| 1 |
Author(s):
Rajana Yamuna.
Page No :
|
Automated Podcast Transcription and Topic Segmentation
Abstract
This paper presents an AI-based system for automated podcast transcription and topic segmentation. The system accepts audio uploads, performs speech-to-text transcription, segments content into topics, extracts keywords, generates summaries, and produces PDF reports through a Flask web application. The architecture integrates audio preprocessing, transcription, NLP-based topic segmentation, and report generation to improve accessibility and content discovery. Experimental evaluation demonstrates efficient processing and useful summaries for educational and media applications.
| 2 |
Author(s):
POTTI PAVAN VENKATA SIMHADRI.
Page No :
|
Prediction of Brain Stroke
Abstract
Brain stroke is one of the leading causes of death and long-term disability worldwide, making early detection and timely intervention essential for improving patient outcomes. This project presents a machine learning-based Brain Stroke Prediction system that estimates an individual's risk of stroke using health and lifestyle parameters such as age, gender, hypertension, heart disease, average glucose level, body mass index (BMI), smoking status, work type, residence type, and marital status. The proposed system is developed using Python, Flask, and Scikit-learn, with a Random Forest Classifier trained on a publicly available healthcare dataset to provide accurate and reliable predictions.
The application features a simple and interactive web interface that allows users to enter their health information and instantly receive a prediction indicating their stroke risk. To protect user privacy, the system operates without a database, ensuring that no personal information or prediction results are stored. Data preprocessing techniques, including handling missing values, encoding categorical features, and feature transformation, are applied to improve the performance of the prediction model. The trained model is integrated into a Flask-based web application to enable fast and efficient real-time predictions.
The proposed system aims to assist healthcare professionals, students, and individuals by providing an accessible tool for preliminary stroke risk assessment. Although it is not intended to replace professional medical diagnosis, it can serve as a valuable decision-support system that promotes early awareness and preventive healthcare. The project demonstrates the practical application of machine learning in healthcare by combining predictive analytics with an easy-to-use web interface, offering a cost-effective, secure, and efficient solution for stroke risk prediction suitable for academic, research, and educational purposes.
| 3 |
Author(s):
Manohar R.
Page No :
|
Recruitment Challenges in Global Capability Centers (GCCs): An Analysis of India’s Talent Acquisition Landscape
Abstract
Global Capability Centers (GCCs) have transformed themselves from cost-reducing back office departments into strategically focused centers of engineering, artificial intelligence, and enterprise innovation, with India itself having over 1,700 centers employing almost 2 million people and contributing annual revenues of over USD 64 billion. Such fast growth has put immense pressure on the recruiting function. This paper aims to explore the main challenges of recruiting in GCCs, paying particular attention to the Indian environment. To do so, secondary information is used: reports from the industry, studies by consultancies, and scholarly literature related to talent acquisition and employer branding. In total, six interconnected challenges of recruiting in GCCs have been identified, namely the shortage of specialized and new technologies' talent, specifically of generative artificial intelligence, cloud architecture, and cybersecurity specialists; high levels of employee turnover and infant turnover among new joiners; leadership readiness problems preventing the localization of senior management positions; increased competition for employers due to the presence of over 1,700 centers competing for one and the same talent pool; infrastructure and quality problems associated with hiring people in Tier-II and Tier-III cities; and legal, taxation, and data compliance problems associated with international recruiting. The results show that talent recruitment under the GCC approach has moved from being a volume-oriented and cost-oriented process to a capabilities-oriented strategic approach, whereby organizations that do not view talent acquisition as a board-level activity could face delayed projects and increased cost inefficiencies.
| 4 |
Author(s):
Sontyana Chakradhar .
Page No : 1-2
|
Personalized Fitness Guide
Abstract
The rapid growth of artificial intelligence and machine learning has enabled the development of intelligent healthcare and fitness applications that provide personalized recommendations based on individual user characteristics. This research presents a Personalized Fitness Guide, a web-based application developed using Python, Flask, and Machine Learning to assist users in achieving their health and fitness goals through customized guidance. The proposed system integrates four major modules: daily calorie requirement prediction, calorie burn estimation, nutrition analysis, and personalized workout plan generation. User-specific information, including age, gender, height, weight, and activity level, is processed using trained machine learning models to produce accurate and personalized fitness recommendations. The nutrition analysis module provides detailed information about calories, proteins, carbohydrates, fats, vitamins, and minerals, enabling users to make informed dietary decisions. Additionally, the workout recommendation module generates structured exercise plans tailored to the user's fitness objectives and physical profile. The application is implemented using HTML, CSS, JavaScript, and Flask, while Scikit-learn, Pandas, NumPy, and Joblib are used for machine learning model development and deployment. Experimental evaluation demonstrates that the proposed system delivers reliable predictions, fast response times, and an intuitive user experience by integrating multiple fitness management functionalities into a single platform. The system minimizes the need for separate fitness applications and offers an intelligent, accessible, and cost-effective solution for personalized health management. The proposed approach has potential applications in personal fitness, wellness centers, healthcare organizations, and sports training environments, while future enhancements may include wearable device integration, cloud deployment, and advanced deep learning models to further improve prediction accuracy and user engagement.
| 5 |
Author(s):
SATISH PILLA.
Page No : 1-2
|
CODE PULSE — ONLINE CODING PRACTICE PLATFORM
Abstract
Abstract—
The rapid growth of the software industry has increased the demand for strong programming and problem-solving skills among students and professionals. Traditional learning methods often provide limited opportunities for practical coding experience and immediate feedback. To address these challenges, CODE PULSE -- ONLINE CODING PRACTICE PLATFORM is proposed as a web-based application that enables users to practice programming problems in an interactive and user-friendly environment. The platform offers features such as user authentication, problem categorization, code submission, automated evaluation, performance tracking, leaderboards, and an administrative management system. It is developed using React.js for the frontend, Spring Boot for the backend, and MySQL for database management, ensuring scalability, security, and efficient data handling. The system helps learners improve coding proficiency, logical thinking, and competitive programming skills through continuous practice and instant feedback. The proposed platform bridges the gap between theoretical learning and practical implementation, making it a valuable tool for academic institutions, training organizations, and aspiring software developers preparing for technical interviews and coding competitions.
| 6 |
Author(s):
JAGARAPU YAMUNA.
Page No : 1-2
|
AgriSathi: An AI-Powered Smart Farming Assistant and Decision Support System
Abstract
Operational information fragmentation in agriculture remains a significant barrier to productivity, crop yield protection, and economic security for smallholder farmers. Traditional farming methods rely heavily on static calendars and localized, often asymmetrical, information regarding soil health, disease diagnostics, weather patterns, and market pricing. This paper presents AgriSathi, a unified, high-performance web portal designed to address these gaps. Implemented with a FastAPI backend and a reverse-proxied Nginx server, AgriSathi integrates eight core agricultural modules: crop recommendations based on multi-parameter environmental rules, non-invasive leaf disease diagnostics with confidence thresholds exceeding 85%, localized weather forecasting with tailored agronomic advisories, real-time wholesale price tracking across multiple weight classes, and a stateful multilingual voice assistant supporting regional languages (specifically Telugu). The system architecture leverages SQLite for low-latency transaction processing. Preliminary validation demonstrates that the unified interface reduces information retrieval latency and empowers farmers with actionable, data-driven decisions to prevent yield loss and middleman exploitation.
Keywords: Precision Agriculture, FastAPI, Convolutional Neural Networks, Multilingual Localization, Leaf Pathology, Mandi Price Index, Agricultural Decision Support Systems.
| 7 |
Author(s):
Battina Reshma.
Page No : 1-2
|
Machine Learning-Based Life Expectancy Prediction in Developed and Developing Regions
Abstract
Life expectancy is a core demographic and public health indicator reflecting the overall quality of healthcare, living conditions, nutrition, socioeconomic progress, and disease control in a country. However, manually analyzing how dozens of health, social, and economic indicators influence lifespan is complex and time-consuming. This paper presents an interactive, web-based intelligence application developed using the Django framework, SQLite database, and machine learning regression algorithms to forecast life expectancy in developed and developing regions. The system utilizes real-world health and development indicators from the World Health Organization (WHO) and United Nations. We implement and evaluate three regression models: Linear Regression, Random Forest Regressor, and XGBoost Regressor. Preprocessing steps handle missing data, normalize features, and split records into training and test sets. XGBoost achieves superior predictive performance due to its robust gradient-boosting trees, yielding the lowest Mean Absolute Error (MAE) and highest R-squared (
R
2
R
2
) value. The system is integrated into a Django web platform, allowing users to view data tables, train models, compare performance metrics via interactive visualizations (correlation matrices, scatter plots, violin plots, and radar charts), and predict life expectancy through user-friendly forms.
| 8 |
Author(s):
DHAMODHARAN.S.
Page No : 1-3
|
ASTUDYONTHEWORKPLACESAFETYANDHEALTH MEASURES IN PRS TYRES PVT LTD AT RASIPURAM
Abstract
The workplace safety and health of employees play a significant role in improving productivity, reducing accidents, and creating a healthy working environment. This study aims to evaluate the workplace safety and health measures adopted in PRS Tyres Pvt Ltd, Rasipuram. The research focuses on employees' awareness of safety policies, the availability of safety equipment, health facilities ,emergency preparedness, and management support. Primary data were collected from
120 employees using a structured questionnaire. Secondary data were collected from books, journals, and company records. The findings reveal that the organization provides satisfactory safety measures, but improvements in training and emergency response systems can further enhance employee well-being. The study concludes with practical suggestions to strengthen workplace safety and health practices.
Keywords: Work place Safety, Employee Health,Occupational Safety,PRS TyresPvtLtd, Rasipuram
| 9 |
Author(s):
Lokesh. R.
Page No : 1-3
|
A STUDY ON EFFECTIVE MARKETING STRATEGY & ITS IMPACT MANAKKADU MASALA, SALEM
Abstract
Marketing strategy plays an important role in improving business performance and customer satisfaction. This study aims to evaluate the effectiveness of the marketing strategies adopted by Manakkadu Masala, Salem. The research is based on primary data collected from 120 respondents using a structured questionnaire and secondary data collected from books, journals, and company records. Percentage analysis was used for data interpretation. The study found that product quality, reasonable pricing, promotional activities, and product availability significantly influence customer satisfaction. The study concludes that effective marketing strategies improve customer loyalty and business growth.
| 10 |
Author(s):
Dr Vandana Khajuria.
Page No : 1-3
|
Anthills of the Savannah: A Critique of Post-Colonial Leadership and Resistance in Chinua Achebe’s Novel
Abstract
Chinua Achebe’s Anthills of the Savannah (1987) is a searing indictment of post-independence African leadership written after a long literary silence. The novel is set in the fictional West African state of Kangan and shows the degeneration of military rule into dictatorship, exposing the betrayal of the ideals of independence. In the neocolonial system, Achebe examines the theme of corrupting power, the alienation of the elite, sycophancy, and exploitation through the intertwined destinies of three friends – Sam, the military ruler, Ikem Osodi, the revolutionary poet and journalist, and Chris Oriko, the passive intellectual – and Beatrice Okoh, a perceptive woman.
The novel is a celebration of the resilience of common people and folk knowledge, represented by the anthills and the folk story of the tortoise and the leopard. It highlights the important role of intellectuals, storytellers, and the ‘New African Woman’ in resisting oppression and in imagining national rebirth. Achebe, using a range of narrative voices, political satire, and Igbo mythology, demonstrates the power of literature as a weapon of social protest and historical memory. Anthills of the Savannah turns out to be a grim diagnosis of Africa’s post-colonial condition, but also a tentative hope for a more inclusive and responsible leadership.
| 11 |
Author(s):
Umadevi Marisa.
Page No : 1-3
|
AI Study Assistant Using Vector Database and Large Language Models for Intelligent Question Answering from PDF Documents
Abstract
The rapid advancement of Artificial Intelligence (AI) and Large Language Models (LLMs) has introduced new possibilities in personalized education and intelligent learning systems. This paper proposes an AI Study Assistant using Vector Database and Large Language Models that enables students to interact with educational documents through intelligent question answering.
The proposed system implements a Retrieval-Augmented Generation (RAG) framework, where uploaded PDF documents are processed, converted into semantic embeddings, and stored in a vector database. When a user submits a query, the system retrieves the most relevant document sections using similarity search and provides accurate responses through an integrated Large Language Model.
Unlike traditional search-based learning platforms, the proposed approach understands the context of user queries and generates meaningful answers from the provided study materials. The system helps students reduce manual searching time, improve knowledge understanding, and achieve personalized learning experiences.
| 12 |
Author(s):
Payal Unmesh Ubale, Dr. Gulshan M. Rathi , Dr. Pawar D. R .
Page No : 1-3
|
METHOD OPTIMIZATION AND VALIDATION OF MIDOSTAURIN USING LCMS
Abstract
Midostaurin (PKC412) is an orally bioavailable multi-kinase inhibitor approved for the treatment of FLT3-mutated Acute Myeloid Leukemia (AML) and advanced systemic mastocytosis. Due to its high lipophilicity ($\log P \sim 4.6$) and narrow nanogram-level therapeutic window, establishing a sensitive and robust analytical method is essential. This study demonstrates the development, optimization, and validation of a Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) method for Midostaurin compliant with ICH Q2(R2) guidelines. Chromatographic separation was achieved on a Hypersil GOLD C18 column ($100\text{ mm} \times 2.1\text{ mm}$, $1.8\,\mu\text{m}$) using an isocratic mobile phase of 0.1% v/v formic acid in water and acetonitrile (25:75 v/v) at a flow rate of $0.30\text{ mL/min}$. Mass spectrometric detection was performed in ESI+ mode utilizing Multiple Reaction Monitoring (MRM) targeting $m/z\ 571.20 \to 398.20$ for Midostaurin and $m/z\ 576.25 \to 310.10$ for Midostaurin-$d_5$ (Internal Standard). Midostaurin eluted at $2.15\text{ min}$ with a total run time of $3.50\text{ min}$. High linearity was obtained over $0.50\text{ ng/mL}$ to $100.00\text{ ng/mL}$ ($r^2 = 0.9994$). Limits of Detection and Quantitation were $0.15\text{ ng/mL}$ and $0.50\text{ ng/mL}$, respectively. Forced degradation testing per ICH Q1A(R2) demonstrated the stability-indicating nature of the method.
| 13 |
Author(s):
Mantripragada Akshaya Pranathi.
Page No : 1-3
|
Resume Ranking engine using ML
Abstract
Recruitment is an important process for organizations, but manually reviewing a large number of resumes takes a lot of time and effort. Recruiters may also miss suitable candidates when screening resumes manually. To solve this problem, this research proposes a Resume Ranking Engine using Machine Learning that automatically analyzes and ranks resumes based on job requirements. The system uses Natural Language Processing (NLP) techniques to extract important information such as skills, education, and experience from resumes
| 14 |
Author(s):
M.DEEPTHI.
