International Journal of AI Electronics and Nexus Energy
Post-COVID Digital Adoption and Omnichannel Customer Experience in Pharmaceutical Marketing: A Systematic Review of Healthcare Professional Enga
The rise of the coronavirus pandemic has been a major game-changer for the pharmaceutical industry, which has seen them turn away from relying on old-fashioned face-to-face interactions with physicians and embrace digital touchpoints that can be integrated into a unified omnichannel marketing strategy. Previously, healthcare professionals (HCPs) had to go through in-person scientific detailing and physical in-person relationship-building, which shifted rapidly and effectively to virtual communications, including webinar platforms, remote detailing systems, mobile platforms, customer relationship management (CRM) tools, and artificial intelligence (AI)-powered personalized engagement tools. This systematic analysis explores digital adoption's effects on omnichannel customer clinical experience and its possible implications for healthcare professional engagement in the context post-COVID. Highly related to the period under study (2020-2025), peer-reviewed literature was doggedly collected and evaluated in compliance with the PRISMA screening guidelines from Scopus, PubMed, Google Scholar, Web of Science, and other platforms. The findings indicated that the omnichannel model positively impacted the accessibility, personalization, continuity of communication, and results of building relationships among healthcare professionals. It’s at the same moment a story about the permanent challenges of digital fatigue, data management challenges, technology interoperability challenges, and evolving regulatory mandates. The future of pharmaceutical marketing is shaped by various aspects such as Artificial Intelligence-powered engagement frameworks, predictive behavioral analysis, and hybrid engagement environments.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
HYBRID LIGHTWEIGHT AI MODELS FOR ACCURATE IMAGE FORGERY DETECTION
The rapid growth of digital image editing tools and social media platforms has significantly increased the spread of manipulated and forged images, creating serious concerns regarding information authenticity and security. Image forgery detection has therefore become an important research area in digital forensics. This paper presents an efficient approach for image forgery detection based on the fusion of lightweight deep learning models. The proposed system combines multiple lightweight convolutional neural network architectures to improve detection accuracy while maintaining low computational complexity and faster processing speed. The fusion strategy enables the extraction of complementary spatial and texture-based features from suspicious images, allowing the system to effectively identify tampered regions and manipulated content. The framework is designed to detect common image forgery techniques such as copy-move forgery, splicing, and image retouching. Data preprocessing and augmentation techniques are applied to improve model robustness and generalization performance. Experimental evaluation demonstrates that the fusionbased lightweight model achieves high detection accuracy with reduced memory consumption compared to traditional deep learning approaches. The proposed method is suitable for real-time and resourceconstrained applications, including mobile devices and cloud-based forensic systems. Overall, the system provides a reliable, scalable, and computationally efficient solution for modern digital image forgery detection challenges.
Image Forgery Detection, Deep Learning, Lightweight CNN, Image Splicing, Copy-Move Forgery, Digital Forensics, Feature Fusion, Image Manipulation Detection, Computer Vision, Artificial Intelligence.
The Multi-Source Content Summarizer is an intelligent system designed to extract, process, and condense information from multiple content formats such as YouTube videos, PDF documents, web articles, and raw text. In today’s digital era, the rapid growth of information across various platforms makes it difficult for users to consume and understand large volumes of data efficiently. This system addresses the problem by integrating automated content extraction techniques with an AI-based summarization engine to generate concise, meaningful, and structured summaries. The system converts different input formats into a unified text representation and produces outputs including titles, key points, paragraph summaries, and keywords. It also provides customizable summary length, enabling users to control the level of detail in the output. A chatbot feature is included to enhance user interaction by answering queries related to the summarized content. Additionally, the system stores summary history in a database, allowing users to access, manage, and download previously generated summaries. The application is built using lightweight technologies, ensuring fast processing and ease of deployment. The modular architecture supports scalability and future enhancements such as multilingual support and real-time summarization. This system is particularly useful for students, researchers, and professionals who require quick insights from extensive information sources. Overall, the project demonstrates an efficient and practical approach to multi-format content summarization using modern Natural Language Processing and Artificial Intelligence techniques.
1Mr.T. JAYARAJAN, 2V. HARSHA VARDHAN,3 P. MEGHANA, 4 MD. MUNEEB
EMAIL SPAM FILTERING SYSTEM USING MACHINE LEARNING
Email spam filtering has become an essential component of modern communication systems due to the rapid increase in unsolicited and malicious emails. This project presents a machine learningbased email spam filtering system designed to automatically classify emails as spam or legitimate (ham). The system utilizes a labeled dataset of emails and applies preprocessing techniques such as tokenization, stop-word removal, and normalization to prepare textual data for analysis. Feature extraction methods like Bag of Words and TF-IDF are used to convert text into numerical representations. Machine learning algorithms including Naive Bayes, Support Vector Machine, and Logistic Regression are employed to train the model and identify patterns associated with spam content. The trained model is then used to classify incoming emails in real-time, ensuring efficient and accurate filtering. Compared to traditional rulebased systems, the proposed approach adapts to new spam patterns and improves performance over time through continuous learning. The system architecture includes modules for email input, preprocessing, feature extraction, model training, prediction, and output classification. Experimental evaluation demonstrates improved accuracy, precision, and recall in detecting spam emails while minimizing false positives. This system enhances user experience by reducing unwanted emails, protecting users from phishing attacks, and maintaining a clean inbox. The project highlights the effectiveness of machine learning techniques in email security and provides a scalable solution for real-world applications.
