Journal of Science and Technology Excellence
Welcome to the Journal of Science and Technology Excellence (JSTE) The Journal of Science and Technology Excellence (JSTE) is an international, peer-reviewed, open-access journal published by Novara Publishers. Our mission is to provide a premier scholarly platform for researchers, engineers, academicians, professionals, and industry experts to publish high-quality, innovative work across all major engineering domains. At JSTE, we believe in advancing the frontiers of technology. Our aim is to promote research that drives innovation and solves real-world challenges — from intelligent computing to sustainable energy. Join us in fostering scientific excellence. Aims & Scope The Journal of Science and Technology Excellence (JSTE) provides a platform for high-quality research in science, engineering, and technology. JSTE publishes original research, review articles, and case studies in a broad range of disciplines, including but not limited to: Science Fields: Physics and Applied Physics Chemistry and Materials Science Environmental Science Biotechnology and Life Sciences Technology & Engineering Fields: Electrical Engineering Mechanical Engineering Civil Engineering Computer Science and Engineering Electronics and Communication Engineering Artificial Intelligence and Machine Learning Internet of Things (IoT) Renewable and Sustainable Energy Other Emerging Engineering Fields JSTE aims to promote interdisciplinary research that bridges scientific discovery with technological innovation, supporting advancements that address real-world challenges. The journal follows a double-blind peer-review process to ensure the highest standards of quality and academic integrity. All published articles are made available under the Creative Commons Attribution (CC BY 4.0) License, enabling unrestricted access, sharing, and distribution with proper attribution. JSTE adheres to the ethical standards recommended by the Committee on Publication Ethics (COPE) and ensures long-term preservation of published content through LOCKSS and CLOCKSS archiving systems. Journal of Science and Technology Excellence (JSTE) Frequency of Publication: Quarterly (January, April, July, October) ISSN: 3108-3102 Publisher Name: NOVARA PUBLISHERS Publisher Address: #605, Jasmine Block, RDB Coconut Grove, Dynamics Colony, Mayuri Nagar, Hyderabad, Telangana – 500049, India Editor-in-Chief: Dr. G. Kishor Kumar Copyright: Journal Science and Technology Excellence Starting Year: 2025 Subject: Multi-disciplinary Language: English Publication Format: Online Phone Number: +91 9030161092 Email IDs: info@novarapublishers.com / novarapublishers@gmail.com Website: jstejournal.com Publisher Website: novarapublishers.com
All articles published in the Journal of Engineering Excellence (JEE) are licensed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). Under this license, authors retain full copyright of their work while granting permission for anyone to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from the publisher or author — provided that the original work is properly cited. This open-access license ensures maximum dissemination and impact of the published research by allowing free and immediate access to scholarly work. For more details, please refer to the official license page: ???? https://creativecommons.org/licenses/by/4.0/
BRIDGING THE BLACK-BOX GAP: EXPLAINABLE AI FOR LARGE LANGUAGE MODELS
Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse natural language processing tasks; however, their transformer-based architectures operate as complex black-box systems with limited transparency into internal reasoning processes. Despite their ability to generate coherent and seemingly logical explanations, such outputs are often not guaranteed to be faithful representations of the model’s true decision pathways, raising critical concerns regarding trust, accountability, bias propagation, hallucination, and regulatory compliance in high-stakes applications. This paper addresses the interpretability gap by proposing a structured Explainable AI (XAI) framework designed to bridge the black-box nature of modern LLMs. The proposed approach integrates intrinsic interpretability mechanisms with post-hoc attribution techniques to produce explanations that are human-understandable, verifiable, and aligned with internal model behavior. A multi-dimensional evaluation strategy is introduced, incorporating faithfulness assessment, robustness testing, explanation consistency analysis, and bias sensitivity measurement. Experimental validation on benchmark natural language tasks demonstrates that the proposed framework enhances explanation reliability without significantly degrading predictive performance. By advancing scalable and verifiable explainability mechanisms, this work contributes toward the development of trustworthy, transparent, and ethically responsible Large Language Models suitable for real-world deployment in safety-critical domains.
