiency reduces network operating cost per GB to ₹1.1, lifting EBITDA margins to 59.2%, expanding 5G subscriber penetration to 88.2%, and elevating ROCE to 25.6% by 2025. The financial model yields a positive NPV of 284.5 Crores and an IRR of 38.6%, far exceeding the 10% discount hurdle rate. The study concludes that investing in 5G network infrastructure is highly viable, driving telecom profitability and shareholder value.

Keywords :

Author : Battula Kalyani, V. Sandeep Kumar, K. Bhargavi Archana

Title : A Study of Financial Performance Evaluation of Telecom Service Providers in the 5G Era at Bharti Airtel Limited

Volume/Issue : 2026;03(09)

Page No : 23-31

This study, titled "Economic Analysis of Renewable Energy-Powered Communication Networks," evaluates the financial viability, cost configurations, and operational savings of deploying hybrid solar-powered base stations for telecom network operations. Base transceiver stations (BTS) consume massive amounts of grid electricity and diesel fuel, particularly off-grid or remote areas in India. Converting these conventional energy sources to green capital assets involves high initial Capital Expenditure (CapEx) for solar PV panels and battery storage banks, but yields substantial Operating Expenditure (OpEx) savings. This research conducts a 5-year capital budgeting analysis (2021-2025) of a hybrid solar-powered tower upgrade program implemented in a leading telecom provider's network portfolio. Project feasibility is evaluated using Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PBP), and Benefit-Cost Ratio (BCR). Quantitative metrics indicate that solar panels and batteries account for 68% of CapEx. Hybrid installations reduce annual energy expenditures per tower site from 17.5 Lakhs to 3.5 Lakhs by 2025, generating significant OpEx reductions. The financial model yields a positive NPV of 28.5 Lakhs per tower site and an IRR of 38.6%, indicating strong project feasibility. The study concludes that transition to renewable energy-powered communication networks represents a highly viable and environmentally sustainable capital project for telecom lenders and operators.

Keywords :

Author : Bidlan Shivani, V. Sandeep Kumar, T.Meghana

Title : Economic Analysis of Renewable Energy -Powered Communication Networks

Volume/Issue : 2026;03(09)

Page No : 32-40

Human Resource (HR) Analytics has emerged as one of the most important technological advancements in modern Human Resource Management. It refers to the systematic collection, analysis, interpretation, and utilization of employee-related data to improve organizational decision-making and workforce management. With the rapid growth of digital technologies, Artificial Intelligence (AI), Machine Learning, Cloud Computing, Big Data Analytics, and Human Resource Information Systems (HRIS), organizations are increasingly adopting HR Analytics to improve employee performance, recruitment, retention, productivity, engagement, workforce planning, and overall organizational effectiveness. HR Analytics enables organizations to make evidence-based decisions by transforming traditional HR practices into data-driven strategies that improve employee management and business performance. In manufacturing organizations such as Bevcon Wayors Pvt. Ltd., HR Analytics plays a significant role in identifying employee performance trends, predicting workforce requirements, measuring employee satisfaction, reducing employee turnover, improving talent acquisition, optimizing training programs, and enhancing operational efficiency. By analyzing workforce data, organizations can identify high-performing employees, monitor attendance patterns, evaluate productivity levels, detect skill gaps, and design effective employee development strategies. HR Analytics also supports strategic workforce planning by providing accurate insights into employee behaviour, organizational performance, compensation management, succession planning, and leadership development. The successful implementation of HR Analytics improves employee engagement, strengthens decision-making, enhances transparency, and enables organizations to respond quickly to changing business requirements. However, organizations also face several challenges during HR Analytics implementation, including data privacy concerns, lack of skilled HR professionals, poor data quality, resistance to technological change, high implementation costs, cybersecurity risks, and integration complexities with existing HR systems.

