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VOLUME 13, ISSUE 9, SEPTEMBER 2025
A multi-protection, FPGA-based and VHDL programmed, object security and surveillance system, using PIR motion sensors, vibration sensor and touch-switches for activating a variety of alarm systems
Dr Evangelos I. Dimitriadis, Leonidas Dimitriadis, U
Machine Learning Models for Chronic Kidney Disease Detection
TANUJA S M, Mr. PRASHANT ANKALKOTI
Real-Time Fire Monitoring System with Arduino
Manish Paul, Priti Singha, Subhrajyoti Bhowmik, Asmit Das, Manjit Parmar Debnath
QUESTION PAPER LEAKAGE PROTECTION USING BLOCKCHAIN
Dr. Tejashwini Y, Mohammed Farzanula, Nikhil S, Shahid A Khan
Design of R-2R Digital to Analog Converter (DAC) using CMOS Technology
Mahantesh H, Aditya U. M, Akash R. M, Anushree A. U, Hanamanth Laddi
Low Power Based Dynamic True Single Phase Clock [Tspc] D Flip Flop For High Performance Application
Ashalatha M E, Abhishek G D, Arun kumar U, Gurukiran P M, Jeetendra M
Design and Implementation of High Performance FIR and IIR Digital Filters for ECG Signal Processing
Dr. Mukthi S L, Sandeep O
A Python-Based Framework for Interactive Real-Time Air Quality Monitoring and Visualization
Narendra M. Jathe
AI-Driven Transformations in Software Engineering
Onyeyili T.I, Ugwuanyi Gilbert
Advanced Digital Signal Processing in the Development and Realization of 5G Mobile Communication Standards
Ugwuanyi Gilbert, Azubogu A.C.O, Nnebe S.U, Oguejiofor O. S
Demystifying PID Controllers: Implementation and Tuning in Embedded System Design
Mr. Yogesh R Chauhan
A Compact Wearable Smart Tag for Monitoring Social Distancing in Pandemic Circumstances
Priyanshu Rudra Pal, Joysree Debnath, Dhriti Rudrapal, Debarshita Biswas
AI-Powered Cyber Threats Security
Prof. Mr.ArsalanA.Shaikh, Miss.Darshali S.Talele
Comparative Analysis of Soil Fertility under Conventional and Organic Farming Systems in Rural Areas of Surguja Division of Chhattisgarh, India
Suresh Kumar, Dr. M.K. Maurya
A Comparative Analysis of Virtual Reality and Augmented Reality in Interactive Learning Environments.
Miss. Chetana Kawale*, Mr.Mayur Suresh Patil
A Comprehensive Study on Predicting Student Academic Performance Using Artificial Intelligence and Educational Data Mining Techniques
Prof. Mr. Vaibhav Chaudhari*, Miss. Prerna M. Patil
Enhancing Cyber Security Through Awareness and Training Programs
Mr. Arsalan A. Shaikh*, Mayur S. Shirsath
Ai based crop disease detection system
Mr. Arsalan A. Shaikh*, Miss. Kajal S. Vichave
Impact of Social Media on Mental Health
Prof. Ms. Chetana Kawale*, Mr. Mahendra Ramesh Patil
Revolutionizing Transformer Health Monitoring Innovations for Enhanced Care and Wellness
Farukh Sheikh, Prof. Trupti F More
How ChatGPT is Changing Education Opportunities and Challenges
Prof. Mr. Arsalan Shaikh*, Miss. Nikita S. Rajput
A Survey of Various Methods and Techniques for Detecting Blur Images
JEBA PRIYA J, Dr. S. PRASANNA
The Impact of Cyber-Physical Attacks on AI-Enabled Business Systems
Praveen Kumar Reddy Gouni, Eraj Farheen Ansari
The Benefits of Cloud Computing
Prof. Mr. Vaibhav Chaudhari, Mr. Milind Narayan Patil
AI-Driven Robotics and Automation
Prof. Mr. Arsalan A. Shaikh*, Miss. Komal Narendra Pawar
Abstract
A multi-protection, FPGA-based and VHDL programmed, object security and surveillance system, using PIR motion sensors, vibration sensor and touch-switches for activating a variety of alarm systems
Dr Evangelos I. Dimitriadis, Leonidas Dimitriadis, U
DOI: 10.17148/IJIREEICE.2025.13901
Abstract: An object security, multi-protection and surveillance system, based on PIR motion sensors, vibration sensor and touch-switches, is presented here. The system uses FPGAs and can protect precious objects or industrial im- portance equipment, as well as laboratory and scientific instruments, against people who may cause damages or steal the above objects. It provides three main protection services. The first is by using its PIR sensors for detection of hu- man presence and second is by activating its vibration sensor, in order to prevent possible moving of the object. It uses two PIR motion sensors for more complete coverage of the area and if human presence is detected four different alarm systems are activated. FPGA board LEDs light up, vibrator starts sounding, a laser module also lights up and step mo- tor activates door closing equipment. Additionally if vibration sensor detects possible movement of the object, another FPGA board LED lights up. System administrator is aware of all the above activations, so he can decide whether to use the third protection ability, which is pressing one or more touch capacity switches, thus activating respective buzzer systems in different security service rooms. The system is programmed using VHDL language and can be implemented in a variety of security applications.