Copyright (c) 2026 Journal of Science and Technology Excellence https://creativecommons.org/licenses/by-nc-nd/4.0
A STUDY ON DIVIDEND POLICIY IN FMCG SECTOR AT HINDUSTAN UNILEVER LIMITED
Investor trust and the methods used to distribute earnings are both affected by a company's dividend policy. In order to foster future growth, fast-moving consumer goods (FMCG) companies should reinvest their earnings while also distributing dividends to shareholders. The dividend policies of Hindustan Unilever Limited (HUL), a prominent Indian FMCG firm, have contributed to this consistency. By looking at its payout ratios and dividend records, HUL shows that profitability and liquidity are its top priorities. Despite letting market forces shape its strategy, the corporation has consistently maintained a solid dividend policy. This consistency not only shows long-term financial stability but also reassures investors and strengthens HUL's brand. Maintaining the company's capacity to secure financing for sustainable expansion allows for consistent distribution of earnings to owners. This policy demonstrates HUL's dedication to aligning its business strategy with the expectations of its investors. It further demonstrates how regulatory frameworks impact fast-moving consumer goods dividend decisions. HUL's approach shows how companies can balance sustainable development with shareholder trust.
A STUDY ON IPO PRICING AND MARKET REACTIONS IN INDIAN EQUITY AT CITI FINANCIAL LTD
The price swings and market response to Citi Financial Ltd.'s first public offering (IPO) on the Indian stock exchange are examined in this essay. A number of factors, such as the issuer's fundamentals, investor mood, the regulatory environment, and general macroeconomic conditions, affect the pricing of an initial public offering (IPO) in India. The inquiry evaluates whether Citi Financial Ltd. was undervalued or overvalued at the time of issuance. It uses publicly available offering data and market performance metrics after listing to analyze investor responses in the short- and medium-term aftermarket. The research looks at how the listing day price is affected by sectoral positioning, issue size, subscription demand, and gray market features. By clarifying the processes of price determination, investor expectations, and the use of IPO values as indicators within India's dynamic financial services sector, the findings advance knowledge of the effectiveness of initial public offerings (IPOs) in developing countries.
Book-Building Mechanism and Market Efficiency
A STUDY ON WORK ENVIRONMENTS AND CAREER DECISIONS OF MILLENNIALS WITH REFERENCE TO WINGS TRAVEL MANAGEMENT
Millennials' ideal work environments and their experiences at Wings Travel Management are the subjects of this research. Company culture, workplace flexibility, leadership style, and opportunities for advancement are examined in this research to determine their effects on millennials' career decisions, job satisfaction, and commitment over the long run. The results demonstrate that employees' decision-making is significantly impacted by an encouraging and innovative work atmosphere. Traditional benefits are less essential to millennials than meaningful employment, a healthy work-life balance, and opportunities for professional growth. Businesses like Wings Travel Management may find success in retaining and engaging millennial workers by shifting their focus and strategies to better suit this generation's priorities and priorities at work. The research provides useful data for business leaders in the travel and corporate services sectors in areas such as strategic talent management, organizational planning, and HR decision-making.
A STUDY ON AI-POWERED TRAINING PROGRAMS FOR EMPLOYEE SKILLS WITH REFERENCE TO GENPACT
Genpact is transforming the landscape of employee training. The rapid advancement of technology and the varied learning objectives frequently surpass the capabilities of traditional training approaches. Genpact's AI-driven solutions facilitate personalized learning tailored to an individual's performance, skill gaps, and preferable learning modalities. These technologies support learning by providing tailored information, prompt feedback, and compelling experiences. Enhanced productivity, expedited skill development, and increased employee engagement are the resulting outcomes. These initiatives promote continuous learning and professional growth, ensuring that employees' competencies align with the organization's objectives and future requirements.
