Journal of Science Engineering Technology and Management Science

AI-powered framework designed to summarize complex legal and financial documents using modern Natural Language Processing techniques. The system combines two advanced models, COLBERT and T5, to improve both the relevance and readability of generated summaries. COLBERT is used to understand the contextual meaning of the document and rank important sentences effectively, while T5 generates concise and meaningful abstractive summaries from the selected content. Unlike traditional extractive methods, this approach focuses on capturing the true intent of the text and expressing it in a simplified manner. The model was trained using a dataset consisting of legal and financial documents along with their summaries. Due to computational limitations in Google Collab, a smaller dataset was used, containing 2,000 training samples and 300 testing samples. Despite these constraints, the system achieved a Rouge-1 score of 30%, indicating promising performance. The results highlight that even with limited resources, transformer-based models can produce useful summaries for domain-specific texts. This framework offers a scalable and cost-effective solution, with potential for significant improvement when trained on larger datasets & powerful hardware.

Keywords : AI-powered summarization, Natural Language Processing, COLBERT, T5 transformer, abstractive summarization, legal documents, financial texts, contextual understanding, sentence ranking, Rouge score evaluation, deep learning, text generation.

Author : Bandi Pranavi, Charan, Geerish Chennale, Gulla Naresh, Mrs. Mercy Joycce

Title : An Automated Summarization Framework for Complex Financial and Legal Texts

Volume/Issue : 2026;03(05)

Page No : 370-375

During disaster search and rescue operations often hinder because of collapse building debris. Rescue team often suffer to reach victim on time which may cause death to victim. To easy search operations we are combining Robotic Hardware along with Machine Learning algorithms. Robots can easily have reached dangerous locations and then apply Machine Learning algorithm to detect presence of human or their body parts. During disaster ML algorithms must work accurately for human detection so we are employing CNN deep learning algorithm which is best known for optimize features extraction and this extracted feature will get trained with various ML algorithms such as Random Forest, SVM and Decision Tree. Each algorithm performance is evaluated in terms of Accuracy, Precision, Recall, Confusion Matrix and FSCORE. All algorithms able to get more than 90% accuracy on CNN optimized features. For accurate prediction Dataset will play an important role but on internet no such dataset exists so we downloaded some disaster images from internet which is created using Virtual Disaster. Dataset size was so small so we applied augmentation algorithm to increase dataset images count.

Keywords : Disaster search and rescue , Robotic hardware, Machine learning, Human detection, Convolutional Neural Network , Feature extraction, Random Forest, Support Vector Machine , Decision Tree, Data augmentation.

Author : G.Pravalika, D.Lakshmi Narasimha, G.Anand, S.Sai Reddy, Mr. S. Bavankumar

Title : AI-Enabled Disaster Prediction and Autonomous Search Operations Platform

Volume/Issue : 2026;03(05)

Page No : 376-381

Demand forecasting plays a crucial role in effective decision-making across industries such as retail, supply chain, and inventory management. This work presents a data-driven machine learning framework for predicting short-term and long-term product demand using multiple algorithms. The system is designed to compare the performance of traditional and advanced models, including Random Forest, Gradient Boosting, LSTM, and XGBoost. Initially, the dataset is pre-processed through normalization, shuffling, and splitting into training and testing sets to ensure reliable evaluation. Each model is trained on 80% of the data and tested on the remaining 20% to measure performance. The evaluation is conducted using metrics such as R² score for accuracy and Root Mean Square Error (RMSE) for prediction error analysis. Experimental results show that Random Forest and Gradient Boosting provide moderate accuracy, while LSTM captures temporal patterns more effectively. However, the XGBoost model outperforms all other methods by achieving the highest accuracy and lowest error rate. The system also includes a user interface that enables real-time demand prediction for selected products. The results demonstrate that advanced ensemble techniques can significantly enhance forecasting performance. This approach provides a reliable and efficient solution for demand prediction in real-world applications. The proposed system can support better planning, reduce uncertainty, and improve operational efficiency.