1Mr. JAHANGEER PASHA, 2M. ANUVIKA, 3MOHAMMAD ISMAIL, 4 P.SHANKAR
Image steganography is an advanced data hiding technique used to securely transmit information by embedding it within digital images in a manner that is imperceptible to the human eye. In the proposed system, secret text messages are hidden inside images using the Least Significant Bit (LSB) technique, which modifies pixel values with minimal distortion while maintaining image quality. Unlike traditional cryptographic methods that expose the existence of encrypted data, steganography conceals both the content and its presence, making it a more secure communication approach. The system is developed using Python with libraries such as Tkinter and PIL, providing a user-friendly graphical interface for encoding and decoding operations. The encoding process converts the secret message into binary form and embeds it into image pixels, while the decoding process extracts the hidden information accurately. The system ensures efficient performance, low computational complexity, and high data security. However, challenges such as limited embedding capacity and vulnerability to image processing attacks still exist. This project demonstrates a balance between simplicity, efficiency, and security, making it suitable for academic and small-scale applications. The results confirm that the hidden data can be retrieved without noticeable image degradation. Overall, image steganography provides an effective solution for covert communication and data protection in modern digital environments.
1Mrs. K. KAVYA, 2M. VIDHYA, 3T. SHIVA SAI, 4MD. UZAIR AGA
REAL TIME BEHAVIOURAL BIOMETRIC FRAUD DETECTION
The rapid growth of digital platforms and online transactions has significantly increased the risk of fraud and unauthorized access, making traditional authentication methods insufficient for modern security needs. Conventional systems rely heavily on static credentials such as passwords and onetime passwords, which are vulnerable to phishing, credential theft, and social engineering attacks. To overcome these limitations, this work presents a real-time behavioural biometric fraud detection system that continuously monitors user interactions throughout a session. The system captures behavioural features such as keystroke dynamics, typing speed, mouse movements, and session activity, which are unique to each user. These features are processed and analyzed using machine learning techniques, particularly unsupervised learning models like Isolation Forest, to identify deviations from normal behaviour patterns. Unlike traditional systems, the proposed approach enables continuous authentication without interrupting user experience. When abnormal behaviour is detected, the system triggers alerts or additional verification mechanisms to prevent fraudulent access. The system is designed to be scalable, efficient, and adaptable to changing user behaviour over time. It can be integrated into banking, e-commerce, and enterprise applications to enhance security and reduce financial losses. Experimental results indicate that the system achieves high detection accuracy while maintaining real-time performance. Overall, the proposed solution provides a reliable and intelligent approach for fraud detection by combining behavioural biometrics with machine learning techniques.
1Mrs.O. SHRAVANI, 2 P.SRI VAISHNAVI, 3M. RAMU, 4 SAI PATHI
The Event Planning System is a web-based application developed to simplify and automate the process of organizing and managing events efficiently. Traditional event management methods rely heavily on manual coordination, which often leads to inefficiencies, communication gaps, and errors. This system addresses these challenges by providing a centralized platform where users can create, customize, and manage events such as weddings, corporate meetings, and social gatherings. It integrates key functionalities including venue selection, guest management, vendor coordination, scheduling, and secure payment processing. The system also enables realtime communication between users and vendors, enhancing coordination and decision-making. Administrative features allow monitoring of system activities, approval of bookings, and maintenance of data integrity. The backend is developed using Django, ensuring secure processing and efficient handling of requests, while MySQL is used for structured data storage. The frontend utilizes HTML, CSS, and JavaScript to provide an interactive user interface. The system also supports real-time notifications and report generation, improving transparency and planning efficiency. By automating repetitive tasks and providing a user-friendly interface, the system reduces manual workload, minimizes errors, and enhances overall user experience. Furthermore, it offers scalability and flexibility for future enhancements such as artificial intelligence-based recommendations and mobile integration. Overall, the Event Planning System provides a reliable, efficient, and costeffective solution for modern event management needs
1Mrs. A. JYOSHNA, 2V. GEETHA SRI, 3 P. NIKHILESH, 4M. MAHENDAR
The HR Resume Screener and Interview Agent is an intelligent recruitment automation system designed to enhance the efficiency and accuracy of hiring processes in modern organizations. Traditional recruitment involves manual resume screening and preliminary interviews, which are time-consuming, prone to human bias, and inefficient when handling large volumes of applications. The proposed system leverages Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) to automate resume analysis, candidate shortlisting, and interview evaluation. The system extracts key information such as skills, education, experience, and certifications from resumes and compares them with job requirements to generate a matching score. Based on this score, candidates are ranked and shortlisted automatically. Furthermore, the integrated AI interview agent conducts preliminary interviews by asking predefined or dynamically generated questions, analyzing candidate responses, and evaluating communication and technical skills. The system stores candidate data, generates performance reports, and provides insights to HR professionals for better decision-making. This approach reduces manual workload, improves consistency in evaluation, minimizes bias, and accelerates the hiring cycle. Additionally, the system supports scalability and can handle large datasets efficiently, making it suitable for organizations of all sizes. Overall, the proposed system provides a smart, reliable, and automated solution for modern recruitment challenges, ensuring faster, fairer, and more accurate hiring outcomes.
1Mrs.G. ARCHANA, 2M. GANESH REDDY, 3M. RANJITH KUMAR, 4L. SAIMANIKANTA
ONLINE STUDENTS BUS TRANSPORT GENERATOR WITH LIVE TRACKER
The Online Students Bus Transport Generator with Live Tracker is a web-based system designed to modernize student transportation management by replacing traditional manual processes with a digital, automated solution. Existing systems rely heavily on paperwork, manual verification, and lack real-time monitoring, leading to inefficiencies, delays, and poor communication. This project integrates an online bus pass generation system with live GPS tracking to improve operational efficiency and student safety. Students can register online, submit applications, upload documents, and receive a digital bus pass embedded with a QR code for authentication and attendance tracking. The administrative module enables efficient management of student records, route allocation, and approval processes through a centralized dashboard. The system also incorporates GPS and IoT technologies to provide real-time bus tracking, allowing students and parents to monitor bus location, estimated arrival time, and route updates. Notifications and alerts are generated for delays, route changes, and emergencies, ensuring better communication. The system reduces paperwork, minimizes errors, and enhances transparency in transport management. Additionally, it ensures scalability and user accessibility through webbased interfaces. Overall, the system offers a reliable, efficient, and smart transportation management solution that aligns with modern digital transformation trends in educational institutions.