Workforce Upskilling and Performance Analytics
A STUDY ON GAMIFICATION IN LEARNING AND DEVELOPMENT WITH REFERENCE TO COGNIZANT
This research analyzes how Cognizant has revolutionized the methods by which its employees cultivate and gain knowledge through the use of games. The organization seeks to improve learning efficacy, sustain employee motivation, and promote engagement by integrating gamified components into training sessions, including challenges, incentives, and progress monitoring. The research employs data gathered from questionnaires, interviews, and employee performance measures to analyze the effects of gamified modules on learning, skill development, and overall performance. The results demonstrate that gamification improves engagement and promotes a culture of ongoing learning and creativity in the workplace. The report offers significant recommendations for improving gamified learning frameworks to better fit with Cognizant's strategic goals. These tips will aid staff in sustaining motivation while concurrently improving productivity and efficiency in a dynamic corporate environment.
Behavioral Change and Performance Improvement
A STUDY ON RBI HOUSING POLICIES AND MORTGAGE CREDIT SUPPLY WITH REFERENCE TO LIC HOUSING FINANCE
The housing finance sector in India helps people achieve their dream of home ownership, which goes beyond mere statistics. This research examines the impact of RBI policy on LIC Housing Finance's lending practices in India. The amount of credit that LIC extends and the interest it charges are directly impacted by any changes to the repurchase rate, lending policies, or liquidity instructions made by the Reserve Bank of India. According to the research, which is based on data collected from papers and articles over a five-year period, these restrictions impact both the amount of mortgage loans and the interest rates that consumers are required to pay. For borrowers, this means that the cost of a mortgage could go up or down depending on RBI policy. Strategically, it directs the risk management and lending policies of LIC Housing Finance. The results demonstrate that the availability of house loans is a key component of monetary policies that impact people's day-to-day lives. People are able to purchase homes with the support of easily accessible financing, which contributes to economic growth. This research's findings can inform policymakers and lenders' efforts to improve the usefulness and adaptability of house loans. The analysis reveals a strong connection between the RBI's actions and the aspirations of millions of Indians to own their own houses.
A STUDY ON LEVERAGED BUYOUT MODELING FOR INFRASTRUCTURE FIRMS AT GVK POWER & INFRASTRUCTURE LTD
A leveraged buyout approach is employed by GVK Power & Infrastructure Ltd. in order to evaluate the potential profitability of the acquisition. A comprehensive analysis of the infrastructure industry's risks, as well as the interplay between the firm's assets, cash flows, and debt, is included in the paper. Projects in this field require a lot of cash and have a lengthy duration, thus finding the optimal debt-to-equity ratio is the main goal of the research. The ability of the organization to continue borrowing money in the face of adverse circumstances is assessed using a battery of scenarios and stress tests. Controlled revenue and prolonged project schedules are two of the financing-related variables examined in the research. Additionally, it delves into the ways in which operational changes, refinancing, or asset divestitures can enhance investor returns. Structured loans, government assistance, and diverse revenue streams are all risk mitigation strategies that can help a business better manage its debt. The research delves deeper into the ways in which synergies and cost savings could enhance the reliability of cash flow. As an additional strategy for increasing value, reorganizing the portfolio is suggested.
AI-DRIVEN MULTILINGUAL DISTRESS DETECTION AND PREDICTIVE WELLBEING ANALYTICS FOR COLLEGE STUDENTS
This paper presents a simple, scalable AI system that supports the mental health of college students through real-time text-based interaction. The system uses a conversational agent with natural language processing to understand student messages, detect emotional distress, and classify risk levels as low, medium, or high. It supports multiple languages so that students can express themselves comfortably and anonymously. The AI then offers basic guidance, coping tips, and, in high‑risk situations, triggers alerts for human support. At the same time, anonymized data from many interactions is converted into predictive wellbeing analytics that show overall stress trends, emerging risk patterns, and engagement levels over time. These insights help institutions plan early interventions, allocate counselling resources, and design more supportive policies without exposing any student’s identity. The paper will describe the technical design, AI and NLP workflow, and how predictive modelling is used to move from reactive crisis response to proactive care in college settings.