Keywords : Demand Forecasting, Machine Learning, XGBoost, Random Forest, Gradient Boosting, LSTM, Time Series Prediction, RMSE, R² Score

Author : H Sreeja, Akhil Reddy, Tharun Teja Rao, P Chandra Kashiyap, Dr. R. Santhoshkumar

Title : Machine Learning-Based Demand Forecasting for Short- and Long-Term Prediction

Volume/Issue : 2026;03(05)

Page No : 382-389

Human-machine interaction systems are growing more and more important in smart places like offices, schools, banks, and hospitals. This project shows an AI-based automatic greeting machine made with Arduino that greets visitors automatically based on the time of day and whether or not a person is there. An ultrasonic sensor is used by the system to find out when someone is getting close to it. A standard RT Clock (RTC) standard module is used to get the right time right now. The Arduino uses the time data to choose the best greeting message, like "Good Morning," "Good Afternoon," or "Good Evening." An LCD screen shows the chosen greeting, and the DF Mini Player component connected to a the speaker plays it at the same time. This automated greeting protocol cuts down on the need for the human receptionists and makes sure that everyone is polite and consistent. The suggested system is affordable, dependable, and works well in smart environments in real time.

Keywords :

Author : E Ashwinin, M Keerthi, R Abhiram, B Nikhitha, Mrs. K Swayam Prabha

Title : Automatic Greeting Machine Based on AI

Volume/Issue : 2026;03(05)

Page No : 390-397

With the overwhelming amount of digital news available today, helping users discover relevant content has become increasingly important. This work proposes a personalized news recommendation system that combines contextual text understanding with graph-based learning. The model leverages the Microsoft MIND dataset to capture user reading patterns, including both interacted and ignored articles, along with rich news attributes such as category and textual content. To better understand the semantics of news articles, a pre-trained BERT model is used to generate meaningful text representations. These representations are then structured into a graph, where relationships between users and news items are modelled and refined through a Graph Convolutional Neural Network . By learning from these complex connections, the system is able to capture deeper patterns in user preferences. Compared to conventional approaches like LSTM and CNN, the proposed method demonstrates improved recommendation quality by effectively integrating relational and contextual information. The results highlight the model’s ability to deliver more accurate and personalized news suggestions, making it a promising solution for modern content recommendation systems.

Keywords :

Author : Mandala Srikaran, Ramagiri Vaman, Muthineni Abhinay, Karingula Ganesh, Mr. B. Karthik Anand

Title : Graph-Based Personalized News Recommendation Using Deep Contextual Embeddings

Volume/Issue : 2026;03(05)

Page No : 423-428

With the rapid growth of digital payments and online banking, detecting fraudulent transactions has become an important concern for financial organizations. Traditional detection methods often fail to identify fraud accurately due to the complex and evolving nature of fraudulent activities. This project presents an improved fraud detection system using the XGBoost machine learning algorithm, combined with Principal Component Analysis (PCA) to enhance feature selection and reduce data complexity. The model is developed and tested using a publicly available fraud transaction dataset from Kaggle. During implementation, various stages such as data preprocessing, feature extraction, model training, and evaluation are carried out systematically. The performance of the proposed model is assessed using key evaluation metrics including accuracy, precision, recall, and F1-score. To validate its effectiveness, the results are compared with the Naïve Bayes algorithm. The comparison shows that the XGBoost model performs significantly better in identifying fraudulent transactions, achieving accuracy above 99% along with improved precision and recall values. Additionally, the system is capable of predicting fraudulent activities in real-time using unseen test data. Overall, the results demonstrate that the proposed approach is efficient and reliable for detecting online transaction fraud, making it suitable for real-world financial applications.

Keywords :

Author : Belige Madhumitha, Jakkidi Bhavya Sri, Nagapuri Pranay, Janne Charan, Mr. G L Singh

Title : Detection of Fraudulent Online Transactions Using Machine Learning Techniques

Volume/Issue : 2026;03(05)

Page No : 429-434

There have been significant changes in the global luxury fashion industry owing to globalization, technological development, changing lifestyles of consumers, and heightened levels of societal consciousness. Today, luxury fashion consumption not only relates to its basic purpose but is also driven by its symbolic values, including prestige, self-expression, social position, and pleasure, among others. This paper analyzes the significance of symbolic value in influencing the behavior of luxury fashion consumption by bringing together relevant theoretical concepts, scholarly studies, and market trends. The review shows that luxury products are symbols of social and psychological nature through which the consumer conveys his/her identity. Based on well-known theories like conspicuous consumption, hedonic consumption, impression management, reference groups, and signaling theory, the paper highlights the multifaceted approach towards luxury consumption behavior. Nevertheless, there are also several important issues that arise from the literature review, among which include problems with measurements, market segmentation, and increasing presence of counterfeits and masstige products, which altogether make the study of symbolic luxury consumption quite problematic. The findings from the literature review reveal that future research should focus on creating culturally adaptive instruments for measuring luxury consumption behavior, further exploring virtual luxury spaces, and implementing more stringent policies.