1Mr. JAHANGEER PASHA, 2B. SNEHITHA, 3Y. YUVRAJ, 4D. NARENDRA
TIMETABLE AUTO GENERATOR FOR COLLEGES CLASH -FREE TIMETABLES
The Timetable Auto Generator for Colleges is an intelligent system designed to automatically create clash-free academic schedules by addressing the complexities involved in manual timetable preparation. Traditional scheduling methods are time-consuming, error-prone, and inefficient due to multiple constraints such as faculty availability, classroom allocation, subject requirements, and institutional policies. The proposed system utilizes advanced computational techniques including genetic algorithms, constraint satisfaction methods, and optimization strategies to generate efficient and conflict-free timetables. It accepts structured input data such as faculty details, course information, time slots, and room capacities, and processes these inputs to produce optimized schedules that eliminate overlaps and maximize resource utilization. The system ensures that no faculty member, student group, or classroom is assigned multiple tasks at the same time while maintaining workload balance and institutional guidelines. It also supports dynamic updates, enabling easy modification when constraints change, such as faculty unavailability or classroom adjustments. The architecture follows a modular approach with input, processing, and output layers, ensuring scalability and flexibility. By reducing scheduling time from days to minutes, the system enhances administrative efficiency and minimizes human intervention. Furthermore, it improves academic planning, ensures fairness in workload distribution, and provides user-friendly outputs in formats like tables, dashboards, and reports. Overall, the timetable auto generator serves as a reliable and efficient solution for modern educational institutions seeking automated scheduling systems.
1Mrs.K. KAVYA, 2B. TEJASRI, 3 J. PRASHANTH, 4A. SHIVANI
HEALTHCARE APPOINTMENT & MONITORING SYSTEM
The rapid evolution of digital healthcare systems has created a demand for efficient, scalable, and intelligent solutions to manage patient care and hospital operations. The Healthcare Appointment and Monitoring System is designed as a web-based platform that integrates appointment scheduling with real-time patient health monitoring to enhance healthcare delivery. Traditional healthcare systems often rely on manual processes, leading to inefficiencies such as long waiting times, scheduling conflicts, and lack of continuous patient monitoring. This system addresses these challenges by providing an automated and centralized solution that enables patients to book appointments online, doctors to manage schedules efficiently, and administrators to oversee system operations seamlessly. The system incorporates a monitoring module that records vital health parameters such as heart rate, temperature, and blood pressure, enabling early detection of abnormalities. A threshold-based alert mechanism ensures that healthcare providers are notified instantly in case of critical conditions, shifting the approach from reactive to proactive care. Developed using a threetier architecture with technologies such as React.js, Node.js, and MongoDB, the system ensures scalability, security, and high performance. Rigorous testing confirms reliability, accuracy, and compliance with healthcare data standards. Overall, the proposed system improves operational efficiency, enhances patient safety, and provides a strong foundation for future advancements such as AI-based prediction and telemedicine integration.
1Mrs.K. DURGA BHAVANI, 2 PANDURAJU BALRAJ,3 KOPPULA SHIVA SAI, 4ARIGELA SAI VARDHAN
The Live Login Anomaly Detector is an intelligent cybersecurity solution designed to enhance authentication mechanisms by detecting suspicious login activities in real time. Traditional authentication systems that rely only on usernames and passwords are increasingly vulnerable to cyber threats such as phishing, brute-force attacks, and credential stuffing. To address these challenges, the proposed system incorporates behavioral analysis by monitoring login parameters such as IP address, device information, login time, location, and frequency of attempts. By analyzing these features, the system assigns a dynamic risk score to each login attempt, categorizing it as low, medium, or high risk. Based on this classification, appropriate actions are taken, including granting access, requesting additional verification such as multifactor authentication, or blocking the login attempt. The system utilizes machine learning techniques to improve detection accuracy over time by learning from historical user behavior patterns. It follows a modular and scalable architecture that supports real-time processing and seamless integration with existing applications. Technologies such as Python, Flask, and database systems are used to ensure efficient performance and secure data handling. Additionally, features like token-based authentication and rate limiting enhance overall system security. The solution is suitable for deployment across various domains, including banking, e-commerce, and enterprise systems, where data security is critical. Overall, the system provides a proactive, adaptive, and efficient approach to authentication security while maintaining a smooth user experience.
1Mrs. A. JYOSHNA, 2G. ABHILASH REDDY, 3B. VIVEK, 4B. SRAVAN YADAV
AUTOMATIC RESULT DISTRIBUTION VIA VERFIED EMAIL
The rapid advancement of digital technologies has transformed the way educational institutions manage academic data and communicate with students. However, traditional result distribution systems such as notice boards and centralized portals remain inefficient, time-consuming, and prone to errors. These systems often lack personalization, real-time communication, and data privacy. To address these challenges, the proposed system titled “Automatic Result Distribution via Verified Email” introduces a fully automated, secure, and scalable solution for processing and distributing student results. The system is developed using a Python-based Flask web framework, which enables efficient handling of backend operations and user interactions. It allows administrators to upload student data in CSV format, which is validated and processed using data handling techniques to compute total scores, averages, and grades accurately. The system further enhances communication by generating personalized HTML-based emails containing detailed results and feedback for each student. A key feature of the system is email verification, ensuring that results are sent only to valid and authorized recipients, thereby maintaining confidentiality and security. The integration of SMTP protocols ensures reliable and secure email delivery. Additionally, logging mechanisms provide transparency and system monitoring capabilities. Overall, the system reduces manual effort, eliminates errors, improves efficiency, and ensures timely communication. It serves as a modern solution for academic institutions seeking automation, accuracy, and secure result distribution.
