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VOLUME 13, ISSUE 8, AUGUST 2025
Implementation of an amorphous silicon carbide, ultra-high-speed switch for improving FPGA’s interconnect switching
Dr Evangelos I. Dimitriadis
CMOS Design and Performance Analysis of Ring Oscillator for Different Stages
Suman B S, Asha K M, Bhumika B, Harshitha B, Preritha G Y
Charge Based Modelling for the Semiconductor Substrate of the Ferroelectric Field Effect Transistor (FEFET)
Shailesh Madhav Keshkamat
LOW POWER 3-BIT ENCODER DESIGN USING MEMRISTOR
Bhagya Shanthakumar, Deeksha V Nyamathi, Bindu D B, Keshava H C, Sachitha B S
Smart Helmet based Accident Detection and Notification
Manish Paul, Satavisha Datta, Manisha Debnath, Rami Das, Sourav Debbarma, Rupan Das, Surabhi Debbarma, Saikat Deb
IoT BASED VEHICLE EMISSION MONITORING SYSTEM
Ali I K, Anjali N R, Anusha V S, Bhagyashree V G, Spoorthi T S
A Real-Time Object Detection System for Assistive Navigation in the Visually Impaired
Abijith R Nair*, Sunitha S Nair
Thyroid Detection System using Machine Learning
Athul V S, Ayswariya V J
LUNGCARE AI: ENHANCING TUBERCULOSIS DETECTION WITH MACHINE LEARNING
Ramraj R J*, Ayswariya V J
VOICE ASSISTANT HOME AUTOMATION
G S Sunitha, Alfiya Taneem, OM Ganesh, Prajwal R H, Sathish P Nidagal
A Multi-dimensional Metaverse Approach in Educational Environments
Dimitrios Magetos, Prof. Sarandis Mitropoulos
DESIGN OF BANDGAP VOLTAGE REFERENCE CIRCUIT
Kiran Kumar G. H, Bhavana P P, J. M. Sukshith Gowda, Niteesh Desai, Rakshit S. Mantanavar
A review on renewable energy awareness and usages patterns in semi urban areas of Ravangla, Sikkim
Dawa Doma Sherpa, Aditya Kumar, Siddharth Kumar Sethi, Sanchay Karmakar
Epileptic seizure detection and Prediction using Deep learning
Syra S Shaji, Goutham Krishna L U
CARBON NANOTUBE FIELD EFFECT TRANSISTOR (CNTFET)-BASED-OPERATIONAL TRANSCONDUCTANCE-AMPLIFIER (OTA) IMPLEMENTATION IN ACTIVE LOW-PASS-FILTER (LPF) AT 14 NM TECHNOLOGY NODE: DESIGNING, SIMULATION, AND PERFORMANCE EVALUATION SUITABLE FOR BIOMEDICAL SIGNAL FILTRATION
Samia Bashiruddin, Pallavi Gupta, M. Nizamuddin
IoT for Smart Agriculture and Crop Monitoring
Chetna Kadam, Rutuja kudse, Rohini V. Daund
Student Emotion Analytics Dashboard for Remote Learning Platforms
Gopika Gopakumar, Goutham Krishna L U
DEVELOPMENT OF DYNAMO-SOLAR POWERED E-BICYCLE
Leela G H, Deekshitha G A, Salma Faiza, Smitha K M, Soujanya Patil
PRIVACY-PRESERVING TECHNIQUES IN HEALTHCARE DATA REPOSITORIES: A COMPARATIVE ANALYSIS
Abdullateef Ajibola Adepoju, Khalid Haruna, Saidu Sunbo Akanji
DESIGNING OF SMART AND SECURE SINGLE ATM CARD FOR MULTIPLE BANK ACCOUNTS
Poornima G N, NivedithA N S, Raksha P, Uma M R, Shreya K S
DESIGN AND INTEGRATION OF A CIRCULAR SMALL LOOP ANTENNA WITH LORA-BASED LIVESTOCK TRACKING SYSTEM USING METHOD OF MOMENTS IN CROSS RIVER STATE
Ofem U. O, Tawo Godwin Ajuo, Osahon Okoro
Smart Shop Cart
Prof Vanishree H V, Khushi V, Pooja D Rao, Uzma Naaz, Varun H S
POWER CONSUMPTION COMPARISON OF COVENTIONAL AND SINGLE-ENDED 6T SRAM CELL USING CMOS
G.S. Sunitha, Bhoomika E, Bhoomika H, Chandan D S, Laxmi B Daddi
“Smart ATM Security System”
