American Journal of Management and IOT Medical Computing
Editor- In-Chief: Dr. K M Jhon Title proper: American Journal of Management and IoT Medical Computing (AJMIMC) Subject: AI ML, Data Science, Management, Medical, Pharmacy, Cyber Security, Computer Science, IOT, Engineering All, Science and Technology Publisher Name: GreenMS Publications ISSN: 3069-0110 Frequency: Quarterly Language: English Country: USA Medium: Online Indexed by: CROSSREF, MDPI Scilit, Google Scholar Ext Title DOI of the Journal: http://doi.org/10.64751/issn. 3069-0110
The research paper on the topic "RECRUITMENT & SELECTION PROCESS" with respect to the "ICICI BANK" situated at Gachibowli, Hyderabad has been prepared. Effective recruitment and selection lead to better organizational outcomes. Focusing on the abovementioned background of the problem, the research paper on the topic recruitment and selection has been made to shed some limelight on the recruitment and selection process. The primary focus is on determining the overall practices adopted by organizations to recruit and select employees and also on understanding the effects of recruitment and selection practices on organizational outcomes at ICICI BANK. The research methodology used is the exploratory one. The data has been collected by properly structured questionnaires. Both primary and secondary were the source of the data. The sample size was 30. It has been observed by the company that portals are the foremost medium of hiring the employees. The employees of the company regard the employee references as one of the most credible source of hiring the novel employees.
WOMEN ENTREPRENEURSHIP IN INDIA CHALLENGES, MOTIVATIONS AND GROWTH OPPORTUNITIES AT HERITAGE FOODS INDIA LTD
Women entrepreneurship has emerged as a significant driver of economic growth and social development in India. This study explores the challenges, motivations, and growth opportunities for women entrepreneurs in India, with a specific focus on Heritage Foods India Ltd. The study adopts a descriptive and analytical research design, relying on secondary data collected from company reports, government publications, industry journals, and academic research papers. The findings reveal that financial constraints, family responsibilities, lack of mentorship, and market competition are the primary challenges faced by women entrepreneurs. Despite these challenges, motivations such as financial independence, passion, work-life balance, and social impact drive women to pursue entrepreneurial ventures. Heritage Foods India Ltd has played a pivotal role in empowering women through its supply chain and community engagement initiatives, creating sustainable livelihood opportunities. The study highlights the growing trend of women entrepreneurship in India and identifies key growth opportunities in sectors such as food processing, e-commerce, healthcare, and education. The research provides valuable insights for policymakers, corporate leaders, and aspiring women entrepreneurs to foster an inclusive entrepreneurial ecosystem.
FINANCIAL FORECASTING AND PLANNING AT ULTRACH CEMENT LTD
Financial forecasting and planning play a vital role in ensuring the long-term growth, stability, and profitability of an organization. This study focuses on the financial forecasting and planning practices at UltraTech Cement Ltd., one of India's leading cement manufacturers. Financial forecasting helps estimate future revenues, expenses, cash flows, and capital requirements based on historical data, market trends, and economic conditions. Effective financial planning enables the company to allocate resources efficiently, manage risks, optimize investments, and achieve strategic objectives. The study examines the financial statements, budgeting techniques, forecasting methods, and financial performance indicators used by UltraTech Cement Ltd. It also evaluates the company's approach to cost management, working capital, and investment planning. The findings indicate that accurate financial forecasting supports better decision-making, improves operational efficiency, and enhances the company's ability to respond to market uncertainties. The study concludes that robust financial planning contributes significantly to sustainable growth and long-term financial success.
A COMPARATIVE STUDY ON EQUITY ANALAYSIS WITH NIFTY STOCKS AT INDIABULLS SECURITIES LTD
Equity analysis is a fundamental aspect of investment decision-making that enables investors to evaluate the performance and potential of stocks in the capital market. This study presents a comparative analysis of equity performance with reference to Nifty stocks at Indiabulls Securities Ltd. The research examines the performance of select Nifty stocks across different sectors including banking, IT, energy, pharma, and FMCG over a five-year period. The study employs various analytical tools including return analysis, risk assessment, beta computation, and portfolio optimization techniques to compare the performance of individual stocks against the Nifty 50 benchmark index. The findings reveal significant variations in risk-return profiles across sectors, with IT and banking stocks demonstrating superior performance while energy stocks show relatively stable but moderate returns. The study concludes that a well-diversified portfolio combining stocks from different sectors can effectively balance risk and return for investors at Indiabulls Securities Ltd.
A Project Report On Home Loans At HDFC Bank Ltd
Housing Development Finance Corporation (HDFC) was the first dwelling finance Company to arrangement movements in India in 1977. After the National Housing Bank Act, 1987, was given NHB happened into life as a Subsidiary of the Reserve Bank of India (RBI) to manage place of accommodation finance guests and supply bureaucracy accompanying refinancing to supplement their fund necessities. Public area banks were admitted to supply shelter loans straightforwardly to sell customers only in 1988. The study is restricted to only HDFC Bank This study is for the most part had connection with the things the one are concerned in obtain loans from banks to achieve their dreams. The study was chiefly attended to accept the idea of loan made by a bank blueprint and the fitness tests of the clients. The study is finished to appreciate the documents complicated in the loan made by a bank blueprint and the reparation methods selected by differing banks and the HFC‘s (Housing Finance Corporations).