1Mrs.G. ARCHANA, 2 K. SRAVYA, 3K. RAGHAVA REDDY, 4D. TARUNCHAND, 5C. SHIVASAI
The rapid transformation of agricultural markets has highlighted the inefficiencies in traditional supply chains dominated by intermediaries, resulting in reduced profits for farmers and increased prices for consumers. The proposed system, Harvest Hub, introduces a digital platform that directly connects farmers with end consumers, thereby eliminating middlemen and ensuring fair pricing mechanisms. The system leverages modern web and mobile technologies to provide features such as product listing, price comparison, market insights, and real-time communication between stakeholders. Farmers can upload product details, set prices, and monitor demand, while consumers gain access to fresh agricultural produce with transparency in sourcing. The platform also incorporates modules for inventory management, order processing, and secure payment handling to streamline the overall transaction lifecycle. Additionally, government authorities can monitor agricultural activities, regulate minimum pricing, and maintain records for better policy implementation. By integrating technologies such as React.js, Spring Boot, and MongoDB, the system ensures scalability, reliability, and efficient data handling. This approach not only enhances farmer income but also improves consumer satisfaction by providing quality products at reasonable prices. Furthermore, the system supports sustainable agricultural practices by encouraging local trade and reducing logistics overhead. The proposed solution addresses critical challenges in agricultural marketing and promotes a more equitable and transparent ecosystem for all stakeholders involved
1Mrs.A. JYOSHNA, 2K. DIVYA, 3A. RAKESH, 4G. AKHIL, 5A.SAI CHARAN
The Effect of Online Sales Growth on Profitability Structure at Nykaa
The rapid proliferation of ecommerce has fundamentally transformed the revenue architecture and cost structure of Indian consumer retail companies. Nykaa (FSN E-Commerce Ventures Ltd), India's leading omnichannel beauty and personal care retailer, presents a compelling case study of the complex relationship between online sales growth and profitability evolution. This study examines the effect of online sales growth on Nykaa's profitability structure over five financial years (FY 2019– 20 to FY 2023–24), analysing revenue composition, gross margin dynamics, operating leverage, advertising intensity, logistics cost efficiency, and net profit margin trajectory. Secondary data sourced from Nykaa's annual reports, SEBI filings, and equity research reports form the analytical foundation. Correlation and regression analyses confirm that online revenue growth is a statistically significant positive predictor of EBITDA margin improvement (β = 0.482, p < 0.01), while advertising spend and logistics costs as a proportion of revenue exert significant negative effects. Gross profit margins have remained stable in the 46–49% range despite scaling, indicating consistent category mix and supplier pricing power. The study finds that Nykaa has achieved a meaningful improvement in EBITDA margin (from – 0.4% in FY 2021–22 to 3.2% in FY 2023– 24) driven by operating leverage on fixed costs, brand maturity reducing customer acquisition cost, and technology-driven fulfilment efficiency.
Nykaa, online sales growth, profitability, e-commerce, gross margin, EBITDA, operating leverage, beauty retail, FSN E-Commerce, digital commerce India
B Veera Reddy1 , Akula Anish Kumar2 , Muthyala Chinna Raju3 , Kandlakoyya Srikanth4 , Dr. B. Narmada5
A STUDY ON RISK AVERSION STRATEGIES FOLLOWED BY HDFC BANK DURING INVESTMENT DECISIONS
Risk aversion is a fundamental principle governing investment strategy in commercial banking. HDFC Bank, India's largest private sector bank by market capitalisation, employs a multi-layered risk management framework to balance return maximisation with capital preservation across its investment portfolio. This paper studies the risk aversion strategies practised by HDFC Bank during investment decisions, encompassing asset allocation discipline, credit risk mitigation, interest rate risk management, liquidity buffer maintenance, and regulatory compliance-driven portfolio constraints. Primary data was gathered through structured questionnaires administered to 120 respondents comprising HDFC Bank investment officers, treasury professionals, and institutional clients. Secondary data was drawn from HDFC Bank Annual Reports (2021–2024), RBI circulars, SEBI guidelines, and peer-reviewed academic literature. Analysis reveals that HDFC Bank systematically prioritises government securities and high-rated corporate bonds, applies strict duration limits to manage interest rate exposure, and maintains a Liquidity Coverage Ratio (LCR) consistently above the regulatory minimum. The study identifies key risk aversion mechanisms including Value-at-Risk (VaR) limits, stress testing, concentration limits, and dynamic hedging strategies. Findings confirm that structured risk aversion significantly reduces portfolio volatility while sustaining competitive risk-adjusted returns.
Risk aversion, HDFC Bank, investment decisions, portfolio management, interest rate risk, credit risk, Value-at-Risk, treasury management, liquidity risk, asset allocation
Chikkondra Nagaraju¹, Gaddam Saibaba², Gunukuntla Mallika³, Dhundra Upendar⁴, Ms. R. Swapna⁵
A STUDY ON DIVIDEND POLICY AND SHAREHOLDER WEALTH: TATA CONSULTANCY SERVICES (TCS)
Dividend policy is one of the most debated topics in corporate finance, influencing how firms distribute earnings and how investors perceive shareholder value creation. Tata Consultancy Services (TCS), India’s largest IT services company and a consistently profitable blue-chip entity, offers a compelling case for studying the relationship between dividend policy and shareholder wealth. This paper examines TCS’s dividend behaviour over a ten-year period from 2014–15 to 2023–24, analysing trends in Dividend Per Share (DPS), Earnings Per Share (EPS), Dividend Payout Ratio, and their relationship with shareholder wealth indicators including stock price, market capitalisation, dividend yield, and Total Shareholder Return (TSR). Secondary data sourced from TCS Annual Reports, BSE/NSE disclosures, and SEBI filings are analysed using correlation analysis, multiple regression modelling, and descriptive statistics. Findings reveal a statistically significant positive correlation between TCS’s dividend per share and its stock price (r = 0.964, p < 0.01), confirming that progressive dividend announcements act as positive signals to the market. The study concludes that TCS’s consistent and growing dividend policy has been a major contributor to long-term shareholder wealth creation, and offers strategic recommendations for dividend management in large-cap Indian IT firms.