Keywords: Object security surveillance system, Sensors, FPGA, VHDL, Buzzer, LEDs, laser, vibrator, touch sensors, step motor.
Keywords: Object security surveillance system, Sensors, FPGA, VHDL, Buzzer, LEDs, laser, vibrator, touch sensors, step motor.
Abstract
Machine Learning Models for Chronic Kidney Disease Detection
TANUJA S M, Mr. PRASHANT ANKALKOTI
DOI: 10.17148/IJIREEICE.2025.13902
Abstract: The CKD will becoming a serious health problem around the world, mostly because it is often diagnosed too late &many people do not have access to early prediction tools. This project was started to beg help to raise toast about kidney health &to use modern technology, like machine learning, to help solve this issue. The goal was to build a system that can predict the chances of a person having CKD &also give them advice on how to live a healthier life to manage or reduce the risk. During the project, we used real patient data that included results from medical tests &other health information. A Gradient Boosting Classifier, which will be a mechanism learning model, was trained using this information to make smart &accurate predictions. The system also gives easy-to-understand & lifestyle tips like changes in diet, exercise, &medicine based on each personās health. This helps people take care of their kidneys before serious problems start.
Keywords: Chronic Kidney Disease Gradient Boosting Random Forest Logistic Regression, Flask Application, Prediction System, Healthcare Technology, Lifestyle Recommendation, Medical Diagnosis.
Keywords: Chronic Kidney Disease Gradient Boosting Random Forest Logistic Regression, Flask Application, Prediction System, Healthcare Technology, Lifestyle Recommendation, Medical Diagnosis.
Abstract
Real-Time Fire Monitoring System with Arduino
Manish Paul, Priti Singha, Subhrajyoti Bhowmik, Asmit Das, Manjit Parmar Debnath
DOI: 10.17148/IJIREEICE.2025.13903
Abstract: This paper aims in designing a fire alarm detector for a modern fire safety systems, designed to identify fire hazards at an early stage and send alerts to remote locations or systems. This project focuses on developing a low-cost, Arduino-based fire alarm detector capable of real-time monitoring and early warning. The system employs flame and smoke sensors to detect signs of fire and immediately activates visual and audible alerts. Additionally, it can be configured to send notifications to mobile devices or security services, ensuring prompt action even when the premises are unoccupied. By leveraging the simplicity and versatility of Arduino, the project demonstrates how embedded technology can enhance fire safety and provide reliable, remote fire hazard detection for residential and commercial applications.
Keywords: Fire alarm detector, notification, accident, Arduino, live monitoring.
Keywords: Fire alarm detector, notification, accident, Arduino, live monitoring.
Abstract
QUESTION PAPER LEAKAGE PROTECTION USING BLOCKCHAIN
Dr. Tejashwini Y, Mohammed Farzanula, Nikhil S, Shahid A Khan
DOI: 10.17148/IJIREEICE.2025.13904
Abstract: Question paper leakage has emerged as a serious challenge in modern educational systems, undermining the credibility of examinations, creating unfair academic outcomes, and eroding public trust in assessment processes. Traditional paper distribution methods, often centralized and lacking robust safeguards, are highly susceptible to insider threats, data tampering, and unauthorized access. To address these vulnerabilities, this project proposes a blockchainābased solution that leverages decentralization, immutability, cryptographic encryption, and smart contracts to securely manage question papers. The system ensures that all papers are encrypted, stored in a tamperāproof distributed ledger, and made accessible only through timeālocked, identityāverified mechanisms. Every access attempt and distribution event is recorded immutably, providing full traceability and eliminating the possibility of undetected leaks. By combining strong security mechanisms with transparent audit trails, the proposed platform not only prevents question paper leakage but also restores examination credibility, minimizes manual handling, and offers a scalable framework for secure academic operations.
Keywords: Blockchain, decentralization, immutability, data integrity, tamper proof.
Keywords: Blockchain, decentralization, immutability, data integrity, tamper proof.
Abstract
Design of R-2R Digital to Analog Converter (DAC) using CMOS Technology
Mahantesh H, Aditya U. M, Akash R. M, Anushree A. U, Hanamanth Laddi
DOI: 10.17148/IJIREEICE.2025.13905
Abstract: This work presents the design and simulation of a two-stage CMOS operational amplifier integrated into an R-2R ladder-based Digital-to-Analog Converter (DAC). Implemented in 180nm CMOS technology, the amplifier comprises a differential input stage and a common-source gain stage, optimized for high gain, stability, and low power. The design supports 4-bit and 8-bit R-2R DAC architectures to achieve accurate digital-to-analog conversion. Simulations using Cadence Virtuoso and Spectre validated DC, AC, and transient performance, confirming linear outputs, low glitch energy, and power efficiency. The study demonstrates an efficient approach to compact analog interface design for modern mixed-signal systems.
Keywords: Amplifier, DAC, CMOS, Power.
Keywords: Amplifier, DAC, CMOS, Power.