ADVANCING VIDEO FORGERY DETECTION VIA DEEP CONVOLUTIONAL NEURAL NETWORKS
The increasing sophistication of editing technologies has raised concerns about video forgeries and their potential impact on digital forensics, media integrity, and security. It is a challenging task as tiny changes are so difficult to identify with traditional detection techniques. This paper's main objective is to investigate how Deep Convolutional Neural Networks (DCNN) might enhance the detection of video counterfeiting. The suggested model can identify instances of deepfake alterations, splicing, and frame tampering with an impressive level of accuracy according to deep learning techniques. The trials' findings suggest that deep convolutional neural networks (DCNN) perform better than more conventional methods, which may make them useful in forensic investigations.
POLYMERIC INORGANIC COMPOSITE MEMBRANES FOR WATER PURIFICATION
The term "selective barrier" refers to an interface or membrane that is very narrow and hence prevents material from moving between phases. The ability of narrow sheets of solution to penetrate particular materials is determined by their unique physical and chemical properties. These materials can be either natural or manmade. The numerous benefits of polymeric-inorganic composites over traditional membranes have propelled them to the forefront of engineered material advancements. Polystyrene, PVC, tungstate, metal phosphate, and arsenate are among the metal oxides that can be used to create synthetic membranes. For separation applications, the vast majority of synthetic membranes now in use are polymer-polymer composites. Their bulk structure, production method, surface chemistry, and shape are the criteria used to classify them. The chemical and physical properties of synthetic membranes and the fundamentals of membrane separation methods are all part of what are known as differentiators. Metals, ceramics, liquids, polymers, or a combination of the two are some examples of inorganic materials that can be used to create artificial membranes. Heterogeneous solids, such as mixed glasses or polymeric mixtures, can be used to produce composite materials, in addition to liquids and uniform polymer sheets. Linear ion-exchanger gel membranes on a disk and a ribbon are indistinguishable from one another. Membranes with distinct histological properties are created when a colloidal ion-exchanger and an inert binder are combined. The biotech, textile, fuel cell, water purification, food, pharmaceutical, and other multibillion dollar industries might all benefit from membrane-based innovations.
CROSS PROJECT LEARNING FOR FAULT PREDICTION WITH IMBALANCED DATA
This study delves into the challenges of dealing with contradictory evidence and generalizing models. In addition, it investigates how incorporating other research efforts could enhance software failure prediction. The inability of traditional failure prediction methods to be highly task-specific stems from the fact that not all tasks share the same data. Machine learning procedures, data resampling methods, and feature selection tactics can help you overcome these challenges and make more accurate predictions. This project has two main goals: first, to improve model training and second, to investigate the usage of multiple datasets in order to hasten problem identification and ensure that solutions operate with varying software configurations. The findings have the potential to enhance and contextualize software quality assurance methods.
ENHANCING CNN PERFORMANCE VIA GENETIC ALGORITHMS FOR FATIGUE MONITORING
The use of evolutionary algorithms improves convolutional neural networks for the detection of sleepy drivers. By identifying the best hyperparameters, detection improves the model's performance. Using Genetic Algorithms (GAs) and Convolutional Neural Networks (CNNs), this method tweaks learning rates, filter dimensions, and layer structure. Genetic algorithms improve detection performance by iteratively adjusting CNN parameters through mutation, selection, and crossover. In order to detect weariness in real-time, the improved CNN records key facial signals such as eye shutting and yawning patterns. This method not only makes things last longer, but it also decreases handling expenses and overfitting. Convergence is faster and more accurate than with traditional CNNs, according to the data. Through continuous driver monitoring, this device enhances traffic safety. In the future, researchers will mainly concentrate on practical uses and methods to include several sensors.