Keywords :

Author : Burusu Sailesh, A. Anil Kumar Reddy, Pavani Mudem

Title : Impact of HR Analytics on Employee Management at Bevcon Wayors Pvt. Ltd

Volume/Issue : 2026;03(09)

Page No : 41-47

Human Resource (HR) Performance Evaluation is one of the most important functions of Human Resource Management, as it helps organizations assess employee performance, improve productivity, identify training needs, and support strategic decision-making. In today's competitive business environment, organizations are increasingly shifting from traditional subjective appraisal methods to quantitative techniques that provide objective, accurate, and data-driven evaluation of employee performance. Quantitative techniques involve the application of statistical methods, mathematical models, performance metrics, key performance indicators (KPIs), Human Resource Analytics, Balanced Scorecards, Data Analytics, Artificial Intelligence (AI), Machine Learning, and Human Resource Information Systems (HRIS) to evaluate employee contributions and organizational effectiveness. These techniques enable organizations to measure employee productivity, attendance, efficiency, quality of work, goal achievement, customer satisfaction, training effectiveness, employee engagement, and overall organizational performance using measurable indicators rather than personal opinions. The adoption of quantitative techniques has significantly improved transparency, fairness, consistency, and reliability in performance evaluation while reducing bias and enhancing employee trust in appraisal systems. Organizations using quantitative performance evaluation methods can identify high-performing employees, recognize skill gaps, develop personalized training programs, optimize workforce planning, improve succession planning, and implement effective reward and compensation strategies. Furthermore, quantitative techniques support evidence-based HR decision-making by enabling managers to analyze workforce trends, predict employee performance, monitor productivity, and improve organizational efficiency. Modern digital technologies, including cloud-based HR platforms, predictive analytics, dashboards, and AI-powered performance management systems, have further enhanced the effectiveness of quantitative HR evaluation.

Keywords :

Author : Kounain Fathima, Amita Johar, Pavani Mudem

Title : Quantitative Techniques in HR Performance Evaluation

Volume/Issue : 2026;03(09)

Page No : 48-54

Green marketing has become an essential business strategy in recent years due to increasing environmental concerns, climate change, and growing consumer awareness regarding sustainable consumption. Consumers today are more conscious of the environmental impact of the products they purchase and are increasingly preferring products that are energy-efficient, eco-friendly, recyclable, and manufactured using sustainable practices. In the consumer electronics industry, energy-efficient appliances such as refrigerators, washing machines, air conditioners, televisions, and microwave ovens have gained significant popularity because they reduce electricity consumption, lower carbon emissions, and contribute to environmental conservation. Companies such as LG Electronics have actively adopted green marketing strategies by promoting environmentally friendly manufacturing processes, energy-saving technologies, eco-labels, sustainable packaging, and awareness campaigns to encourage consumers to choose energy-efficient appliances. Green marketing not only focuses on promoting environmentally responsible products but also emphasizes corporate social responsibility, sustainable production, waste reduction, and efficient resource utilization. These initiatives help organizations improve their brand image, enhance customer trust, and gain a competitive advantage in the marketplace. Consumer purchase intention refers to the likelihood that a consumer will purchase a product based on various influencing factors such as environmental awareness, product quality, price, brand reputation, perceived value, and promotional activities. Green marketing campaigns can positively influence purchase intention by educating consumers about the long-term economic and environmental benefits of energyefficient appliances. The present study aims to examine the impact of green marketing on consumer purchase intention for energy-efficient appliances at LG Electronics Company. It investigates the influence of environmental awareness, green advertising, eco-labels, energy efficiency ratings, corporate sustainability initiatives, and consumer trust on purchasing decisions.

Keywords :

Author : Gudati Shiva Krishna, Katha Shiva Kesava Reddy, Pavani Mudem

Title : A Study on Impact of Green Marketing on Consumer Purchase Intention for Energy Efficient Appliances

Volume/Issue : 2026;03(09)