Abstract
Low Power Based Dynamic True Single Phase Clock [Tspc] D Flip Flop For High Performance Application
Ashalatha M E, Abhishek G D, Arun kumar U, Gurukiran P M, Jeetendra M
DOI: 10.17148/IJIREEICE.2025.13906
Abstract: D flip-flop is viewed as the most basic memory cell in by far most of computerized circuits, which brings it broad usage, particularly under current conditions where high-thickness pipeline innovation is as often as possible, utilized in advanced coordinated circuits. As a constant research center, various types of zero flip-flops have been explored, and the ongoing exploration pattern has gone to rapid low-control execution, which can come down to low power-defer item.To actualize superior VLSI, picking the most proper D flip-flop has clearly become an incredibly huge part in the structure stream.This work includes to design and development of a Low Power Dynamic Power Based True Single Phase D Flip Flop [TSPC] for High Performance Application using Cadence Tool. The design has been tested and verified using Cadence Virtuoso. The developed TSPC D Flip Flop model can be used in the design of sequential circuits with enhanced performance.
Keywords: TSPC D Flip-Flop, Low Power VLSI, Design, High-Speed Sequential, Circuits, Cadence Virtuoso Simulation
Keywords: TSPC D Flip-Flop, Low Power VLSI, Design, High-Speed Sequential, Circuits, Cadence Virtuoso Simulation
Abstract
Design and Implementation of High Performance FIR and IIR Digital Filters for ECG Signal Processing
Dr. Mukthi S L, Sandeep O
DOI: 10.17148/IJIREEICE.2025.13907
Abstract: The design and implementation of digital filtering methods to improve electrocardiogram (ECG) signals. MATLAB is used for the development and analysis of low-pass filters with finite impulse response (FIR) and infinite impulse response (IIR) of different orders. The main goal is to eliminate noisy elements like high-frequency interference and baseline drift while keeping the therapeutically meaningful aspects of ECG signals. In terms of signal preservation and noise reduction, the FIR filters with Kaiser windows and the IIR filters based on elliptic responses perform better. The comparative study provides valuable insights for biomedical applications by highlighting the trade-off between filter, complexity, and signal clarity. The results demonstrate that digital filtering techniques are useful instruments for processing ECG signals accurately, which are use in diagnostic and healthcare monitoring systems.
Keywords: Digital Filters, FIR, IIR, ECG Signal Processing, MATLAB, Vivado.
Keywords: Digital Filters, FIR, IIR, ECG Signal Processing, MATLAB, Vivado.
Abstract
A Python-Based Framework for Interactive Real-Time Air Quality Monitoring and Visualization
Narendra M. Jathe
DOI: 10.17148/IJIREEICE.2025.13908
Abstract: Air pollution has emerged as a major global challenge, severely impacting human health, environmental quality, and climate stability. Rapid industrialization, urban expansion, and increased vehicular emissions have contributed to rising levels of pollutants, including particulate matter (PM2.5 and PM10), carbon monoxide (CO), nitrogen dioxide (NO2), and ozone (O3). Monitoring air quality in real-time is essential for timely interventions, public health advisories, and informed policy-making. This research presents a comprehensive Python-based system for real- time air quality monitoring and visualization. The system fetches live data from the OpenAQ API, with a simulation fallback to ensure continuous operation during data unavailability. Key pollutants are monitored, logged in CSV files for historical analysis, and visualized using interactive multi-line graphs. The approach enables dynamic observation of pollution trends, identification of peak pollution periods, and facilitates informed decision-making to mitigate environmental and health risks.
Keywords: Air Pollution, Real-Time Monitoring, Python, Data Visualization, PM2.5, PM10, CO, NO2, O3, Environmental Health, Urban Air Quality, Public Health, Pollution Trends.
Keywords: Air Pollution, Real-Time Monitoring, Python, Data Visualization, PM2.5, PM10, CO, NO2, O3, Environmental Health, Urban Air Quality, Public Health, Pollution Trends.
Abstract
AI-Driven Transformations in Software Engineering
Onyeyili T.I, Ugwuanyi Gilbert
DOI: 10.17148/IJIREEICE.2025.13909
Abstract: Artificial Intelligence (AI) is increasingly being integrated into modern software engineering (SE) processes, transforming the way systems are designed, developed, tested, and maintained. The growing usability and computational power of AI technologies enable the automation of complex engineering tasks, the enhancement of software adaptability, and the optimization of development lifecycles. However, this integration also introduces new challenges such as security vulnerabilities, ethical concerns, and risks of excessive automation. Despite significant research efforts, there is no unified framework for categorizing AI applications in SE or systematically analyzing their risks and benefits. This paper explores the evolving role of AI in software engineering, emphasizing its applications in intelligent automation, testing, optimization, and quality assurance. It further identifies opportunities, unresolved challenges, and the need for balance between human intervention and machine-driven decision-making. Finally, the study highlights future directions where AI could redefine traditional engineering paradigms and foster the emergence of autonomous, self-improving software systems.