Mahendrachri, Abhishek S Shingadi, Bhoomika N, Chethan Kumar K H,Keshava P M
SOLAR POWERED SMART GRASS CUTTER
Prakash K M, Nisarga B K, Pavitra S A, Adifa Hussian
SMART VEHICLE IGNITION SYSTEM
Prakash K M, Bhagya D B, Rani C O, Sindhu B S, Srushti S G
A Hybrid Deep Learning Framework for Personalized Women’s Nutrition Recommendation Based on Menstrual and Health Parameters
Mr. Abhinav N D, Ms. Charishma R, Ms. Kruthi R
HUMANOID ROBOT
Shubha V Patel, Prajwal S, Raghavendra V Revankar, Renukaradhya M S
Voice Assistant Smart Navigation Stick for Blind Using IoT
Avinash K G, Manasa S M, Roopa N A, Shabrien Taj T, Meghana V
TIQ Based Flash ADC
Leela G H, Kruthika N, Kruthika S G, Nandini S, Parvathi R Andanur
Abstract
Implementation of an amorphous silicon carbide, ultra-high-speed switch for improving FPGA’s interconnect switching
Dr Evangelos I. Dimitriadis
DOI: 10.17148/IJIREEICE.2025.13801
Abstract: A novel solution for improving FPGA’s operation is proposed here, for the first time. The use of a crystalline silicon based switch, which incorporates in fabrication procedure a 0.1 microns thin film of amorphous silicon carbide, is presented in this paper. The above switch exhibits a rise time value of about 10ps, which is the lowest ever reported and compared with switching time of 50-300ns of MOSFETs used in FPGAs interconnect switching, can lead to very fast FPGA’s operation. The switch can be triggered from OFF to ON state by using a gate electrode. It can also be triggered by incident light, thus making it capable for future FPGAs photonic application.
Keywords: FPGAs, interconnect switches, amorphous silicon carbide ultra-high-speed switch.
Keywords: FPGAs, interconnect switches, amorphous silicon carbide ultra-high-speed switch.
Abstract
CMOS Design and Performance Analysis of Ring Oscillator for Different Stages
Suman B S, Asha K M, Bhumika B, Harshitha B, Preritha G Y
DOI: 10.17148/IJIREEICE.2025.13802
Abstract: In the evolving landscape of Very Large Scale Integration (VLSI), ring oscillators play a critical role in evaluating process, voltage, and temperature (PVT) variations, and are widely used in applications such as clock generation, frequency synthesis, and on-chip testing. This project presents a comprehensive CMOS-based design and performance analysis of ring oscillators with varying stages using the Cadence design suite. The primary objective is to study how the number of stages impacts key performance parameters such as oscillation frequency, power consumption, propagation delay, and area.The design methodology involves the implementation of odd-stage CMOS inverter chains (3, 5, 7, and 9 stages) in the Cadence Virtuoso platform using a standard 180nm technology library. Post-layout simulations are performed using Spectre simulator to ensure accurate timing and power analysis. The frequency of oscillation is observed to decrease with an increasing number of stages, while power dissipation and area show a proportional rise. This study provides valuable insights into the trade-offs involved in the design of ring oscillators, aiding in the selection of optimal configurations for various low-power and high-speed VLSI applications.