Technical analysis plays a significant role in helping investors and traders make informed decisions by examining historical price movements, trading volume, and market trends. This study, titled "Technical Analysis W.R.T. Angel Broking," aims to understand the application of technical analysis tools and techniques in the trading environment offered by Angel Broking. The research focuses on identifying market trends, evaluating stock price movements using technical indicators such as Moving Averages, Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Bollinger Bands, and candlestick patterns, and assessing their effectiveness in supporting investment decisions. The study adopts a descriptive research approach based on both primary and secondary data. Primary data are collected through structured questionnaires administered to Angel Broking clients and active traders, while secondary data are obtained from company reports, financial publications, research articles, and stock market databases. The collected data are analyzed using statistical techniques and graphical representations to understand investor awareness, trading behavior, and the practical usefulness of technical analysis tools. The findings are expected to demonstrate that technical analysis enables investors to identify potential entry and exit points, manage investment risk, and improve trading efficiency. However, the study also recognizes that technical indicators should not be relied upon independently, as market conditions are influenced by economic, political, and company-specific factors. Therefore, combining technical analysis with fundamental analysis can lead to more effective investment decisions. Overall, this research provides valuable insights into the role of technical analysis in modern stock trading and highlights how Angel Broking's trading platform supports investors in making data-driven and timely investment decisions.
Study On Merchant Banking In India At Hsbc Bank
A merchant bank is a capital-providing financial institution to corporations in the form of shareholding rather than a loan. A merchant bank also offers corporate advisory to the companies in which they invest. In the United Kingdom, the older term "merchant bank" is used to describe a bank. A Merchant Bank may be loosely defined as a private equity investment arm of a financial services company offering investment banking and ancillary services as well. Since a merchant bank plays the role of an advisor and broker as well as a principal, a merchant bank has a longer-term orientation than an ordinary investment bank and takes a keen interest in the viability of every investment opportunity and giving the correct advice to form a strong alliance with each client company. In banking, a merchant bank is an older term for an Investment Bank. It may also be used to describe banking's private equity activities. This article is concerned with the history of banking as established by the merchants, dating back from the middle Ages. Merchant banking is a valuable service rendered by a group of financial institutions that facilitates the development of the corporate sector which finally translates into the overall economic development of the nation. Merchant banks were supposed to undertake various functions like issue management, underwriting, portfolio management, loan syndication, consultant, advisor and host of other functions. Merchant banking is a blend of banking and consultancy services.
Impact Of Gst Implementation On Retail Entrepreneurs In India
Goods and Services Tax (GST) is one of the most important tax reforms brought to bear by India at present, to make a comprehensive and transparent, consolidated tax system. The objective of this paper is to evaluate the effect of GST on retail entrepreneurs and the effects of the new tax system on retail-oriented economy. The study discusses the structure and framework of GST, its aims, its characteristics and its mechanism of application. The research estimates the impact of GST and examines the cost, price and profitability of retail businesses, compared to pre-GST and post-GST taxation systems. It checks if GST has made tax compliance easier and trade between retailers more efficient. This research also highlights some of the challenges such retail entrepreneur’s face under the GST environment: compliance as well as technology adaptation, filing processes and tax intricacies. In addition, the study explores changes in retail sales and the following expansion/deterioration patterns that the industry is experiencing post GST, including its sales activity, cost of operations, and expansion possibilities for its business. Through analysis of this the research presents a wide view of the pros and cons of GST for retail entrepreneurs. The results will be helpful for the retailers, policymakers, researchers and stakeholders to evaluate the effectiveness of GST, how effectively it contributed to the development of the retail landscape.
STUDY ON KEY PERFORMANCE INDICATORS FOR SMALL BUSINESS W.R.T. ZESSTA SOFTWARE SERVICES PVT LTD
Business arranging must obtain effective and productive processes so that accomplish vying positions. The conduct of these processes is determinable through key conduct signs (KPIs). KPIs are very main to administration administrative and are relied upon by all levels of an arranging to measure progress in carrying out effects. An arrangement needs expected intelligent to label allure extreme-profit extreme-risk dossier character (DQ) issues that support each KPI. Thus, it is idea that a severe foundation that guides the plan of DQ measure to KPIs is wanted to support assurance hesitation-making. In order to evolve a foundation that maps DQ issues to KPIs, the current state of DQ in Australian institutions and the grade of DQ drives that are connected to administrative KPIs will be examined. A projected country with its own government-expansive survey and record of what happened judgments will be secondhand together to cultivate the foundation for plan DQ works to administrative KPIs. This research will further address the question of by means of what tests that link DQ pushs to administrative KPIs may be settled.