Page No : 1-3
|
SMART SPIRITUAL NAVIGATION SYSTEM
Abstract
Religious tourism has grown significantly, increasing the need for smart digital guidance systems for pilgrims. This paper presents the Smart Spiritual Navigation System, a web-based platform designed to assist devotees visiting Tiruvannamalai by providing temple information, Girivalam guidance, hotel booking, travel assistance, sacred place details, QR-based temple booking, and AI-powered chatbot support
The proposed system integrates location-based navigation, online booking, multilingual support, and AI technologies to enhance the pilgrimage experience. It offers users easy access to spiritual information while reducing manual effort and improving travel planning. The platform provides an efficient, user-friendly, and centralized solution for pilgrims.
| 15 |
Author(s):
shital sanjay lumade.
Page No : 1-3
|
Smart attendance System Using RFID and Google Sheets with ESP8266
Abstract
Attendance management plays a vital role in
academic and organizational environments, yet
traditional methods such as manual roll calls and
signature sheets are inefficient, error-prone, and
susceptible to manipulation. This research presents the
design and implementation of a smart attendance system
that integrates RFID technology with NodeMCU
ESP8266 and Google Sheets for real-time data logging.
The system employs RFID tags for identification, an
ESP8266 microcontroller for processing and Wi-Fi
connectivity, and Google Apps Script for cloud
integration. A 16×2 LCD and buzzer provide immediate
feedback to users. Experimental results demonstrate
that the system records attendance within 2–3 seconds
under stable network conditions, achieving 100%
accuracy in data transmission. The proposed system is
cost-effective, portable, and scalable, offering a practical
solution for educational institutions and workplaces.
| 16 |
Author(s):
1. Dr.M.R.PRAKASH MА., M.Com., B.Ed., MBA., M.Phil. Ph.D., FSIESRP (Malaysia)., Head, Department of Business Administration, School of Management, 2.M. Sundramurthy M. Com., M. Phil., Assistant professor Department of B. Com (Accounting & Finance),.
Page No : 1-3
|
Analyzing the Association Between Commercial and Economic Status (B2G Relations) and the E-Government Inclination Index Using Neural Networks
Abstract
The E-specialists Readiness Index is a composite record made from the Neural Index, the Telecommunication Foundation Index and the Human Resources Index. Ascertained every year by methods for the World Bank, this record gives actualities on such issues in light of the fact that the obtainment of open organizations and the use of those organizations by methods for individuals and establishments. In this analyze, we inspect whether the E-government Readiness Index offers actualities at the consistence of the business venture lives of countries with an e-government shape by utilizing neural structures.
| 17 |
Author(s):
1.S. Sujatha M. Com., M. Phil., MBA., P.hD., Assistant professor & Head, Department of B. Com (Accounting & Finance), 2.U. Anbarasi M.Com., M.Phil.,PGDRB., PGDPD., Ph.D Assistant professor Department of B. Com (Accounting & Finance),.
Page No : 1-3
|
Statistical Inference Methods for Triangle Taste Test Data and Their Applications
Abstract
This paper plots techniques for thing differentiations testing and tendency testing by method for using triangle taste appraisals records. Binomial dispersal theory and hypothesis test system are used since that the current truthful induction procedures for triangle trials methodologies for material and quality examination of cigarette things have a couple of deficiencies. Finally, two logical examinations are given, which have indispensable authoritative repercussions to cigarette endeavors.
| 18 |
Author(s):
Abdush Shaaf.
Page No : 1-3
|
Memes as a Marketing Tool: Examining Brand Recall, Trust, and Purchase Intent Among Gen Z Social Media Users
Abstract
This study examines the influence of meme marketing on brand engagement among Gen Z social media users. As brands increasingly adopt memes to connect with younger, digitally native audiences, this research investigates how such content shapes brand recall, emotional connection, and purchase intent. A structured questionnaire was administered to 215 Gen Z respondents through Google Forms, covering meme exposure, platform usage, trust in branded content, and behavioural responses. The study employed non-probability convenience sampling and analysed both quantitative and qualitative responses. Findings indicate that branded memes strengthen brand relatability and recall, particularly when humour and cultural relevance are present. A majority of respondents reported that meme content positively influenced their perception of brands, with a considerable proportion stating that memes influenced a purchase decision. The results suggest that meme marketing, when executed authentically, can meaningfully enhance engagement and loyalty among Gen Z consumers, offering practical insights for marketers seeking to build stronger relationships with this audience through culturally embedded content strategies.
| 19 |
Author(s):
INDUJA.S.
Page No : 1-4
|
A Study and Analysis of Employee Satisfaction at XASCOM Info Solutions LLP
Abstract
Employee satisfaction is an important factor that influences organizational performance and employee productivity. Satisfied employees contribute positively to the success of an organization by improving efficiency, teamwork, and commitment. This study aims to analyze the level of employee satisfaction at XASCOM Info Solutions LLP. The study focuses on various factors such as salary, working conditions, career growth, employee welfare, and management support. Primary data were collected through a structured questionnaire, and secondary data were obtained from books, journals, and company records. Percentage analysis was used to interpret the collected data. The findings indicate that most employees are satisfied with the work environment and organizational policies. The study also suggests improvements in career development and
| 20 |
Author(s):
NATHEESH M.
Page No : 1-4
|
A Study on the Workplace Culture and its Impact on Employees’ Retention at Hosur
Abstract
Workplace culture plays a significant role in influencing employee satisfaction, motivation, and retention. A positive work environment encourages employees to remain committed to the organization and improves productivity. This study aims to examine the relationship between workplace culture and employee retention in selected organizations at Hosur.
The study is based on primary data collected from 120 employees through a structured questionnaire and secondary data collected from books, journals, company records, and websites. Percentage analysis has been used to analyze the data. The findings indicate that a supportive work environment, effective communication, career development opportunities, recognition, and work-life balance positively influence employee retention. The study concludes that organizations with a healthy workplace culture are more successful in retaining skilled employees.
| 21 |
Author(s):
SRINITHI. M .
Page No : 1-4
|
A STUDY ON SALES PROMOTION TOWARDS SKA DAIRY FOODS INDIA PRIVATE LIMITED AT SALEM
Abstract
Sales promotion is one of the most important marketing tools that helps organizations increase product awareness, attract new customers, and improve sales performance. The present study focuses on the sales promotion activities of SKA Dairy Foods India Private Limited at Salem. The main objective of the study is to analyze the effectiveness of promotional strategies adopted by the company and to evaluate customer satisfaction towards these promotional activities. The study is based on both primary and secondary data. Primary data were collected from 120 respondents using a structured questionnaire, while secondary data were collected from books, journals, company records, and websites. Percentage analysis was used to interpret the collected data. The findings reveal that promotional activities positively influence customer buying behaviour and improve customer loyalty.
| 22 |
Author(s):
AJITH S .
Page No : 1-4
|
A STUDY ON EMPLOYEE RETENTIONAMONGWORKERS IN I MARQUE SOLUTIONSAT CHENNAI
Abstract
Employee retention has emerged as one of the most critical challenges facing contemporary organizations, particularly in highly dynamic sectors like business process outsourcing and IT-enabled services. This research aims to analyze the various factors determining employee retention strategies within | Marque Solutions, Chennai. A sample size of 120 respondents was gathered using a descriptive research design coupled with non-probability convenience sampling. A structured served as the questionnaire primary instrumentfordataacquisition. Statisticalmethodologies, including SimplePercentage Analysis, Chi-Square Test, and Pearson Correlation Analysis, were meticulously applied to interpret the empirical dataset. The statistical findings revealed that while basic compensation packages create a primary baseline, career development opportunities, organizational culture, and professional relationship dynamics play an overwhelming role in fostering long-term organizational commitment. The study highlights that targeted interactive interventions and transparent appraisal frameworks effectively lower turnover intent. Stringent retention strategic models are recommended to nurture a resilient workforce and sustain competitive advantages.
| 23 |
Author(s):
Jenifer F.
Page No : 1-4
|
A STUDY ON THE IMPACT OF WORK LIFE BALANCE ON EMPLOYEES PERFORMANCE AT CHRISTY FRIED GRAM INDUSTRY, TIRUCHENGODE
Abstract
In the highly competitive modern industrial ecosystem, maintaining an optimal equilibrium between professional obligations and personal well-being has emerged as an indispensable comerstone for enhancing institutional productivity. This empirical research systematically investigates the critical impact of work-life balance vectors on overall employee performance frameworks with specific reference to Christy Fried Gram Industry across the Tiruchengode region.
Employing a descriptive research design, primary quantitative data was successfully gathered from a well-structured sample of exactly 120 active employees via a standardized convenience sampling methodology. Advanced statistical analysis operations, including frequency percentage distribution tests, bivariate Chi-Square hypotheses evaluations, and Pearson product-moment correlation tracking loops, were applied to the dataset. The scientific findings demonstrate that core work-life balance elements-specifically flexible shifts, stress management protocols, health welfare setups, and rapid grievance redressal exercise an overwhelming, mathematically verifiable influence over employee performance and organizational commitment. Bivariate checks established that employee age cohorts and tenure parameters significantly govern work-life satisfaction levels, highlighting strategic focal points for HR technical teams. The study concludes by proposing an optimized corporate framework tailored to compress operational stress and escalate retention benchmarks.
| 24 |
Author(s):
Dr. Satyabrata Dash.
Page No : 1-4
|
Strategic Partnerships Drive Technology Adoption in Indian Industry
Abstract
This document shows the required format and appearance of a manuscript prepared for SPIE e-journals. The abstract should consist of a single paragraph containing no more than 200 words. It should be a summary of the paper and not an introduction. Because the abstract may be used in abstracting and indexing databases, it should be self-contained (i.e., no numerical references) and substantive in nature, presenting concisely the objectives, methodology used, results obtained, and their significance. A list of up to six keywords should immediately follow, with the keywords separated by commas and ending with a period.
Key Words: Indian industry, strategic partnership, corporate partnership, Commercial Alliance, manufacturing sector
| 25 |
Author(s):
Saroj.
Page No : 1-4
|
Financial Literacy and Inclusion: A Driver of Economic Empowerment in Rural India
Abstract
Abstract:
This research paper investigates the pivotal role of financial literacy and financial inclusion in fostering economic empowerment among rural populations in India. Using updated data from RBI's Financial Inclusion Index, NABARD surveys, and NSSO reports, it analyzes the influence of financial awareness and access to services on household income, savings, insurance coverage, and consumption patterns. Tables, graphs, and authentic statistical data enrich the findings. The study aims to offer policymakers a clear framework for enhancing rural economic development through targeted financial strategies.
Keywords: Financial Literacy, Financial Inclusion, Rural India, Economic Empowerment, Digital Banking
| 26 |
Author(s):
Ramya S.
Page No : 1-4
|
Unequal Earths: Climate Justice and the Politics of Vulnerability in Select Indian Climate Fiction
Abstract
Climate change is increasingly recognised not merely as an environmental crisis but as a profoundly unequal social phenomenon. The discourse of climate justice foregrounds how marginalised communities disproportionately bear the burdens of ecological degradation despite contributing least to its causes. This paper examines the representation of climate justice and vulnerable communities in select Indian literary texts, including The Hungry Tide (2014), Gun Island (20196), Latitudes of Longing (2019), The Butterfly Effect (2019), and A Guardian and a Thief. Drawing on postcolonial ecocriticism, eco-narratology, and intersectionality, the study explores how Indian fiction narrates environmental injustice through caste, class, gender, and indigeneity. The paper argues that these texts not only depict ecological crises but also critique structural inequalities and advocate for a more inclusive and ethical environmental imagination.
| 27 |
Author(s):
NavyaShree S.
Page No : 1-4
|
An evaluation of employee engagement rate and employee engagement strategies on deriving organizational succes
Abstract
This study evaluates the employee engagement rate and employee engagement strategies in achieving organizational success. Primary data were collected through a structured questionnaire, and secondary data were obtained from books and journals. The findings indicate a strong positive relationship between employee engagement and organizational success. The study concludes that effective engagement strategies improve employee satisfaction, performance, commitment, and overall organizational growth.
| 28 |
Author(s):
BODDEDA.PAVAN KUMAR SURYA.
Page No : 1-4
|
Smart Interview Practice System
Abstract
The increasing demand for placement preparation and interview readiness has created the need for intelligent systems that provide personalized interview practice and real-time performance evaluation. This project presents a Smart Interview Practice System, an AI-powered web application designed to help students and job seekers improve their interview skills through interactive practice sessions and automated feedback.
| 29 |
Author(s):
Manohar V .
Page No : 1-4
|
A Study on the Impact of Data Accuracy and Quality Control on Financial Market Operations and Information Reliability
Abstract
Financial market operations increasingly depend on the accuracy, timeliness and integrity of the data that underpins trade execution, settlement, regulatory reporting and investor disclosure, yet data-quality failures remain a persistent source of operational and reputational risk. This study examines the impact of data accuracy and quality-control practices on financial market operations and information reliability, situating firm-level survey evidence within the broader trend of rising reported data-quality incidents across Indian financial market intermediaries. A log-linear regression on five years of reported data-quality-incident data (FY 2020-21 to FY 2024-25) confirms significant year-on-year growth (β̂ = 0.2005, t = 13.509, p = 0.0009, R² = 0.984), corresponding to an average annual growth rate of approximately 22.2%. Primary data collected from 90 respondents working in financial-market operations, compliance and data-management roles show that 61.1% experience a data-accuracy issue “often” or “always” in at least one core process, reconciliation and trade-matching is the process most frequently affected, and firms without a formal data-governance framework are significantly more likely to have received a regulatory query or notice (χ² = 4.484, p = 0.034). Chi-square tests of independence further find that firm size is significantly associated with the frequency of data-accuracy issues (χ² = 16.642, p = 0.011), while the verification method used (manual, system-automated, or hybrid) is not significantly associated with reporting-error incidence (χ² = 1.522, p = 0.467). The findings indicate that data-quality risk in financial market operations is real, growing, and structurally linked to firm size and governance maturity rather than to the verification method alone, and the study recommends prioritising formal data-governance frameworks and automated reconciliation controls, particularly for smaller intermediaries.
| 30 |
Author(s):
Chaman k k.