Keywords: VLSI, Ring Oscillator, PVT, Power Consumption, Propagation Delay, Cadence Virtuoso, Spectre simulator.
Keywords: VLSI, Ring Oscillator, PVT, Power Consumption, Propagation Delay, Cadence Virtuoso, Spectre simulator.
Abstract
Charge Based Modelling for the Semiconductor Substrate of the Ferroelectric Field Effect Transistor (FEFET)
Shailesh Madhav Keshkamat
DOI: 10.17148/IJIREEICE.2025.13803
Abstract: This paper presents the modelling of FEFET by using the BSIM for the semiconductor layer while dealing with the Landau – Khalatnikov formulation for the Ferroelectric layer. Results are presented for the simulation for the charge-based evaluation of the semiconductor substrate, while the challenges to be addressed for the FE layer are enumerated.
Keywords: Ferro-Electric Field Effect Transistor, Landau – Khalatnikov Formulation, Berkeley Short Channel IGFET Model (BSIM), Interstitial Layer, Polarization of Domains.
Keywords: Ferro-Electric Field Effect Transistor, Landau – Khalatnikov Formulation, Berkeley Short Channel IGFET Model (BSIM), Interstitial Layer, Polarization of Domains.
Abstract
LOW POWER 3-BIT ENCODER DESIGN USING MEMRISTOR
Bhagya Shanthakumar, Deeksha V Nyamathi, Bindu D B, Keshava H C, Sachitha B S
DOI: 10.17148/IJIREEICE.2025.13804
Abstract: Combinational circuits are the fundamental building blocks for almost all digital electronic systems. In this project, we are proposing one of the combinational circuit 3-bit encoder using Memristor. Here, we are designing 3-bit encoder in CMOS logic, Pseudo NMOS logic and Memristor (MRL) logic. A comparative analysis also have done among these configurations of design. From this analysis, we are able to choose best configuration of design that applicable for specified areas of applications.
Keywords: Memristor, 3-bit Encoder, Low Power Design, CMOS, Pseudo NMOS, MRL
Keywords: Memristor, 3-bit Encoder, Low Power Design, CMOS, Pseudo NMOS, MRL
Abstract
Smart Helmet based Accident Detection and Notification
Manish Paul, Satavisha Datta, Manisha Debnath, Rami Das, Sourav Debbarma, Rupan Das, Surabhi Debbarma, Saikat Deb
DOI: 10.17148/IJIREEICE.2025.13805
Abstract: The paper aims to address the increasing rate of accidents by designing and implementing a smart helmet system that integrates sensors and wireless communication to detect accidents in real-time and promptly notify emergency services. An intelligent helmet stands as a crucial safety accessory for riders, emphasizing the utmost importance of their well-being while on the road. Through the incorporation of advanced technologies, it elevates the features of a traditional helmet and transforms a regular bike into a smart vehicle. This groundbreaking headgear seamlessly incorporates features such as alcohol sensing, identifying accidents, tracking location, and detecting falls, effectively operating as a hands-free device.
Keywords: Smart helmet, notification, accident, Arduino, location tracking.
Keywords: Smart helmet, notification, accident, Arduino, location tracking.
Abstract
IoT BASED VEHICLE EMISSION MONITORING SYSTEM
Ali I K, Anjali N R, Anusha V S, Bhagyashree V G, Spoorthi T S
DOI: 10.17148/IJIREEICE.2025.13806
Abstract: The rapid increase in vehicular traffic has led to significant environmental concerns, particularly due to the emission of harmful gases such as carbon monoxide (CO), nitrogen oxides (NOx), and hydrocarbons (HC). Traditional methods of emission monitoring are often periodic and fail to provide real-time data, limiting their effectiveness in controlling pollution. This project presents an IoT-based Vehicle Emission Monitoring System designed to continuously track and report emission levels from individual vehicles. Using gas sensors integrated with a microcontroller and IoT module, the system detects pollutant levels and transmits the data to a cloud platform for real-time analysis and storage. Users and authorities can access this information via a web or mobile application, enabling immediate identification of vehicles exceeding emission norms. The proposed system aims to promote environmental awareness, support regulatory compliance, and contribute to cleaner air by facilitating timely maintenance and stricter enforcement of emission standards.
