← Back to Archives
VOLUME 13, ISSUE 2, FEBRUARY 2025
Boosting Decision-Making with LLM-Powered Prompts in PowerBI
Abdul Khaleeq Mohammed, Siraj Farheen Ansari, Mohammed Imran Ahmed, Zubair Ahmed Mohammed
Line Following Food Serving Robot
Divyashree L. K, Keerthana K. V, Vyshnav E.
ACCIDENT DETECTION AND ALERT SYSTEM
Divyashree L. K, Akhil Sunny, Muhammed Munazir
Securing IoT Networks: A Blockchain-Driven Approach to Decentralized Communication
Syed Mubeen Ali, Mohammed Nomaan, Khaja HameedUllah, Syed Omer Shah, Mohammed Zakwan Khan
ARTIFICIAL INTELLIGENCE (AI) IN EDUCATION: USING AI TOOLS FOR TEACHING AND LEARNING PROCESS
Dr Akhilesh Saini, Mrs. Deepika Sharma, Mrs. Kritika Tripathi
Advancing Lithium-Ion Battery Recycling in India Through Technological Innovations Economic Feasibility and Policy-Driven Sustainability
Priyanka Singh, Saanjhree R, PS Nagachyuth, Rakshith Krishna G, Rohith Chelva, S Prena, Dr. Gopalakrishnan Chinnasamy
Understanding Consumer Preferences and Technology Readiness for AI-Based Emotional Wellness Apps
Sharmila K, Shreyas, Shrushti S Naduvinamath, Sinchana B, Sirisha S, Sivan Vishnu Balan, Sneha T, Dr.Gopalakrishnan Chinnasamy
A novel, time controlled, four digit PIN input system with reset function, for multiple security applications, using VHDL and FPGAs
Dr Evangelos I. Dimitriadis, Leonidas Dimitriadis
DC Homes with Scope of AI for Energy Security in Developing Nations
Akash Sao, Satish Sahu
ANTHROPOMETRIC VARIATIONS AND SPORTS PERFORMANCE: A REVIEW OF RELATED LITERATURE
Jai Bhagwan Singh Goun
The Effect of Capital Structure on Investment Performance: A Study of Listed Companies in the Financial Sector
Joel Adetokunbo, Olakunle Sobowale
Abstract
Boosting Decision-Making with LLM-Powered Prompts in PowerBI
Abdul Khaleeq Mohammed, Siraj Farheen Ansari, Mohammed Imran Ahmed, Zubair Ahmed Mohammed
DOI: 10.17148/IJIREEICE.2025.13201
Abstract: Integration of AI into Power BI by Microsoft has revolutionized traditional business intelligence tools into sophisticated decision support systems. Organizations are enabled to make their decision-making processes accurate, efficient, and real-time by using AI-powered prompts related to predictive analytics, anomaly detection, natural language nodes, and Smart Narratives. This paper discusses the operations, applications, and implications of AI-driven prompts in Power BI, more specifically their democratizing effect to place the non-technical user on equal footing with the technical user in the analysis of data. There exist measurable improvements in decision accuracy, operational efficiency, and user satisfaction cutting across different sections of the economy, including healthcare, retail, and manufacturing. However, there still exist a lot of challenges: in fields like data quality, AI biases, and ethical concerns, among many others. Directions for improving the possibilities of Power BI with respect to each significant example include generative AI, real-time analytics, and industry-specific solutions. All these innovations place AI-powered tools as the center of shaping the future in data-driven decision-making or organizational success.
Keywords: Artificial Intelligence, Power BI, Predictive Analytics, Anomaly Detection, Natural Language Querying, Smart Narratives, Business Intelligence, Decision-Making, LLM.
Keywords: Artificial Intelligence, Power BI, Predictive Analytics, Anomaly Detection, Natural Language Querying, Smart Narratives, Business Intelligence, Decision-Making, LLM.
Abstract
Line Following Food Serving Robot
Divyashree L. K, Keerthana K. V, Vyshnav E.