Page No : 1-4
|
Confirmation and Matching in Post-Trade Operations: Employee Perceptions of Settlement Risk Reduction at a Global Custodian
Abstract
This study, undertaken during a six-week internship in the post-trade operations function of State Street Corporation, Bengaluru, examines the role of trade confirmation and matching in reducing settlement risk, and situates employee-level perception evidence within the broader regulatory and academic literature on post-trade operational risk. A structured five-point Likert questionnaire covering ten confirmation- and matchingrelated statements was administered to 150 employees drawn from Trade Operations, Middle Office, Back Office, Custody Services, Risk Management and other operational functions. A one-sample t-test confirms that the composite perception score (M = 3.96, SD = 0.29) is significantly above the neutral midpoint (t = 40.146, p < 0.001), indicating strong overall employee confidence that confirmation and matching reduce settlement risk. However, internal-consistency reliability of the ten-item scale was low (Cronbach's α = −0.046), and neither Pearson correlation nor multiple regression (R² = 0.015, F(6,143) = 0.371, p = 0.896) found significant relationships among individual perception items, indicating that the items function as distinct judgements rather than a single unified attitude. One-way ANOVA found no significant difference in perception across departments (F = 0.45, p = 0.812) or years of experience (F = 0.749, p = 0.525), while the difference between male and female respondents approached but did not reach significance (t = 1.847, p = 0.067). The findings reveal a consistent "principle versus practice" gap: employees strongly endorse the conceptual importance of confirmation and matching but rate current matching timeliness, discrepancy resolution and system reliability more moderately. The study recommends prioritising matching turnaround time, strengthening exception-resolution workflows, continuing automation investment, and redesigning future perception instruments as validated, multi-dimensional scales.
| 31 |
Author(s):
Chaman K K .
Page No : 1-4
|
Behavioural Biases and Investor Perception in Retail Equity Investment Decisions: A Study Among Individual Investors in Bengaluru
Abstract
Classical finance theory assumes investors process information rationally and act to maximise expected utility, yet a substantial behavioural-finance literature shows that psychological biases systematically shape real investment decisions. This study examines the influence of common behavioural biases, namely overconfidence, herding, loss aversion and the disposition effect, on the investment decisions and self-reported satisfaction of individual retail investors in Bengaluru. A structured five-point Likert questionnaire covering demographic, investment-behaviour and bias-related statements was administered to 150 individual equity and mutual-fund investors. A one-sample t-test confirms that the composite bias-influence score (M = 3.71, SD = 0.34) is significantly above the neutral midpoint (t = 25.86, p < 0.001), indicating that respondents recognise behavioural biases as a material influence on their own investment decisions. One-way ANOVA found a significant difference in bias-influence scores across income groups (F = 3.128, p = 0.016) but not across investing-experience bands (F = 1.104, p = 0.351). Pearson correlation found a significant positive relationship between self-reported overconfidence and trading frequency (r = 0.34, p < 0.001), and a significant negative relationship between risk tolerance and portfolio diversification (r = −0.21, p = 0.010), while a supplementary multiple regression of investment satisfaction on four bias dimensions was significant overall (R² = 0.187, F(4,145) = 8.34, p < 0.001), with loss aversion and herding emerging as the strongest individual predictors. No significant gender difference was found (t = 1.12, p = 0.264). The findings indicate that behavioural biases are a recognised and measurable influence on retail investment behaviour in this sample, vary systematically with income, and are more closely tied to trading and diversification patterns than to demographic profile alone. The study recommends structured investor-education interventions targeted at high-income, high-turnover investor segments and closer integration of behavioural coaching into financial-advisory practice.
| 32 |
Author(s):
Aditya Hulkoti.
Page No : 1-4
|
Digital Transformation in Loan Processing: Employee Perceptions of Operational Efficiency
Abstract
Abstract – This study, undertaken during a six-week internship at IDF Financial Services Pvt. Ltd., Dharwad, examines the role of digital transformation in loan processing and its impact on operational efficiency in microfinance institutions. A structured five-point Likert questionnaire covering ten statements related to digital loan processing was administered to 150 employees working across Credit Operations, Branch Operations, Field Operations, Recovery, Risk Management, and other operational functions. A one-sample t-test confirmed that the overall perception score (M = 4.09, SD = 0.30) was significantly above the neutral midpoint (t = 41.863, p < 0.001), indicating strong employee confidence that digital loan processing improves operational efficiency. Cronbach's alpha (α = 0.841) indicated satisfactory internal consistency, while Pearson correlation and multiple regression identified a significant positive relationship between digital loan processing and operational efficiency. One-way ANOVA found no significant difference in perception across departments (F = 0.517, p = 0.762) or years of experience (F = 0.698, p = 0.594), while the difference between male and female respondents was not statistically significant (t = 1.276, p = 0.204). The findings indicate that employees believe digital transformation has improved loan processing speed, documentation accuracy, and customer service, while emphasizing the need for continuous system upgrades and employee training to further enhance operational efficiency..
Key Words: Digital Transformation, Loan Processing, Operational Efficiency, Microfinance, Employee Perception.
| 33 |
Author(s):
Jayachitra G .
Page No : 1-5
|
A STUDY ON EMPLOYEES JOB SATISFACTION USING BUSINESS ANALYTICS WITH REFERENCE TO XASCOM INFO SOLUTIONS LLP AT CHENNAI
Abstract
In the hyper-competitive software engineering and knowledge-services vertical, maximizing employee job satisfaction functions as a primary indicator for reducing operational talent drain and driving organizational innovation. This empirical research explores the core determinants of employee job satisfaction inside Xascom Info Solutions LLP, Chennai, by integrating modern business analytics data frameworks. Moving away from standard descriptive HR practices, this investigation combines descriptive percentage distributions, bivariate Chi-Square optimization matrices, and Pearson product-moment correlation tracking to analyze a representative cross-sectional sample of 120 full-time technology professionals. Data was systematically gathered using a structured questionnaire that translates traditional human relations indicators such as technological infrastructure support, compensation health, management transparency, and work-life balance frameworks into discrete analytics variables. The empirical findings indicate that while financial compensation defines the baseline layer, predictive analytics tools demonstrate that career path clarity and data-driven management frameworks exert a dominant influence over long-term retention intent. Bivariate testing confirmed that educational qualifications do not systematically skew employee satisfaction with analytics-driven corporate learning systems. The study concludes by outlining an actionable data-driven talent management architecture designed to optimize operational workplace health.
Keywords: Job Satisfaction, Business Analytics, Predictive HR Metrics, Workforce Optimization, Xascom Info Solutions.
| 34 |
Author(s):
SOUNDHARRAJAN A D .
Page No : 1-5
|
A study on the customer satisfaction towards product in thangavelu textile private limited
Abstract
Customer satisfaction plays an important role in the success and growth of every business organization. In the textile industry, maintaining product quality and customer satisfaction is essential due to increasing market competition. This study focuses on analyzing the level of customer satisfaction towards the products of Thangavelu Textile Private Limited at Salem. The study aims to identify customer opinions regarding product quality, pricing, durability, and service.
| 35 |
Author(s):
Shyamala C.
Page No : 1-5
|
Design of an FPGA-Based ECG Signal Analysis System
Abstract
ECG signals are very small in amplitude and are easily affected by noise, which can make heart-rate measurement and R-peak detection unreliable. In this paper, a real-time ECG acquisition and processing system is developed using the Nexys A7 FPGA to achieve accurate and fast cardiac monitoring. The ECG signal is collected through surface electrodes and conditioned using the AD8232 module, then digitized with the Artix-7 XADC. A 0.5–40 Hz bandpass filter is applied to remove noise and baseline drift. R-peaks are detected using an adaptive thresholding method combined with an exponential moving average and a refractory period, ensuring reliable detection. The heart rate is calculated from RR-intervals and displayed on the onboard 7-segment display, while the processed ECG signal is converted back to analog for oscilloscope observation. The results show that the FPGA-based system provides accurate ECG processing with low delay, making it suitable for portable and embedded cardiac monitoring applications.
| 36 |
Author(s):
SARAVANAN N.
Page No : 1-5
|
A Study on the Impact of Digital Banking and Fintech Services on Customer Satisfaction with Reference to ESAF Small Finance Bank at Salem
Abstract
The rapid evolution of digital infrastructure has completely transformed the traditional banking landscape, driving financial institutions toward absolute financial inclusion and seamless digital service architecture. This dynamic empirical research systematically investigates the critical impacts of digital banking channels and fintech services on overall customer satisfaction frameworks with specific reference to ESAF Small Finance Bank across the Salem region. Employing a comprehensive descriptive research design, primary quantitative intelligence was successfully gathered from a well-structured sample of exactly 120 active retail banking customers via a standardized convenience sampling methodology. Advanced statistical analysis operations, including frequency percentage distribution tests, bivariate Chi-Square hypotheses evaluations, and Pearson product-moment correlation tracking loops, were applied to the empirical datasets. The scientific findings demonstrate that core digital banking elements specifically transaction security architecture, application processing agility, user-interface accessibility, and rapid customer care support operations-exercise an overwhelming, mathematically verifiable influence over customer satisfaction and institutional trust. Bivariate checks firmly established that customer education profiles and financial parameters critically govern digital adaptation levels, highlighting strategic focal points for bank technical teams. The study concludes by frameworking an optimized, user-centric integrated fintech satisfaction delivery matrix specifically designed to minimize transactional attrition and escalate retention benchmarks.
| 37 |
Author(s):
Manjula Devi K N.
Page No : 1-5
|
AquaLens: A Machine-Learning Decision-Support System for Industrial Water Footprint Reduction
Abstract
I'm pursuing an M.Tech in Computer Science
| 38 |
Author(s):
L Chavan.
Page No : 1-5
|
AI Resume Screener and Student Resume Optimizer
Abstract
The recruitment process has become increasingly challenging due to the large volume of resumes received for every job opening. Manual screening of resumes is time-consuming, prone to human errors, and may result in overlooking qualified candidates. Similarly, students often face difficulties in tailoring their resumes according to specific job requirements, reducing their chances of being shortlisted by Applicant Tracking Systems (ATS). To address these challenges, this project roposes an AI Resume Screener and Student Resume Optimizer that automates resume screening, candidate ranking, and resume enhancement. The system extracts information from PDF and DOCX resumes using multiple text extraction techniques, including pdfplumber, pypdf, pdfminer, and Optical Character Recognition (OCR) using Tesseract for scanned documents. It identifies candidate skills, educational qualifications, and contact information, and compares them with job requirements to calculate a match score. The proposed system provides separate functionalities for HR users and students. HR users can upload
job descriptions and multiple resumes to obtain ranked candidate lists based on a scoring algorithm that evaluates skills, education, and contact information. Students can upload their resumes and receive AI-powered optimization suggestions generated using OpenAI GPT-4o-mini, resulting in ATS friendly resumes while preserving the original formatting. The system uses SQLite for data storage and
supports efficient management of users, screening records, and resume submissions. By automating resume analysis and optimization, the proposed system reduces recruitment effort, improves candidate selection accuracy, and enhances students' employability by helping them create job-specific professional resumes
| 39 |
Author(s):
Ms. Ruchita G. Jogdand .
Page No : 1-5
|
Design and Implementation of a Smart Blind Assistance Stick Using Arduino Uno
Abstract
Blind and visually impaired individuals face significant challenges while navigating independently. Traditional walking sticks can only detect obstacles after physical contact, often leading to accidents or injuries. This paper presents the design and implementation of a Smart Blind Assistance Stick using Arduino Uno that integrates an ultrasonic sensor, water sensor, buzzer, vibration motor, and a GSM module. The system detects nearby obstacles using ultrasonic waves, identifies water or wet surfaces using a water sensor, and provides real-time audio and vibration alerts. In emergency situations, a push button activates the GSM module to send an SOS SMS to a predefined contact number. The proposed system improves mobility, safety, and confidence for visually impaired users and offers a cost-effective and user-friendly assistive solution.
| 40 |
Author(s):
Shiwani R, Yalini B, Sanjay S , Pranesh IB, and Ms. Naveena.
Page No : 1-5
|
Integrated MRI-Cognitive Analysis Platform for Early Alzheimer’s Diagnosis and Personalized Care
Abstract
Alzheimer’s disease (AD) gradually impairs memory, cognition, and functional independence, representing one of the most pressing neurological health challenges globally. In this paper, we present a multi-modal diagnostic framework that combines deep learning-based MRI analysis with standardized cognitive assessment scoring to produce a fused severity index for AD stage classification. Our system classifies patients into four stages: Non Demented, Very Mild Demented, Mild Demented, and Moderate Demented. We evaluate the framework on clinical case data, demonstrating 100% MRI classification confidence on representative cases. The cognitive scoring module implements a Mini-Mental State Examination (MMSE)-style assessment across seven cognitive domains. A weighted fusion mechanism integrates both modalities into a unified severity index on a 0–100 scale. Our results suggest that multi-modal fusion significantly enhances diagnostic reliability compared to single-modality approaches.
| 41 |
Author(s):
Shubhangi Bhale.
Page No : 1-5
|
Intellivent Adaptive Fan Controller
Abstract
This paper presents an IntelliVent Adaptive Fan
Control system that automatically controls fan
speed according to room occupancy and
temperature conditions. IR Sensor Module
sensors are used to detect the number of people
entering and leaving the room, while the
DHT11 monitorsroom temperature
continuously. The ESP32 processes the sensor
data and controls the fan automatically for
better comfort and energy efficiency. The
system also includes a 16x2 LCD to display
real-time information such as people count and
temperature. A Piezo Buzzer is used for alert
conditions when temperature exceeds a certain
limit. Additionally, a Wi-Fi-based web
dashboard provides remote monitoring and
control. The proposed system reduces energy
consumption, improves smart automation, and
demonstrates an effective IoT-based solution
for modern indoor environments
| 42 |
Author(s):
Ankur Gatpalli.
Page No : 1-5
|
Smart Door Lock System Using Face & Fingerprint Authentication
Abstract
This paper presents a Smart Door Lock System based on fingerprint and face recognition techniques to enhance security and access control in modern environments. The proposed system utilizes an Android application with an inbuilt fingerprint sensor for fingerprint authentication, while Python OpenCV algorithms are employed for facial recognition. The ESP8266 NodeMCU acts as the central processing and communication unit that receives authentication results from both biometric modules. A relay module interfaced with the ESP8266 controls the electronic door lock powered by a 12V battery supply. The system grants access only when both fingerprint verification and facial recognition are successfully completed, thereby reducing the possibility of unauthorized entry. Unlike conventional lock systems that rely on physical keys or passwords, the proposed approach offers a secure, reliable, and automated access control solution. The developed prototype demonstrates the practical application of IoT and biometric technologies in creating intelligent security systems suitable for homes, offices, laboratories, and other restricted areas.
| 43 |
Author(s):
MOGGA DILEEP.
Page No : 1-5
|
Secure Digital Evidence Verification System using Digital Signatures
Abstract
The increasing use of digital images as evidence in legal, forensic, and organizational applications has raised significant concerns regarding image authenticity and integrity. Modern image editing tools make it easy to manipulate digital evidence, creating the need for a secure and reliable verification system. This paper presents a Secure Digital Evidence Verification System using Digital Signature, a web-based platform designed to ensure the authenticity, integrity, and traceability of digital image evidence throughout its lifecycle. the image using DWT watermarking, while a digital signature is generated and securely stored in both the image metadata and the centralized database. A SHA-256 hash of the sealed image is also generated and stored to detect any unauthorized modifications. The system implements role-based access control, enabling General Public users to submit evidence, Public Authorities to verify authenticity, and Administrators to manage users, approvals, and audit logs. The proposed framework effectively detects image tampering, preserves The proposed system integrates Digital Signatures, Discrete Wavelet Transform (DWT)-based invisible watermarking, and SHA-256 cryptographic hashing to provide a multi-layered security framework. During the image sealing process, a unique Submission ID is invisibly embedded into evidence authenticity, and provides a secure workflow for digital evidence submission and verification. It offers a practical solution for digital forensic investigations, law enforcement agencies, and judicial systems requiring trustworthy digital evidence management.
| 44 |
Author(s):
YOGEETA BC, Dr. Leela M.H.