Keywords : Luxury fashion consumption, symbolic value, consumer behaviour, prestige, social status, identity signaling, conspicuous consumption, hedonic consumption, cultural influence, digital luxury, social media.

Author : Jagadishwar Reddy REDDYGARI

Title : Understanding the Role of Symbolic Value in Shaping Luxury Fashion Consumption Patterns

Volume/Issue : 2026;03(05)

Page No : 434-441

This study assesses the impact of technology-enabled training systems on employee motivation, competency, and performance in service sector organizations. The study is descriptive in nature and is based on primary data collected through a structured questionnaire administered to 120 employees drawn from IT/ITES, banking and financial services, hospitality and tourism, retail and e-commerce, and healthcare service organizations, supplemented by secondary data drawn from journals, industry reports, and other published sources. The analysis covers the demographic profile of respondents, their exposure to and engagement with technology-enabled training systems such as e-learning modules, virtual instructor-led training, mobile learning applications, and AI-based coaching, and the perceived impact of such systems on employee motivation, competency development, and job performance. The results show that e-learning modules and learning management systems remain the most widely used training format, that employees report strong motivation and engagement gains following technology-enabled training, and that such systems are perceived to meaningfully strengthen technical, communication, and problem-solving competencies as well as day-to-day job performance. The study concludes that technology-enabled training systems have a strong and largely positive impact on the motivation, competency, and performance of employees in service sector organizations, while also revealing a clear demand among employees for more advanced, personalised, and interactive training content, underscoring the need for organizations to continue investing in accessible and engaging digital learning ecosystems

Keywords : Technology-Enabled Training, Employee Motivation, Employee Competency, Job Performance, Service Sector Organizations, E-Learning, Human Resource Development.

Author : Reecha Singh, Shruti Grover

Title : ASSESSING THE IMPACT OF TECHNOLOGY-ENABLED TRAINING SYSTEMS ON EMPLOYEE MOTIVATION, COMPETENCY, AND PERFORMANCE IN SERVICE SECTOR ORGANIZATIONS

Volume/Issue : 2026;03(05)

Page No : 442-456

Foreign exchange (Forex) currency and interest rate futures play a significant role in modern financial markets by helping investors, businesses, and financial institutions manage risks arising from fluctuations in exchange rates and interest rates. This study focuses on the concept, functioning, and importance of Forex currency futures and interest rate futures in the financial system. Forex currency futures are standardized contracts used to hedge against currency risk caused by changes in foreign exchange rates, while interest rate futures are financial derivatives designed to protect investors from interest rate volatility. The study examines the role of these futures in risk management, price discovery, and market stability. It also highlights the advantages of futures trading, including liquidity, transparency, and hedging efficiency, along with the challenges such as market uncertainty and speculative risks. The research concludes that Forex currency and interest rate futures contribute significantly to financial market development by reducing financial risk, improving investment opportunities, and supporting economic growth.

Keywords : Foreign Exchange (Forex), Currency Futures, Interest Rate Futures, Financial Derivatives, Risk Management, Hedging, Exchange Rate Risk, Interest Rate Risk, Price Discovery, Financial Markets, Market Liquidity, Market Stability, Speculation, Investment Opportunities, Economic Growth.