Keywords: harmful gases, individual vehicles, emission standards
Keywords: harmful gases, individual vehicles, emission standards
Abstract
A Real-Time Object Detection System for Assistive Navigation in the Visually Impaired
Abijith R Nair*, Sunitha S Nair
DOI: 10.17148/IJIREEICE.2025.13807
Abstract: One of the biggest challenges facing blind assistance systems is how they can navigate with safety and independence in such complicated real-world scenarios, given that traditional tools that assist these users are usually simplistic. Among many techniques that emerge as essential to upgrading these systems are machine learning and deep learning. These methods introduce considerable object detection, voice recognition, and distance measurement capabilities. This review summarizes the findings of recent studies in the application of neural networks, such as convolutional neural networks (CNNs), and advanced models in real-time object recognition and environmental awareness. Models like Faster R-CNN, SSD, and DenseNet have shown exceptional performance in object detection and segmentation with high accuracy rates and reliability. However, the challenges include diversity in datasets, limitations in real-time processing, and user adaptability. Furthermore, computational efficiency and optimizing deep learning models for low-power devices remain crucial areas for improvement. Enhancing multimodal feedback, integrating adaptive learning models, and improving response time are essential for real-world deployment. This review represents a great step forward in assistive technology, providing real-time, reliable feedback to help visually impaired users navigate their surroundings with greater independence and confidence.
Keywords: Visually Impaired, Computer Vision, Deep Learning, Object Detection, YOLO Algorithm, Real time.
Keywords: Visually Impaired, Computer Vision, Deep Learning, Object Detection, YOLO Algorithm, Real time.
Abstract
Thyroid Detection System using Machine Learning
Athul V S, Ayswariya V J
DOI: 10.17148/IJIREEICE.2025.13808
Abstract: Thyroid disease is a widespread endocrine disorder that often goes undiagnosed due to the limitations of conventional diagnostic methods. This project proposes a machine learning-based web application for automated detection of thyroid disorders using both clinical data and medical images. The system integrates Fuzzy C-Means clustering for analyzing hormone-level data and Convolutional Neural Networks (CNN) for classifying thyroid ultrasound images. It supports role-based access for technicians, doctors, and patients, streamlining diagnosis, prescription, and feedback. The proposed model achieved over 96% accuracy in classification, outperforming traditional algorithms. With modules for appointment booking and real-time doctor-patient chat, the system offers a user-friendly and scalable solution for intelligent healthcare. It aims to reduce human error, improve early detection, and make thyroid diagnosis more accessible, especially in resource-limited settings.
Keywords: Thyroid Disease, CNN, Fuzzy C Means Clustering.
Keywords: Thyroid Disease, CNN, Fuzzy C Means Clustering.
Abstract
LUNGCARE AI: ENHANCING TUBERCULOSIS DETECTION WITH MACHINE LEARNING
Ramraj R J*, Ayswariya V J
DOI: 10.17148/IJIREEICE.2025.13809
Abstract: Lung diseases such as Tuberculosis (TB) and Pneumonia pose major health challenges, particularly in countries like India. Chest X-rays are widely used for diagnosis, but manual interpretation can be time-consuming and inconsistent. This study presents a hybrid ensemble model combining InceptionV3 and VGG16 Convolutional Neural Networks (CNNs) for classifying TB, Pneumonia, and Normal lung conditions. The dataset undergoes advanced preprocessing and augmentation, followed by an 80:10:10 train-validation-test split. Using a majority voting strategy, the ensemble achieves around 95% overall accuracy and a 0.996 ROC AUC score. The model demonstrates strong potential for scalable, automated lung disease diagnosis in real-world clinical settings.