Page No : 1-5
|
“A Conceptual Study on Non-Performing Assets in Cooperative Banking: Challenges, Prevention Strategies and Best Practices”
Abstract
Non-Performing Assets (NPAs) continue to be one of the major challenges affecting the performance and sustainability of the banking sector, particularly cooperative banks. An increase in NPAs reduces the quality of loan portfolios, affects operational efficiency, and limits the ability of banks to extend fresh credit. This conceptual study aims to examine the nature, causes, challenges, and preventive strategies related to NPAs in cooperative banking based on secondary data collected from published research articles, reports, books, and official publications. The study discusses the classification of NPAs, the factors responsible for loan defaults, and the significance of effective credit appraisal, loan monitoring, and technological support in minimizing NPAs. It also highlights various best practices adopted by banks to improve recovery mechanisms and strengthen risk management. Since the study is conceptual in nature, it does not include confidential organizational information or financial data from any specific bank. The findings indicate that timely monitoring, proper credit assessment, digital technologies, and customer awareness are essential for improving asset quality and maintaining financial stability. The study concludes that a proactive approach towards credit management and effective governance can significantly reduce the occurrence of NPAs and contribute to the long-term growth of cooperative banks..
| 45 |
Author(s):
Pranav Daithankar.
Page No : 1-5
|
Gesture Recognition and Translate into Regional Language System
Abstract
Communication through hand
gestures is a natural and intuitive form of
human interaction, yet individuals with
speech and hearing impairments face
significant barriers in expressing themselves
to the general public who may not
understand sign language. Traditional sign
language interpretation requires human
translators, which is costly, limited in
availability, and unsuitable for real-time,
everyday communication. To address these
challenges, this paper proposes an
automated Gesture Recognition and
Translate into Regional Language System
that integrates flex sensor-based finger
gesture detection with accelerometer-based
hand movement tracking. The system
employs an Arduino Nano microcontroller
to process sensor data and recognizes
predefined gestures corresponding to letters,
words, or phrases. Recognized gestures are
translated into regional languages such as
Hindi, Marathi, Tamil, or Kannada and
displayed on a 16x2 LCD screen. An
optional audible output provides spoken
translation for enhanced accessibility. By
combining flex sensors for finger bending
detection and an accelerometer for hand
orientation, the system achieves accurate,
low-cost, and real-time gesture recognition
suitable for daily use. This portable,
cost-effective, and user-friendly solution is
ideal for hearing and speech-impaired
individuals, classroom settings, and public
assistance centers.
| 46 |
Author(s):
Keerti Appanna Uttur.
Page No : 1-5
|
“THE IMPACT OF DIGITAL PAYMENT ADOPTION ON CONSUMER FINANCIAL BEHAVIOUR IN INDIA”
Abstract
Digital payment systems have changed how Indian consumers manage money. UPI, mobile wallets, internet banking, debit cards, and other electronic payment tools have made transactions quicker and more accessible, but this shift also raises questions about how people spend, save, and track their expenses once cash stops being the default. This paper looks at digital payment adoption in India and its connection to consumer financial behaviour, focusing on convenience, perceived security, ease of use, financial awareness, trust, transaction speed, and accessibility. The proposed study uses a quantitative design, with primary data gathered through a structured questionnaire administered to digital payment users. Descriptive statistics and suitable inferential tests will be applied to examine how adoption relates to spending patterns, budgeting, saving habits, transaction frequency, and overall financial management. The results should be useful to financial institutions, fintech firms, policymakers, and consumers looking to encourage safer and more financially sound use of digital payments.
Key Words: Digital Payments, Consumer Behaviour, UPI, Financial Behaviour, Fintech, India.
| 47 |
Author(s):
Mohammed Saboor.
Page No : 1-5
|
Gold as a Safe Haven during Geopolitical Tensions: Evidence from Gold Futures Trading in India
Abstract
This study examines the role of gold as a safe haven asset during periods of geopolitical and economic uncertainty. Using secondary data on gold futures trading in India from 2015 to 2025, the study analyzes traded contracts, total value, and average daily turnover along with gold price trends. The methodology includes trend analysis, growth rate analysis, turnover intensity analysis, and comparative analysis. The findings indicate that gold trading activity and capital inflows increase significantly during crisis periods such as 2020 and 2025. The results also show a positive relationship between gold prices and trading activity, suggesting increased investor participation during uncertain times. The study concludes that gold continues to function as a reliable safe haven asset, particularly during periods of heightened geopolitical risk.
| 48 |
Author(s):
GURUSARAN.P.
Page No : 1-6
|
A STUDY ON THE CUSTOMER SATISFACTION TOWARDS AFTER SALES, SERVICE AT SRIRAM BAJAJ SHOWROOM AT RASIPURAM
Abstract
This study focuses on the organizational performance and customer satisfaction of Bajaj Auto. The main objective of the study is to understand the company's services, product quality, employee performance, and customer satisfaction level. Data were collected through questionnaires and customer feedback methods The study analyzes important factors such as product quality, service satisfaction, pricing, and staff behavior.
| 49 |
Author(s):
Prof. Geetha C.V, Ajithesh A.
Page No : 1-6
|
A Study on the Effectiveness of AI-Enabled Recruitment Practices
Abstract
Artificial Intelligence (AI) is increasingly transforming recruitment by automating candidate sourcing, screening, and selection processes. This study examines the effectiveness of AI-enabled recruitment practices in improving hiring efficiency, candidate quality, decision-making, and overall recruitment outcomes. A descriptive research design was adopted, and primary data was collected from 70 respondents using a structured questionnaire. Statistical tools such as Chi-square, correlation, and regression were used for data analysis. The findings indicate that AI-enabled recruitment significantly reduces hiring time, improves candidate shortlisting quality, and enhances recruitment decision-making. The study concludes that AI adoption positively influences recruitment effectiveness and stakeholder satisfaction. However, organizations must address concerns related to transparency, trust, and ethical implementation to maximize the benefits of AI-driven recruitment systems.
| 50 |
Author(s):
Akshay Kumar.
Page No : 1-6
|
A Correlation Analysis of Service Parameters Driving Passenger Satisfaction Across 16 AAI Airports
Abstract
This study examines Airport Service Quality (ASQ) and its impact on overall passenger satisfaction at 16 major airports. Using data collected through the Airport Council International (ACI) ASQ survey, the research analyzes 32 service quality parameters, including check-in, security, cleanliness, staff behavior, and terminal facilities. Pearson's correlation coefficient is used to identify the relationship between each service quality parameter and overall passenger satisfaction. The findings help identify the key factors influencing passenger experience and provide recommendations for improving airport service quality and customer satisfaction
| 51 |
Author(s):
Mr. Ayush A. Padmawar.
Page No : 1-6
|
VIBE ROOMMATE: A Compatibility-Based Roommate Matching System
Abstract
Selecting a compatible roommate is a critical factor in enhancing students' accommodation experience and overall well-being. However, most existing roommate-finding platforms primarily emphasize accommodation availability, rental cost, and location while providing limited support for evaluating lifestyle compatibility. This paper presents VIBE ROOMMATE, a compatibility-based roommate matching platform designed for college students. The proposed system employs a weighted score-based compatibility algorithm that evaluates users across ten lifestyle attributes, including sleep schedule, cleanliness, study habits, social behaviour, food preference, and budget, to generate transparent and explainable roommate recommendations. The platform is developed using the MERN technology stack and integrates JWT-based authentication, bcrypt password encryption, MongoDB Atlas, Cloudinary, and Socket.IO to provide secure profile management, accommodation listing, cloud-based storage, and real-time communication within a unified web application. A functional prototype involving ten test users successfully validated the integration of the proposed modules and demonstrated the technical feasibility of the platform. The proposed solution provides a secure, scalable, and user-centric foundation for compatibility-driven roommate matching and supports future integration of AI-based recommendation techniques.
| 52 |
Author(s):
Keerti Appanna Uttur.
Page No : 1-6
|
The Impact of Social Media Financial Influencers on the Investment Decisions of Retail Investors: An Empirical Study
Abstract
Social media has changed how retail investors find, interpret, and share financial information. Financial influencers — "finfluencers" — use YouTube, Instagram, LinkedIn, X, and similar platforms to share investment opinions, education, and market commentary. This study looks at how these influencers affect retail investors' decisions, focusing on perceived credibility, financial knowledge, information quality, trust, social influence, and how useful investors find influencer content. The approach is quantitative: primary data collected from retail investors through a structured questionnaire, analysed with descriptive statistics and appropriate inferential tests. The aim is to see how much retail investors actually lean on finfluencers when picking investment products, reading market signals, or deciding to buy, hold, or sell. The results should be useful to investors, financial educators, regulators, and the content creators themselves in encouraging more informed, responsible investing.
Keywords: Financial Influencers, Retail Investors, Social Media, Investment Decisions, Investor Behaviour, Financial Literacy
| 53 |
Author(s):
Mohammed Saboor.
Page No : 1-6
|
GST Return Filing Process and Compliance Challenges in Small Businesses: Evidence from a Chartered Accountancy Practice in Bengaluru, India
Abstract
The Goods and Services Tax (GST) has transformed India's indirect tax system by introducing a unified, technology-driven framework, yet small businesses continue to face real difficulties in meeting GST return-filing obligations. This study, undertaken during an internship at Nouman Sait & Associates, Chartered Accountants, Bengaluru, examines the GST return-filing process and the compliance challenges faced by small businesses, and situates these firm-level findings within the broader national trend of GST base expansion. A log-linear regression on five years of official GST collection data (FY 2020-21 to FY 2024-25) confirms significant year-on-year growth (β̂ = 0.1635, t = 7.531, p = 0.0049, R² = 0.95), corresponding to an average annual growth rate of approximately 17.8%. Primary data collected from 82 small-business respondents via a structured questionnaire show that 77% rely on an accountant or chartered accountant firm for compliance, 48.7% experience filing difficulty “often” or “always”, and Input Tax Credit (ITC) reconciliation is the single most difficult stage of compliance. Chi-square tests of independence find that annual turnover is significantly associated with filing-difficulty frequency (χ² = 21.611, p = 0.042) and that GST registration scheme is significantly associated with receipt of a GST notice (χ² = 7.524, p = 0.023), while who manages compliance is not significantly associated with late-fee incidence (χ² = 0.128, p = 0.938). The findings indicate that GST compliance difficulty is real, statistically linked to firm characteristics, and not fully resolved by outsourcing compliance alone, and the study recommends targeted simplification of the ITC/GSTR-2B reconciliation workflow and compliance support directed at Regular-scheme taxpayers.
| 54 |
Author(s):
Akshatha N.
Page No : 1-6
|
Digital Twin Maturity and Trading Decision-Making: Scale Development, Validation, and Experimental Evidence from Financial Market Simulation
Abstract
Digital twin (DT) technology is increasingly applied to financial markets to create dynamic, simulation-based representations of trading environments, yet no validated instrument exists to measure how DT maturity shapes trader behavior. Objective: This study develops and validates a multidimensional Digital Twin Maturity (DTM) scale and examines its influence, alongside AI trust and simulation quality, on trading decision-making within a financial market simulation. Methodology: A quantitative, cross-sectional survey-experimental design was employed. Sample: A structured questionnaire measured on a five-point Likert scale was administered to 150 active retail traders who participated in a simulated trading environment. Statistical techniques: Reliability analysis, descriptive statistics, Pearson correlation, multiple regression, independent samples t-test, and one-way ANOVA were computed using Microsoft Excel. Key findings: All constructs demonstrated strong internal consistency (Cronbach's alpha 0.84-0.93). Digital twin maturity, AI trust, and simulation quality significantly and positively predicted trading decision-making (R2 = 0.612, p < .001), while risk perception exerted a significant negative influence. No significant gender difference emerged, whereas trading experience produced significant group differences in decision quality. Practical implication: The validated scale offers financial institutions, FinTech developers, and brokerage platforms a diagnostic tool to benchmark digital twin maturity and design simulation-based trading environments that strengthen trader confidence and decision accuracy.
| 55 |
Author(s):
Author : Abhishek Channappa Borakanavar , co author : Dr. Vinod Krishna M U .
Page No : 1-6
|
A Study on the Impact of AI-Driven KYC Verification on Customer Onboarding Efficiency in Digital Payment Platforms
Abstract
Digital payment platforms have witnessed rapid growth over the past decade, and customer onboarding has emerged as a critical determinant of user acquisition and retention. Know Your Customer (KYC) verification, a mandatory regulatory requirement, has traditionally been a slow, manual, and error-prone process. The emergence of Artificial Intelligence (AI) technologies such as optical character recognition, facial recognition, liveness detection, and machine learning has transformed KYC verification into a fast, accurate, and secure process. This study examines the impact of AI-driven KYC verification on customer onboarding efficiency in digital payment platforms. The objective of the study is to analyze how AI-based verification affects onboarding speed, accuracy, security, and customer experience, while also identifying the associated challenges. The study adopts a descriptive research design based entirely on secondary data collected from academic journals, industry reports, and regulatory publications. The findings reveal that AI-driven KYC substantially reduces onboarding time, improves document verification accuracy, strengthens fraud detection, and enhances overall customer satisfaction, although concerns relating to data privacy and algorithmic bias remain. The study concludes that AI-driven KYC verification is a critical enabler of efficient and secure customer onboarding, and its continued refinement will play a decisive role in the future of digital identity verification within the financial services sector.
| 56 |
Author(s):
Gopal Chand, Deputy General Manager (Corporate Planning & Management Services),.
Page No : 1-6
|
IMPACT OF COVID-19 AND POST-PANDEMIC RECOVERY OF AIR TRANSPORT MOBILITY IN INDIAN AIRPORTS
Abstract
The COVID-19 pandemic produced the sharpest disruption in the history of commercial aviation and severely affected airport activity, passenger mobility, and aviation-linked tourism in India. This paper examines the trajectory of Indian air transport mobility from the collapse of operations during March-April 2020 to the post-pandemic recovery achieved by 2025-26, using airport traffic statistics reported for domestic and international aircraft movements and passenger volumes. The analysis shows that, relative to 2019-20, total traffic fell dramatically in 2020-21, with international passenger traffic declining by 84.8% and domestic passenger traffic by 61.7%, followed by a sustained recovery led initially by the domestic market. By 2022-23, domestic passenger traffic had recovered to 98.5% of the pre-pandemic benchmark, while total passenger traffic reached 96% of the 2019-20 level. In 2023-24, total passenger throughput surpassed the pre-COVID baseline, and in 2025-26 Indian airports handled 420.09 million passengers, with domestic and international traffic reaching 124% and 121%, respectively, of pre-pandemic levels. The paper argues that the Indian aviation sector has moved beyond recovery into a phase of structural expansion, supported by airport infrastructure growth, regulatory reforms, and airline fleet expansion. The findings contribute to ongoing discussion on transport resilience, post-crisis mobility restoration, and the transformation of emerging aviation markets.