Dividend Policy, Shareholder Wealth, TCS, Dividend Per Share, Payout Ratio, Earnings Per Share, Market Capitalisation, Total Shareholder Return, Signalling Theory, Dividend Discount Model.
Rasala Srividya¹, Pavude Manmath², Erram Sai Tharun³, Halli Umakanth⁴, Madishetty Sushmitha⁵, Dr. Y. Azith⁶
ANALYZING THE IMPACT OF CORPORATE EARNINGS ANNOUNCEMENTS ON STOCK PRICE AT EMBRIZON
Corporateearningsannouncements represent one of the most significant scheduled information events in equity markets, triggering rapid price discovery as investors interpret reported earnings relative to prior market expectations. The degree and direction of stock price reaction to earnings announcements has been extensively studied in developed markets; however, evidence from emerging mid-cap companies listed on Indian exchanges remains comparatively sparse. This study analyses the impact of quarterly earnings announcements on the stock price of Embrizon—a mid-cap technology and services company listed on the National Stock Exchange of India— across eight quarterly announcement events spanning Q1 FY 2021–22 to Q4 FY 2022– 23. Employing the event study methodology with a market model estimation window of 120 trading days, the study computes abnormal returns in a ±5 day event window surrounding each earnings announcement date and tests for statistical significance using the standardised abnormal return test. Regression analysis is additionally employed to quantify the relationship between the magnitude of earnings surprise (actual EPS minus consensus estimate, scaled by stock price) and the cumulative abnormal return in the event window. Findings reveal that positive earnings surprises generate average cumulative abnormal returns of 7.36% over the five-day post-announcement window, while negative earnings surprises generate average cumulative abnormal returns of - 6.84%, confirming statistically significant stock price reactions to unexpected earnings information. The earnings surprise coefficient in the regression model is 0.842 (p < 0.001), indicating that each 1% earnings surprise generates approximately 0.84% abnormal return. The study provides insights into the price formation process at Embrizon and offers practical implications for investors, corporate finance managers, and market microstructure researchers.
earnings announcement, stock price reaction, event study, abnormal return, earnings surprise, Embrizon, market model, EPS, price discovery, NSE India, corporate finance.
B Varunkumar¹, Edlapally Sri Harshitha², Pasumarthi Shivarani³, Sampangi Pooja⁴ Ms. P. Amulya⁵
A STUDY ON FINANCIAL LEVERAGE ON COMPANY PROFITABILITY AT STATE BANK OF INDIA
Financial leverage—the extent to which a firm finances its assets through debt relative to equity—is one of the most consequential determinants of corporate profitability, risk profile, and shareholder value creation. In the banking sector, financial leverage assumes a uniquely central role, as debt in the form of deposits and borrowings constitutes the primary raw material of the lending business and the principal source of interest income generation. State Bank of India (SBI), India's largest public sector bank and the country's most systemically important financial institution, provides an ideal empirical context for examining how the degree of financial leverage influences profitability outcomes across varying macroeconomic and credit cycle conditions. This study analyses the impact of financial leverage on the profitability of SBI over the five-year period FY 2018–19 to FY 2022– 23, employing leverage ratio analysis, profitability ratio computation, DuPont decomposition, and regression modelling to quantify the leverage-profitability relationship. Secondary data sourced from SBI Annual Reports, RBI regulatory returns, and NSE/BSE filings forms the empirical basis. Findings reveal that SBI's Debt-toEquity ratio declined from 15.24x in FY19 to 12.62x in FY23, coinciding with a dramatic improvement in Return on Equity from 0.39% to 15.54% and Return on Assets from 0.02% to 0.98%—confirming that deleveraging, improved asset quality, and enhanced net interest margin together drove the bank's profitability recovery. Regression analysis confirms a statistically significant negative relationship between the Debt-toEquity ratio and ROE (coefficient -1.842, p = 0.018), consistent with the theory that excessive leverage amplifies risk and constrains profitability in banking when asset quality deteriorates.
financial leverage, profitability, SBI, State Bank of India, debt-to-equity ratio, return on equity, return on assets, DuPont analysis, capital adequacy, NIM, banking sector.
Erugu Srikanth¹, Mengani Uday Kiran², Veda Sri Kande³, Kappala Uday Kumar⁴ Ms. P. Amulya⁵
A STUDY ON CYBERSECURITY CHALLENGES AND THREATS IN THE DIGITAL TRANSFORMATION
Digital transformation— encompassing the adoption of cloud computing, Internet of Things (IoT), artificial intelligence, big data analytics, remote work infrastructures, and API-driven digital platforms—has fundamentally restructured the technological landscape of contemporary organisations while simultaneously expanding the attack surface available to malicious actors. As organisations accelerate the integration of digital technologies into core business processes, the frequency, sophistication, and financial impact of cybersecurity threats have grown commensurately, making cybersecurity risk management a strategic imperative rather than a purely technical function. This study examines the nature, scope, and organisational impact of cybersecurity challenges and threats arising from digital transformation, drawing on primary survey data collected from 100 IT professionals and business analysts across technology-enabled organisations and secondary data from global cybersecurity reports including IBM Cost of a Data Breach Report 2023, Verizon DBIR 2023, and CERT-In Annual Report 2022–23. The research employs descriptive analysis, threat taxonomy framework development, and perception-based survey analysis to document the threat landscape, identify sector-wise vulnerability profiles, assess organisational cybersecurity readiness, and evaluate the effectiveness of mitigation frameworks including NIST CSF 2.0, Zero Trust Architecture, and AI-driven Security Operations Centres. Findings reveal that 86% of respondents agree that digital transformation increases cybersecurity risk, ransomware and supply chain attacks are the most economically impactful threat categories, and only 54% of surveyed organisations have implemented a Zero Trust policy—underscoringa significant cybersecurity readiness gap relative to the pace of digital adoption.
cybersecurity, digital transformation, ransomware, Zero Trust Architecture, cloud security, IoT threats, NIST CSF, data breach, threat intelligence, business analytics, information security.