Author : Dr. D.N.V.Krishna Reddy, Dr.D.Srinivasarao

Title : Evaluating Forex Currency and Interest Rate Futures as Strategic Instruments for Financial Risk Management

Volume/Issue : 2026;03(05)

Page No : 482-490

Mergers and Acquisitions (M&A) have become important strategic tools for organizations to achieve growth, improve operational efficiency, and strengthen market position. In the Indian banking sector, the merger of associate banks and Bharatiya Mahila Bank with the State Bank of India (SBI) marked a significant step toward banking consolidation. This study analyzes the impact of mergers and acquisitions on SBI in terms of financial performance, market share, operational efficiency, customer base, and profitability. The research evaluates the pre-merger and post-merger performance of SBI using key financial indicators such as deposits, advances, net profit, and total assets. The findings indicate that the merger enhanced SBI's competitiveness, expanded its branch network, increased operational synergies, and strengthened its position as India's largest public sector bank. However, challenges such as integration costs, employee adjustments, and management complexities were also observed. The study concludes that the merger has positively contributed to SBI's long-term growth and financial stability.

Keywords : Mergers and Acquisitions, State Bank of India, Banking Sector, Financial Performance, Consolidation, Profitability, Assets, Deposits, Operational Efficiency, Synergy

Author : Mrs.A.Ramyakrishna, Mr.V.Rambabu

Title : Assessing the Strategic and Financial Outcomes of Mergers and Acquisitions at State Bank of India

Volume/Issue : 2026;03(05)

Page No : 473-481

The introduction of the Goods and Services Tax (GST) in India has brought significant changes to the taxation system by replacing multiple indirect taxes with a unified tax structure. The hotel, tourism, and travel industries are among the major sectors affected by the implementation of GST due to their strong contribution to the Indian economy and employment generation. This study examines the impact of GST on the hotel industry, tourism sector, and travel services in terms of pricing, tax structure, customer demand, operational efficiency, and overall business growth. The research highlights how GST has simplified the taxation process by eliminating cascading taxes and improving transparency in transactions. At the same time, varying GST rates on hotel tariffs, restaurant services, transport facilities, and tour packages have influenced consumer spending patterns and business profitability. The study also analyzes the advantages and challenges faced by hotels, travel agencies, tour operators, and tourists after GST implementation. Findings indicate that GST has contributed to better tax compliance and organized business practices, while higher tax slabs on luxury services have affected affordability and customer preferences. Overall, the study concludes that GST has had both positive and negative impacts on the hospitality and tourism sectors, with long-term benefits depending on policy reforms, industry adaptation, and effective tax management practices.

Keywords : Goods and Services Tax (GST), Hotel Industry, Tourism Sector, Travel Services, Hospitality Industry, Tax Reforms, Indirect Taxation, Customer Demand, Business Growth, Tax Compliance, Operational Efficiency, Tourism Development, Travel Agencies, Tour Operators, Consumer Spending, Economic Impact.

Author : Mrs.D.Naga Teja, Mr. V. Suresh

Title : GST Reforms and Sectoral Transformation: An Assessment of India’s Hospitality, Tourism and Travel Industries

Volume/Issue : 2026;03(05)

Page No : 464-472

Employee motivation is one of the most important factors that influence organizational productivity, employee performance, workplace satisfaction, and business success. This study focuses on motivating employees through incentives and other non-monetary techniques within an organization. Employee motivation refers to the process of encouraging employees to perform their work efficiently and contribute positively toward organizational goals. Organizations use both monetary incentives such as bonuses, rewards, allowances, and performance incentives, as well as non-monetary techniques such as recognition programs, career development opportunities, flexible work environments, appreciation systems, leadership support, training programs, and employee engagement activities to improve employee morale and workplace productivity. In modern organizations, motivated employees show better commitment, teamwork, creativity, workplace discipline, and operational efficiency. The study examines employee perceptions regarding incentive systems, employee recognition, communication practices, career growth opportunities, workplace support, and motivational programs. Technological advancements and modern human resource management practices have significantly improved employee engagement, performance monitoring, reward systems, and communication management within organizations. The study concludes that incentives and non-monetary motivational techniques positively influence employee satisfaction, workplace morale, organizational commitment, productivity, and long-term organizational sustainability.

Keywords : Employee Motivation, Incentive Systems, Non-Monetary Rewards, Employee Satisfaction, Workplace Productivity, Employee Engagement, Career Development, Recognition Programs, Organizational Commitment, Human Resource Management.

Author : Dr. D. N. V. Krishna Reddy, Mrs. T. Naga Lakshmi

Title : Igniting Workforce Excellence: A Strategic Study on Employee Motivation Through Incentives and Non-Financial Engagement Practices

Volume/Issue : 2026;03(05)

Page No : 457-463