Page No : 55-62

Human Resource Analytics (HR Analytics) has emerged as a strategic tool that enables organizations to make data-driven decisions for improving workforce performance and organizational effectiveness. In the highly competitive electronics industry, where innovation, technological advancement, and operational efficiency are critical to success, HR Analytics plays a vital role in optimizing talent management, employee productivity, recruitment, performance evaluation, training, and retention. This study examines the role of Human Resource Analytics in improving the performance of electronics companies by analyzing its impact on employee productivity, decision-making, workforce planning, and overall organizational performance. A quantitative research approach was adopted using a structured questionnaire to collect primary data from employees and HR professionals, while secondary data were gathered from journals, books, company reports, and credible online sources. The collected data were analyzed using descriptive and inferential statistical techniques to evaluate the relationship between HR Analytics practices and organizational performance. The findings indicate that effective implementation of HR Analytics enhances workforce planning, improves employee performance, supports evidence-based HR decisions, reduces employee turnover, and increases operational efficiency. The study also reveals that organizations using HR Analytics achieve better talent acquisition, employee engagement, performance management, and strategic workforce development. However, challenges such as data privacy, lack of analytical skills, technology integration, and resistance to organizational change require continuous investment in digital infrastructure and employee capability development. The study concludes that Human Resource Analytics is a valuable strategic resource that enables electronics companies to enhance employee performance, improve organizational efficiency, and achieve sustainable competitive advantage in the rapidly evolving industrial environment.

Keywords :

Author : Kolthur Pranathi, V.Sandeep Kumar, Pavani Mudem

Title : A Study on Role of Human Resource Analytics in Improving Performance of Electronics Companies

Volume/Issue : 2026;03(09)

Page No : 63-70

This study, titled "The Impact of UPI Technology on Banking Profitability at Punjab National Bank (PNB)," evaluates digital channel transaction volume shares, payment processing cost reductions, operating profitability expansion, and financial feasibility of Unified Payments Interface (UPI) infrastructure in public sector banking. Commercial banks experience massive digital transaction shifts, where UPI payments represent 48% and mobile banking apps account for 26% of digital payment volumes. A five-year project lifecycle (2021-2025) of an enterprise UPI switch infrastructure upgrade at Punjab National Bank (PNB) is evaluated using capital budgeting parameters: Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PBP), and Benefit-Cost Ratio (BCR). Quantitative analysis indicates that processing payments via real-time UPI APIs reduces transaction cost per ₹1,000 to ₹0.3 compared to ₹45.0 under branch teller operations. High UPI transaction processing (38.5 Billion transactions) drives PNB operating profit to 34,500 Crores while compressing the Cost-to-Income Ratio (CIR) to 38.2%, expanding UPI payment penetration to 94.5% by 2025. The financial model yields a positive NPV of 284.5 Crores and an IRR of 38.6%, far exceeding the 10% discount hurdle rate. The study concludes that investing in highthroughput UPI payment infrastructure is highly viable, driving cost efficiency and public bank profitability

Keywords :

Author : Vaishnavi Madoori, V. Shiva Narayana Reddy, K. Bhargavi Archana

Title : The Impact of UPI Technology on Banking Profitability at Punjab National Bank (PNB)

Volume/Issue : 2026;03(09)

Page No : 71-78

The COVID-19 pandemic brought unprecedented changes to organizational work practices, leading many companies to adopt remote work as a long-term human resource (HR) strategy. This study examines the impact of remote work on employee performance by analyzing post-pandemic HR practices at HCL Technologies. The research focuses on understanding how flexible work arrangements, digital collaboration tools, employee engagement initiatives, performance evaluation methods, and work-life balance have influenced employee productivity and organizational effectiveness. A quantitative research approach was adopted, and primary data were collected through structured questionnaires administered to employees of HCL Technologies. Secondary data were obtained from company reports, academic journals, books, and credible online sources. Statistical techniques such as percentage analysis, mean analysis, and graphical representation were used to interpret the collected data. The findings indicate that remote work has positively influenced employee performance by improving flexibility, reducing commuting time, and enhancing work-life balance. However, challenges such as communication barriers, collaboration issues, employee isolation, and performance monitoring continue to affect overall productivity. The study concludes that the success of remote work depends on effective HR policies, robust digital infrastructure, continuous employee engagement, and transparent performance management systems. The research provides valuable insights for HR professionals and organizational leaders in designing sustainable hybrid work models that enhance employee satisfaction and long-term organizational performance in the postpandemic business environment.

