Volume 13 | Issue 8 |

 Abstract

This paper presents an innovative, art-integrated pedagogical method for teaching complex geographical concepts through multisensory engagement. The case study highlights a classroom demonstration on volcanic eruptions incorporating music, hands-on modeling, and creative expression. Conducted as part of the SAMMRIDHI National Event organized by NCERT-CIET, this approach aligns with the National Education Policy (NEP) 2020, emphasizing experiential, interdisciplinary learning. The overwhelmingly positive response underscores the potential of art-integrated teaching to enhance conceptual clarity and student engagement in geography.

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 Keywords

Art-Integration, Geography Teaching, Volcanic Eruptions, Experiential Learning, NEP 2020, SAMMRIDHI, Multisensory Education, NCERT-CIET

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Artificial intelligence (AI) is fundamentally reshaping the landscape of professional softwaredevelopment, moving beyond mere automation to redefine workflows, roles, and strategiccapabilities across the entire software development lifecycle (SDLC). This report provides acomprehensive analysis of AI's pervasive integration, detailing its transformative impact onrequirements gathering, design, code generation, testing, debugging, code review,documentation, security, and deployment. It explores the significant benefits, includingenhanced productivity, improved code quality, accelerated time-to-market, and augmenteddecision-making, while also critically examining the inherent challenges such as accuracyconcerns, security vulnerabilities, intellectual property complexities, the risk of over-reliance, andcrucial ethical considerations. Furthermore, the report delves into the evolving roles andessential skill sets for software engineers in an AI-driven era, highlighting the shift towards anintelligence-centric development paradigm. Finally, it forecasts emerging trends, including therise of agentic AI, multimodal and customized models, self-healing software, and thedemocratization of AI tools through open-source initiatives, concluding with actionablerecommendations for practitioners and organizations navigating this profound technological evolution.

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 Keywords

Artificial Intelligence (AI), Software Development Lifecycle (SDLC), Large Language Models (LLMs), Generative AI (GenAI), Code Generation, AI Coding Assistants, Automated Testing, Debugging and Error Resolution, Code Review, Security Analysis, Intellectual Property (IP), Algorithmic Bias, Agentic AI Systems, Self-Healing Software, Open-Source AI

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Creative Commons Attribution 4.0 and The Open Definition

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The shift towards online education, driven by technological advancements and the global pandemic, has introduced innovative teaching-learning practices including online teaching and it needs a different assessment method such as the Online Unrestricted-Type Open Book Examination (OUTOBE). The growing reliance on online education has led to the adoption of various assessment formats, including the OUTOBE, which allows students access to unlimited resources during the examination. This study explores the attitudes of student-teachers studied in the B.A. B.Ed. / B.Sc. B.Ed. (4 Year Integrated) Programme of Doctor Harisingh Gour Vishwavidyalaya (MP) India towards OUTOBE, focusing on their attitude of its fairness, effectiveness, and impact on learning outcomes. Purposive sampling technique was used for selection of the sample. Total 66 student-teachers in which 24 female and 42 male student-teachers who had both the experience of online unrestricted type open book and traditional (closed book) examination, were included in this study. Using a quantitative survey to capture diverse perspectives of student-teachers. To compare the means of scores, t-test is used for analysis of data. The findings revealed that while many student-teachers appreciate the flexibility and reduced anxiety associated with OUTOBE, there are some concerns about its effectiveness in evaluating true understanding and critical thinking skills. Despite these concerns, the majority of student-teachers expressed a preference for OUTOBE over traditional examinations, citing its potential to assess applied knowledge and practical skills. It offers valuable insights for educators and policymakers seeking to enhance the assessment strategies in higher education, ensuring them align with 21st-century teaching and learning paradigms.

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 Keywords

OUTOBE, Online Unrestricted-Type Open Book Examination, Attitude, Student-teacher and Innovative Assessment.