Keywords: artificial intelligence, software engineering, machine learning, neural networks, intelligent automation, software testing, optimization, big data analytics, fuzzy logic, quality assurance
Keywords: artificial intelligence, software engineering, machine learning, neural networks, intelligent automation, software testing, optimization, big data analytics, fuzzy logic, quality assurance
Abstract
Advanced Digital Signal Processing in the Development and Realization of 5G Mobile Communication Standards
Ugwuanyi Gilbert, Azubogu A.C.O, Nnebe S.U, Oguejiofor O. S
DOI: 10.17148/IJIREEICE.2025.13910
Abstract: The unprecedented growth in smart device usage has accelerated demand for ultra-reliable, high-speed wireless connectivity. Fifth-Generation (5G) mobile communication systems address this demand by delivering multi- gigabit throughput, ultra-low latency, and massive device connectivity. Achieving these stringent requirements relies heavily on advanced Digital Signal Processing (DSP) techniques. This paper investigates the fundamental and advanced roles of DSP in the development and realization of 5G standards, highlighting its contributions to Orthogonal Frequency Division Multiplexing (OFDM), Massive Multiple-Input Multiple-Output (MIMO), and digital beam forming. Further emphasis is placed on adaptive channel estimation, signal detection, and interference mitigation strategies that enable robust operation in dynamic environments. Hardware design considerations involving DSP-ASIC co-integration are examined, alongside the emerging synergy between DSP and Machine Learning (ML) for adaptive, intelligent signal processing. The findings demonstrate that DSP forms the backbone of 5G physical layer design, ensuring scalability, efficiency, and adaptability, while laying the foundation for beyond-5G (B5G) and 6G networks.
Keywords: 5G networks, digital signal processing (DSP), OFDM, massive MIMO, beamforming, channel estimation, LDPC, ASIC, machine learning (ML), adaptive signal processing.
Keywords: 5G networks, digital signal processing (DSP), OFDM, massive MIMO, beamforming, channel estimation, LDPC, ASIC, machine learning (ML), adaptive signal processing.
Abstract
An Analysis of Failure Modes, Economic Impact, and Regulatory Gaps of Photovoltaic Junction Boxes, Future Scope in the Indian Solar Sector
Ravi Verma
DOI: 10.17148/IJIREEICE.2025.13911
Abstract: Photovoltaic (PV) junction boxes play a crucial role as Balance of System (BoS) components, facilitating the safe and efficient transmission of power from solar modules. Junction boxes play a vital role, yet they frequently encounter reliability issues, particularly in the face of India's varied and severe environmental conditions. This analysis explores the main failure modes of PV junction boxes within the Indian solar industry, assesses their economic impacts, and highlights significant regulatory deficiencies. The examination uncovers prevalent failure mechanisms, including thermal degradation, diode burnout, moisture ingress, and corrosion, which are intensified by elevated ambient temperatures, dust, and humidity. The economic implications of these failures include diminished energy production, escalated operations and maintenance costs, and an increased risk of fire hazards, all of which directly impact return on investment. Furthermore, the absence of enforceable standards in India and the lack of policy incentives for BoS components have resulted in an influx of substandard imports and constrained domestic innovation. This paper emphasizes the critical necessity for strong regulatory frameworks, which should encompass mandatory BIS certification, targeted incentives for specific components, and enhanced investment in quality testing infrastructure. It is crucial to tackle these gaps to guarantee the enduring performance, safety, and sustainability of solar installations as India moves forward in its shift towards a clean energy future.
Keywords: Sustainability, green energy, failure mechanisms, Policy management, Regulatory, Circular Economy
Keywords: Sustainability, green energy, failure mechanisms, Policy management, Regulatory, Circular Economy
Abstract
Demystifying PID Controllers: Implementation and Tuning in Embedded System Design
Mr. Yogesh R Chauhan
DOI: 10.17148/IJIREEICE.2025.13912
Abstract: Proportional-Integral-Derivative (PID) controllers are needed for ensuring the accuracy and stability of embedded systems. In this research, we explore the role of PID controllers in robust feedback control for a range of applications. We explore the theoretical underpinnings of the proportional, integral, and derivative terms, evaluating their respective contributions and their combined effects on system response. This paper also includes practical implementation approaches for embedded design. It discusses significant challenges faced in embedded control systems, such as integral wind-up, the impacts of sampling intervals, output saturation, and noise reduction for the derivative term. Finally, two well-known tuning algorithms, Manual Tuning and the Ziegler-Nichols, are set out as systematic approaches to maximum controller performance. This research presents an applied manual for engineers designing embedded systems for implementing and tuning PID controllers effectively to deliver stable, accurate, and responsive system performance.
Keywords: PID Control, BLDC Motor, PID Design, Embedded Systems, Motor Control Tuning
Keywords: PID Control, BLDC Motor, PID Design, Embedded Systems, Motor Control Tuning
Abstract
A Compact Wearable Smart Tag for Monitoring Social Distancing in Pandemic Circumstances
Priyanshu Rudra Pal, Joysree Debnath, Dhriti Rudrapal, Debarshita Biswas
DOI: 10.17148/IJIREEICE.2025.13913
Abstract: In the context of recurrent outbreaks of infectious diseases, protecting public health has emerged as a paramount concern. The smart tag is a small, wearable device expertly engineered to provide safe spacing between individuals in congested settings. The system integrates an Arduino Uno microcontroller, an ultrasonic sensor, an LED, and a buzzer to establish an efficient proximity alarm system. The ultrasonic sensor quantifies the distance between humans. And if an individual encroaches upon the designated unsafe range, the device promptly activates alarms via visual (LED) and audio (buzzer) messages. This allows users to quickly implement corrective actions in order to maintain a sufficient amount of space between themselves and others. The system is designed with portability, cost-effectiveness, and a lightweight construction in mind. This makes it very accessible for everyday use in a wide range of contexts, including schools, hospitals, businesses, and other public spaces. It has the potential to greatly reduce the risk of illness transmission by promoting awareness and adherence to social distancing practices. This Social Distancing Smart Tag combines technical innovation with public safety in order to provide a proactive and realistic strategy for preventing sickness. This initiative serves as an example of the intersection of innovation and societal demands, addressing current public health challenges through the use of technology that is both accessible and dependable.