Keywords :

Author : Silveru. Sai Tejaswini, K. Shashidhar, K. Bhargavi Archana

Title : Remote Work and Employee Performance an Analysis of Pandemic Hr Practices a Hcl Technologies

Volume/Issue : 2026;03(09)

Page No : 79-85

Artificial Intelligence (AI) has emerged as a transformative technology in Human Resource Management, particularly in recruitment and talent acquisition, by enabling organizations to improve the efficiency, accuracy, and effectiveness of hiring processes. AI-powered tools such as resume screening systems, applicant tracking systems, chatbots, predictive analytics, and automated candidate assessments have significantly reduced recruitment time while enhancing the quality of hiring decisions. This study examines the role of Artificial Intelligence in recruitment and talent acquisition by evaluating its impact on candidate sourcing, screening, selection, hiring efficiency, recruiter productivity, and overall organizational performance. The research also explores employees' and HR professionals' perceptions regarding the adoption of AI in recruitment, along with the opportunities and challenges associated with its implementation. A descriptive research design was adopted using both primary and secondary data. Primary data were collected through structured questionnaires administered to HR professionals and employees, while secondary data were gathered from academic journals, books, industry reports, and credible online sources related to AI and human resource management. The collected data were analyzed using percentage analysis, mean analysis, and graphical representations to assess the effectiveness of AIdriven recruitment practices. The findings indicate that AI significantly improves recruitment efficiency by reducing hiring time, enhancing candidate matching, minimizing administrative workload, and supporting data-driven decision-making. However, challenges such as algorithmic bias, data privacy concerns, lack of transparency, and the need for human oversight continue to influence AI adoption in recruitment. The study concludes that integrating Artificial Intelligence with human expertise can create a more efficient, fair, and strategic recruitment process, enabling organizations to attract, identify, and retain high-quality talent while strengthening their overall talent acquisition strategy.

Keywords :

Author : Thanneru Anuradha, M.Hari Prasad, K. Bhargavi Archana

Title : A Study on Artificial Intelligence in Recruitment and Talent Acquisition

Volume/Issue : 2026;03(09)

Page No : 86-91

Packaging design has become one of the most influential marketing tools in today's highly competitive consumer goods industry. Beyond its traditional role of protecting products during storage and transportation, packaging now serves as a powerful medium for communication, brand differentiation, and consumer engagement. Attractive packaging design influences customers by creating positive first impressions, communicating product quality, providing essential product information, and strengthening brand recognition. In the fast-moving consumer goods (FMCG) sector, where consumers are exposed to numerous competing brands, packaging plays a vital role in influencing purchase intentions and final buying decisions. Hindustan Unilever Limited (HUL), one of India's leading FMCG companies, manufactures a wide range of products including personal care items, food products, beverages, home care products, and health & hygiene products. HUL continuously invests in innovative packaging designs that combine visual appeal, functionality, convenience, sustainability, and consumer safety to attract customers and improve brand loyalty. Modern packaging incorporates attractive colors, creative graphics, informative labels, premium materials, ergonomic shapes, eco-friendly packaging solutions, and tamper-proof features that enhance customer confidence and satisfaction. Consumer purchase intention refers to the likelihood that a consumer will purchase a product based on several influencing factors such as packaging design, brand image, product quality, price, promotional activities, and personal preferences. Packaging design influences consumer behaviour by communicating product benefits, increasing perceived value, differentiating products from competitors, and creating emotional connections with customers. Eco-friendly packaging has also become increasingly important because environmentally conscious consumers prefer products packaged using recyclable, biodegradable, and sustainable materials.

Keywords :

Author : Gourelli Dinesh, A. Anil Kumar Reddy, Pavani Mudem

Title : A Study on Packaging Design in Consumer Purchase Intentions at Hul Company Ltd

Volume/Issue : 2026;03(09)

Page No : 92-98

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, chatbots 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 : Dr. Endirala Swathin Goud

Title : Artificial Intelligence Adoption in Human Resource Management: An Empirical Study of Its Impact on HR Practices and Organizational Outcomes

Volume/Issue : 2026;03(09)

Page No : 99-107

Hybrid work which combines office work with remote and flexible working is likely to be a permanent feature of the work landscape for knowledge workers in a post-pandemic world․ Hybrid work impacts health and well-being in a genuinely mixed way․ Despite offering freedom and avoiding commuting‚ the role of telework in disrupting boundaries‚ increasing isolation‚ and inducing techno-stress are also mentioned․ The review summarizes the literature from a Job Demands-Resources model perspective․ Our aim is to better understand hybrid work as a job demand and job resource and how its balance is shaped by hybrid work design and management․ In a review of telework‚ work-from-home‚ and flexible work arrangements we explore the well-being ledger linking HRM practices to engagement‚ burnout‚ satisfaction‚ performance and retention․ We argue that such an outcome is contingent‚ and will be determined by the design of HRM within individual organizations․