INVESTIGATION OF WEB ATTACK VULNERABILITIES MAN IN THE MIDDLE AND SESSION HIJACKING
The online infrastructure is at risk from flaws in web security. Session hijacking and man-in-the-middle (MITM) assaults are two of the most dangerous dangers. Man-in-the-middle tactics enable attackers to intercept user-website conversations and manipulate or pilfer data. Attackers can gain access to another user's session and carry out harmful operations by using login tokens. The inquiry looks into the consequences, risks, and functionality of online apps. Many security techniques, including intrusion detection systems, private session management, and encryption, are now being investigated. Real-world case studies are used to demonstrate the issues and solutions. To improve safety procedures, a thorough understanding of these risks is required. The investigation focuses mostly on self-governing security systems. The principal aim of the research years will be to improve security by applying artificial intelligence.
LEVERAGING MACHINE LEARNING FOR FRAUD DETECTION IN BANKING
The detection of bank misconduct is more critical than ever as the number of digital activities increases. In a world where cyber threats are evolving at a dizzying rate, machine learning (ML) has emerged as a powerful tool for detecting suspicious activity. In this study, we examine how businesses can rapidly detect fraud by analyzing massive datasets with machine learning techniques such as random forests, decision trees, neural networks, and support vector machines. Machine learning (ML) enhances accuracy, decreases false positives, and speeds up reaction time through feature selection, preprocessing, and model performance review. Ultimately, machine learning facilitates the protection of consumer transactions, the elimination of fraud, and the improvement of banking security and usability.
Anomaly Detection and Classification Algorithms
NEURAL APPROACHES TO HATE SPEECH AND CYBERBULLYING DETECTION
The widespread use of social media has resulted in a surge in hate speech and cyberbullying, therefore robust detection methods are critical. The dynamic character of the language and the ambiguity included in categorization problems are too much for conventional approaches to handle. Investigating if neural networks and uncertainty estimations together can increase detection accuracy is the aim of this study. Using Bayesian deep learning and probabilistic models is one method to facilitate scenario management. The system is more resistant to unwanted inputs and configuration modifications when this tactic is used. A dataset created from real social media comments is used in the evaluation. The findings imply that both memorization and accuracy have improved. This approach can be modified to take into account recently developed hate speech formats. The automatic moderation mechanism performs better as a result. Real-time deployment and multilingual support will be features of future versions of the application.
CYBERBULLYING DETECTION IN SOCIAL NETWORKS USING MULTIMODAL TECHNIQUES
The issue of cyberbullying on social media is on the rise, and it is imperative that we develop more effective methods to identify it. To improve results, a multi-modal method combines text, pictures, and user behavior. Computer vision and natural language processing are both capable of detecting incorrect text and images. Machine learning systems can detect patterns indicative of cyberbullying by analyzing user connections. This approach improves detection accuracy by considering a wide range of materials. By absorbing data from their environments, deep learning systems enhance categorization accuracy. Stopping online harassment is a breeze with real-time tracking. In order to put an end to various forms of cyberbullying using this approach, continuous instruction is required. With this strategy, internet users will be better protected. In order to improve detection, future research should make use of state-of-the-art AI systems.
FAULT-TOLERANT AND SECURE TERNARY CONTENT ADDRESSABLE MEMORY FOR HIGH-RELIABILITY APPLICATIONS
Ternary content addressable memory, also known as TCAM, is a vital component of systems that have a high level of reliability. This is due to the fact that it is extremely secure and can tolerate errors. These applications include, but are not limited to, real-time data processing settings, aviation electronics, cybersecurity infrastructure, and networking systems. Because the data is retrieved according to its content rather than its location in memory, TCAM makes it feasible to perform searching operations in parallel that are extremely fast. As a result of this, it is an excellent option for applications that require accurate pattern matching and retrieval immediately. Traditional TCAM systems, on the other hand, have a number of significant shortcomings, such as the potential for data manipulation and unauthorized access, the chance of human error, and the possibility of hardware failure. Additionally, they have the ability to consume an excessive quantity of power throughout their operation.