Keywords: Arduino Uno, Ultrasonic sensor, Social distancing, Smart tag, Microcontroller.
Keywords: Arduino Uno, Ultrasonic sensor, Social distancing, Smart tag, Microcontroller.
Abstract
AI-Powered Cyber Threats Security
Prof. Mr.ArsalanA.Shaikh, Miss.Darshali S.Talele
DOI: 10.17148/IJIREEICE.2025.13914
Abstract: The integration of Artificial Intelligence (AI) across various domains has significantly enhanced productivity and development. However, this advancement has also introduced a surge in cybersecurity threats, particularly those driven by AI itself. These AI-powered threats take advantage of technological advancements to compromise computerized systems, thereby undermining their integrity. This systematic review explores the complexities of AI- driven cyber threats, which utilize sophisticated AI capabilities to execute intricate and deceptive cyberattacks. Our review consolidates existing research to examine the scope, detection methods, impacts, and mitigation strategies associated with AI-induced threats. We emphasize the dynamic relationship between AI development and cybersecurity, stressing the need for advanced protective systems that evolve alongside the growing risks. Our findings highlight the critical role of AI in both facilitating and defending against cybersecurity measures, demonstrating a dual impact that necessitates the continuous evolution of cybersecurity.
Abstract
Comparative Analysis of Soil Fertility under Conventional and Organic Farming Systems in Rural Areas of Surguja Division of Chhattisgarh, India
Suresh Kumar, Dr. M.K. Maurya
DOI: 10.17148/IJIREEICE.2025.13915
Abstract: The present study provides a comparative analysis of soil fertility under conventional and organic farming systems in rural areas of the Surguja division of Chhattisgarh, based on soil samples collected from Khairbar (Surguja), Pathalgaon (Jashpur), and Mainpat (Surguja). Soil parameters such as pH, electrical conductivity, organic carbon, available nitrogen, phosphorus, potassium, calcium, magnesium, sulphate, and micronutrients (Zn, Cu, Fe, Mn, B, Mo) were analyzed. Results reveal significant differences in soil fertility status across locations, influenced by both soil management practices and inherent soil characteristics. Mainpat soils showed relatively higher levels of organic carbon (0.84%), nitrogen (302 kg/ha), and available potassium (305 kg/ha), indicative of better fertility under organic amendments. In contrast, Khairbar soils had lower organic carbon (0.24%) and nitrogen (150.55 kg/ha), reflecting nutrient depletion under conventional practices.
The study highlights that integrating organic amendments such as compost and cow dung manure can improve soil fertility by enhancing organic carbon and macro/micro-nutrient availability. It is concluded that organic farming practices offer long-term benefits for sustaining soil fertility and crop productivity in Surguja division.
Keywords: Soil fertility, conventional farming, organic farming, Surguja division, Chhattisgarh, soil nutrients, sustainable agriculture
The study highlights that integrating organic amendments such as compost and cow dung manure can improve soil fertility by enhancing organic carbon and macro/micro-nutrient availability. It is concluded that organic farming practices offer long-term benefits for sustaining soil fertility and crop productivity in Surguja division.
Keywords: Soil fertility, conventional farming, organic farming, Surguja division, Chhattisgarh, soil nutrients, sustainable agriculture
Abstract
A Comparative Analysis of Virtual Reality and Augmented Reality in Interactive Learning Environments.
Miss. Chetana Kawale*, Mr.Mayur Suresh Patil
DOI: 10.17148/IJIREEICE.2025.13916
Abstract: Virtual Reality (VR) and Augmented Reality (AR) are two significant educational technologies that provide different avenues to rethink familiar education problems based on interactive and immersive experiences. VR enables learners to interact in completely simulated environments, offering opportunities for learners to engage in immersive ways with abstract or complex ideas, whereas AR extends the possibilities in physical world contexts by augmenting an environment with digital objects to ground theoretical abstract constructions in practice. This research article highlights a comparative analysis of VR and AR applications in interactive learning contexts with respect to their impact on pedagogy, system design and framework for use, implementation, and some of the challenges of each. The work is situated in a projectābased approach to address the feasibility of implementation, system or architecture, algorithms, and testing. The findings indicate that while VR environments are much richer in immersion, AR provides a low cost, expandable, and more accessible form. VR and AR both demonstrate an evidence based positive effect on motivation, retention and participation in active learning in comparison to traditional methods. The paper concludes that VR and AR can co-develop and that hybrid XR solutions can leverage the strengths of each technology and represent a more sustainable approach to education.
Keywords: Virtual Reality, Augmented Reality, Interactive Learning, XR, Pedagogy.
Keywords: Virtual Reality, Augmented Reality, Interactive Learning, XR, Pedagogy.