Keywords :

Author : Aseel Hadi Mahmood

Title : The Well-Being Ledger of Hybrid Work: A Concise Job Demands-Resources Review of HRM Practices for the PostPandemic Workplace

Volume/Issue : 2026;03(09)

Page No : 113-119

Privacy-preserving data collection is essential for IoT environments, where massive volumes of key-value data are continuously generated and transmitted across distributed devices. Local Differential Privacy (LDP) provides protection without requiring trusted data collectors, but conventional mechanisms often introduce substantial estimation errors and reduced data utility, particularly for composite key-value structures. This work addresses these limitations through an accuracy-oriented privacy-preserving collection framework using a key-value dataset containing categorical keys and associated values collected over a defined observation period. The data is preprocessed by removing missing and duplicate records, validating key-value pairs, encoding categorical attributes, and applying normalization where required. The implemented models include Randomized Response, Generalized Randomized Response, Optimized Unary Encoding, and proposed hybrid mechanisms combining key and value perturbation with sampling-based processing. Performance is evaluated using privacy level, hit rate, estimation variance, precision, and utility preservation. Among the evaluated approaches, the proposed hybrid key-value perturbation mechanism achieves the highest hit rate and the lowest estimation variance, demonstrating improved estimation reliability under privacy constraints. The results indicate that coordinated sampling and perturbation can provide stronger privacy protection while maintaining high data utility, offering an effective approach for accurate and privacy-preserving keyvalue data collection in IoT environments.

Keywords :

Author : M.Thangavel, A.M.Kalpana, S.Ananth

Title : Privacy-Enhanced Key-Value Data Collection for Large-Scale IoT Environments

Volume/Issue : 2026;03(09)

Page No : 108-112

The Pharmacy Sales and Inventory Trend Dashboard is designed to provide an interactive and efficient platform for analyzing pharmacy sales, inventory levels, and product trends. The system collects and organizes pharmacy transaction data such as medicine names, sales quantities, prices, stock levels, purchase dates, and sales dates. The dashboard transforms this data into meaningful visual reports that help users understand sales performance and inventory movement. Key indicators such as total sales, total quantity sold, available stock, fast-moving medicines, and low-stock products can be displayed through charts, tables, and summary cards. Sales trends can be analyzed over different time periods to identify changes in customer demand and business performance. Inventory analysis helps identify products that require restocking and medicines with slow or excess movement. Product-wise and categorywise analysis can also be used to compare sales performance across different medicine groups. Interactive filters allow users to view information based on date, product, category, or other relevant parameters. The dashboard supports data-driven decision-making by presenting complex pharmacy data in a simple and understandable format. It can help pharmacy managers improve stock planning, monitor sales performance, reduce inventory-related issues, and identify important business trends. Overall, the project demonstrates how data visualization and analytical dashboards can be used to improve pharmacy sales and inventory management.

Keywords : Pharmacy sales and inventory management involves handling a large amount of information related to medicines, sales transactions, stock availability, prices, and customer demand.

Author : 1M Shirisha, 2 C Eswar Charan, 3 I Adithya, 4 S Siddu, 5 J Adesh

Title : PHARMACY SALES AND INVENTORY TREND DASHBOARD

Volume/Issue : 2026;03(09)

Page No : 120-126

The SaaS Subscription Trend Dashboard is designed to analyze and visualize subscription-related business data in an interactive and user-friendly format. The system focuses on understanding subscription growth, customer behavior, revenue performance, plan-wise distribution, and subscription trends over different time periods. Data such as customer details, subscription plans, subscription dates, renewal status, cancellations, monthly recurring revenue, and payment information can be organized and processed for analysis. The dashboard presents important key performance indicators such as total subscribers, active subscriptions, new subscriptions, cancellations, churn rate, and recurring revenue. Time-based charts help identify subscription growth patterns and changes in customer demand. Planwise analysis allows users to compare the performance of different subscription packages and identify popular plans. Customer and subscription analysis can also help identify retention patterns and cancellation trends. Interactive filters enable users to analyze specific plans, customer segments, and time periods. Visual reports using charts, graphs, tables, and KPI cards convert complex subscription data into meaningful business insights. The dashboard can support SaaS businesses in monitoring performance, understanding customer trends, and improving subscription management strategies. Overall, the project demonstrates how data analytics and visualization can be used to develop an effective dashboard for monitoring SaaS subscription trends and supporting data-driven business decisions.