ETHNOMEDICINAL PLANTS USED BY CHENCHU TRIBAL COMMUNITIES OF THE NAGARJUNASAGAR – SRISAILAM REGION, INDIA: DOCUMENTATION OF TRADITIONAL HEALTHCARE PRACTICES
The Nagarjunasagar Srisailam Tiger Reserve (NSTR) area extends through Nallamalais and a part of Yerramalai hills of Eastern Ghats, spreading over 5 districts, viz.. Mahabubnagar, Prakasam, Kurnool, Guntur and Nalgonda covering an area of 3568 sq. km. Chenchus are predominant and stand as a unique tribe among others in Andhra Pradesh. They occupy larger part of Nallamalais, naturalising in its glorious environs and rich biodiversity. They hold the forests around them in great esteem and have learnt to live with plants and wild animals. In this close association over centuries, they have nurtured their skills in identifying the plants for food, medicine, house building, agricultural operations, domestic uses, trade and barter, magico-religious beliefs, taboos, etc. A majority of the Chenchus are well versed in natural folk medicinal practices. From time immemorial, they have accumulated in-depth, time-tested knowledge about the medicinal plants in their habitat, which has enabled them to survive deadly diseases (Hemalatha & Subba Reddy 1982). Information pertaining to ethnomedicinal practices among Chenchus is available, though limited (Hemadri & Rao 1984; Hemadri 1994 Hemalatha & Subba Reddy 1982, Morris 1989, Rama Rao & Henry 1996, Reddy et al. 1988, Reddy et of 1996). But information pertaining to remedies for cuts, wounds and boils in particular is lacking. Since the tribal people in the course of their dayto-day traverse into the forests for their needs often encounter injuries through cuts and wounds. besides infectious boils, the present study especially elicits knowledge about Chenchu ethnomedicinal practices. Here, 30 remedies have been described from different Chenchu tribal pockets of NSTR for the treatment of cuts, wounds and boils.
IMPACT OF BUY BACK OF SHARES ON THE SHARE PRICE BAHAVIOUR- AN EVIDENCE FROM INDIAN STOCK MARKET
Buy back is one of the important corporate actions through which company buys back its shares either from open market. Companies go for buy back of shares in two conditions generally, one is negative situation of continuous drop in share price due to negative rumors in markets, and on the other condition is effective utilization of idle cash by reducing number of owners. In both cases, share price behavior analysis very crucial for investors for taking investment decisions such as hold, buy or sold. However, there is very few studies have done on measuring the effect of buy back of shares on share price behavior particularly in Indian context. Therefore, present study focused on this by taking two companies went to buy back of shares during the period of 2020. The study analysis the share price behavior in share price returns during pre and post buy back event, including very short period, short period and long period. The study used the T-test to measure the pre and post event stock price returns from Indian stock market context. The outcome of the study proved that buy back of share is good indicator that company will generated good wealth in long period.
IMPACT OF EMOTIONAL INTELLIGENCE AND COMMUNICATION SKILLS ON JOB SATISFACTION AMONG ACADEMIC LIBRARIANS
Objective: The present study explores the relationship between Emotional Intelligence (EI), Communication Skills and Job Satisfaction among library professionals working in university, college, and school. Methodology: To conduct this study the investigators collected the data using Google Forms from 55 library professionals working academic libraries of Telangana. The data was analysed using statistical techniques – mean, standard deviation, ANOVA (to compare differences between institutions) and Pearson Correlation to determine the impact of independent variables on the dependent variable. Findings: The findings shows that school library staff demonstrated significantly higher total Emotional Intelligence (p=.049) and Verbal Communication Skills (p=.022) than their counterparts in higher education. Correlation analysis revealed a high degree of interdependence between EI and Communication Skills (r=0.782, p<.01). However, in terms of impact on Job Satisfaction, Communication Skills emerged as a significant predictor (r=.284, p<.05) while Emotional Intelligence did not reach statistical significance (p=.069). Conclusion: The study concludes that while EI and communication are deeply integrated, targeted communication training may be a more direct pathway to enhancing staff satisfaction. Library administrators are encouraged to address the "satisfaction gap" in school environments by looking beyond skill development to organizational support factors.
SOMA SHEKHAR GOURISHETTY1 , G. RAVI KUMAR2
DIRECT AND INDIRECT IMPACT OF PERFORMANCE MANAGEMENT SYSTEM DIMENSIONS ON EMPLOYEE RETENTION IN THE BUSINESS PROCESS OUTSOURCING SECTOR
The Business Process Outsourcing (BPO) sector faces persistent employee turnover, demanding effective human resource practices such as Performance Management Systems (PMS) to enhance retention. This study examines the direct and indirect impact of several dimensions of PMS on employee retention. A quantitative cross-sectional survey was conducted among 202 employees in BPO organizations in Bangalore, India. Data were analysed using descriptive statistics, correlation, and multiple regression techniques. The correlation and regression analysis revealed that H5: procedural justice (fairness) (r = 0.44, β = 0.183, p < 0.05), h3: employee engagement (r = 0.43, β = 0.188, p < 0.05), h12: retention intention due to employee engagement activities (r= 0.30, β = 0.246, p < 0.01), and H11: Reverse turnover intention due to PMS policy such as distributive justice (r = 0.55, β = 0.363, p < 0.001) positively influenced employee retention. The study contributes to the existing literature by providing empirical evidence from the BPO context and offers practical implications for organizations seeking to strengthen retention through effective performance management practices.