Dubbaka Poojitha¹, Sura Pravalika², Kuntala Mohandas³, Thaneti Sravani⁴ Mr. G. Mahesh⁵
A Study on Impacts of Reward and Recognition on Employee Retention at INFOSYS
Employee retention has emerged as one of the most critical human resource challenges facing multinational information technology corporations. In an industry characterised by intense talent competition, high attrition rates, and significant knowledge capital embedded in experienced employees, the design and implementation of effective reward and recognition programmes constitute a key strategic lever for workforce stability. Infosys Limited, one of India's largest IT services companies, employs over 340,000 professionals globally and operates in an environment of persistent talent demand from domestic and international competitors. This study examines the impact of reward and recognition practices on employee retention at Infosys, focusing on how monetary rewards, non-monetary recognition, career advancement opportunities, and managerial appreciation behaviours influence employees' intention to remain with the organisation. A structured questionnaire was administered to 150 employees across technical and managerial grades. Data analysis employs percentage analysis, weighted average ranking, and chisquare tests. Findings reveal that career growth opportunity, performance-based monetary rewards, and managerial recognition are the top three retention drivers, while inadequate recognition and limited growth pathways are the leading disengagement factors. Recommendations include a structured recognition framework, transparent performance-linked compensation, and leadership development in appreciation practices.
Reward and Recognition, Employee Retention, Infosys, HR Management, Motivation, Performance Appraisal, Talent Management, Employee Engagement, IT Sector, Attrition.
Nandhyala Keerthana Reddy1 , Rendla Manasa2 , Mahima Kumari3 , Bingi Harini4 , Badugu Bhuvaneshwari5 , Dr. A. Jyothi6
A STUDY ON DIGITAL TRANSFORMATION IN BANKING AT STATE BANK OF INDIA
Digital transformation in banking represents the comprehensive integration of digital technologies across all banking operations, fundamentally altering how financial institutions create value, deliver services, engage customers, and manage risk. State Bank of India (SBI), India's largest commercial bank with total assets exceeding ₹62 lakh crore and over 500 million customers, has undertaken one of the most ambitious digital transformation programmes in Indian banking history, transitioning from a predominantly branchcentric service model to a digital-first omnichannel banking ecosystem. This study examines SBI's digital transformation journey, analysing technology investments, digital banking platform adoption, operational efficiency gains, customer experience improvements, financial inclusion outcomes, and cybersecurity governance. Primary data was collected through structured questionnaires administered to 120 respondents comprising SBI employees across digital banking, IT, and operations functions, and SBI customers using digital banking channels. Secondary data was sourced from SBI Annual Reports (2021–2024), RBI Digital Payments reports, NASSCOM banking technology publications, and academic literature on digital transformation in banking. Findings indicate that SBI's YONO (You Only Need One) platform has crossed 78 million registered users, digital transaction share has grown from 52% to 84% of total transactions, and digital banking has reduced SBI's cost per transaction from ₹45 (branch) to ₹2.1 (digital), generating annual operating cost savings exceeding ₹18,000 crore. Recommendations address AI-driven personalisation, cybersecurity framework strengthening, rural digital banking expansion, and API-enabled ecosystem partnership development.
Digital transformation, SBI, YONO, digital banking, fintech, UPI, mobile banking, financial inclusion, cybersecurity, banking technology
Bodige Uday Goud1 , Gatla Naresh2 , Usolla Ashok3 , Kale Rahul4 , Siripuram Sai Rajeev Reddy5 , A. Jyothsna6
A STUDY ON JOB STRESS ON WORKING EMPLOYEES AT ZEST WINGS INFORMATIVES (PVT.) LTD.
Job stress has emerged as one of the most significant occupational health challenges in modern information technology and services organisations, with far-reaching consequences for employee wellbeing, organisational productivity, talent retention, and healthcare costs. Zest Wings Informatives Private Limited, a Hyderabadbased IT services and software solutions company, operates in a high-pressure project-driven environment characterised by tight deadlines, client-driven scope changes, on-call availability expectations, and remote working dynamics that collectively create elevated stress exposure for its workforce. This study examines the nature, sources, and consequences of job stress among employees at Zest Wings Informatives (Pvt.) Ltd., analysing role overload, role ambiguity, interpersonal conflict, work-life imbalance, and organisational culture as primary stress antecedents. Primary data was collected through structured questionnaires administered to 100 respondents from Zest Wings Informatives across technical, functional, and managerial roles. Secondary data was sourced from academic literature on occupational stress, WHO workplace mental health guidelines, NASSCOM employee wellbeing reports, and HR industry publications. Findings indicate that 67% of Zest Wings employees report moderate to high stress levels, with deadline pressure (82%), role overload (74%), and work-life imbalance (68%) identified as the three dominant stress sources. Stress significantly impacts employee performance (reported by 71%), job satisfaction (64%), and intention to quit (58%). Recommendations address workload management, role clarity enhancement, flexible working adoption, and structured employee wellness programme implementation for reducing job stress and improving workforce wellbeing at Zest Wings Informatives.