Keywords : Software as a Service (SaaS) has become an important business model in which customers access software applications through subscription-based plans.

Author : 1Malleswar Rao, 2 G Asritha, 3 K Deekshitha, 4 B Srija, 5 P Manasa

Title : SAAS SUBSCRIPTION TREND DASHBOARD

Volume/Issue : 2026;03(09)

Page No : 127-134

The Personal Budget and Spending Category Analysis system is designed to help individuals monitor their income, expenses, budgets, and spending patterns through an organized analytical platform. The system collects financial information such as income, expense amounts, transaction dates, payment methods, and spending categories. The collected data is processed and categorized to provide a clear understanding of how money is being used across different expense areas. Users can set budget limits for categories such as food, transportation, shopping, bills, entertainment, and other personal expenses. The system compares actual spending with planned budgets to identify areas of overspending or savings. Monthly and category-wise spending trends can be analyzed using charts, graphs, tables, and key performance indicators. Important metrics such as total income, total expenses, remaining budget, savings, and category-wise expenditure are presented through an interactive dashboard. The system helps users identify high-spending categories and understand changes in their financial habits over time. Interactive filters can be used to analyze expenses based on dates, categories, or payment methods. The visual presentation of financial information makes it easier to understand complex spending data and supports better financial planning. Overall, the project demonstrates how data analysis and visualization can be used to improve personal budget management, spending awareness, and informed financial decision-making.

Keywords : Managing personal finances effectively requires a clear understanding of income, expenses, savings, and spending habits. Many individuals find it difficult to track their daily expenses and determine where most of their money is being spent.

Author : 1 B Vijay Kumar, 2 A Guruswami, 3 S Naveen, 4 D Arjun Teja, 5 M Puneeth

Title : SAAS SUBSCRIPTION TREND DASHBOARD

Volume/Issue : 2026;03(09)

Page No : 135-141

The SME Loan Application Volume & Approval Rate Dashboard is designed to analyze and visualize small and medium enterprise loan application data in an interactive and user-friendly format. The system focuses on monitoring loan application volume, approval rates, rejection rates, loan amounts, processing trends, and applicant characteristics. Data such as application date, business type, requested loan amount, loan status, applicant location, credit information, and processing duration can be organized and processed for analysis. The dashboard presents important key performance indicators such as total applications, approved applications, rejected applications, approval rate, and total approved loan amount. Time-based analysis helps identify changes in application volume and approval performance across different periods. Loan amount and business-category analysis can be used to understand the distribution of financing requirements among SMEs. Approval and rejection trends provide insights into the overall performance of the loan application process. Interactive filters allow users to analyze applications based on date, business category, location, loan status, and other relevant attributes. Charts, graphs, tables, and KPI cards transform raw loan application data into meaningful visual insights. The dashboard can help financial institutions monitor lending performance, identify application trends, and understand approval patterns. Overall, the project demonstrates how data analytics and visualization can support efficient monitoring of SME loan applications and improve data-driven lending analysis.

Keywords : Small and Medium Enterprises (SMEs) play an important role in economic development and often require financial support to establish, operate, and expand their businesses.

Author : 1 A Rajashekar, 2 K Shravani, 3 G Sathvika, 4 M Lakshmi Prasanna, 5 D Tarun Reddy

Title : SME LOAN APPLICATION VOLUME & APPROVAL RATE DASHBOARD

Volume/Issue : 2026;03(09)