Understanding Organic Food Awareness and Purchase Factors among Collegians
The increasing popularity of organic food makes it crucial to understand why people buy it, or why they don't. Even though people are concerned about their health due to poor diets and pollution, and they often think positively about organic options, there are real hurdles. Things like high prices, not being able to find organic food easily, and general lack of trust make it difficult for people to actually buy it. Compounding this is the practice in India where farmers rely on toxic pesticides to keep up with food production, which not only harms the soil but eventually gets into our bodies. Embracing the idea of giving back to nature, this research reviews what is currently known about organic food in India. It highlights the importance of how consumers feel about organic food and the many factors that influence their decision to purchase it. Consumers overwhelmingly like the idea of organic food, but this feeling is pulled in two directions. On one side, factors like better health and caring for the environment encourage buying. On the other, the obstacles of high cost and limited availability make it tougher. While the health benefits are a big selling point, and thus drive demand, some people still hesitate because they aren't fully satisfied. The core reasons people choose organic are the hope for a healthier life and supporting production methods that are kinder to the planet.
EXPLORING THE ROLE OF DIGITAL RECRUITMENT IN SHAPING JOB SEARCH BEHAVIOR OF POST-GRADUATE JOBSEEKERS: A STUDY IN NAGPUR CITY
The rapid advancement of digital technologies has fundamentally transformed recruitment practices across the globe. In India, the proliferation of e-recruitment has reshaped how jobseekers search, evaluate, and apply for employment opportunities. This study explores the role of digital recruitment in shaping job search behavior among post-graduate jobseekers in Nagpur City. Drawing on the Technology Acceptance Model (TAM), the study examines key variables — perceived usefulness, ease of use, credibility, and accessibility — and their influence on job search behavior. Primary data were collected from 150 post-graduate respondents using a structured questionnaire. Statistical tools including Pearson correlation, multiple regression (with VIF multicollinearity diagnostics), KMO–Bartlett factor validation, and reliability analysis (Cronbach's α) were applied using SPSS v26. The model explained 67.3% of variance in job search behavior (R² = 0.673; F = 38.74; p < .001). All five hypotheses were supported. Perceived usefulness (β = .42) emerged as the strongest predictor, followed by ease of use (β = .31). Data privacy concerns (68%), fake job postings (63%), and absence of post-application feedback (58%) are the dominant adoption barriers. The study contributes an empirically validated, tier-2-city-specific understanding of digital recruitment behavior and offers actionable implications for recruiters, policymakers, and educational institutions.
Digital Recruitment; E-Recruitment; Job Search Behavior; TAM; Post-Graduate Jobseekers; Nagpur City; Perceived Usefulness; E-HRM
Automated Medicine Dispenser and Reminder IoT System for Elderly Care
The administration of medicines at the correct time is essential for effective treatment and patient recovery. Over the years, medication management has evolved from manual schedules and written prescriptions to electronic reminder systems aimed at improving adherence. However, many patients, particularly elderly individuals and those with chronic illnesses, still face difficulties in remembering complex medication routines, leading to missed doses, incorrect intake, and reduced treatment effectiveness. Traditional medication management methods primarily rely on manual supervision, alarm clocks, or basic reminder applications, which often lack automation, real-time monitoring, and remote notification capabilities. These limitations create a need for a more intelligent and reliable solution that can ensure timely medicine administration while reducing human dependency. To address these challenges, this study presents an IoTenabled automated medication management system that integrates a microcontroller, Real-Time Clock (RTC), Wi-Fi communication module, LCD display, buzzer, and motor-driven dispensing units. The system continuously monitors predefined medication schedules and automatically generates reminders when the scheduled time is reached. Simultaneously, it dispenses the appropriate medicine and transmits notifications to remote users or caregivers through wireless communication channels. The integration of automation and IoT technology enhances medication adherence, minimizes dosing errors, and supports continuous patient monitoring. The proposed approach offers a cost-effective, user-friendly, and dependable healthcare solution suitable for home-based and assisted-care environments. Its implementation contributes significantly to improving patient safety, treatment compliance, and overall healthcare management through intelligent and timely medication administration.