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The cement industry in India faces constant pressure to enhance productivity, workplace safety, and cost efficiency. This case study presents the implementation of the 5S methodology ata mid-sized cement manufacturing plant in India. It highlights how the 5S approach -- Sort, Set in Order, Shine, Standardize, and Sustain -- was used to improve housekeeping, reduce waste, and create a culture of continuous improvement. The case study offers insights into practical challenges, solutions, and measurable outcomes.This case study examines the systematic implementation of 5S methodology in a cement manufacturing plant in India to improve workplace organization, safety, and operational efficiency. The research investigates the challenges and benefits of implementing Sort (Seiri), Set in Order (Seiton), Shine (Seiso), Standardize (Seiketsu), and Sustain (Shitsuke) principles in a complex industrial environment. The study was conducted over an eight-month period at ABC Cement Limited, focusing on critical production areas including raw material handling, kiln operations, and cement grinding sections. Results demonstrate significant improvements in workplace safety (45% reduction in minor accidents), equipment efficiency (18% improvement in Overall Equipment Effectiveness), and employee productivity (22% increase in output per man-hour). The implementation achieved substantial cost savings of 38 million annually through waste reduction, improved equipment reliability, and enhanced operational efficiency.

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5S Methodology, Workplace Organization, Cement Manufacturing, Lean Manufacturing, Safety Improvement, Operational Efficiency, India

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The rapid development in commercial and social areas of modern society has affected the air quality adversely. The pollutant concentration due to industries and transport continue to grow and have an effect on human life. According to the global pollution database maintained by the World Health Organization (WHO), India has the greatest number of polluted cities globally. Higher levels of the Air Quality Index (AQI), which indicates increased risk due to pollutant exposure, are an important indicator that directly affects public health. Thus, tracking air quality index is essential in developing countries so that measures to curb air pollution could be tackled. In this research paper the primary aim is to anticipate the levels of Air Quality Index using classification techniques namely Decision Tree, Random Forest, Linear Regression and Support Vector Machines. For the experimental work the dataset of Delhi: Ashok Vihar and 2 stations of Kolkata: Ballygunge and Victoria has been gathered from Central Pollution Control Board Website. It has been concluded that Random Forest depicts maximum performance than the other classification techniques based on accuracy, precision and recall and SVM exhibits the lowest prediction accuracy.

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 Keywords

Classification, Support Vector Machine, Decision Tree, Linear Regression ,Random Forest, Air Quality Index

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Creative Commons Attribution 4.0 and The Open Definition

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This study investigates and compares user preferences, satisfaction levels, and usability factors between mobile banking applications and online banking platforms in public sector banks in India. In the world of banking, where services appear to be digitalized at a rapid pace, it is important to know customers' behavior in each of the channels as such knowledge can propel service delivery and user experience. A stratified random sample of 150 respondents from urban and semi urban areas that have diverse demographic profile and banking habits was formed. The structured questionnaire was used to collect the primary data, concerning the platform usage, service satisfaction, as well as the security perceptions and the influential factors of adoption. Descriptive and inferential statistical tools, i.e. t-tests and regression analysis were used to analyze the data. Specifically, findings show that mobile banking is the favored platform since it is easy to use, fast, accessible, and with a better user interface, more particularly younger and more experienced users. Key determinants of preference of platform have been security, convenience, and service range. Despite both platforms being commonly used, mobile banking has a lot more user satisfaction and engagement. Based on the study, strategic investments are made in mobile technologies and user centered amendment. In future, there_is going to be more personalized, secure, AI integrated mobile services and at the same time there will be inclusive and innovative digital banking experience.

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 Keywords

Mobile Banking, Online Banking, User Preference, Public Sector Banks, Digital Adoption

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Creative Commons Attribution 4.0 and The Open Definition

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This study presents an interactive, real-time simulation combining deep reinforcement learning and adversarial dynamics in a multi agent treasure maze setting. Built with PyTorch for learning and Pygame for rendering, the system instructs Deep Q Networks (DQN) to properly negotiate randomly generated mazes while reacting to deceptive elements like concealed agents and false items. Agents' environments force them to identify real targets, avoid adversaries, and be agile decision-makers in the face of ambiguity. Constraints on energy limit how activities may be performed, hence motivating the strategic use of current knowledge. Using Matplotlib, a Jupyter Notebook grabs and shows a range of real-time game data including survival performance, choice accuracy, treasure gathering, and fake player detection. The game also offers in-game commentary, a real-time scoreboard, and status overlays so as to enhance feedback and study. Along with offering a modular, expandable architecture for adversarial reinforcement learning experiment testing, the system helps with manual and AI-controlled gaming