Keyword: Tuberculosis, Deep learning, Machine learning, Early detection.
Keyword: Tuberculosis, Deep learning, Machine learning, Early detection.
Abstract
VOICE ASSISTANT HOME AUTOMATION
G S Sunitha, Alfiya Taneem, OM Ganesh, Prajwal R H, Sathish P Nidagal
DOI: 10.17148/IJIREEICE.2025.13810
Abstract: This project presents a hybrid home automation system using the ESP32 microcontroller, combining voice control (via Google Assistant and IFTTT) and a custom mobile app to manage appliances like lights and fans. It enhances convenience, especially for the elderly and differently-abled, by enabling hands-free and remote operation. The system uses relay modules for device control and connects to Firebase Realtime Database for cloud-based monitoring and real- time status updates. It offers reliable performance even during internet outages and supports both individual and group device control, making it a flexible and user-friendly IoT solution for modern smart homes.
Keyword: hybrid home automation system, Firebase Realtime Database, real-time status updates.
Keyword: hybrid home automation system, Firebase Realtime Database, real-time status updates.
Abstract
A Multi-dimensional Metaverse Approach in Educational Environments
Dimitrios Magetos, Prof. Sarandis Mitropoulos
DOI: 10.17148/IJIREEICE.2025.13811
Abstract: Metaverse, as an emerging, interactive and immersive 3D virtual space technology of Web 3.0, is expected to lead to a profound transformation of the educational process. Its ability to combine digital presence, direct interaction and experiential learning offers new perspectives for active student participation and personalized learning in virtual environments. Within this context, the present study focuses on examining the integration of Metaverse into education, exploring its potential and advantages, as well as the issues and challenges that may arise from its use. To support the research process, a mixed methodology was used, which included a literature review, a qualitative study, and a quantitative study. In addition, pilot virtual worlds were created on the Spatial.io platform, using the ADDIE method, offering teachers the opportunity to test and gain experience. The results show that Metaverse contributes substantially to the improvement of learning experience, offering realistic and interactive learning environments, while enhancing social interaction and promoting experiential learning. However, potential risks must be taken into account, such as digital inequality, malicious use of educational data, prevention of cyberbullying, and unnecessary and excessive use of the metaverse by students, such as isolation from the natural environment and obsession with virtual worlds and our digital persona. In conclusion, the empirical research reveals a positive response of Greek teachers to the proposed Metaverse model, predicting improvements in the learning experience and student interest.
Keywords: Web 3.0, Metaverse, Avatar, Spatial.io, ADDIE, Virtual Worlds.
Keywords: Web 3.0, Metaverse, Avatar, Spatial.io, ADDIE, Virtual Worlds.
Abstract
DESIGN OF BANDGAP VOLTAGE REFERENCE CIRCUIT
Kiran Kumar G. H, Bhavana P P, J. M. Sukshith Gowda, Niteesh Desai, Rakshit S. Mantanavar
DOI: 10.17148/IJIREEICE.2025.13812
Abstract: The project focuses on designing and simulating a Bandgap Voltage Reference (BGR) circuit using 180nm CMOS technology to achieve a temperature-independent and precise voltage reference. This is crucial for reliable operation of analog and digital components. The design uses Complementary-To-Absolute-Temperature (CTAT) and Proportional-To-Absolute-Temperature (PTAT) characteristics to cancel out temperature effects and generate a constant reference voltage. The circuit is suitable for various applications, including Analog-to-Digital Converters, voltage regulators, temperature sensors, and battery-operated systems. The work not only demonstrates temperature compensation techniques but also enhances understanding of analog circuit behaviour under real-world conditions.
Keywords: Cadence, simulation, circuit, voltage.
Keywords: Cadence, simulation, circuit, voltage.