DOI: 10.17148/IJIREEICE.2025.13202
Abstract: Line Following is one of the most important aspects of robotics. A Line Following Robot is an autonomous robot which is able to follow either a black or white line that is drawn on the surface consisting of a contrasting color. It is designed to move automatically and follow the made plot line. The robot uses several sensors to identify the line thus assisting the robot to stay on the track. The array of four sensor make sits movement precise and flexible. The robot is driven by DC gear motors to control the movement of the wheels. The Arduino Uno interface is used to perform and implement algorithms to control the speed of the motors, steering the robot to travel along the line smoothly. This project aims to implement the algorithm and control the movement of the robot by proper tuning of the control parameters and thus achieve better performance. In addition the LCD interface is added in order to display the distance travelled by the robot. It can be used industrial automated equipment carriers, small household applications, tour guides in museums and other similar applications, etc. It is a mobile machine that can detect and follow the line drawn on the floor.
Keywords: Line following, Arduino Uno, LCD interface, serving robot
Keywords: Line following, Arduino Uno, LCD interface, serving robot
Abstract
ACCIDENT DETECTION AND ALERT SYSTEM
Divyashree L. K, Akhil Sunny, Muhammed Munazir
DOI: 10.17148/IJIREEICE.2025.13203
Abstract: Speed is one of the basic reasons for vehicle accident. Many lives could have been saved if emergency services could get accident information and reach in time. This project deals with accident detection system when an accident occurs it uses various components and alerts the Rescue team for help. An efficient automatic accident detection with an automatic notification to the emergency service with the accident location is a prime need to save the precious human life. The proposed system deals with accident alerting and detection. It reads the exact latitude and longitude of the vehicle involved in the accident and sends this information to nearest emergency service provider. The goal of the project is to detect accidents and alert the rescue team in time.
Keywords: Accident detecting, ARDUINO ,GSM, GPS.
Keywords: Accident detecting, ARDUINO ,GSM, GPS.
Abstract
FACEMASK INTELLIGENCE ALERT SYSTEM USING DEEP LEARNING
G SHANTHI
DOI: 10.17148/IJIREEICE.2025.13204
Abstract: Face detection has grown in popularity as a challenge in image processing and computer vision. Convolutional architectures are being used in many new algorithms to make them as accurate as feasible. These convolutional designs have allowed even pixel information to be extracted. We want to create a binary face classifier that can detect any face in the frame, regardless of alignment. We show how to make accurate face segmentation masks from any input image of any size. For feature extraction, the approach starts with an RGB image of any size and employs Predefined Training Weights of VGG - 16 Architecture.
Keywords: HAAR Cascade, Facial recognition, Deep Learning, CNN.
Keywords: HAAR Cascade, Facial recognition, Deep Learning, CNN.
Abstract
Securing IoT Networks: A Blockchain-Driven Approach to Decentralized Communication
Syed Mubeen Ali, Mohammed Nomaan, Khaja HameedUllah, Syed Omer Shah, Mohammed Zakwan Khan
DOI: 10.17148/IJIREEICE.2025.13205
Abstract: The Internet of Things has changed device interaction and communication faces and brought an equally great challenge in security. Traditional models of centralized security cannot work in decentralized and expansive IoT networks; hence, they leave open several cyber threats. This paper proposes a blockchain-based approach for securing IoT through decentralized communication. The data integrity, authentication, and resistance to tampering are guaranteed through blockchain technology; at the same time, smart contracts enable the automation of safety policies. We demonstrate that this model enhances security without sacrificing performance by simulation and real-world testing.