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 Keywords

Deep Q-Network (DQN), Camouflage Deception, Fake Player Detection, Multi-Agent Reinforcement Learning, Power-Up Management, Jupyter Notebook Simulation, Pygame

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The rapid generation of industrial and mine wastes such as Fly Ash, Red Mud, and Slag presents both environmental challenges and opportunities for resource recovery. This study explores their potential utilization as sustainable materials in line with circular economy principles. Drawing on secondary data from previous research and case studies, the work critically evaluates the physicochemical properties, environmental impacts, and engineering suitability of these wastes for applications in construction, including bricks, road sub-base layers, and embankments. Performance comparisons indicate that Slag demonstrates superior mechanical strength and environmental stability, while Fly Ash shows consistent pozzolanic behavior and Red Mud requires treatment before reuse. A compliance-based assessment aligned with USEPA/CPCB standards was used to ensure safety and feasibility. The findings support the strategic transformation of industrial waste into valuable resources, promoting sustainable mining practices, reducing landfill dependency, and contributing to responsible resource utilization. The research also recommends policy incentives to accelerate adoption in the industrial sector.

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 Keywords

Circular Economy, Mining Waste Management, Fly Ash, Red Mud, Slag, Resource Recovery, Industrial Waste Utilization, Sustainable Construction Materials, Waste Valorization

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Creative Commons Attribution 4.0 and The Open Definition

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The increasing complexity of modern electronic systems necessitates efficient and reliable testing mechanisms during printed circuit board (PCB) development and manufacturing. This project, titled "FPGA-Based Signal Generation for PCB Testing Simulators," aims to design and implement a high-speed, versatile signal generator using FPGA technology. The system can generate various waveforms, including sine, square, triangular, and pulse signals, with precise control over parameters such as frequency, amplitude, and duty cycle. The FPGA-based implementation employs techniques such as lookup tables (LUTs) for waveform synthesis and programmable logic for customizable signal parameters. The generated signals are output through a digital-to-analog converter (DAC) and verified using visualization tools like oscilloscopes. This project emphasizes simplicity and flexibility, reducing complexity compared to existing automated test systems while ensuring high accuracy and real-time signal generation capabilities. Applications include PCB testing, educational tools for understanding signal processing and FPGA programming, and pre-processing in communication systems. The results demonstrate the feasibility of a cost-effective, configurable signal generator that serves as a foundation for future enhancements, such as feedback analysis and adaptive testing.

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 Keywords

FPGA, Signal Generation, PCB Testing, UART, VHDL, DAC, Logic Analyzer, Waveform Synthesis, Logic Locking, LFSR.

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 Abstract

Early detection of heart disease plays a vital role in preventing life-threatening cardiovascular events and reducing healthcare burdens. This research presents a machine learning-based approach to predict the 10-year risk of heart disease using clinical data from the Framingham Heart Study. A Logistic Regression model was implemented due to its simplicity, interpretability, and suitability for binary classification tasks in healthcare. The dataset was preprocessed through normalization and feature selection, focusing on key indicators such as age, blood pressure, cholesterol, and glucose levels. The model was trained and evaluated using accuracy, confusion matrix, and other classification metrics. The Logistic Regression model achieved an accuracy of 100%, demonstrating its effectiveness in identifying at-risk individuals. Although more complex algorithms like XGBoost offer higher accuracy, the transparency of Logistic Regression makes it a valuable tool for real-world clinical applications. The study concludes by suggesting future enhancements such as handling data imbalance, integrating ensemble models, and deploying explainable AI to improve prediction reliability and clinical trust.

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 Keywords

Heart Disease Prediction, Logistic Regression, Framingham Dataset, Machine Learning, Clinical Risk Factors, Explainable AI, Binary Classification.

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