Abstract
A review on renewable energy awareness and usages patterns in semi urban areas of Ravangla, Sikkim
Dawa Doma Sherpa, Aditya Kumar, Siddharth Kumar Sethi, Sanchay Karmakar
DOI: 10.17148/IJIREEICE.2025.13813
Abstract: This paper explores how residents of Ravangla, a semi-urban town in Sikkim, perceive and use renewable energy. Drawing from in-person surveys and community observations, it was found that many people have encountered solar lanterns and water heaters, mainly due to government support or word of mouth, but deeper technical knowledge is rare. Households often rely on these renewables as backup sources during frequent power outages, rather than as their main source of power. While government schemes and local examples can spark interest, obstacles remain: high initial costs, limited product choices in the market, and lack of nearby maintenance support deter widespread adoption. Some confusion persists about what truly counts as renewable energy, with products like LED bulbs or even diesel generators occasionally misidentified. Residents indicate that hands-on demonstrations and training in local languages would help bridge the knowledge gap. The study suggests local entrepreneurs could be trained to provide maintenance and promote awareness. Aligning clean energy products with the community’s daily needs—such as solar-powered tools for small businesses or agriculture—may drive higher adoption. Ultimately, the region’s journey offers lessons for similar towns, highlighting the value of locally led efforts and practical education over top-down campaigns
Background and Rationale
India has set progressive renewable energy targets as part of its Nationally Determined Contributions (NDC), seeking to reach 50% cumulative electric power capacity from non-fossil sources by 2030. Si,kkim, as a hilly Northeastern state, stands out for its existing reliance on renewable sources—mainly hydro—but is also challenged by weather, terrain, and limitations in grid extension, making off-grid solar and other distributed renewables especially relevant. According to the Sikkim Renewable Energy Development Agency (SREDA), solar potential remains high though actual adoption rates still lag, particularly in semi-urban and rural regions.
Background and Rationale
India has set progressive renewable energy targets as part of its Nationally Determined Contributions (NDC), seeking to reach 50% cumulative electric power capacity from non-fossil sources by 2030. Si,kkim, as a hilly Northeastern state, stands out for its existing reliance on renewable sources—mainly hydro—but is also challenged by weather, terrain, and limitations in grid extension, making off-grid solar and other distributed renewables especially relevant. According to the Sikkim Renewable Energy Development Agency (SREDA), solar potential remains high though actual adoption rates still lag, particularly in semi-urban and rural regions.
Abstract
Epileptic seizure detection and Prediction using Deep learning
Syra S Shaji, Goutham Krishna L U
DOI: 10.17148/IJIREEICE.2025.13814
Abstract: Numerous methods, including electroencephalography (EEG) and magnetic resonance imaging (MRI), have been proposed to diagnose epileptic seizures. Deep knowledge (DL) is one of the many subfields of artificial intelligence. Conventional machine learning algorithms involving point birth were used prior to the emergence of DL. As a result, their performance was restricted to what the people creating the features by hand could do. However, in DL, the creation of features and type is completely automated. Similar to how the theory of epileptic seizures has advanced significantly, these methods have appeared in numerous medical fields. This study presents a thorough overview of a factory focused on automated epileptic seizure discovery using neuroimaging modalities and DL methods. Different approaches have been suggested to diagnose epilepsy.
Keywords: LSTM, EEG modalities, MRI modalities.
Keywords: LSTM, EEG modalities, MRI modalities.