Abstract
Performance Analysis of LTE Systems with Convolutional Encoding over Fading Channels
P. Sreesudha
DOI: 10.17148/IJIREEICE.2025.13917
Abstract: The advanced wireless systems feature Long-Term Evolution (LTE) is one of the wide band standards. This paper delivers detailed performance evaluations of LTE systems which implement 2X2 and 4X4 Multiple-Input Multiple- Output (MIMO) configurations through Rayleigh fading channels. The system uses convolutional encoder and viterbi decoder to achieve better system performance. The proposed test method evaluates system performance through the implementation of convolutional encoding and decoding under multiple modulation types QPSK, 16-QAM and 64-QAM. The simulation results presents how the error control coding method benefits the performance in severe fading wireless environment.
Keywords: Convolutional Encoder, LTE, MIMO, OFDM
Keywords: Convolutional Encoder, LTE, MIMO, OFDM
Abstract
A Comprehensive Study on Predicting Student Academic Performance Using Artificial Intelligence and Educational Data Mining Techniques
Prof. Mr. Vaibhav Chaudhari*, Miss. Prerna M. Patil
DOI: 10.17148/IJIREEICE.2025.13918
Abstract: Artificial Intelligence (AI) has become a potent instrument in education, facilitating predictive modeling, adaptive learning, and early recognition of student at-risk status. Conventional means of performance assessmentāe.g., tests and teacher ratingsāare typically reactive, giving feedback only after students have performed below potential. This paper discusses the use of AI methodologies to predict student academic performance on the basis of several parameters such as attendance, assignment grades, and previous grades. Three machine learning modelsāDecision Tree, Random Forest, and Neural Networkāwere tested and compared on a student dataset. The models were compared on a variety of performance metrics such as accuracy, precision, recall, and F1-score to cover all aspects of evaluation. Experimental findings indicate that the Neural Network performed better than the Decision Tree and Random Forest, with the highest rate of accuracy in forecasting student outcomes. Such findings propose that AI is able to improve proactive education initiatives immensely by foreseeing struggling students early and facilitating customized learning interventions. The research concludes that the incorporation of AI-based predictive systems in academic institutions is capable of revolutionizing the evaluation process from reactive measurement to proactive student intervention.
Conventional evaluation toolsālike tests, manual grading, and teacher observationāare usually reactive in nature, recognizing performance problems only after students underperform. AI can, on the other hand, change the paradigm of education to proactive and preventive by projecting outcomes beforehand and suggesting individualized interventions. This work investigates the use of AI methods for the prediction of student academic performance based on attendance, assignment grades, previous grades, and learning activity as input features of primary importance. Three machine learning algorithmsāDecision Tree, Random Forest, and Neural Networkāwere trained and tested on student datasets. Performance was compared on several measures, such as accuracy, precision, recall, and F1-score, to provide a thorough evaluation.
Experimental findings reveal that the Neural Network outperformed Decision Tree and Random Forest models consistently, yielding the maximum accuracy for predicting outcomes. The results show the promise of AI-based prediction systems to revolutionize pedagogy. With the ability to initiate interventions early on, AI can address issues related to low dropout rates, enhance student motivation, and increase academic achievement. The research infers that the use of AI-based predictive systems by institutions can aid in more adaptive, data-driven, and student-centric education.
Conventional evaluation toolsālike tests, manual grading, and teacher observationāare usually reactive in nature, recognizing performance problems only after students underperform. AI can, on the other hand, change the paradigm of education to proactive and preventive by projecting outcomes beforehand and suggesting individualized interventions. This work investigates the use of AI methods for the prediction of student academic performance based on attendance, assignment grades, previous grades, and learning activity as input features of primary importance. Three machine learning algorithmsāDecision Tree, Random Forest, and Neural Networkāwere trained and tested on student datasets. Performance was compared on several measures, such as accuracy, precision, recall, and F1-score, to provide a thorough evaluation.
Experimental findings reveal that the Neural Network outperformed Decision Tree and Random Forest models consistently, yielding the maximum accuracy for predicting outcomes. The results show the promise of AI-based prediction systems to revolutionize pedagogy. With the ability to initiate interventions early on, AI can address issues related to low dropout rates, enhance student motivation, and increase academic achievement. The research infers that the use of AI-based predictive systems by institutions can aid in more adaptive, data-driven, and student-centric education.
Abstract
Enhancing Cyber Security Through Awareness and Training Programs
Mr. Arsalan A. Shaikh*, Mayur S. Shirsath
DOI: 10.17148/IJIREEICE.2025.13919
Abstract: One of the most important aspects of the rapid transformation into the digital world is the security concerns that come with it. The exploitation of phishing, ransomware, social engineering, and identity theft, in addition to the use of human neglect on concerns, is tantamount to most of the breaches. More than 80% of the breaches are a product of carelessness, which is a studied fact. Devices like intrusion detection systems, firewalls, and encryption are all extremely important, but without human-centered defenses, all these technologies will fail. The focus of this research is on the impact of cyber security awareness and training as the primary level of defense. With the use of mixed-method analysis, the study evaluates behavior through the use of structured awareness sessions, surveys, phishing simulations, and training workshops. The participants were trained on incident reporting, safe browsing, phishing, and password management. As phishing detection rates improved from 35% to 78% and acceptance rates of multi-factor authentication increased, the results exhibited a marked increase in user vigilance.