Keywords: IoT security, Blockchain, Decentralized communication, Network security, Cybersecurity
Keywords: IoT security, Blockchain, Decentralized communication, Network security, Cybersecurity
Abstract
ARTIFICIAL INTELLIGENCE (AI) IN EDUCATION: USING AI TOOLS FOR TEACHING AND LEARNING PROCESS
Dr Akhilesh Saini, Mrs. Deepika Sharma, Mrs. Kritika Tripathi
DOI: 10.17148/IJIREEICE.2025.13206
Abstract: Artificial Intelligence (AI) is transforming the education sector by enhancing teaching methodologies and improving learning experiences. AI-powered tools, such as intelligent tutoring systems, adaptive learning platforms, and automated grading systems, provide personalized learning experiences, enabling students to learn at their own pace. Teachers benefit from AI-driven analytics that offer insights into student performance, helping them tailor instructional strategies. Additionally, AI chatbots and virtual assistants enhance student engagement and support. Despite its advantages, challenges such as data privacy concerns, the digital divide, and the need for teacher training must be addressed to ensure effective AI integration in education. This paper explores the impact, benefits, and challenges of AI in the teaching and learning process, highlighting its potential to revolutionize education.
Abstract
Advancing Lithium-Ion Battery Recycling in India Through Technological Innovations Economic Feasibility and Policy-Driven Sustainability
Priyanka Singh, Saanjhree R, PS Nagachyuth, Rakshith Krishna G, Rohith Chelva, S Prena, Dr. Gopalakrishnan Chinnasamy
DOI: 10.17148/IJIREEICE.2025.13207
Abstract: The fast growth of electric vehicles (EVs), consumer electronics, and renewable energy storage in India has resulted in a huge volume of lithium-ion battery (LIB) waste, which demands effective recycling. This work examines the LIB recycling scenario in India with an emphasis on technological advancement, financial viability, policy regime, and environmental stewardship. The work concentrates on hydrometallurgical over pyro-metallurgical processes, AI- driven automation, and predictive modeling to achieve maximum material recovery and minimum carbon footprint. A business model is envisioned, with cost structures, revenue streams, and investment strategies, illustrating the feasibility of a financially viable and sustainable LIB recycling system. Quantitative estimates an increase in LIB waste to 50,000 metric tons by 2025, highlighting the importance of policy-backed incentives, public-private collaborations, and infrastructure investments. Lifecycle Assessment (LCA) studies also suggest that LIB recycling can reduce carbon emissions by 60%, according to India's net-zero emission goals. The study concludes that the integration of global best practices, government incentives, and advanced technologies can position India as a global leader in sustainable battery waste management, ensuring resource security, economic viability, and environmental sustainability.
Keywords: Lithium-ion Battery Recycling, Sustainability, Resource Recovery, Circular Economy, India, Environmental Impact
Keywords: Lithium-ion Battery Recycling, Sustainability, Resource Recovery, Circular Economy, India, Environmental Impact
Abstract
Understanding Consumer Preferences and Technology Readiness for AI-Based Emotional Wellness Apps
Sharmila K, Shreyas, Shrushti S Naduvinamath, Sinchana B, Sirisha S, Sivan Vishnu Balan, Sneha T, Dr.Gopalakrishnan Chinnasamy
DOI: 10.17148/IJIREEICE.2025.13208
Abstract: With the rise of artificial intelligence (AI) in mental health and emotional well-being, AI-based emotional wellness apps have gained significant traction. This research explores consumer preferences, demographic influences, and technology readiness for adopting such applications. By analyzing survey data and statistical findings, we examine how personalized features, age groups, and occupational backgrounds impact the adoption and willingness to pay for these solutions. The findings provide insights into how AI-driven emotional well-being apps can better cater to diverse consumer needs and foster long-term engagement through innovative technologies. Additionally, this study examines how AI-powered interventions contribute to stress management and mental well- being by offering tailored experiences through Augmented Reality (AR), Voice Recognition, and Natural Language Processing (NLP). These technologies enhance accessibility and efficiency in emotional self-care, making wellness applications more effective and inclusive for different user groups.
Keywords: AI-driven wellness, emotional well-being, technology readiness, consumer preferences, augmented reality, voice recognition, natural language processing.
Keywords: AI-driven wellness, emotional well-being, technology readiness, consumer preferences, augmented reality, voice recognition, natural language processing.