Job stress, occupational stress, Zest Wings Informatives, IT sector, role overload, work-life balance, employee wellbeing, stress management, turnover intention, organisational health.
Thatikayala Ashwini1 , Amula Nithisha2 , Vayuvegula Akhila3 , Y Jyothi4 , O. Shalini5
A STUDY ON IMPACT OF STRESS MANAGEMENT PRACTICES ON EMPLOYEES AT YASHODA HOSPITAL
Stress is an inevitable reality in the high-stakes environment of healthcare, where employees are subjected to long working hours, emotional labour, patient-care pressures, and organisational demandssimultaneously. Yashoda Hospital, one of the prominent multispecialty hospital chains in Telangana and Andhra Pradesh, employs a large and diverse workforce spanning clinical, administrative, and support functions, all of whom are exposed to varying degrees of occupational stress. This study examines the nature, sources, and intensity of occupational stress experienced by employees at Yashoda Hospital and evaluates the effectiveness of the stress management practices currently adopted by the organisation. A structured questionnaire was administered to a sample of 120 employees drawn from clinical and non-clinical departments. Data was analysed using percentage analysis, weighted average method, and chi-square test. Findings reveal that workload, shift timing irregularities, interpersonal conflicts, and inadequate recognition are the primary stressors. The study further identifies that while formal stress management programmes exist, awareness and participation rates remain sub-optimal. Recommendations include structured wellness initiatives, regular counselling sessions, workload rebalancing mechanisms, and a performance-linked recognition framework. The findings contribute to the growing body of evidence on healthcare occupational stress management in the Indian hospital context.
Stress Management, Occupational Stress, Healthcare Employees, Yashoda Hospital, Employee Wellbeing, Workplace Stress, Hospital Administration, Work-Life Balance, Stress Reduction, Indian Healthcare.
Kalluri Sravani1 , PV Aravind Babu2 , Deshaboina Manisha3 , Rotte Neha4 Ms. A. Swathi5
Work Life Balance And Its Influence On Employee Satisfaction And Retention At Kesoram Industries Limited
Work-life balance (WLB) has emerged as a critical determinant of employee satisfaction, organisational commitment, and voluntary retention in contemporary industrial settings. Kesoram Industries Limited, a diversified conglomerate with operations spanning tyre manufacturing, cement, and rayon, faces the dual challenge of retaining a technically skilled workforce in a competitive labour market while managing the operational demands inherent in large-scale continuous-process manufacturing. This study investigates the nature and extent of work-life balance practices at Kesoram Industries, their influence on employee satisfaction levels, and their role in reducing voluntary attrition. Primary data was collected through structured questionnaires administered to 150 respondents comprising shop-floor workers, supervisors, technical staff, and managerial personnel across Kesoram’s Hyderabad and Basantnagar facilities. Secondary data was drawn from company HR records, Ministry of Labour reports, and peer-reviewed academic literature. Weighted average analysis reveals an overall WLB satisfaction score of 3.76 out of 5.0, indicating moderate satisfaction with significant improvement opportunities. Findings identify excessive overtime demands (cited by 68% of respondents), inadequate leave flexibility (54%), and limited childcare provisions (47%) as the three primary WLB pain points. Statistical analysis confirms a strong positive correlation (r = 0.74) between WLB satisfaction and retention intention. The study provides evidence-based recommendations for structural WLB improvements including flexible shift scheduling, enhanced leave management, and targeted employee assistance programmes.
Work-life balance, employee satisfaction, employee retention, Kesoram Industries, manufacturing sector, flexible working, organisational commitment, attrition, human resource management.
Amreen Begum¹, Yaseen Hussain², Thikka Balasri³, Saidigari Charan⁴, Dabbu Sharanya⁵, Ms. S. Ramya⁶
DESIGN AND ANALYSIS OF MANAGEMENT INFORMATION SYSTEM FOR DECISION MAKING
Management Information Systems (MIS) play a pivotal role in modern organisations by transforming raw operational data into structured, actionable information that supports effective managerial decision-making at strategic, tactical, and operational levels. This paper presents a comprehensive design and analysis of an MIS framework tailored for organisational decision-making, examining its architectural components, functional modules, implementation challenges, and measurable impact on decision quality and speed. The study employs a mixed-methods research design combining a structured survey of 100 respondents across managerial levels with secondary analysis of MIS performance literature. Primary data was gathered through a validated 30-item questionnaire administered to managers in ITenabled service organisations in Hyderabad. Findings indicate that MIS implementation reduces average decision time by 66.3%, improves decision accuracy by 24.4%, and achieves a strong positive correlation (r = 0.876) between MIS usage frequency and decision speed. Key barriers including inadequate training, resistance to change, and poor data quality are identified, with targeted recommendations for overcoming adoption challenges. The study concludes that a well-designed MIS architecture significantly enhances organisational intelligence, managerial effectiveness, and competitive responsiveness.
Management Information System, Decision Making, Decision Support System, Executive Information System, Data Quality, Organisational Intelligence, MIS Implementation, Information Technology, Business Intelligence, Knowledge Management.
Anaveni Praveen¹, Sannilla Vinoothna², Lukalapu Janeshwar³, Burra Sai Sujitha⁴, Dr. B. Narmada⁵
A STUDY ON EMPLOYEE WELFARE PRACTICES AT INFOSYS
Employee welfare constitutes a cornerstone of human resource strategy in knowledgeintensive industries, directly influencing workforce productivity, organisational commitment, and talent retention. Infosys Limited, one of India’s largest and most globally recognised information technology services companies, has developed a comprehensive welfare ecosystem encompassing financial benefits, health and wellness programmes, learning and development initiatives, work-life balance provisions, and diversity and inclusion mechanisms. This paper examines the structure, implementation, and effectiveness of employee welfare practices at Infosys. Primary data was collected through structured questionnaires administered to 120 respondents comprising Infosys employees across multiple experience levels and functional domains. Secondary data was sourced from Infosys Annual Reports (2021–2024), ESG disclosures, academic journals, and industry research. Findings indicate that Infosys’s welfare programmes significantly enhance employee satisfaction (weighted average score 4.11/5), reduce voluntary attrition, and strengthen employer brand equity. Quantitative analysis reveals that health and wellness benefits (ranked first), professional development opportunities (ranked second), and flexible work arrangements (ranked third) are the most valued welfare dimensions. The study identifies implementation challenges including programme awareness gaps, geographical equity issues, and wellness resource utilisation disparities, and recommends targeted improvements to maximise welfare programme impact across Infosys’s global workforce
Employee welfare, Infosys, human resource management, employee satisfaction, work-life balance, health and wellness, talent retention, organisational commitment, IT sector India.