TCAM (ternary content addressable memory)
CLOUD-INTEGRATED WEARABLE SENSOR NETWORK SYSTEM FOR REAL-TIME DATA ANALYSIS
An IoT-enabled system of ubiquitous sensors for health and safety monitoring is detailed in this research. Protecting workers' well-being is of the utmost importance in industrial settings. That is why it is possible to greatly enhance workplace safety standards by setting up an IoT network architecture to track physiological and environmental variables. In order to track physiological and environmental variables, the suggested network system incorporates a number of wearable sensors. A number of people are linking their wearable sensors via Bluetooth in order to create an Internet of Things platform that includes various gadgets. A LoRa network facilitates communication and data exchange among these sensors. When a potentially dangerous situation is detected, the sensor node will quickly alert and provide advice to the users. In order to facilitate data processing, cloud connectivity, and local web server capabilities, an intelligent IoT gateway is set up. The data collected by the wearable sensors will be transmitted to an IoT cloud by the gateway, where it will be analyzed, stored, and displayed.
AI POWERED DYNAMIC IRRIGATION SCHEDULING USING REAL TIME WEATHER DATA
A dynamic irrigation scheduling system that optimizes crop output and water utilization by utilizing real-time weather data and AI is described in this paper. The proposed system encompasses machine learning algorithms, sensors, and Internet of Things devices. We will closely monitor environmental variables such as soil moisture, temperature, humidity, and anticipated rainfall. Based on these inputs, the system will autonomously determine the irrigation water requirements, ensuring that neither an excessive or insufficient amount of water is wasted. This system is designed to adapt to changing weather patterns and crop requirements in order to guarantee that resources are utilized efficiently and agricultural methods are durable. This system is more effective than traditional irrigation methods in intelligent agricultural applications due to its increased decision-making capacity, reduced water consumption, and increased crop productivity, as demonstrated by experimental data.
INTEGRATING MACHINE LEARNING AND NETWORK ANALYSIS FOR FINANCIAL DISTRESS PREDICTION
Financial institutions need crisis prediction capabilities to mitigate risk and respond quickly. Traditional models sometimes run into problems due to the fact that unexpected data makes it impossible for many different financial variables to interact. This research introduces a method that integrates network analysis and machine learning to enhance the precision of predictions. To begin, we may determine which financial indicators are most important by using feature selection approaches. Next, machine learning methods such as XGBoost, Random Forest, and Neural Networks are used to categorize the data. Identifying patterns in the propagation of issues and financial connection modeling are two of the many applications of network analysis. When it comes to predicting future occurrences using real-world financial data, the hybrid method performs better than individual machine learning models. The results show that by combining network knowledge with statistical learning, financial problem estimates can be made more precise.
DEVELOPMENT OF A SMARTPHONE-CONTROLLED HOME AUTOMATION SYSTEM USING BLUETOOTH TECHNOLOGY
Home automation is a system that is widely used and significant in the current era of science, technology, and expansion due to its ability to remotely operate and evaluate domestic equipment. It is expanding and evolving at a swift pace. This article delves into the development and execution of a smart home system that employs Bluetooth to regulate household appliances. As a result, the project's affordability and user-friendliness are improved by the Bluetooth integration of the system. Not only is the system user-friendly and comfortable, but it also includes a security feature that allows the user to detect intrusions while away from home and activates when the user exits the house. This feature is maintained by an independent Android application.
AUTOMATIC DETECTION OF CYBERBULLYING IN SOCIAL NETWORKS
The increased use of the internet in modern times has created a lot of data. The virtual world has both positive and negative aspects. One of the worst sides of web 4.0 is cyberbullying, a form of cybercrime. The use of technology in bullying situations is known as cyberbullying. This research compiled the findings of thirty separate investigations into cyberbullying and documented the wide range of methods used to identify instances of bullying. By adding textual, behavioral, and demographic aspects, this research departs from a previous analysis of the same dataset that only included linguistic variables. Some terms in the text could be used in a way that could be considered cyberbullying. People who have experienced bullying firsthand are more prone to bullying others. A person's gender, age, and residence are some of the demographic details provided by the dataset. In order to evaluate the two classifiers, the system makes use of various performance metrics. With an overall accuracy of 87.14, the Support Vector Machine classifier outperforms the Bernoulli NB classifier.