Abstract
A novel, time controlled, four digit PIN input system with reset function, for multiple security applications, using VHDL and FPGAs
Dr Evangelos I. Dimitriadis, Leonidas Dimitriadis
DOI: 10.17148/IJIREEICE.2025.13209
Abstract: A novel, time controlled, four digit PIN input system, suitable for many security applications, was programmed and put in use, for the first time, in this work. The DE10-Lite FPGA board was used here. Administrator has the pro- graming ability of creating PIN1 and PIN2 and setting the time needed for user to correctly create one of the above PINs. User handles FPGA’s SW switches in conjunction with key buttons, in order to achieve the correct creation of one pin, in a few minutes time, also being able to see his creation in seven segment displays. If PIN input is wrong FA (FALSE) is displayed, while at the opposite case all LEDs light up. In the case that user’s time runs out without correct PIN creation, system locks up and administrator must press a third key button connected to FPGA via a pull-down resistance, if he wants to provide user with extra time.
Keywords: PIN input, FPGA, VHDL, time controlled, reset function, security applications.
Keywords: PIN input, FPGA, VHDL, time controlled, reset function, security applications.
Abstract
DC Homes with Scope of AI for Energy Security in Developing Nations
Akash Sao, Satish Sahu
DOI: 10.17148/IJIREEICE.2025.13210
Abstract: Nation development is closely tied to electrical energy consumption, prompting improvements in generation, transmission, and utilization to meet rising demand. In the U.S., demand doubled every decade until the early 1970s, while developing countries like India see a doubling every seven years, necessitating significant investments in the power sector. To meet rising energy demand affordably, reliance on fossil fuel-based power plants—such as thermal, diesel, and hydroelectric—has increased. However, their consumption significantly contributes to global warming and environmental damage, highlighting the need for alternative, eco-friendly energy sources like renewables. Many individuals are now adopting green energy solutions, such as solar, wind, and hydropower, with government initiatives supporting this shift. Additionally, dependence on fossil fuels raises national security concerns. Conventional power plants are often located far from load centers, primarily due to environmental factors. For example, large hydroelectric stations are typically situated hundreds of kilometers away from where the electricity is consumed. This results in significant costs and energy losses during the lengthy transmission process from generation to utilization. This paper addresses the challenges of long-distance power transmission, including the selection of suitable transmission systems and issues with electrification in difficult geographical areas. It explores Solar DC Homes as an alternative solution and discusses how integrating Artificial Intelligence with solar PV systems can enhance efficiency through various research and applications
Keywords: DC Homes, Artificial Intelligence, Solar PV System
Keywords: DC Homes, Artificial Intelligence, Solar PV System
Abstract
ANTHROPOMETRIC VARIATIONS AND SPORTS PERFORMANCE: A REVIEW OF RELATED LITERATURE
Jai Bhagwan Singh Goun
DOI: 10.17148/IJIREEICE.2025.13211
Abstract: Anthropometry — the measurement and analysis of human body dimensions — has long been central to understanding athletic potential, optimizing training, and guiding talent identification. This review synthesizes theoretical foundations and empirical findings on how anthropometric variations (height, body mass, limb lengths, body composition, somatotype, and proportionality) relate to performance across a range of sports. Evidence indicates that certain anthropometric profiles confer advantages in specific activities: taller stature and longer limb segments favor basketball, volleyball, rowing, and sprinting start mechanics; mesomorphic build and greater lean mass are advantageous in power and contact sports (e.g., weightlifting, rugby, throwing); ectomorphic profiles commonly characterize endurance runners; while a compact, hyperflexible body often benefits gymnasts and divers. However, relationships are complex and moderated by factors such as sex, age, training history, biomechanics, physiological capacities (VO₂max, anaerobic power), and technical skill. Talent identification models that rely solely on static body measurements can misclassify potential because anthropometry interacts dynamically with growth, maturation, and training-induced adaptations. Practical applications include position-specific profiling, individualized strength and conditioning plans, equipment and technique optimization, and long-term athlete development frameworks that account for maturation. The review also highlights methodological challenges in the literature — inconsistent measurement protocols, small and heterogeneous samples, cross-sectional designs, and limited longitudinal tracking — and calls for multimodal, longitudinal research that integrates anthropometry with biomechanical, physiological, and psychosocial variables. Finally, ethical considerations (labeling, early specialization, and discrimination) and culturally sensitive approaches to anthropometric assessment are discussed. Recommendations are provided for researchers, coaches, and sport scientists to use anthropometric information responsibly to enhance performance and athlete well-being.