Akarapu Akhila¹, Elthuri Bindhiya², Bandala Saikiran³, Kottakota Nagaveni⁴, Ms. A. Jyothsna⁵
ANALYSIS OF CREDIT APPRAISAL TECHNIQUES IN CORPORATE BANKING
Credit appraisal in corporate banking constitutes the systematic evaluation of a borrowing entity's financial health, repayment capacity, management quality, and collateral adequacy before the extension of credit facilities. Unlike retail lending, corporate credit appraisal demands a multi-dimensional assessment encompassing quantitative financial analysis, qualitative risk evaluation, industry dynamics, and regulatory compliance. This study analyses the credit appraisal techniques employed in corporate banking, examining frameworks such as the 5-C model, financial ratio benchmarking, Debt Service Coverage Ratio (DSCR) analysis, credit risk rating, and post-sanction monitoring. Primary data was gathered through structured interactions with corporate banking professionals across leading Indian banks. Secondary data was sourced from RBI master circulars, Basel Committee guidelines, published annual reports, and academic literature spanning 2018–2024. Findings establish DSCR, Interest Coverage Ratio (ICR), and collateral quality as primary determinants of corporate credit decisions. Average loan appraisal turnaround time of 24.3 days and an overall approval rate of 76.7% across 60 sampled corporate loan cases were recorded. The study identifies key challenges including information asymmetry, valuation subjectivity, and regulatory compliance burden, and recommends adoption of AIdriven credit underwriting models, real-time financial monitoring, and standardized sector-specific appraisal templates.
Credit appraisal, corporate banking, DSCR, credit risk rating, collateral valuation, financial ratio analysis, NPA, Basel III, loan sanctioning, banking sector India.
Hamilpur Deepthika1 , Bollu Laharika2 , Kashi Nagesh3 , Asadhi Nikitha Rani4 , Ms. R. Swapna5
A Study on Artificial Intelligence in Training and Development Programs at Amazon
Artificial Intelligence (AI) is rapidly transforming human resource management practices, particularly in the domain of training and development. Amazon, as one of the world's most technologically advanced companies, has been at the forefront of integrating AI-driven tools into its employee learning and development ecosystem. This study examines how Amazon leverages AI technologies such as machine learning, natural language processing, adaptive learning systems, and predictive analytics to enhance training effectiveness, personalize learning pathways, and optimize workforce performance. A mixed-method research design was employed, combining secondary data analysis with structured survey-based primary data collection from 120 respondents comprising Amazon employees and HR professionals. The study explores the impact of AI on training efficiency, employee skill development, cost reduction, and overall organizational productivity. Findings indicate that AI-enabled training systems significantly improve learning outcomes, reduce training time by up to 40%, and enhance employee engagement compared to traditional methods. The paper also identifies key challenges such as data privacy concerns, algorithmic bias, resistance to technological change, and implementation costs. Suggestions for more equitable and effective AI deployment in L&D are provided, along with a comprehensive conclusion on the future trajectory of AI in organizational learning environments.
Artificial Intelligence, Amazon, Training and Development, Machine Learning, Adaptive Learning, Natural Language Processing, Human Resource Management, Employee Performance, Organizational Learning.
M Rajitha1 , Bijjamula Divya2 , Kadthala Pavan Kumar3 , Kompally Sreeja4 Dr. Y. Azith5
A STUDY ON THE IMPACT OF RBI MONETARY POLICY ON THE PROFITABILITY OF STATE BANK OF INDIA
Monetary policy serves as one of the most critical instruments through which central banks influence economic activity, credit availability, and the financial performance of banking institutions. The Reserve Bank of India (RBI) employs a range of monetary policy tools — including the repo rate, reverse repo rate, Cash Reserve Ratio (CRR), and Statutory Liquidity Ratio (SLR) — to regulate liquidity, inflation, and economic growth. The State Bank of India (SBI), as the largest public sector bank and a systemically important financial institution, is significantly impacted by these policy decisions. This study empirically investigates the relationship between RBI's monetary policy instruments and the profitability of SBI over a ten-year period from 2014-15 to 2023-24. Using secondary data sourced from RBI annual reports, SBI annual reports, and SEBI disclosures, the research employs correlation analysis and multiple regression modelling to quantify the impact of policy rate changes on key profitability indicators, namely Net Profit, Net Interest Margin (NIM), Net Non-Performing Assets (NPA), and Return on Assets (ROA). The findings reveal a statistically significant negative correlation between repo rate increases and SBI's net profit, while NIM shows a positive association with rising rate environments. The study concludes that RBI's monetary stance plays a decisive role in shaping SBI's financial outcomes and offers practical recommendations for policy calibration and bank management strategies.
RBI Monetary Policy, Repo Rate, SBI Profitability, Net Interest Margin, NPA, Return on Assets, CRR, SLR, Correlation Analysis, Regression Analysis
Banoth Varalakshmi1 , Bethi Anvitha2 , Saniya Ruksaar3 , Mohammad Sohail4 , K.V. Chiranjeevi5