The paper further examines the structural transformation now underway: the Air India–Vistara merger completed in November 2024, unprecedented aircraft order books exceeding 1,600 pending deliveries across IndiGo, Air India and Akasa, and an aviation infrastructure programme targeting 350–400 operational airports by 2047 (up from 164 in 2025), anchored by the commissioning of Navi Mumbai International Airport (December 2025) and Noida International Airport at Jewar (March 2026). The study concludes that Indian civil aviation has not merely recovered from the pandemic but has entered a structurally higher growth trajectory, supported by regulatory reform including the Bharatiya Vayuyan Adhiniyam 2024 and the Protection of Interest in Aircraft Objects Act 2025.
KEYWORDS: COVID-19, mobility, airport, India, recovery, aviation infrastructure, civil aviation; airport traffic
| 57 |
Author(s):
Vasu c.
Page No : 1-7
|
A Comprehensive Empirical Study on Employees Stress Management in Work Place at Jeyam Multi Speciality Hospital, Mallur, Salem
Abstract
In the modern, high-pressure healthcare sector, managing workplace stress among medical and administrative staff has emerged as a primary challenge linked directly to patient care quality, organizational efficiency, and clinical safety. This extensive empirical research investigates the multi-dimensional workplace stressors and coping mechanisms determining employee profiles within Jeyam Multi Speciality Hospital located at Mallur, Salem. Employing a strict descriptive research methodology, primary quantitative datasets were mobilized from a structured cross-sectional sample size of 120 full-time active hospital staff via non-probability convenience sampling channels across varying emergency and inpatient shifts.
| 58 |
Author(s):
Prabakaran V.
Page No : 1-7
|
A Study on the Effectiveness of Customer Relationship Management (CRM) Strategies on Customer Loyalty with Reference to ESAF Small Finance Bank at Salem
Abstract
In the highly competitive and dynamically evolving retail banking industry, Customer Relationship Management (CRM) has emerged as a core strategic mandate for ensuring long-term institutional sustainability, market penetration, and customer retention. This empirical study comprehensively. investigates the structural effectiveness of CRM strategies and their direct downstream impact on fostering long-term customer loyalty within ESAF Small Finance Bank situated across branches in Salem, Tamil Nadu. By leveraging a strict descriptive research methodology, primary quantitative responses were compiled utilizing a structured cross-sectional digital questionnaire administered to a sample size of exactly 120 active retail banking customers chosen via non-probability convenience sampling parameters. Advanced statistical tools, including percentage distribution testing, bivariate Pearson Chi-Square verification arrays, and Pearson product-moment correlation tracking loops, were used to examine the underlying behavioral datasets. The analytical outcomes reveal that key transactional metrics specifically digital banking interface ease, service delivery responsiveness, transparency in financial charges, and customized financial solutions exert an overwhelming and highly visible influence over individual customer loyalty vectors. The research concludes by formulating robust and integrated strategic CRM roadmap precisely customized to bolster operational banking efficiency, maximize touchpoint engagement,
| 59 |
Author(s):
Madhu SivaSelvi S.
Page No : 1-7
|
LUNA-X: A Hybrid Deep Learning Framework for Automated Lung Nodule Detection, Disease Classification, and Three-Dimensional Pulmonary Visualization from CT Images
Abstract
LUNA-X is a hybrid deep learning framework that couples a ResNet backbone with a self-attention Vision Transformer (ViT) to automate pulmonary nodule detection and classification in CT scans. The system improves clinical decision support by combining detailed local texture analysis with global spatial dependencies, achieving a 95.60% Dice Similarity Coefficient on the LIDC-IDRI dataset for identifying malignancies, pneumonia, and fibrosis.
| 60 |
Author(s):
Dr. Satyabrata Dash.
Page No : 1-7
|
Impact of Digitalization in Marketing Communication on Consumer Perception in Southern Odisha
Abstract
Digitalization of marketing is an approach used by organizations to brand and coordinate their communication efforts. In the latest couple of years, there has been a quantum growth in the number of internet users and the awareness towards the World Wide Web has increased in India. The various opportunities that it presents have been recognized and companies have started making plans to include internet, e-commerce and e-business in their scheme of things. This paper aims at exploring detailed information on major domains of the dissertation topic by reviewing past research, books and related articles. The study aims to understand the massive contribution of digital marketing in terms of consumer value; the researcher has used stratified probability sampling with a sample size of 200 numbers across Brahmapur, a major commercial city in Southern Odisha. Thus, the present study adopts descriptive research design and undertakes the survey method with questionnaire as a research instrument in order to collect primary data required in the research. The study finds that consumers rely upon more than one medium in order to enhance their brand related knowledge. The study also reveals that main reason for growing importance of Digital marketing is the increasing literacy about internet among people. The major benefits of Digital marketing are its capability of interaction between consumers and marketers followed by availability of wide range of information & ease of shopping. These benefits make Digital marketing superior than traditional marketing.
Key Words: Key words: digital marketing communication, consumer perception, DMC, IMC, digitalization.
| 61 |
Author(s):
Dr. Satyabrata Dash.
Page No : 1-7
|
An Empirical Study on Urban Materialism and Consumer Opportunity
Abstract
Materialism is the ‘devotion to material desires, to the neglect of spiritual matters; a way of life, based entirely upon material interests’ (The Oxford English Reference Dictionary, 1995). The materialism of the consumer represents the degree of attachment a consumer has to worldly possessions. The increased exposure of the consumer to occidental culture through media and in the backdrop of rapid economic growth and transforming societal norms in the urban parts of the country, an empirically oriented analysis was required to understand the details of the consumer materialistic tendencies. The tendency of materialism fetches both novelty and challenges for marketers in order to stabilize the demand and adding values to their products.
The study is confined only to Bhubaneswar city and attempted to investigate the significance of gender difference in terms of materialistic tendencies of urban consumers to ensure availability of a valid and reliable means of measuring individual differences. The sample size consisted of 96 respondents under the age of 30, residing in the city of Bhubaneswar. Materialism was measured using the Richins and Dawson (1992), 18 - item, 5 - point, agreement - disagreement scale and subjected to t - test in order to compare materialism among the two independent samples consisting of men and women. The study revealed that in two of the eighteen variables used to measure materialism, there exists a significant difference between the materialistic tendencies of men and women living in urban India.
Key-words: urban consumers, wellbeing, Novelty, Materialism, values, satisfaction.
| 62 |
Author(s):
Vikas Dubey.
Page No : 1-7
|
TRAC: AN INTELLIGENT DECOUPLED IAM FRAMEWORK FOR SECURE CLOUD INFRASTRUCTURE
Abstract
The foundation of cloud infrastructure is made up of cloud access control protocols. Industries have been taught to utilize role-based access to provide people permissions for decades. Despite decades of development, role explosions that lead to excessive user privileges continue to plague cloud identity and access management systems. The entire cloud infrastructure may be at risk due to this issue. This study presents an ultra-modern framework based on the fundamental ideas of decoupling and dynamic job assignment to address this problem. Instead of considering roles, the suggested framework bases user access on the tasks that users must do.
| 63 |
Author(s):
Ritu Singh.
Page No : 1-8
|
Reimagining Educational Assessment through Human–AI Collaborative Intelligence
Abstract
Abstract
The evolving educational landscape demands assessment systems that transcend traditional measures of academic achievement and actively contribute to improving learning outcomes. Despite sustained curriculum reforms, a significant proportion of learners continue to progress through school without mastering foundational competencies, resulting in cumulative learning deficits that impede higher-order thinking and problem-solving abilities. Diagnostic assessment, integrated with systematic remediation, offers a learner-centred approach that identifies individual learning gaps and enables targeted instructional interventions. This paper examines the conceptual underpinnings, design, implementation, and educational implications of a comprehensive diagnostic assessment framework developed through collaborative efforts involving curriculum experts, psychometricians, technology specialists, and educators. Drawing upon implementation experiences and contemporary educational research, the paper discusses how diagnostic assessment supports competency-based education, aligns with the vision of India's National Education Policy (NEP) 2020, and contributes to preparing learners for the demands of the twenty-first century. The paper also analyses implementation challenges, including teacher readiness, educational change management, and post-pandemic learning recovery, while proposing future directions for data-informed personalized learning.
Keywords: Diagnostic assessment, remediation, competency-based education, psychometrics, formative assessment, NEP 2020, future-ready learners.
| 64 |
Author(s):
Rupesh Chandrasen Londhe.
Page No : 1-8
|
Comparative Analysis of Indian Higher Education Rankings: IIRF, Outlook-I Care, and India Today–MDRA
Abstract
This study critically examines three major private, non governmental Indian higher education ranking frameworks: the Indian Institutional Ranking Framework (IIRF), Outlook–ICARE, and India Today–MDRA. As higher education in India expands, rankings have become essential tools for benchmarking institutional performance, guiding student choices, and shaping institutional strategy. Unlike government endorsed systems such as NIRF, these private rankings operate independently, blending objective data with perceptual surveys and emphasizing parameters such as pedagogy, innovation, employer perception, infrastructure, and governance.
| 65 |
Author(s):
Ranjitha M.
Page No : 1-8
|
Performance Evaluation of Top 5 Large-Cap Mutual Funds in India
Abstract
The acceptance of mutual funds among investors has grown consistently over the years among retail and institutional investors due to the benefits they offer portfolio diversification, professional fund management, and opportunities for long-term capital growth. Assessing the performance of these investment schemes is important for helping investors make well-informed financial decisions. This study evaluates and compares the investment efficiency of choosen large-cap mutual fund schemes operating in India by applying risk-adjusted performance measures. The analysis draws upon secondary information obtained from Net Asset Values (NAVs) associated with five chosen large-cap mutual fund schemes over one-year, three-year, and five-year periods. To measure performance, the study employs widely recognized indicators such as the Sharpe, Treynor indices, and Jensen's Alpha, which assess returns while considering the degree of investment risk. Research also examines key variables including portfolio returns, market returns, the risk-free rate, beta and standard deviation to ensure a detailed assessment of the choosen mutual fund schemes. By adopting a risk-adjusted approach, the study offers a systematic comparison of mutual fund performance over varying investment time frames and provides meaningful guidance for investors, researchers and financial professionals.
| 66 |
Author(s):
Narmadha K.
Page No : 1-8
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Predictive and Prescriptive Analytics for Customer Churn: A Machine Learning and Business Intelligence Framework for the Telecommunications Sector
Abstract
Customer churn remains one of the most significant threats to profitability in subscription-driven industries such as telecommunications. This paper presents an end-to-end analytics framework that combines descriptive business-intelligence dashboards with predictive machine learning and prescriptive decision support to address churn on a real-world telecommunications dataset comprising 7,043 customers and 21 attributes. Interactive dashboards were first developed to characterise churn across demographic, tenure, and service-contract dimensions, revealing an overall churn rate of 26.54%. To predict individual churn probability, three ensemble classifiers — Random Forest, XGBoost, and LightGBM — were trained on a class-balanced dataset produced using the Synthetic Minority Over-Sampling Technique (SMOTE), achieving a peak ROC-AUC of 83.5%. SHapley Additive exPlanations (SHAP) were applied to the best-performing model to render predictions interpretable, identifying contract type, monthly charges, and tenure as the dominant churn drivers. Building on these predictions, a rule-based prescriptive analytics engine automatically generated individualised retention offers for 1,585 high-risk customers, and a Customer Lifetime Value (CLTV) and risk-cohort segmentation prioritised 404 customers for immediate intervention. The results demonstrate that integrating descriptive, predictive, and prescriptive analytics into a single pipeline yields actionable, quantified retention strategies that outperform descriptive reporting alone.
| 67 |
Author(s):
Nagesh Soule.
Page No : 1-8
|
Automation of Reporting Tasks and Its Impact on Bandwidth for Customer Experience Managers
Abstract
- Organizations increasingly automate reporting to reduce the manual effort of collecting, verifying, and circulating operational data, a burden that falls heavily on Customer Experience (CX) managers in Software-as-a-Service firms. This study examines whether employee perceptions of automation across five dimensions - Productivity/Time Savings, Ease of Use, Speed & Timeliness, Quality & Error Reduction, and Trust & Satisfaction - predict their overall perception that automation improves operational efficiency and releases bandwidth for higher-value work. Data were collected from 84 working professionals using a 20-item, five-point Likert-scale questionnaire administered through Google Forms. The instrument demonstrated excellent reliability (Cronbach's alpha = 0.918). Descriptive statistics, Pearson correlation, and multiple linear regression were used for analysis, with regression assumptions (normality, multicollinearity, homoscedasticity, autocorrelation) verified. All five constructs correlated positively and significantly with the overall outcome (r = 0.498-0.646), and together explained 50.3% of its variance (R² = 0.503). Speed & Timeliness (β = 0.526), Productivity/Time Savings (β = 0.450), and Ease of Use (β = 0.413) emerged as the strongest predictors. Findings suggest that organizations seeking to maximize perceived bandwidth release should prioritize automating high-frequency, time-consuming tasks and keep interfaces simple, rather than investing primarily in trust-building initiatives.
| 68 |
Author(s):
1.J S Vikas 2. Dr Vinod Krishna M U.
Page No : 1-8
|
Collaborative FinTech Models: Banks, NBFCs, and Startups in Driving Financial Inclusion in India
Abstract
Background: Financial inclusion remains a central policy objective in India, and the emergence of FinTech has reshaped how banks, non-banking financial companies (NBFCs), and technology startups collaborate to extend financial services to underserved populations. Objective: This study examines collaborative FinTech models involving banks, NBFCs, and startups, and evaluates their contribution to financial inclusion in India. Methodology: A descriptive research design with a quantitative approach was adopted. Data were collected from 150 respondents using a structured questionnaire and a convenience sampling technique, and were analysed using MS Excel and SPSS through frequency distributions, percentage analysis, and chi-square testing. Findings: The results indicate that collaborative FinTech arrangements, supported by infrastructure such as UPI, Aadhaar-based verification, and the Account Aggregator framework, significantly improve access to credit, digital payments, and formal banking services, particularly for semi-urban and rural populations. Trust in digital platforms and awareness of government schemes were found to be key determinants of adoption. Conclusion: The study concludes that collaboration among banks, NBFCs, and startups produces a more effective financial inclusion ecosystem than any single actor could achieve independently, though challenges around trust, digital literacy, and regulatory coordination persist and require continued policy attention.
Keywords: FinTech, Financial Inclusion, NBFC, Digital Lending, UPI, Collaborative Ecosystem
| 69 |
Author(s):
Dr. Shally Gupta.