Keywords: anthropometry, body composition, somatotype, talent identification, sports performance, body proportions, maturation
Keywords: anthropometry, body composition, somatotype, talent identification, sports performance, body proportions, maturation
Abstract
The Effect of Capital Structure on Investment Performance: A Study of Listed Companies in the Financial Sector
Joel Adetokunbo, Olakunle Sobowale
DOI: 10.17148/IJIREEICE.2025.13212
Abstract: Capital structure and investment performance have remained at the center of corporate finance, particularly in the financial industry where funding strategies directly influence organizational performance. This study analyzes the effect of capital structure on investment performance of listed companies in the financial industry. On the basis of Modigliani–Miller theory, trade-off theory, and pecking order theory, the research examines how financing choices influence key performance metrics like Return on Assets (ROA), Return on Equity (ROE), Earnings per Share (EPS), and Tobin's Q. The research employs the quantitative approach on the basis of secondary data from selected listed financial institutions' financial reports and filings. Regression analysis and correlation methods are used to test the association between debt–equity structures and returns on investment, while adjusting for firm-specific factors like size, growth, and liquidity. Evidence suggests that a balance between debt and equity maximizes investment performance, yet overdependence on debt funding worsens profitability and long-term viability. The results are similar to some degree with previous empirical findings, concluding that capital structure decisions are context-dependent and influenced by market and regulatory frameworks. The current research adds to the field of financial management by offering industry-specific findings and offering practical recommendations to managers and policymakers looking to optimize funding practices.
Keywords: Capital structure; Financial performance; Investment; Listed companies; Risk management
Keywords: Capital structure; Financial performance; Investment; Listed companies; Risk management
Abstract
SECURING THE NATION: AI-ENHANCED DEVSECOPS FOR LEGACY CRITICAL INFRASTRUCTURE
Kiran Kumar Yakkali
DOI: 10.17148/IJIREEICE.2025.13213
Abstract: Critical infrastructure systems, which were built decades before, such as power grids, water treatment systems, transport networks, industrial control systems, etc., are also still crucial to national security but more susceptible to advanced cyber attacks. The old-fashioned architectures, proprietary protocols, and patching possibilities render the traditional security approaches to be inadequate. The paper will discuss the potential of using AI-assisted DevSecOps to change the state of security of these old environments by embedding continuous monitoring, threat detectors, and secure-by-design within the entire system life cycle. We discuss recent innovations in machine learning- based anomaly detection, predictive maintenance, and automation in CI/CD pipelines, which have the potential to make them more resilient without affecting the continuity of the operation. The research suggests a hybrid AI-DevSecOps framework based on the current research and practical applications, allowing the implementation of this model in legacy systems. The framework focuses on safe configuration, model driven threat intelligence, auto compliance checking, and persistent checking. The findings indicate that a combination of AI and DevSecOps could help shorten the time to respond to the threat to a minimum, increase the visibility of the OT/IT boundaries, and allow proactive defense mechanisms. The paper finds that AI-assisted DevSecOps is a feasible and scalable solution that can be used to update the cybersecurity of legacy critical infrastructure without jeopardizing reliability, safety, and mission-critical performance.
Keywords: AI-enhanced security; DevSecOps; Legacy critical infrastructure; Industrial control systems; Machine learning; Cybersecurity automation; Anomaly detection
Keywords: AI-enhanced security; DevSecOps; Legacy critical infrastructure; Industrial control systems; Machine learning; Cybersecurity automation; Anomaly detection