Keywords: Cyber Security; Awareness; Cyber Attacks; Security Training; Safety Measures.
Keywords: Cyber Security; Awareness; Cyber Attacks; Security Training; Safety Measures.
Abstract
Ai based crop disease detection system
Mr. Arsalan A. Shaikh*, Miss. Kajal S. Vichave
DOI: 10.17148/IJIREEICE.2025.13920
Abstract: An AI-based crop disease detection system makes use of artificial intelligence, specifically computer vision and machine learning, to identify and categorize crop diseases. The main goal is to help farmers and agricultural specialists identify plant diseases early so that they can take quick action and reduce crop losses.These systems usually use photos taken with smartphones or cameras to analyze visible symptoms on plant parts like leaves, stems, or fruits using image processing techniques. Convolutional neural networks (CNNs), a type of deep learning model, are specifically trained to identify visual patterns linked to particular plant diseases or nutrient deficiencies. In certain applications, environmental elements such as humidity and temperature are also included to improve diagnostic precision. India is an agricultural country. A total of 17% of the GDP comes from agriculture. As a result, it is a significant area of the Indian economy. In terms of global agricultural production, India came in second. Every crop is susceptible to specific diseases that will impact the potential yield's quantity and quality. Crop diseases account for approximately 42% of crop failure and cause the average yield loss for the majority of important food crops. Crop diseases frequently cause the entire crop production to be destroyed. Numerous diseases have an impact on crop production globally. Early disease detection will make it possible to monitor and implement control measures more effectively.
Abstract
Impact of Social Media on Mental Health
Prof. Ms. Chetana Kawale*, Mr. Mahendra Ramesh Patil
DOI: 10.17148/IJIREEICE.2025.13921
Abstract: Social media has transformed human interaction by providing platforms for communication, information sharing, and self-expression. However, its growing influence on psychological well-being has raised concerns among researchers, health professionals, and policymakers. This study explores the impact of social media on mental health, focusing on both positive and negative effects. A mixed-methods approach was adopted, combining surveys of 300 university students with secondary data from recent studies. Findings reveal that while social media enhances connectivity, peer support, and awareness, it also contributes to anxiety, depression, sleep disturbances, and reduced self- esteem due to excessive use, cyberbullying, and social comparison. Statistical analysis indicated that students spending more than 4 hours daily on social platforms reported significantly higher stress levels (p < 0.05). This research highlights the dual nature of social media, suggesting the need for balanced use, digital literacy, and intervention strategies to mitigate risks and promote mental well-being.
Abstract
Revolutionizing Transformer Health Monitoring Innovations for Enhanced Care and Wellness
Farukh Sheikh, Prof. Trupti F More
DOI: 10.17148/IJIREEICE.2025.13922
Abstract: The Internet of Things (IoT) technology will be used in the project to improve the monitoring and maintenance of distribution transformers. In power distribution networks, distribution transformers play a crucial role, and the reliability and efficiency of the entire system are directly impacted by the performance of these transformers. Traditional monitoring techniques frequently rely on recurrent manual inspections, which can be laborious, ineffective, and subject to human mistake. Distribution utilities may increase the dependability of their transformer assets, optimize maintenance schedules, decrease downtime, and boost overall operational efficiency by putting this smart monitoring system in place. The initiative also establishes the groundwork for future research and development in this area and advances IoT technology in the electricity sector.
Keywords: Arduino Uno, Distribution Transformer, IOT.
Keywords: Arduino Uno, Distribution Transformer, IOT.
Abstract
How ChatGPT is Changing Education Opportunities and Challenges
Prof. Mr. Arsalan Shaikh*, Miss. Nikita S. Rajput
DOI: 10.17148/IJIREEICE.2025.13923
Abstract: Artificial Intelligence (AI) is quickly transforming how education is taught, accessed, and experienced. One of the most notable tools in this space is ChatGPT, a language model developed by OpenAI that can generate responses similar to how humans speak and write. In education, ChatGPT brings many benefits for both students and teachers. It supports personalized learning, offers round-the-clock help for student questions, assists with exam preparation, and helps reduce teachersā workload by generating quizzes, assignments, and lesson plans. Its ability to understand and communicate in multiple languages also makes learning more accessible for students from diverse backgrounds.Despite these advantages, using ChatGPT in education also comes with challenges. Issues like plagiarism, cheating, over-reliance on AI, occasional inaccuracies in responses, and broader ethical concerns raise important questions about its impact on students' critical thinking and creativity. Many educators are also concerned that too much dependence on AI could reduce meaningful interactions between teachers and studentsāsomething essential for a well-rounded education.This paper explores both the opportunities and challenges of using ChatGPT in educational settings. It uses a qualitative approach, drawing on existing research papers, reports, and case studies. The findings suggest that while ChatGPT has the potential to greatly improve learning, it must be used responsibly and ethically. Ultimately, the future of education will rely on striking the right balance between AI tools and human involvement, ensuring that technology enhances, rather than replaces, the role of teachers.