Page No : 1-9
|
“Boundary Regularity of Optimal Transport Maps in the Monge Problem on Riemannian Manifolds”
Abstract
We study the boundary regularity of the optimal transport maps in the Monge problem on Riemannian manifolds. For the quadratic cost induced by the Riemannian distance, we show that the optimal map is Hölder continuous up to the boundary when the source domain has a strictly convex boundary and the ambient manifold has bounded sectional curvature. Our argument is based on a combination of c-convex analysis and barrier constructions adapted to the Fermi coordinates in a neighbourhood of the boundary. We also give explicit counterexamples showing that regularity may fail when the convexity of the boundary is violated. These results generalise classical boundary regularity results of the Euclidean theory to curved geometries and clarify the geometric conditions required for continuity of optimal maps. We discuss applications to geometric PDE's on manifolds with boundary, and further open questions.
| 70 |
Author(s):
Lennon Raj.
Page No : 1-9
|
Sculpt Studio: An AI-Powered Web Platform for Personalized Fitness Coaching, Scheduling, and Conversational Support
Abstract
Maintaining a consistent and effective fitness routine is difficult for most people without access to
personalized coaching. In many settings, users rely on generic workout plans found online or on rigid gym
schedules, which do not adapt to individual goals, fitness levels, or availability. While personal trainers can
provide tailored guidance, this option is often expensive and difficult to schedule consistently. This paper
presents Sculpt Studio, a practical AI-based web platform designed to make personalized fitness coaching
accessible and easy to use. The system analyses user profile data, activity history, and stated goals to
generate adaptive workout recommendations. Instead of focusing only on recommendation accuracy, the
design also considers how easily the guidance can be understood and acted upon by everyday users. The
platform combines three core capabilities: an AI-driven workout recommendation engine, an integrated
booking and scheduling module for sessions and classes, and a conversational AI chatbot that answers
fitness-related queries and assists with navigation. Initial testing shows that recommendations remain
consistent across repeated use and that the scheduling and chatbot features reduce the effort required to
plan and maintain a routine. Overall, the study highlights that combining AI-driven personalization with a
user-friendly design approach can make fitness coaching more accessible, consistent, and scalable.
| 71 |
Author(s):
Lakshan A.
Page No : 1-9
|
A Moving-Average Crossover Analysis of the Nifty 50 (2010–2022)
Abstract
Abstract - This study provides a comprehensive, long-horizon empirical evaluation of the 50-day/200-day simple moving-average (SMA) crossover — popularly known as the “Golden Cross” and “Death Cross” — on the Nifty 50, India's benchmark equity index, using 3,077 daily observations from January 2010 to June 2022. Beyond documenting the timing of eleven Golden Cross and eleven Death Cross events, this paper constructs and backtests a full systematic trading strategy built on the crossover rule, benchmarks it against a passive buy-and-hold approach, decomposes performance by market regime and sub-period, tests sensitivity to four alternative moving-average pairings, quantifies transaction-cost drag, and examines volatility dynamics surrounding signal events. The mechanical crossover strategy is found to underperform buy-and-hold substantially on a raw-return basis (4.01% vs. 9.90% CAGR) while achieving materially lower realised volatility (12.54% vs. 22.52% annualised) — a trade-off that nets out to a lower risk-adjusted (Sharpe) ratio for the trend-following approach in this sample. We place these findings in the context of efficient-markets and technical analysis literature, explain the rationale for why the signal always has a time lag and thus cannot be considered a true leading indicator, and describe the implications of the findings both in practice and in the context of academic research using a developed-market signal and data applied to an emerging market that experiences greater price volatility.
Key Words: technical analysis, moving average crossover, Golden Cross, Death Cross, Nifty 50, trend following, emerging markets, market efficiency, backtesting.
| 72 |
Author(s):
Hamsa K.
Page No : 1-9
|
Innovation-Oriented Employer Branding and Its Influence on Employee Retention: A Study of ABB India Limited
Abstract
Organizations operating in technology-intensive sectors increasingly rely on employer branding to attract and hold on to skilled talent, and innovation has become one of the central themes shaping how employees judge a workplace. This study examines how four dimensions of innovation-oriented employer branding, exposure to technological learning, employee involvement in innovation, the Organization's sustainability image, and an innovation-driven work environment, relate to employee retention at ABB India Limited. A descriptive, quantitative design was used, with a structured questionnaire administered through Google Forms to 190 employees of the company. Responses were examined using mean scores, Pearson correlation, and multiple linear regression in Microsoft Excel. Employees rated the Organization favorably across all four dimensions, with the innovation-driven work environment receiving the strongest agreement. All four dimensions showed moderate positive correlations with retention, and together they accounted for close to a third of the variation in retention scores, with the innovation-driven work environment standing out as the only statistically significant predictor. The findings suggest that retention in engineering-led Organizations depends less on any single branding initiative and more on whether employees experience day-to-day work as genuinely innovative, a distinction with practical implications for how such firms design their people strategies.
| 73 |
Author(s):
A SANTHOSHI.
Page No : 1-9
|
Human-AI collaboration in supply chain decision-making
Abstract
This study examines the role of Human–AI collaboration in improving decision-making effectiveness in supply chain management. The objective of the study is to understand how artificial intelligence capabilities and human expertise work together to support better decisions in areas such as demand forecasting, inventory management, logistics, and risk management. A quantitative research approach was adopted, and primary data was collected through a structured questionnaire from 120 respondents familiar with supply chain activities. The collected data was analyzed using percentage analysis, reliability testing, descriptive statistics, correlation analysis, regression analysis, t-test, and ANOVA. The findings indicate that Human–AI collaboration, trust in AI, transparency, and organizational support have a positive influence on decision-making effectiveness. The results highlight that AI improves data analysis and prediction capabilities, while human involvement provides experience, judgment, and contextual understanding. This study emphasizes that successful supply chain decisions can be achieved through effective collaboration between humans and AI rather than replacing human decision-makers.
| 74 |
Author(s):
Diwakar H G.
Page No : 1-9
|
Awareness of Machine Learning for FinTech App Engagement: A Study of User Trust and Perception
Abstract
Background: The rapid embedding of machine learning (ML) within FinTech applications — powering investment recommendations, copy-trading signals, and automated savings nudges — has outpaced users' recognition of when and how these algorithms operate. Objective: This study measures the level of ML awareness, understanding, and trust among FinTech app users and identifies the demographic and usage-based factors that shape user engagement with ML-powered financial features. Methodology: A descriptive, quantitative research design was adopted. Primary data were collected from 101 FinTech app users through a structured Google Form questionnaire comprising demographic items and nine Likert-scale statements. Data were analysed using Python (pandas, SciPy, scikit-learn) through descriptive statistics, independent-samples t-tests, one-way ANOVA, Pearson correlation, Markov Chain modelling of trust-state transitions, and linear-programming-based prescriptive optimisation.
| 75 |
Author(s):
Lakshan A.
Page No : 1-10
|
Rupee-Cost Averaging versus Lump-Sum Investing in a Structurally Bullish Emerging Market: Evidence from the Nifty 50 (2012–2022)
Abstract
Abstract - Systematic Investment Plans (SIPs) are marketed widely to Indian retail investors as a disciplined, risk-reducing alternative to lump-sum investment through periodic fixed-amount purchases, commonly termed rupee-cost averaging. This paper presents a holistic empirical test of that claim using Nifty 50 index data from January 2010 to June 2022. Beyond a single-window comparison, the analysis constructs 91 overlapping five-year and 31 overlapping ten-year rolling windows across the full sample to assess how consistently lump-sum investing outperforms SIP across different starting points; computes annualised volatility, maximum drawdown, and Sharpe ratios for both strategies; stress-tests outcomes using a crash-timed worst-case start date; and decomposes results across two five-year sub-periods. Across the primary January 2012–June 2022 window, lump-sum investing produced a terminal value of ₹49.3 lakh against ₹25.0 lakh for an equivalent-capital SIP, and lump sum outperformed SIP in 97.8% of five-year and 100% of ten-year rolling windows tested. Despite this consistent terminal-value advantage, SIP’s money-weighted annualised return (XIRR) exceeded lump sum’s CAGR in the median rolling window, a result reconciled by the differing amounts of time capital is at risk under each strategy. We discuss why SIP nonetheless retains behavioural and cash-flow-management value for the large share of retail investors who do not have a lump sum available at the outset, and outline the market conditions under which SIP’s relative performance would improve.
Key Words: systematic investment plan, rupee-cost averaging, dollar-cost averaging, lump-sum investing, Nifty 50, retail investing, emerging markets, behavioural finance, rolling-window analysis.
| 76 |
Author(s):
Chinmayi G M.
Page No : 1-10
|
A Systematic Literature Review on Non-Performing Assets in the Indian Banking Sector
Abstract
The Indian banking sector plays a crucial role in the economic development and financial stability of the country. However, the rising amount of NPAs has posed a severe challenge to the financial performance, liquidity, portfolio quality, and profitability of banks. The current research presents a comprehensive review of the existing literature on Non-Performing Assets in the Indian banking industry. The review study is based on secondary information obtained from published research articles in national and international journals. The selected studies include many elements of NPAs such as their origin, drivers, effect on bank profitability, public vs commercial banks, recovery process, legislative changes and risk monitoring techniques.
| 77 |
Author(s):
Shoba HN.
Page No : 1-10
|
The Impact of Artificial Intelligence-Based Recruitment Tools on Talent Acquisition Efficiency and Candidate Experience
Abstract
Artificial Intelligence (AI) is transforming recruitment by automating activities such as candidate sourcing, resume screening, candidate matching, interview scheduling, and communication. The present study, titled “The Impact of Artificial Intelligence-Based Recruitment Tools on Talent Acquisition Efficiency and Candidate Experience,” examines how AI-enabled recruitment technologies influence recruitment effectiveness and candidates’ overall hiring experience.
A quantitative research approach was adopted, with primary data collected through a structured questionnaire from 100 respondents. The study focuses on three major variables: AI-Based Recruitment Tools, Talent Acquisition Efficiency, and Candidate Experience. The collected data was analyzed using descriptive statistics, reliability analysis, correlation, regression, and hypothesis testing.
The findings indicate that AI-based recruitment tools have a positive and significant impact on Talent Acquisition Efficiency and Candidate Experience. AI contributes to faster recruitment, reduced administrative effort, improved recruiter productivity, timely communication, and streamlined hiring processes. The study concludes that combining AI-based technologies with appropriate human involvement can create a more efficient, responsive, and candidate-centric recruitment process.
| 78 |
Author(s):
G.Vigneshwaran.
Page No : 1-10
|
Digital Transformation in Cooperative Banking: An Empirical Analysis of Digital Banking Services in the Tamil Nadu State Cooperative Bank and District Central Cooperative Banks
Abstract
Digital transformation has reshaped Cooperative baking by improving the accessibility and efficiency of financial services. This study examines the digital banking performance of the Tamil Nadu State Cooperative Bank and District Central Cooperative Bank using secondary data for February- March 2026. The findings show that total digital transactions increased from 13.83 million to 14.51 million, recording an overall growth of 4.92%, with UPI emerging as the dominant payment platform. The study concludes that digital banking has strengthened customer convenience, operational efficiency, and financial inclusion in the Tamil Nadu cooperative banking sector.
| 79 |
Author(s):
Naman Kulshrestha, Dr Gopal Chand.
Page No : 1-11
|
Airport Service Quality Assessment: A study of ASQ parameters at major AAI airports (2024-2025)
Abstract
This study offers a data-driven analysis of Airport Service Quality (ASQ) performance at 16 major airports operated by the Airports Authority of India (AAI) during the 2024-2025 period. The research draws on eight quarters of standardized ACI World survey data. Covers 32 service parameters and includes 120 pooled cross-sectional observations. The study isolates the true structural drivers of Overall Passenger Satisfaction by applying correlation analysis, ordinary least squares regression, standard deviation-based volatility modelling, and gap analysis.
The analysis reveals that staff interactions dominate as predictors of Overall Satisfaction. Courtesy and Helpfulness of Airport Staff (r = 0.742), Ambience (r = 0.695), and Health Safety (r = 0.708) together explain 54.4% of the variance in a parsimonious OLS model. Meanwhile, Wi-Fi Service Quality and Availability of Charging Stations present a different picture. Their base scores were low in 2024, 3.91 and 4.32, respectively, and they emerged as the highest-volatility, highest-improvement parameters across both years. This points to a pattern of systemic underinvestment in digital infrastructure that is now being corrected, though unevenly. Srinagar (SXR) stood out as a statistically significant underperformer relative to the system mean (t = 4.42, p = 0.022), with a structural deficit concentrated in commercial amenities, entertainment, and digital connectivity. Indore (IDR) and Goa (GOI) anchor the performance frontier and provide replicable benchmarks.
A behavioural economics framework distinguishes Hygiene Factors from Delighter parameters. Deficiencies in Hygiene Factors produce disproportionate dissatisfaction, while Delighters generate satisfaction uplift when exceeded. Applied strategically, this framework supports a targeted capital expenditure prioritization matrix. The matrix recommends an infrastructure investment sequence designed to maximize returns on Overall Satisfaction. By the ACI World elasticity estimate, each percentage point of satisfaction gained produces a 1.5x multiplier effect on non-aeronautical revenue.
| 80 |
Author(s):
Dhanushya.
Page No : 1-11
|
A STUDY ON IMPACT OF SOCIAL MARKETING ON CONSUMER BUYING BEHAVIOUR INSTANT CHAPTHI PRODUCES TOWARDS ATVE FOODS, AT SALEM.
Abstract
Social media marketing has emerged as one of the most influential promotional tools in the modern business environment. The rapid growth of social networking platforms such as Facebook, Instagram, YouTube, and WhatsApp has significantly changed the way organizations communicate with consumers and promote their products. Businesses increasingly utilize social media marketing to create brand awareness, engage customers, and influence purchasing decisions. In the food industry, social media plays a vital role in shaping consumer perceptions and encouraging product adoption, particularly for convenience food products that cater to the fast-paced lifestyle of modern consumers.
| 81 |
Author(s):
Deepashree P Kulkarni.
Page No : 1-11
|
Impact of Digital Automation on Operational Efficiency: The Mediating Role of IT Investment Intensity and the Moderating Role of Firm Size
Abstract
Abstract-
Digital automation - comprising artificial intelligence, robotic process automation, enterprise resource planning, cloud computing, and Internet of Things - is changing how businesses manage production, delivery of services, and decision-making processes. In this research paper, a theoretical framework and a testable model have been developed to investigate the effect of firm-level Digital Automation Index (DAI) based on content analysis of corporate disclosures on Operational Efficiency (OE) of Indian listed companies involved in automation-intensive industries such as industrial technology, manufacturing, engineering, automobiles, and electronics. Based on the Resource-Based View and Dynamic Capabilities perspective, the testable model asserts that IT Investment Intensity (ITI) acts as a mediator between DAI and OE, and Firm Size (FS) moderates their relationship. A panel data approach for the years 2020-2025 involving 30-50 listed firms (180-300 observations) has been suggested, using secondary data sources such as annual reports, sustainability reports, and financial statements. This paper describes how Digital Automation Index will be calculated, operationalize all the constructs, provides five hypotheses, and presents a nine-phase statistical analysis method including descriptive statistics, panel diagnostics, fixed or random-effects regressions, mediation and moderation analysis, and robustness tests.Key Words: digital automation, operational efficiency, IT investment intensity, firm size, Industry 4.0, panel data, Indian listed companies.
| 82 |
Author(s):
Manohar R.