IoT, Automated Medication Management, Smart Medicine Dispenser, RTC, Medication Adherence, Remote Patient Monitoring
Adaptive Latent Motion Intelligence for Multi-Gait Behavioral Understanding in Sensor-Embedded Wearable Ecosystems
Human activity recognition based on wearable sensor data has become increasingly important due to its applications in areas such as healthcare, fitness tracking, and intelligent environments. The widespread use of sensor-equipped devices has led to the generation of large volumes of continuous motion data, making efficient analysis methods essential. Earlier approaches to activity recognition primarily depended on manual observation or rule-based techniques that relied on predefined thresholds. However, these methods struggled to capture complex activity patterns, handle noisy signals, and scale effectively to large datasets, often resulting in limited accuracy and poor generalization. A major challenge in this domain is the accurate classification of activities from highdimensional, continuously streaming sensor data while maintaining scalability and reliability. Conventional techniques lack flexibility and tend to perform poorly when faced with variations such as changes in user behavior or differences in sensor positioning. This highlights the need for intelligent, data-driven systems capable of automatically learning meaningful patterns. To overcome these limitations, this study proposes a machine learning-based framework for activity classification that employs multiple models, including Greedy Tree (GT), K-Nearest Neighbors (KNN), Logistic Regression (LR), Naive Bayes (NB), and Adaptive Boosting (AB). The system incorporates data preprocessing, feature normalization, model training, evaluation, and prediction within an integrated pipeline. Experimental findings reveal that the Greedy Tree (GT) model achieves the highest performance, with an accuracy of 99.00% on the “activity” target variable, surpassing KNN (77.85%), NB (57.40%), LR (51.10%), and AB (51.02%). Overall, the proposed approach enhances classification accuracy, robustness, and scalability compared to traditional methods. It effectively manages continuous sensor data and supports both real-time and offline prediction scenarios.
Human Activity Recognition (HAR), Sensor Data Analysis, Machine Learning, Ensemble Learning, Fitness Monitoring.
ICU Connect: IoT-Enabled Remote ICU Patient Monitoring System
Continuous monitoring of patient health, especially in intensive care environments such as the Intensive Care Unit (ICU), is essential for timely diagnosis and effective medical intervention. Traditional healthcare systems rely on manual observation and periodic measurements, which can delay the detection of critical changes in patient conditions. These methods lack real-time tracking, remote accessibility, and automated alert mechanisms, increasing the risk of complications. Therefore, there is a growing need for a smart and connected healthcare solution that enables continuous and accurate monitoring. The proposed system introduces a wearable health monitoring network based on the Internet of Things (IoT). It utilizes wearable sensors to measure key physiological parameters such as heart rate, body temperature, and movement, which are processed using an Arduino microcontroller (open-source embedded system platform). The collected data is transmitted via the ESP8266 Wi-Fi module (Wireless Fidelity communication module) to an IoT cloud platform for storage, processing, and visualization. A smart IoT gateway is also implemented to support local processing, web server functionality, and cloud integration. The system continuously analyzes patient data and generates alerts when abnormal conditions are detected, ensuring immediate notification to authorized personnel. Additionally, Machine Learning (ML) techniques are incorporated to improve prediction accuracy and health assessment. The wearable and portable design allows patients to be monitored remotely from anywhere in the world. Overall, the system provides a reliable, real-time, and cost-effective solution that enhances patient safety and supports modern smart healthcare applications.
Internet of Things (IoT), Intensive Care Unit (ICU), Arduino Microcontroller, ESP8266 WiFi Module, Machine Learning (ML), Remote Patient Monitoring, Vital Sign Monitoring.
Intelligent Clinical Response Orchestration via Adaptive AI-Driven Inference for Emergency Service Coordination
The growing prevalence of chronic conditions such as hypertension and diabetes has intensified the need for intelligent healthcare solutions capable of supporting early-stage detection and informed medical decision-making. Continuous monitoring of multiple patient health indicators is essential, yet traditional diagnostic practices largely depend on manual assessment and basic statistical analysis, limiting their effectiveness in large-scale and data-intensive environments. These conventional systems often fail to capture complex interdependencies within medical data, struggle with imbalanced datasets, and lack the capability to deliver timely predictions in distributed settings. To overcome these challenges, this study presents a real-time clinical decision support framework driven by Artificial Intelligence (AI) and structured on a dual client–server model. The backend server is responsible for data preprocessing, model training, and prediction using Machine Learning (ML) techniques, including Complement Naive Bayes (CNB), Multinomial Naive Bayes (MNB), Perceptron, and a Tao Tree Classifier (TTC). Data preprocessing is performed using Label Encoding and K-Means Synthetic Minority Oversampling Technique (KMeans-SMOTE) to address categorical data handling and class imbalance issues. Communication between system components is facilitated through a Flask-based Application Programming Interface (API) utilizing Hypertext Transfer Protocol (HTTP), ensuring efficient data exchange. The client interface enables users to upload medical datasets, which are remotely processed to generate predictions for blood pressure levels and diabetic conditions. Additionally, Lightning Memory-Mapped Database (LMDB) is integrated to provide secure, highspeed data storage and retrieval. The proposed system enhances predictive accuracy, ensures real-time accessibility, and supports scalable, reliable healthcare delivery.