Abstract
A Survey of Various Methods and Techniques for Detecting Blur Images
JEBA PRIYA J, Dr. S. PRASANNA
DOI: 10.17148/IJIREEICE.2025.13924
Abstract: The quality of images is essential in computer vision, image processing, and other related fields. Many digital images contain blurred regions, which are caused by motion or defocus. The blur detection algorithms are found very helpful in real-life applications and therefore have been developed in various multimedia-related research areas, including image restoration, image enhancement, and image segmentation. Image restoration is one of the categories in image processing, where the quality of an image plays a vital role in the process. Blur detection is a pre-processing stage in image restoration. Blur detection techniques are used to remove the blur from a blurred region of an image, which is due to the defocus of a camera or the motion of an object. In this paper we represent some methods of blur detection, such as Blind image de-convolution, Low depth of field, Edge sharpness analysis, and Low directional high frequency energy, Haar Wavelet Transform (HWT), Fast Fourier transform (FFT), Laplacian operator, Modified Laplacian (MLAP), Tenengrad (TEN), Gaussian Blurring, Median Blur, Bilateral Blur. After studying all these techniques, we have found that a lot of future work is required for the development of a perfect and effective blur detection technique.
Keywords: Blur detection, Blur image, Image processing, Blur classification, Blind image deconvolution, Edge sharpness analysis, DOF, HWT, FFT, MLAP, TEN
Keywords: Blur detection, Blur image, Image processing, Blur classification, Blind image deconvolution, Edge sharpness analysis, DOF, HWT, FFT, MLAP, TEN
Abstract
The Impact of Cyber-Physical Attacks on AI-Enabled Business Systems
Praveen Kumar Reddy Gouni, Eraj Farheen Ansari
DOI: 10.17148/IJIREEICE.2025.13925
Abstract: Now, companies in industries including manufacturing, shipping, supply chain, healthcare, and critical infrastructure can attain previously unheard-of levels of automation, efficiency, and predictive capabilities because of the convergence of artificial intelligence (AI) and cyber-physical systems (CPS). But this connection brings with it serious new security issues. Cyber-physical attacks that modify digital control levels, take advantage of AI models, or interact with physical devices can seriously impair company operations, leading to monetary losses, security threats, and damage to one's reputation. The impact of such attacks on AI-enabled business systems is examined in this article through the development of a comprehensive threat vector taxonomy that covers the cyber, physical, and AI/model layers. We provide impact metrics that link technical disruptions to measurable business consequences, such as operational inefficiencies, economic costs, downtime, and fines from the government. We use real-world occurrences, benchmark CPS datasets (SWaT, WADI, BATADAL) for experimental evaluations, and controlled attack scenarios to show how vulnerable AI- driven decision-making pipelines are too adversarial and supply-chain threats. We also examine mitigation strategies like secure model lifecycle management, anomaly detection, robust machine learning, and sensor redundancy. According to the study, in order to preserve the credibility of AI-enabled business CPS, extensive defences, regulatory standards, and a strong system architecture are essential.
Keywords: Cyber-Physical Systems (CPS); AI Security; Business Systems; Adversarial Machine Learning; Cyber- Physical Attacks; Supply Chain Security; Industrial Control Systems (ICS); Anomaly Detection; Model Poisoning; Resilient AI; Critical Infrastructure Protection; Business Risk; Operational Technology (OT) Security; Secure AI Lifecycle.
Keywords: Cyber-Physical Systems (CPS); AI Security; Business Systems; Adversarial Machine Learning; Cyber- Physical Attacks; Supply Chain Security; Industrial Control Systems (ICS); Anomaly Detection; Model Poisoning; Resilient AI; Critical Infrastructure Protection; Business Risk; Operational Technology (OT) Security; Secure AI Lifecycle.
Abstract
The Benefits of Cloud Computing
Prof. Mr. Vaibhav Chaudhari, Mr. Milind Narayan Patil
DOI: 10.17148/IJIREEICE.2025.13926
Abstract: Cloud computing has emerged as a transformative paradigm in the field of information technology, enabling organizations and individuals to access scalable computing resources over the internet. This paper explores the benefits of cloud computing, highlighting its advantages in terms of cost efficiency, scalability, flexibility, collaboration, disaster recovery, and environmental sustainability. The study draws from existing literature and case studies to illustrate how cloud adoption enhances business operations, fosters innovation, and supports digital transformation. Findings suggest that cloud computing not only reduces operational costs but also provides competitive advantages by improving agility and enabling global access to resources.
Keywords: Cloud Computing, Scalability, Cost Efficiency, IT Infrastructure, Digital Transformation
Keywords: Cloud Computing, Scalability, Cost Efficiency, IT Infrastructure, Digital Transformation
Abstract
AI-Driven Robotics and Automation
Prof. Mr. Arsalan A. Shaikh*, Miss. Komal Narendra Pawar
DOI: 10.17148/IJIREEICE.2025.13927
Abstract: Research Problem: The need for greater flexibility, intelligence, and adaptability in robotic systems beyond traditional, pre-programmed automation. Objectives : To explore how AI, specifically machine learning and computer vision, enhances robotic capabilities, to evaluate the performance of an AI-driven system, and to discuss its implications. Methods: Briefly describe the research approach, such as a simulation-based experiment using reinforcement learning to train a robotic arm, a case study analysis of a specific industry, or a comprehensive literature review. Key Findings: State the primary results, for example, "The AI-driven system achieved a 25% increase in task completion speed and a 40% reduction in error rates compared to conventional automation. Conclusion: Summarize the overall significance of the findings for the field.