Page No : 1-11
|
GenAI HRM Recruitment Article
Abstract
The development of generative artificial intelligence (GenAI) has seen a swift transition from a novel HR pilot study to an operational application, with a dramatic increase in GenAI adoption in human resources management in less than one year. This paper will review how the adoption of GenAI is transforming the quality of hiring and decisions made by managers in the field of human resource management (HRM). In order to do this, I will use evidence from industry benchmarking reports, market research on the HR-technology landscape, as well as scientific literature on algorithmic hiring, technology acceptance, and human-algorithm collaboration. It becomes evident from the evidence below that the application of GenAI provides quantifiable benefits in terms of efficiency improvements throughout the hiring process, including decreases in time-to-hire, cost-per-hire, and resume screening times, as well as increases in quality-of-hire and retention among early hires if used as part of a properly governed workflow. At the same time, the reviewed evidence also uncovers several consistent and even growing risks, including disparate impact created through algorithmic bias in the training datasets and algorithm itself, as well as human-in-the-loop studies suggesting high propensity to replicate the recommendations of biased algorithms. In this paper, I refer to the Technology Acceptance Model, the Unified Theory of Acceptance and Use of Technology, and Structuration Theory as explanations for not only what causes adoption but also when the use of GenAI improves versus diminishes the quality of decision-making. The conclusion from this analysis is that the net impact of GenAI on recruitment quality depends on governance decisions made by people rather than the impact of the technology per se.
| 83 |
Author(s):
Kowsalya S.
Page No : 1-11
|
MRCL-FECG: Morphology–Rhythm Contrastive Representation Learning for Label-Efficient Fetal Arrhythmia Classification
Abstract
Fetal electrocardiogram (FECG) analysis is a valuable method for evaluating fetal cardiac activity and diagnosing abnormal cardiac rhythms in pregnancy. Reliable fetal arrhythmia classification models are, however, hampered by the lack of labeled FECG data because obtaining such data is challenging and time-consuming. Existing deep learning (DL) approaches predominantly rely on supervised learning and may suffer from overfitting and limited generalisation when trained on small datasets. Moreover, fetal arrhythmia is also reflected in a morphological variation of the FECG waveform and temporal abnormalities of cardiac rhythm. Most of the classification methods used, however, only learn these features from a small amount of labelled data, and they fail to explicitly make use of the relationship between the morphology of the waveforms and the rhythm pattern to which they belong. This article introduces a Morphology–Rhythm Contrastive Representation Learning framework (MRCL-FECG) for label-efficient fetal arrhythmia classification to overcome these drawbacks. Pre-processing and segmentation of FECG are performed first to get fixed-length windows, in which cardiac rhythm information is represented by the corresponding RR-interval and Fetal Heart Rate (FHR) sequences. The FECG waveforms are used to extract the morphological representation using lightweight one-dimensional convolutional neural networks (1D-CNNs) and the temporal rhythm using the RR-interval and FHR sequence. The resulting morphology and rhythm representations are projected in a shared feature space that encourages agreement among complementary representations derived from the same cardiac segment, and differentiates the representation of different segments, while being unrelated to one another. The learned representations are then fine-tuned for fetal arrhythmia classification with a few-shot learning method with a small number of labelled fetal data.
| 84 |
Author(s):
Dasari Manasa,shreevamshi.
Page No : 1-11
|
Whole-Person Leadership Spirit, Soul, and Body
Abstract
Abstract—The rapid integration of artificial intelligence and
digital technologies into organizational environments has transformed the nature of leadership, creating a growing need for
approaches that emphasize uniquely human capabilities. This
paper introduces the Whole-Person Leadership Framework, a
holistic model that conceptualizes effective leadership as the
integration of three interconnected dimensions: spirit, soul, and
body. The spirit dimension represents purpose, vision, ethical
awareness, and the pursuit of meaning; the soul dimension
encompasses identity, values, emotional intelligence, empathy,
and relational competence; while the body dimension focuses
on disciplined action, presence, resilience, and the practical
execution of leadership responsibilities.
Drawing upon contemporary leadership theories, humancentered management principles, and emerging discussions on
leadership in the age of artificial intelligence, this conceptual
study examines how the integration of these dimensions contributes to ethical decision-making, adaptability, employee engagement, and organizational effectiveness. As intelligent systems
increasingly perform analytical and routine tasks, leadership
success depends more heavily on human qualities such as compassion, moral judgment, self-awareness, and authentic relationships. The proposed framework offers a comprehensive perspective for leadership development by aligning personal transformation with organizational performance. The study contributes to
leadership literature by providing a holistic and human-centered
approach that enables leaders to navigate technological change
while fostering trust, resilience, and sustainable organizational
growth.
Index Terms—Whole-Person Leadership, Human-Centered
Leadership, Spiritual Leadership, Emotional Intelligence, Artificial Intelligence, Leadership Development, Organizational Behavior, Ethical Leadership
| 85 |
Author(s):
Sunnaina R.
Page No : 1-11
|
AI-Driven Economic Decision-Making in Smart Cities: Balancing Efficiency, Sustainability, and Inclusive Governance
Abstract
The rapid advancement of Artificial Intelligence (AI) is transforming the way cities plan, manage, and deliver public services, giving rise to the development of smart cities that are more efficient, sustainable, and citizen-centric. By integrating AI with technologies such as the Internet of Things (IoT), big data analytics, cloud computing, digital twins, and intelligent decision-support systems, governments can analyze large volumes of real-time data to make informed economic decisions, optimize resource allocation, improve service delivery, and enhance urban governance. AI-driven solutions are increasingly being applied in transportation, healthcare, energy management, waste management, water conservation, environmental monitoring, disaster response, and digital public administration. These applications contribute to reducing operational costs, improving infrastructure planning, enhancing public service quality, and promoting long-term economic growth while addressing complex urban challenges such as rapid population growth, climate change, traffic congestion, pollution, and limited public resources.
Despite these benefits, the growing adoption of AI in smart cities raises significant governance and ethical concerns. The extensive use of data-driven technologies creates challenges related to data privacy, cybersecurity, algorithmic bias, transparency, accountability, digital inequality, and public trust. In many developing countries, additional barriers such as inadequate digital infrastructure, limited technical expertise, regulatory gaps, and unequal access to digital technologies further complicate AI implementation. Therefore, the successful integration of AI into urban governance requires robust policy frameworks that balance technological innovation with ethical responsibility, social inclusion, environmental sustainability, and citizen participation.
This chapter examines the role of AI in economic decision-making within smart cities by exploring how technological efficiency can be balanced with sustainability and inclusive governance. It highlights the importance of responsible AI adoption in supporting evidence-based policymaking, improving resource utilization, strengthening financial and environmental management, and enhancing the overall quality of urban life. The chapter also investigates how transparent governance, stakeholder collaboration, digital literacy, and citizen engagement contribute to building trust in AI-enabled public administration.
The study adopts a qualitative research approach based on a comprehensive review of peer-reviewed literature, government reports, policy documents, international publications, and selected smart city case studies. Comparative insights are drawn from globally recognized smart cities such as Singapore, Barcelona, Amsterdam, and Dubai, along with selected Indian Smart Cities developed under the Smart Cities Mission. These examples illustrate successful AI applications in urban governance while highlighting implementation challenges and best practices relevant to both developed and developing economies.
Furthermore, the chapter proposes a conceptual framework linking AI adoption, digital governance, efficient resource allocation, sustainable development, citizen trust, and economic decision-making. The framework demonstrates that responsible AI governance can improve decision quality, strengthen public service delivery, promote environmental sustainability, and support inclusive urban development. Based on the findings, the chapter offers practical recommendations for governments, policymakers, urban planners, and technology developers to strengthen AI governance, protect citizen data, improve digital infrastructure, encourage ethical AI practices, and foster public participation in urban decision-making. By integrating perspectives from economics, artificial intelligence, public administration, sustainability, and digital governance, this chapter contributes to both academic research and policy practice, emphasizing that the future of smart cities depends not only on technological advancement but also on transparent governance, ethical decision-making, and inclusive, sustainable urban development.
Keywords: Artificial Intelligence (AI), Smart Cities, Economic Decision-Making, Digital Governance, Sustainable Development, Resource Allocation, AI Ethics.
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Author(s):
Deepashree P Kulkarni.
Page No : 1-12
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AI IN FORECASTING GEOPOLITICAL RISK AND ITS IMPACT ON ESG AND CORPORATE FINANCIAL STRATEGY EVIDENCE FROM THE INDIAN ENERGY SECTOR
Abstract
Abstract – Geopolitical instability has become one of the determinants of business financial decisions; thus, businesses use artificial intelligence (AI) to forecast their risks. This paper aims to find out if the usage of greenwashing, being a proxy for the credibility gap between ESG positioning and actual risk exposure of the firm, has an effect on stock price volatility at 20 energy firms in India, listed on the exchange for the 2019-2023 period (100 firm-year observations). The research investigates the moderating role of board independence and the mediating role of the market-to-book ratio. The sample companies' financial performance is predicted using Artificial Intelligence techniques, including Linear Regression, Random Forest, Gradient Boosting and XGBoost. Out-of-sample predictions are provided for 2024-2026. Panel data tests include panel diagnostics, correlation, variance inflation factor (VIF) test, fixed/random effects regression, Hausman test, Sobel/bootstrap mediation test and moderated regression. It can be seen from the above-mentioned results that the ratio of market to book is a significant predictor of volatility and significantly mediates the effect of greenwashing on volatility, and at the same time board independence is a positive predictor of volatility; the Random Forest model demonstrates the best out-of-sample predictive stability and market to book, as well as firm size, is found to be the most significant predictor of volatility.
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Author(s):
Achhuth N.
Page No : 1-12
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Funding Innovation for Future-Ready Healthcare Systems
Abstract
Healthcare has a lot of issues that need to be fixed. Some people believe that smart
capital platforms can help with these healthcare problems. They use a combination of
ways to pay for things like centers that use artificial intelligence to make decisions
and technology to verify things. These smart capital platforms can really help with
healthcare issues. They have centers that use artificial intelligence to make decisions
and technology to verify things in healthcare. Healthcare is not working well. Experts say there are many reasons for this. For
instance old computer systems are still being used. New rules are taking a time to be
made. People who live in cities are getting healthcare than people who live in rural
areas. Healthcare is not working well because people who live in cities are getting
care. There are also rules, about keeping personal information of healthcare private. Healthcare has problems and one of them is that people who live in rural areas are not
getting good healthcare. These problems are not because the technology is bad. We already have things that
work. For example Singapore’s Health Hub, Apollo TeleHealth is making it easier for
people to get healthcare. There are also computer programs like blockchain oracles
that help figure out what works and what does not work in healthcare. (Singapore
Ministry of Health, 2024; Apollo Hospitals Enterprise Ltd., 2025; VillageReach, 2025)
Healthcare and smart capital platforms can really help people. Studies have shown
that these ideas are good and the numbers show they make things more fair for
everyone. Healthcare is important. These strategies can make a big difference, in
healthcare. Now, leaders need to roll out strong governance systems and ramp up
rural programs to build digital health solutions that last(Bain & Company, 2026;
Rajasthan Health Investment Fund, 2024).
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Author(s):
Priya Ranjan.
Page No : 1-13
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Green Banking 5.0: An AI-Driven ESG and Climate Risk Framework for Sustainable Banking in India
Abstract
The transition toward sustainable finance has become a strategic priority for the global banking industry due to increasing climate change risks, stricter environmental regulations, and the growing demand for responsible investment practices. In India, banks are progressively adopting digital technologies and environmentally sustainable financial services; however, the integration of Artificial Intelligence (AI), Environmental, Social and Governance (ESG) analytics, climate risk assessment, and carbon-aware lending into a unified decision-making framework remains limited (1). Existing studies primarily focus on digital banking and paperless operations, with comparatively less attention given to AI-enabled sustainability intelligence and predictive environmental risk management (2).
The present study proposes a Green Banking 5.0 framework that integrates AI-driven analytics, ESG evaluation, climate-risk assessment, and carbon-conscious lending to support sustainable financial decision-making in the Indian banking sector. The study employs a descriptive and analytical research design using both primary data collected from 120 banking customers through a structured questionnaire and secondary data obtained from published literature, banking reports, government publications, and sustainability documents (3). Statistical analysis includes percentage analysis, comparative interpretation, and graphical representation to evaluate customer awareness, adoption of green banking services, and perceptions regarding sustainable banking initiatives (4).
The findings indicate that customers increasingly prefer digital and environmentally friendly banking services because of their convenience, operational efficiency, and reduced environmental impact. Furthermore, AI-assisted ESG assessment and climate-risk analytics have significant potential to improve credit appraisal, strengthen risk management, and encourage sustainable investment decisions while supporting India's long-term climate and net-zero objectives (5). Despite these opportunities, several challenges remain, including inadequate ESG data availability, cybersecurity concerns, technological infrastructure limitations, and the absence of standardized sustainability reporting frameworks (6).
The proposed Green Banking 5.0 framework provides a comprehensive conceptual model that combines digital transformation with sustainable finance and offers practical guidance for banks, regulators, and policymakers seeking to strengthen environmentally responsible financial systems. The study contributes to the emerging literature on AI-enabled sustainable banking by presenting an integrated approach for future-ready banking aligned with Industry 5.0 principles and global sustainability goals (7).
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Author(s):
G S RANGASWAMY.
Page No : 1-13
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Artificial Intelligence Adoption and Corporate Financial Decision-Making: Examining the Influence of Digital Maturity on Investment, Budgeting, Risk Management, and Strategic Planning in Indian Companies
Abstract
Artificial Intelligence (AI), covering areas like machine learning, natural-language processing, predictive analytics, and increasingly agentic decision-support systems, is gradually becoming an integral part of the finance function of Indian corporations, going far beyond back-office automation to become an integral part of the decision-making process involved in investment analysis, budgeting and forecasting, risk management, and financial planning, to name a few. The proposed paper will develop a conceptual framework and a survey-based model, testing the impact of AI Adoption (AIA) on the Quality of Corporate Financial Decision-Making (FDMQ) among Indian companies. Building on theories from the Resource-Based View, Dynamic Capabilities, and Technology-Organization-Environment models, the proposed research will suggest that the mediating role in the relationship between AIA and FDMQ is played by AI-Enabled Analytics Capability (AEAC), while the moderating role in this relationship belongs to the firm's Digital Maturity (DM). The novelty of this research project compared to the existing literature lies in its intention to collect primary data, using the structured questionnaire to survey the CFOs, controllers, FP&A managers, risk managers, and financial analysts at Indian corporations, suggesting 250-400 survey respondents. In this research paper, an outline of the development and validation process of the four scales - AI Adoption, Analytics Capability, Digital Maturity, and Financial Decision Making Quality, and formulation of five hypotheses have been done along with a nine-step analysis plan using PLS-SEM methodology for the purpose of testing mediated and moderated mediation.
| 90 |
Author(s):
GAHAN K NAIK.