Hypertension Prediction, Diabetes Detection, Machine Learning (ML), Client–Server Architecture, LMDB, Flask Framework
A Hybrid Deep-Boosting Framework for Context-Aware Severity Classification in Healthcare Security Narratives
Healthcare infrastructures are becoming prime targets for cyber threats, as evidenced by the massive exposure of patient records and the continuous rise in reported vulnerabilities within hospital systems. The growing dependence on digital platforms has intensified the need for efficient mechanisms to detect and prioritize security issues. Traditional manual methods for assessing bug severity are not only time-intensive but also inconsistent, particularly when dealing with large volumes of unstructured security reports generated in medical environments. To address these challenges, this research introduces an intelligent Natural Language Processing (NLP)-based framework designed to automate bug severity classification. The approach utilizes a specialized dataset containing medical incident reports, security advisories, and vulnerability disclosures. Initially, the data undergoes systematic preprocessing and Exploratory Data Analysis to ensure cleanliness, consistency, and meaningful representation. For capturing contextual semantics, a lightweight transformer-based model inspired by Robustly Optimized bidirectional encoded representation of transformers (RoBERTa) is employed, enabling efficient generation of text embeddings with reduced computational overhead. To further enhance predictive performance, the framework integrates a Deep Neural Network (DNN) for feature refinement, which identifies and extracts the most relevant patterns from the embedding space. These optimized features are then passed to a probabilistic boostingbased classifier, improving classification reliability compared to conventional standalone models. The system categorizes vulnerabilities into two classes normal and severe facilitating rapid prioritization of critical issues. The framework demonstrates improved accuracy, reduced misclassification, and faster processing capability. By combining efficient language modelling with advanced feature learning, this approach offers a scalable and practical solution for strengthening cybersecurity management in healthcare environments.
Healthcare Cybersecurity, Bug Severity Classification, Vulnerability Detection, Natural Language Processing (NLP), Feature Extraction, Deep Neural Networks (DNN)
A Scalable Attention-Driven Architecture for Modeling Complex Disease Transmission Dynamics at Population Scale
The rapid expansion of unstructured clinical text within healthcare systems has created a critical need for automated solutions capable of extracting meaningful insights for clinical decision support. Medical transcription data contains essential information regarding patient conditions, treatments, and associated domains, yet its unstructured nature makes accurate interpretation complex. The problem addressed in this study is the automatic classification of clinical narratives into appropriate medical specialties. In a manual system, healthcare professionals or administrative staff read and interpret each document individually to assign categories based on their expertise, which is time-consuming, inconsistent, and unsuitable for large-scale data processing. This manual approach suffers from limitations such as lack of scalability, high dependency on human judgment, susceptibility to errors, and absence of standardization, thereby emphasizing the need for an efficient automated system. To overcome these challenges, the proposed framework integrates natural language processing techniques with advanced machine learning methods. Initially, text data is pre-processed using tokenization, stopword removal, and lemmatization to generate clean input. Contextual feature representations are then extracted using Efficiently Learning an Encoder that Classifies Token Replacements Accurately (ELECTRA), capturing semantic relationships within the text. To handle class imbalance, Synthetic Minority Over-sampling Technique (SMOTE) and K-Means Synthetic Minority Over-Sampling Technique (KMeansSMOTE) are applied. The resulting features are utilized to train multiple classifiers, including Adaptive Boosting Classifier (ABC), Extremely Randomized Trees Classifier (ETC), Random Forest Classifier (RFC), and Tree Alternating Optimization Classifier (TTC), enabling robust comparative analysis and improved predictive performance. The proposed system significantly enhances classification accuracy, reduces manual workload, and provides a scalable and reliable solution for intelligent healthcare text analytics
Clinical Text Classification, Medical Transcription Analysis, Natural Language Processing (NLP), Ensemble Learning, Healthcare Text Analytics. Synthetic Minority Over-sampling Technique (SMOTE).
Synergistic Modeling of PPG Morphophysiological Patterns and Clinical Parameters for Fine-Grained Glycemic State Inference
The increasing prevalence of diabetes and related metabolic disorders has created a strong demand for intelligent systems capable of accurately predicting blood glucose levels using non-invasive physiological signals. Continuous monitoring and timely prediction are essential to prevent complications and support effective clinical decision-making. However, many earlier approaches rely on basic statistical analysis and manual interpretation, which struggle to handle complex relationships among multiple health parameters such as Photoplethysmography (PPG) signal characteristics, heart rate, and patient demographics. These methods often lack scalability, adaptability, and predictive accuracy when applied to real-world healthcare datasets. Additionally, the absence of automated systems limits real-time analysis and delays preventive interventions. To address these challenges, this project proposes a machine learning-based predictive framework that leverages multiple regression models, including Linear Regression (LR), Ridge Regression, Least Absolute Shrinkage and Selection Operator (Lasso) Regression, Decision Tree Regressor (DTR), and an ensemble-based approach using Natural Gradient Boosting (NGB). The system processes physiological signals such as PPG along with attributes like age, gender, height, and weight to estimate glucose levels. Data preprocessing techniques such as missing value handling, feature scaling, and train-test splitting are applied to ensure robust model performance. The framework is deployed through a Flask based web application that enables model training, performance evaluation using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²) score, and real-time prediction for both single and batch inputs. The proposed system enhances prediction accuracy, reduces manual dependency, and provides a scalable solution for healthcare monitoring, enabling early detection and improved patient outcomes
glucose level prediction, PPG signal, non-invasive monitoring, machine learning, regression models, healthcare analytics, Flask Framework.