Abstract
CARBON NANOTUBE FIELD EFFECT TRANSISTOR (CNTFET)-BASED-OPERATIONAL TRANSCONDUCTANCE-AMPLIFIER (OTA) IMPLEMENTATION IN ACTIVE LOW-PASS-FILTER (LPF) AT 14 NM TECHNOLOGY NODE: DESIGNING, SIMULATION, AND PERFORMANCE EVALUATION SUITABLE FOR BIOMEDICAL SIGNAL FILTRATION
Samia Bashiruddin, Pallavi Gupta, M. Nizamuddin
DOI: 10.17148/IJIREEICE.2025.13815
Abstract: The frequency overlap between biomedical signals and noise highlights the critical need for effective signal processing and filtering to ensure precision in radio diagnostics. This work introduces the design of a 14 nm CNFET- OTA-based low-pass filter (LPF) suitable for physiological signal filtration. A second-order LPF architecture integrating CNFET-OTA was proposed and simulated using HSPICE. The filter achieved a cut-off frequency of 100.7 Hz, an average power dissipation of 141.8 nW, a quality factor of 0.707, and a phase margin of 134°, confirming its applicability in biomedical domains. Low power consumption, low cut-off frequency, with an appropriate quality factor, makes the proposed LPF well-suited for real-time bio signal processing. Aiming for low power consumption while achieving low cut-off frequency from a 0.5V power supply, CNFET-OTA-based LPFs ensure resilience in real-world applications, paving the way for future advancements in this field.
Keywords: CNTFET, OTA, HSPICE, Stanford Model, Filter, Bio Signal
Keywords: CNTFET, OTA, HSPICE, Stanford Model, Filter, Bio Signal
Abstract
IoT for Smart Agriculture and Crop Monitoring
Chetna Kadam, Rutuja kudse, Rohini V. Daund
DOI: 10.17148/IJIREEICE.2025.13816
Abstract: The growth of the global population coupled with a decline in natural resources, farmland, and the increase in unpredictable environmental conditions leads to food security is becoming a major concern for all nations worldwide. These problems are motivators that are driving the agricultural industry to transition to smart agriculture with the application of the Internet of Things (IoT) and big data solutions to improve operational efficiency and productivity. The IoT integrates a series of existing state-of-the-art solutions and technologies, such as wireless sensor networks, cognitive radio ad hoc networks, cloud computing, big data, and end-user applications. This study presents a survey of IoT solutions and demonstrates how IoT can be integrated into the smart agriculture sector.
Keywords: IoT in agriculture, Smart agriculture, Precision agriculture, Smart farming, IoT crop monitoring.
Keywords: IoT in agriculture, Smart agriculture, Precision agriculture, Smart farming, IoT crop monitoring.
Abstract
Student Emotion Analytics Dashboard for Remote Learning Platforms
Gopika Gopakumar, Goutham Krishna L U
DOI: 10.17148/IJIREEICE.2025.13817
Abstract: The rapid transformation of education into digital platforms has emphasized the need to improve virtual learning experiences by understanding students' emotions during lectures. Emotional states directly impact students’ focus, engagement, and learning outcomes, making real-time emotion analysis a valuable tool for enhancing teaching methodologies. This research presents an advanced emotion-based interactive dashboard designed to analyse students' facial expressions during online lectures, offering actionable insights to educators for improving teaching strategies and engagement. The system uses Convolutional Neural Networks (CNNs) for facial expression analysis and classifies emotions into categories such as happiness, sadness, anger, surprise, fear, and neutrality. These models are trained using both the FER-2013 and CK+ datasets, which provide robustness across varied image quality and expression types. To optimize training performance and improve model convergence, the system employs the Adam Optimizer, an adaptive learning rate optimization algorithm that combines the benefits of both AdaGrad and RMSProp, ensuring faster and more reliable training of deep neural networks. The processed emotional data is integrated into an intuitive dashboard that combines contextual details, such as the subject being taught, teaching faculty, and session-specific parameters. The dashboard offers dynamic visualization of emotion distribution, engagement trends, and real-time analytics, enabling educators to identify patterns in student behaviour. The system demonstrated high accuracy in emotion classification under various conditions. The integration of emotion-based analytics provides a unique approach to monitoring class engagement, identifying struggling students, and fostering personalized learning experiences. By combining advanced deep learning techniques with real-time analytics, the proposed system has the potential to redefine the future of online education, making it more responsive, adaptive, and student-centered