Page No : 1-14
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AI-Driven Predictive Material Planning in Construction Projects: A Framework for Reducing Project Delays
Abstract
Material shortages, late deliveries and mis-timed procurement remain among the most persistent causes of schedule overrun in building and infrastructure projects. Traditional material planning still depends on static lead times taken from vendor quotations, bills of quantity that are frozen early, and uniform inventory buffers that treat all materials equally despite different risk levels. This study examines how Artificial Intelligence (AI), Machine Learning (ML), and Explainable AI (XAI) can be integrated into a predictive material planning framework to reduce project delays. Using an empirical illustration based on 1,850 procurement records across twelve material categories, the proposed framework demonstrates significant improvements in lead-time prediction and risk-based inventory allocation. The findings show that AI-driven predictive planning can substantially reduce project delays while improving procurement decision-making and supply chain efficiency.
| 91 |
Author(s):
Pranav P.
Page No : 1-15
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Energy Shocks, Sovereign Capital Structure, and Impacts on Blue Economy Investment”, “Managing Sovereign Debt under Energy Shocks: Fiscal Stability and Policy Intervention
Abstract
Green bonds have emerged as an important financial instrument for mobilising capital towards environmentally sustainable projects. In India, the growing focus on renewable energy, clean transportation, sustainable infrastructure, energy efficiency, and climate-related initiatives has increased interest in green finance and green bonds. Green bonds provide investors with an opportunity to earn financial returns while contributing to projects that generate environmental benefits.
This study examines the role of green bonds in promoting sustainable investment in the Indian financial market. It focuses on factors such as investor awareness, perceived environmental benefits, risk perception, expected returns, trust, transparency, financial knowledge, and investor perception towards green financial instruments. The study follows a quantitative research approach using primary data collected from investors through a structured questionnaire. The collected data can be analysed using descriptive statistics, reliability analysis, correlation, regression, and other appropriate statistical techniques. The study aims to understand whether awareness and perception of green bonds influence investors' willingness to consider sustainable investment options. The findings are expected to be useful for investors, financial institutions, policymakers, regulators, and issuers in understanding the opportunities and challenges associated with the development of India's green bond market.
Keywords: Green Bonds, Sustainable Investment, Green Finance, Indian Financial Market, ESG, Investor Awareness, Environmental Sustainability, Financial Markets
| 92 |
Author(s):
Ayesha Khanam.
Page No : 1-16
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Influence of Perceived Organizational Support on Employee Engagement, Job Satisfaction, and Employee Retention Across Different Generations Working in Indian Organizations Today
Abstract
This study examines the influence of Perceived Organizational Support (POS) on employee engagement, job satisfaction, and employee retention across different generations working in Indian organizations. The research aims to understand how employees' perceptions of organizational support shape their workplace attitudes and retention intentions while identifying generational differences in these relationships. A quantitative research design is proposed, using a structured questionnaire to collect primary data from employees across diverse industries in India. The collected data will be analyzed using descriptive statistics, reliability analysis, correlation, regression, and Structural Equation Modeling (SEM) to examine the direct and indirect relationships among the study variables. The findings are expected to demonstrate that higher perceived organizational support significantly enhances employee engagement and job satisfaction, which in turn strengthen employee retention. The study also anticipates that generational differences influence the magnitude of these relationships, reflecting varying workplace expectations and motivational factors among employees. The research contributes to human resource management literature by providing empirical evidence on the strategic role of organizational support in building an engaged and committed multigenerational workforce and offers practical implications for designing inclusive HR policies that improve employee retention and organizational effectiveness in the Indian context.
| 93 |
Author(s):
Ayesha Khanam.
Page No : 1-16
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Influence of Flexible Work Policies on Employee Retention, Job Satisfaction, and Organizational Commitment Among Employees in Indian Organizations Today
Abstract
The proposed study attempts to explore the impact of Flexible Work Policies (FWP) on Job Satisfaction, Organizational Commitment and Retention of the employees working in the organizations of India, with Employee Engagement being the mediating variable. With the rise of flexible working arrangements like telecommuting, hybrid work schedule, flexitime and compressed work week adopted by Indian organizations post the COVID-19 pandemic, it is imperative to find out the effect of such policies on the attitudes of the employees and retention intention. A quantitative and cross sectional research method will be employed in the study, with a structured questionnaire survey carried out among employees from different sectors in India. The data obtained will be subjected to statistical analysis using descriptive statistics, reliability test, correlation, multiple regression and SEM to analyze the relationship among the variables considered in the study. The results of the study are anticipated to prove that the adoption of flexible work policies by the organizations increases the engagement of employees that result in better job satisfaction, commitment and retention intention of the employees, whereas the flexible work policies directly affect the three outcomes. This paper is based on Social Exchange Theory and Work/Family Border Theory and illustrates the concept of organizational flexibility being paid back in terms of positive attitudes of employees and lower intentions to leave the organization. It will be hoped that the current study will add value to the existing knowledge about human resource management by providing a comprehensive framework of relations between flexible working policies and several factors at once and give practical recommendations for Indian firms.
| 94 |
Author(s):
R K Shreya.
Page No : 1-17
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AI-Driven Employee Performance Evaluation: Building a Fair and Transparent HR Analytics System
Abstract
Artificial intelligence (AI), machine learning, and explainable AI (XAI) techniques are transforming employee performance evaluation from subjective, periodic appraisals into data-driven, continuous, and transparent systems. While individual technologies offer significant benefits in predictive accuracy and efficiency, their combined potential for achieving both fairness and transparency in HR analytics remains underexplored, particularly when integrated with strategic organizational considerations. This chapter critically examines the convergence of AI-driven predictive modeling, algorithmic bias mitigation, and explainable AI approaches, evaluated through theoretical frameworks, literature synthesis, and empirical illustration using the IBM HR Analytics Employee Attrition & Performance dataset. Strategic analysis frameworks (PESTEL, Porter’s Five Forces, and SWOT) are applied to assess macro- and micro-environmental factors influencing responsible adoption. Findings demonstrate that integrated models can achieve perfect predictive accuracy while eliminating observed demographic bias and providing clear, interpretable explanations via SHAP, thereby addressing long-standing limitations of traditional performance systems. The chapter offers theoretical contributions by unifying bias management, explain ability, and strategic HR perspectives into a cohesive framework and provides practical guidance for organizations seeking to implement fair, transparent, and ethically responsible AI-driven performance evaluation systems in the digital era.
| 95 |
Author(s):
Priya Ranjan.
Page No : 1-18
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Integrated Project Delivery Readiness and Schedule Risk Index (IPDRSRI): A DPMS and Primavera P6-Based Predictive Framework for Engineering, Procurement, Construction, Commissioning, and Contract Closure in IISCO Steel Plant Projects
Abstract
Since large EPC projects for steel almost always experience delays, cost overruns, design blockages, material supply issues, and contractual problems, oversight becomes crucial. Today, most projects monitor progress by percentages, milestones, and historical timeline reviews, which look backward quite effectively but are unaware of upcoming issues such as delayed drawings, shortages of materials, on-site congestion, unaddressed blockers, or conflicts in agreement management. Now, imagine combining Oracle Primavera P6 schedules and DPMS readings into one consolidated metric. Here, all aspects—engineering progress, procurement status, construction, pre-startup, money management, contract conditions, schedule variances, critical path, post-legal activities, and post-completion verification—get integrated into a single indicator representing a project's readiness instead of many separate, growing reports. Instead of relying on speculation, teams see genuine cross-functional synchronization, as each part of the operation remains compartmentalized. Since timing impacts the bottom line, tracking the critical path adds leverage where it is needed most. Added to that is the advantage of a DPMS feeding real-time updates, which then drive predictive behavior. This becomes a physical reality rather than an abstract model driven by multiple interactions simultaneously. Signs of weakness are generally observed relatively late in the design phase, but not in this instance. Here, the data stream originates from DPMS readings for the entirety of the work—the cost structure, blocked areas, engineering designs, and delivery information. If delays occur, they are instantly noted. Rather than waiting until the deadline passes, critical alerts are transmitted in advance of key dates. Easily fractured timelines are clearly identified. All transactional documents, including contracts and invoices, are fed into the model, along with time spent during inspections and engineering task status. Team communications are also processed to track such things as changes in the longest path and delays in shipments and dispatch. Any signs of risk are immediately highlighted. This allows managers to clearly see areas that require attention before the negative impact grows. The use case for the model clearly applies to the broader industrial complex that involves steel production. That can encompass areas where molten metal flows under the rigors of temperatures that rework materials. It extends to facilities that convert molten steel into a solidified form through various steps. Similarly, coke plant operations using coal as input have a direct application. Facilities involved with burning limestone or dolomite may also find its structure suitable for their processes. The framework easily extends to facilities managing significant tonnages of ore, scrap, or flux, as well as their associated auxiliary works.
| 96 |
Author(s):
Keerthana B, Co author - Dr. Shreevamshi N.
Page No : 1-25
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Intercultural Human Resource Management Strategies for Organizations
Abstract
The increasing globalization of business has transformed workforce diversity into a strategic organizational asset, requiring organizations to adopt effective Intercultural Human Resource Management (IHRM) strategies to manage employees from diverse cultural backgrounds. This study investigates the influence of Intercultural Human Resource Management Strategies (independent variable) on Organizational Performance (dependent variable) in culturally diverse workplaces. The research specifically examines the role of cross-cultural training, cultural intelligence, diversity, equity and inclusion (DEI) initiatives, culturally inclusive recruitment and selection, performance management, employee engagement, and digital HR practices in enhancing employee satisfaction, organizational commitment, teamwork, workforce productivity, employee retention, leadership effectiveness, and sustainable organizational performance.
A quantitative research approach with a descriptive research design was employed for the study. Primary data were collected from 250 employees and HR professionals working in multicultural organizations using a structured questionnaire based on a five-point Likert scale. A simple random sampling technique was adopted to select the respondents. The collected data were analysed using IBM SPSS Statistics, employing descriptive statistical techniques such as frequency, percentage, mean, and standard deviation, along with inferential statistical methods including Pearson's correlation and multiple linear regression analysis. The reliability of the research instrument was assessed using Cronbach's Alpha, which indicated satisfactory internal consistency.
The findings reveal that Intercultural Human Resource Management strategies have a significant positive influence on organizational performance by improving employee satisfaction, communication, collaboration, leadership effectiveness, organizational commitment, innovation, and workforce productivity. The study further identifies communication barriers, cultural misunderstandings, and resistance to change as key challenges affecting the implementation of intercultural HRM practices. The research concludes that organizations that integrate culturally inclusive HR policies, cross-cultural learning, diversity management, and culturally intelligent leadership into their strategic HR systems are better positioned to achieve sustainable organizational performance and long-term competitive advantage. The study contributes to the existing body of knowledge by providing empirical evidence on the strategic importance of Intercultural Human Resource Management in contemporary multicultural organizations.
Keywords: Intercultural Human Resource Management, Organizational Performance, Cultural Intelligence, Cross-Cultural Training, Diversity, Equity and Inclusion, Employee Satisfaction, Employee Engagement, Organizational Commitment, Multicultural Workforce.
| 97 |
Author(s):
Keerthana B, MBA Student, Co Author - Dr. Shreevamshi N, Assistant Professor.
Page No : 1-51
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“Eco-Friendly Practices and Green Policies for Sustainable Human Resource Management”
Abstract
The increasing emphasis on environmental sustainability has transformed Human Resource Management (HRM) from a traditional administrative function into a strategic driver of sustainable organizational development. Green Human Resource Management (Green HRM) has emerged as an important approach that integrates environmentally responsible practices into human resource functions, enabling organizations to reduce their environmental impact while enhancing employee engagement, organizational performance, and long-term sustainability. Despite the growing adoption of Green HRM, limited empirical research has examined its influence on Sustainable Human Resource Management (Sustainable HRM), particularly within India's digital marketing and service sector. Addressing this gap, the present study investigates the relationship between Green HRM practices and Sustainable HRM, using Green Human Resource Management as the independent variable and Sustainable Human Resource Management as the dependent variable.
The study aimed to evaluate employees' perceptions of Green HRM practices, examine their influence on Sustainable HRM, and determine the extent to which environmentally responsible HR initiatives contribute to organizational sustainability. A quantitative research design was adopted, and primary data were collected through a structured Google Forms questionnaire comprising demographic and Likert-scale questions. The study employed a convenience sampling technique, and data were collected from 120 respondents, including 50 employees of Innovkraft Inc. and 70 employees from other digital marketing agencies and related organizations. Secondary data were obtained from peer-reviewed journals, books, conference proceedings, and reputable online sources to establish the theoretical foundation of the study. The collected data were analysed using IBM SPSS Statistics Version 29, employing descriptive statistics, Cronbach's Alpha to assess instrument reliability, One-Sample t-test for hypothesis testing, Pearson Correlation Analysis, and Simple Linear Regression Analysis.
The findings revealed that respondents held positive perceptions of Green HRM practices, including green recruitment, paperless HR processes, environmental training, employee participation in sustainability initiatives, green performance management, and green reward systems. The One-Sample t-test confirmed that these practices were perceived as statistically significant contributors to sustainable workplace management. Pearson Correlation Analysis indicated a strong positive relationship between Green HRM and Sustainable HRM (r = 0.728, p < 0.001), while the regression analysis demonstrated that Green HRM explained 53% of the variance (R² = 0.530) in Sustainable HRM, confirming its significant predictive influence on sustainable human resource outcomes. These findings suggest that organizations implementing comprehensive Green HRM practices are more likely to strengthen employee engagement, environmental responsibility, organizational commitment, and overall sustainability performance.
The study contributes to the existing literature by providing empirical evidence on the strategic role of Green HRM in promoting Sustainable HRM within the digital marketing industry. It extends the application of sustainability-oriented HR practices to service-based organizations and supports the Resource-Based View (RBV), Ability–Motivation–Opportunity (AMO) Theory, and Employee Engagement Theory, demonstrating that environmentally responsible HR practices create long-term organizational value. The study concludes that integrating Green HRM into organizational strategy is essential for fostering sustainable workplace practices, enhancing organizational effectiveness, and achieving long-term competitive advantage. The findings provide valuable implications for researchers, HR professionals, organizational leaders, and policymakers seeking to strengthen sustainability through human resource management.
Keywords: Green Human Resource Management, Sustainable Human Resource Management, Green HRM, Sustainable HRM, Environmental Sustainability, Employee Engagement, Green Recruitment, Sustainable Workplace, Organizational Sustainability, Digital Marketing Industry, Human Resource Management, SPSS.