A Privacy-Centric Edge Intelligence Paradigm for Instantaneous Clinical Insight Generation via Secure Data Transformation
The rapid integration of digital technologies in healthcare has intensified concerns surrounding data security and patient privacy, especially in predictive diagnostic systems. Conventional machine learning approaches typically rely on direct access to raw medical data, making them vulnerable to data breaches, unauthorized usage, and privacy compromises. Additionally, many existing solutions lack comprehensive authentication protocols and fail to safeguard sensitive information during both training and inference stages. These limitations highlight the necessity for intelligent frameworks that can deliver high predictive performance while maintaining strict data confidentiality and secure system access. To overcome these issues, this study introduces a secure and privacy-aware healthcare prediction framework that combines machine learning with encryption and authentication mechanisms. The system is implemented using the Flask web framework and integrates an email-based One-Time Password (OTP) verification process via Simple Mail Transfer Protocol (SMTP) to ensure secure user authentication. To protect sensitive medical data, a Lightweight Privacy-Preserving Machine Learning Encryption (LPME) approach is employed, which transforms input features using polynomial-based cryptographic techniques before they are utilized for model training. The framework is evaluated on healthcare datasets related to heart disease and hypothyroidism. Prior to model development, the data undergoes preprocessing steps such as normalization, categorical encoding, and class imbalance handling using the Synthetic Minority Oversampling Technique (SMOTE). For predictive modelling, Extreme Gradient Boosting (XGB) is adopted as the primary classifier due to its effectiveness in modelling complex relationships, while Gaussian Naive Bayes (GNB) is used as a baseline for comparison. The system’s performance is assessed using standard evaluation metrics including accuracy, precision, recall, and F1-score.
THE IMPACT OF EMPLOYEE SATISFACTION ON ORGANIZATIONAL PERFORMANCE: A STUDY OF CONTRIBUTING FACTORS AND OUTCOMES AT IIFL
Human resources are one of the most important assets of the organization & assessing it current value is both important and difficult, but it must be done if this resources utility is to be optimized, the performance of their resource will determine the overall effectiveness of the organization. On method of evaluating their value is through the assessment of the climate of the organization. They are working in the research data shows that the job satisfaction does have an equates on the overall organization effectiveness. Organization is made up of people there for if people do not change then the organization can not change for obtaining the full co-operation & enthusiastic support of the members in achieving the organizational objectives, the organization must satisfy their needs and insurer their feelings. Every organization is different and less a unique feelings and character beyond its structures characters these each org, deals with its members in a distinct way through its policies on allocation of resources, commune action pattern reward and penalty leader ship and decision making style etc. the org policy and connection with regard to all these and a cluster of other related activities influence the feelings attitudes and behavior of its members and results in the creation of a unique organizational climate. Hence job satisfaction is a product of leader ship practices communication further, enduring the systemic characteristics f the working relationship among person and divisions of the organization
A COMPREHENSIVE ANALYSIS OF CUSTOMER BUYING BEHAVIOR: UNDERSTANDING DECISION-MAKING PROCESSES AND INFLUENCING FACTORS AT HERO MOTO CORP Ltd.
Customer buying behavior is a critical area of study in marketing as it helps organizations understand how consumers select, purchase, use, and evaluate products and services. Consumer decisions are influenced by a combination of psychological, social, cultural, personal, and economic factors. Understanding these influences enables businesses to develop effective marketing strategies, improve customer satisfaction, and build longterm relationships with consumers. This study provides a comprehensive analysis of customer buying behavior by examining the decision-making process and the various factors that influence purchasing decisions. The research highlights the importance of consumer perceptions, attitudes, motivations, and external influences such as family, reference groups, and social media. The findings contribute to a better understanding of customer preferences and help organizations design products, pricing strategies, promotional campaigns, and distribution channels that align with consumer expectations.
THE EFFECTIVENESS OF DIGITAL ADVERTISING ON MODERN CONSUMER BUYING BEHAVIOR: A CASE STUDY OF AIRTEL
Advertising plays a crucial role in influencing consumer buying behavior by creating awareness, providing information, and shaping consumer perceptions about products and services. In today's highly competitive market environment, businesses use various advertising media such as television, newspapers, radio, social media, and digital platforms to attract potential customers and influence their purchasing decisions. Effective advertising helps consumers recognize product features, compare alternatives, and develop preferences for specific brands. The study focuses on analyzing the impact of advertising on consumer buying behavior and understanding how different advertising strategies affect consumer attitudes, preferences, and purchase intentions. It examines the role of advertising in building brand awareness, enhancing customer trust, and encouraging repeat purchases. The study also explores factors such as advertising appeal, message content, celebrity endorsements, and digital marketing techniques that influence consumer decision-making. The findings indicate that advertising significantly affects consumer buying behavior by increasing product knowledge, creating brand loyalty, and motivating purchase decisions. Consumers are more likely to purchase products that are advertised effectively and align with their needs and expectations. The study concludes that advertising remains an essential marketing tool for businesses seeking to attract customers, improve sales performance, and maintain a competitive advantage in the marketplace.