Page No : 142-149

The Patient Wait-Time Analysis system is designed to analyze and visualize the time patients spend waiting at different stages of a healthcare service process. The system focuses on understanding patient arrival patterns, registration time, consultation waiting time, service duration, and overall waiting time. Patient-related operational data such as arrival time, registration time, appointment time, consultation start time, and service completion time can be collected and organized for analysis. The system calculates important metrics such as average waiting time, maximum waiting time, minimum waiting time, and total patient volume. Waiting-time trends can be analyzed across different days, time periods, departments, doctors, or service areas. The dashboard uses charts, graphs, tables, and key performance indicators to present the analyzed information clearly. It can help identify peak hours and departments where patients experience longer waiting periods. Analysis of patient flow can also support better scheduling and resource allocation. Interactive filters allow users to examine waiting-time patterns based on specific dates, departments, or other relevant parameters. The system converts raw healthcare service data into meaningful operational insights without requiring manual calculations. Overall, the project demonstrates how data analytics and visualization can support efficient patient-flow monitoring and help healthcare facilities identify opportunities for reducing unnecessary waiting time.

Keywords : Patient waiting time is an important factor in evaluating the efficiency and quality of healthcare services.

Author : 1 D Saritha, 2 M Siri, 3 T Firdouse, 4 K Shiva Prasad, 5 M Yousf Babu

Title : PATIENT WAIT-TIME ANALYSIS

Volume/Issue : 2026;03(09)

Page No : 150-156

Patient waiting time is an important operational measure used to understand the efficiency of healthcare services. Long waiting periods may occur because of high patient volume, appointment delays, registration procedures, limited staff availability, or inefficient service workflows. Analyzing waiting-time information can help healthcare facilities understand where delays occur and identify areas that require improvement. The Patient Wait-Time Analysis system is designed to collect, process, analyze, and visualize patient-flow and waiting-time information. The system can use data such as patient arrival time, registration time, appointment time, consultation start time, department, doctor, and service completion time. These records are processed to calculate individual and overall waiting durations. The system analyzes important measures such as average waiting time, minimum waiting time, maximum waiting time, patient volume, service duration, and department-wise performance. Waiting-time trends can be examined across different dates, time periods, departments, doctors, and service stages. Peak hours can also be identified by analyzing patient arrival patterns and waiting-time variations. An interactive dashboard can present the analyzed information using charts, graphs, tables, and key performance indicators. Users can apply filters to examine specific departments, doctors, dates, or service stages. This makes it easier to identify departments or periods where patients experience longer waiting times. Overall, the project demonstrates how data analytics and visualization can support healthcare operational management. The system provides a centralized method for understanding patient-flow patterns and service delays. The insights generated from the analysis can support better scheduling, staff allocation, resource planning, and service monitoring. Therefore, the Patient Wait-Time Analysis system provides a useful analytical approach for improving healthcare service efficiency and reducing unnecessary patient waiting.

Keywords : Patient waiting time is an important aspect of healthcare service management because it directly reflects how efficiently patients move through different stages of a healthcare facility.

Author : 1 M Jyothi, 2 P Sai Ganesh, 3 G Abhinay, 4 P Maneesh, 5 S Shruthi

Title : DIGITAL PAYMENT TRANSACTION TRENDS DASHBOARD

Volume/Issue : 2026;03(09)

Page No : 157-164

The API Usage Volume and Response Time Dashboard is a monitoring and analytics system designed to track API performance, usage patterns, and service reliability in real time. Modern applications depend heavily on APIs to exchange data between clients, servers, databases, and external services. As API traffic increases, it becomes important to understand how frequently APIs are being called and how quickly they respond to requests. The proposed dashboard provides a centralized platform for collecting, processing, analyzing, and visualizing API performance information. The system focuses primarily on two important performance indicators: API usage volume and response time. API usage volume represents the number of requests received or processed during a specific period, while response time represents the time taken by an API to return a response. By monitoring these values continuously, administrators and developers can identify traffic spikes, slow endpoints, unusual usage patterns, and potential performance problems. The dashboard presents collected API data through interactive charts, graphs, tables, and summary metrics. Users can analyze API requests according to time intervals, endpoints, status codes, response-time ranges, and request volumes. The system can also identify high-traffic APIs and endpoints with poor response performance. This makes it easier for technical teams to understand system behavior without manually examining large amounts of server or application logs. The proposed solution helps organizations improve API reliability and application performance by providing meaningful information in a simple visual format. Historical data can be used to compare performance across different time periods and identify recurring issues. Overall, the dashboard acts as a practical monitoring and decision-support system for maintaining efficient, scalable, and reliable API-based applications.

Keywords : Application Programming Interfaces (APIs) have become an essential component of modern software systems.

Author : 1 Dr B Anil, 2 D Varshith, 3 K Shiva Kumar, 4 Ch Krishnaveni, 5 Md Furqan Ahmed

Title : API USAGE VOLUME AND RESPONSE TIME DASHBOARD

Volume/Issue : 2026;03(09)

Page No : 165-172

The Vendor Performance Comparison Dashboard is a data-driven system designed to evaluate, compare, and monitor the performance of multiple vendors using a centralized dashboard. Organizations often depend on several vendors for supplying products, materials, services, or other business requirements. Evaluating these vendors manually can be difficult because performance information may be distributed across invoices, purchase records, delivery reports, quality records, and other documents. The proposed dashboard brings these important performance indicators together in a single platform. The system focuses on key vendor performance metrics such as on-time delivery, product quality, order fulfillment, cost, response time, and overall vendor rating. These metrics are collected from available vendor and procurement records and processed to generate meaningful performance scores. By applying consistent evaluation criteria, the system enables organizations to compare vendors using standardized measurements rather than relying only on manual judgment. The dashboard presents vendor information through interactive charts, graphs, tables, ranking views, and performance indicators. Users can compare vendors based on individual metrics or an overall performance score. Historical data can also be analyzed to understand whether a vendor's performance is improving, declining, or remaining stable. Filters can be provided for vendor, product category, date range, location, and performance metric to support detailed analysis. The main purpose of the proposed system is to help procurement managers and business administrators make informed vendor-selection and vendor-management decisions. By identifying reliable and underperforming vendors, organizations can take appropriate corrective actions and improve procurement efficiency. Overall, the Vendor Performance Comparison Dashboard provides a centralized, transparent, and systematic approach to vendor performance evaluation.

Keywords : Vendor management is an important activity for organizations that depend on external suppliers for products, raw materials, equipment, or services.

Author : 1 G Praveen, 2 I Meghana, 3 G Srija, 4 K Samhitha Reddy, 5 R Nikhitha

Title : VENDOR PERFORMANCE COMPARISON DASHBOARD

Volume/Issue : 2026;03(09)

Page No : 173-180

The Mobile App Crash Report Trend Dashboard is a software monitoring and analytics system designed to collect, analyze, and visualize crash information generated by mobile applications. Mobile applications may experience crashes because of programming errors, memory limitations, device incompatibility, operating-system differences, network conditions, or unexpected user inputs. As the number of users and supported devices increases, manually analyzing crash information becomes difficult. The proposed dashboard provides a centralized platform for understanding mobile application crash behavior. The system focuses on important crash-related metrics such as total crashes, affected users, crash frequency, crash-free sessions, operating-system version, device model, application version, and crash type. Crash reports collected from mobile applications are processed and organized so that developers can identify recurring problems. The system can group crashes based on application versions, devices, operating systems, dates, and error categories. The dashboard presents the analyzed information using interactive charts, graphs, tables, and summary cards. Trend charts can show whether the number of crashes is increasing or decreasing over time, while ranking charts can identify application versions or device models associated with a higher number of crashes. Users can apply filters based on date, application version, device, operating system, or crash category to perform detailed analysis. The proposed system helps development teams identify critical crash patterns and prioritize issues that require immediate attention. Historical crash information can also be compared across application releases to determine whether a new version has improved or negatively affected application stability. This provides useful information for debugging, testing, and release management. Overall, the Mobile App Crash Report Trend Dashboard provides a centralized and visual approach to mobile application stability monitoring. It reduces the effort required to analyze raw crash reports and helps developers make data-driven decisions. The system can contribute to improved application reliability, better user experience, and faster identification of recurring technical problems.

Keywords : Mobile applications have become an important part of everyday digital services, providing users with communication, entertainment, financial, educational, shopping, and business functionalities.

Author : 1 Sachin Chowhan, 2 Ahtesam Siddique, 3 K Raja Sulochana, 4 K Hrishikesh Goud

Title : MOBILE APP CRASH REPORT TREND DASHBOARD

Volume/Issue : 2026;03(09)

Page No : 181-188