ANALYZING THE IMPACT OF PERSONALIZED MARKETING STRATEGIES ON CUSTOMER SATISFACTION AT TATA MOTORS
Personalized marketing has emerged as a powerful strategy for organizations seeking to enhance customer satisfaction and build long-term relationships with consumers. By utilizing customer data, preferences, purchase history, and behavioral insights, businesses can deliver customized products, services, and promotional messages that meet individual customer needs. This study examines the impact of personalized marketing strategies on customer satisfaction and explores how tailored marketing efforts influence consumer perceptions, engagement, and loyalty. The research focuses on understanding the effectiveness of personalization techniques such as targeted advertisements, personalized recommendations, customized email campaigns, and individualized customer experiences. Data collected from consumers is analyzed to evaluate their responses to personalized marketing initiatives and the extent to which these strategies contribute to satisfaction and purchasing decisions. The findings indicate that personalized marketing significantly enhances customer satisfaction by improving relevance, convenience, and overall customer experience. Customers are more likely to engage with brands that understand their preferences and provide personalized solutions. The study also highlights the role of technology, data analytics, and customer relationship management systems in implementing successful personalization strategies. However, concerns related to data privacy and information security remain important challenges that organizations must address. The study concludes that personalized marketing is an effective tool for increasing customer satisfaction, strengthening customer relationships, and gaining a competitive advantage in today's dynamic marketplace. Businesses that successfully implement personalized marketing strategies can improve customer retention, brand loyalty, and overall organizational performance
A COMPREHENSIVE ANALYSIS OF SALES PROMOTIONAL ACTIVITIES: STRATEGIES AND THEIR IMPACT ON CONSUMER BEHAVIOR AT HYUNDAI MOTORS
Sales promotional activities play a vital role in modern marketing by influencing consumer behavior, increasing product awareness, and boosting sales performance. Organizations use various promotional strategies such as discounts, coupons, free samples, contests, cashback offers, loyalty programs, and seasonal campaigns to attract customers and encourage purchases. These activities help businesses create brand visibility, enhance customer engagement, and gain a competitive advantage in the marketplace. This study presents a comprehensive analysis of sales promotional activities and their impact on consumer behavior. It examines how different promotional strategies influence customers' purchasing decisions, brand preferences, and buying frequency. The study also explores the effectiveness of both traditional and digital promotional methods in attracting and retaining customers. Data collected from consumers provides insights into their perceptions, preferences, and responses toward various promotional tools. The findings indicate that sales promotional activities significantly affect consumer purchase intentions and contribute to increased sales and customer satisfaction. Price-based promotions such as discounts and cashback offers are found to be highly influential, while loyalty programs and personalized promotions help in building long-term customer relationships. The study concludes that well-planned sales promotion strategies are essential for enhancing market performance and achieving organizational objectives in a highly competitive business environment.
A COMPREHENSIVE STRATEGIES FOR BUILDING AND MEASURING BRAND AWARENESS IN COMPETITIVE MARKETS AT TATA MOTORS
This research project deals measuring brand awareness of Heritage, the study is intended to access brand awareness lives of different car brands available in the market and also about Heritage, to identify the factors/source creating brand awareness and also to know importance of brand awareness role in the buying decision. It is found that majority of respondent owns Heritage coming to brand awareness, almost all respondent are aware about most of the car brands available in the market advertising plays important role in the creating brand awareness among the respondents. Price, Quality, Durability factors are considered by majority of respondent before making purchasing decision. Over all Heritage could able to create significant levels of brand awareness among the public. It is suggested to focus more on television media for advertising apart from other traditional media.
IMPLEMENTING EFFECTIVE CUSTOMER RELATIONSHIP MANAGEMENT: BEST PRACTICES AND CASE STUDIES AT ICICI BANK
Customer Relationship Management (CRM) is a strategic approach focused on building and maintaining longterm, profitable relationships with customers by understanding and responding to their needs. This study examines the role of CRM in enhancing customer satisfaction, loyalty, and retention while improving business performance. It explores key components of CRM systems, including customer data management, sales automation, marketing integration, and customer service support. The research highlights how CRM technology helps organizations track customer interactions, personalize communication, and deliver value-added services. It also discusses the shift from traditional CRM models to more advanced, AI-driven platforms that offer real-time insights and predictive analytics. Through case studies and performance metrics, the project evaluates the impact of CRM implementation across various industries, identifying both challenges and success factors. Findings suggest that effective CRM practices lead to increased customer engagement, better resource allocation, and a stronger competitive advantage. The study concludes that CRM is not just a tool, but a strategic asset essential for sustainable business growth in today’s customer-centric environment
Dr. T. Sundeep Prasad, Komma Sai Keerthana