Keywords: Convolutional Neural Network, Analytical Dashboard, Adam Optimizer, Emotion Classification
Keywords: Convolutional Neural Network, Analytical Dashboard, Adam Optimizer, Emotion Classification
Abstract
DEVELOPMENT OF DYNAMO-SOLAR POWERED E-BICYCLE
Leela G H, Deekshitha G A, Salma Faiza, Smitha K M, Soujanya Patil
DOI: 10.17148/IJIREEICE.2025.13818
Abstract: In the current situation, a hybrid bicycle powered by solar and dynamo will assist in resolving the main issue of fuel prices, particularly the continually rising cost of petrol. Once more, vehicle-related pollution in urban areas and metropolises is steadily rising. An attempt is being made to look into some other alternate energy sources to power the bicycle in order to get around these issues. Once more, buying cars (motorcycles, scooters, or mopeds) for every social class is likewise out of reach. In light of this, efforts were underway to find a remedy for the environmental pollution and a means of helping these economically disadvantaged individuals. The direct current motor installed in the front axle housing powers the solar and dynamo-assisted hybrid bicycle utilising electrical power. The battery will be charged by the solar panels on the carriage, which will then power the hub motor. A pair of 48-volt dynamos mounted on the back wheel of the bicycle will charge the battery when it is travelling on the road, and the solar panel will charge the battery while the bike is parked or idle. For a two-wheeler or a bicycle with a gear shifting system, this setup will take the place of the gasoline engine, gearbox and fuel tank.
Keyword: Fuel prices, solar, charge, bicycle, pollution.
Keyword: Fuel prices, solar, charge, bicycle, pollution.
Abstract
PRIVACY-PRESERVING TECHNIQUES IN HEALTHCARE DATA REPOSITORIES: A COMPARATIVE ANALYSIS
Abdullateef Ajibola Adepoju, Khalid Haruna, Saidu Sunbo Akanji
DOI: 10.17148/IJIREEICE.2025.13819
Abstract: Digital healthcare systems generate vast amounts of sensitive patient data, creating significant privacy challenges including data breaches, re-identification attacks and regulatory compliance issues. This study evaluates privacy-preserving techniques (PPTs) for healthcare data repositories, including anonymization, homomorphic encryption, differential privacy, federated learning and blockchain solutions. Each technique was assessed based on privacy guarantees, data utility, scalability, implementation feasibility and regulatory compliance. Results demonstrate substantial improvements in privacy measures: encryption effectiveness increased to 90%, access controls reached 85%, and user satisfaction improved by 30%. No single technique provides optimal solutions across all criteria; hybrid approaches offer the best privacy-utility balance. The study provides guidance for implementing secure, compliant healthcare data systems.
Keywords: Privacy-preserving techniques, Healthcare data repositories, Differential privacy, Federated learning, HIPAA compliance.
Keywords: Privacy-preserving techniques, Healthcare data repositories, Differential privacy, Federated learning, HIPAA compliance.
Abstract
DESIGNING OF SMART AND SECURE SINGLE ATM CARD FOR MULTIPLE BANK ACCOUNTS
Poornima G N, NivedithA N S, Raksha P, Uma M R, Shreya K S
DOI: 10.17148/IJIREEICE.2025.13820
Abstract: In this project, a smart and secure single ATM card system with multi-factor authentication that can access various bank accounts is designed and implemented. An Arduino Mega microcontroller is used in the system, along with an RFID module, fingerprint sensor, keypad, LCD display, GSM module, and many auxiliary parts. Users can select between using their fingerprints or entering their password to validate their identification when they swipe an RFID card. Following authentication, users can choose from a number of connected bank accounts and carry out financial tasks including checking their balance and making cash withdrawals. After transactions, the system refreshes and shows the remaining amount. In the event of successful withdrawals or unauthorised access, it also sends out alert messages via GSM. By combining different bank access into a single smart card system, this project improves banking security and ease while supporting numerous users with separate credentials.
Keywords: ATM card system, RFID card, alert messages.
Keywords: ATM card system, RFID card, alert messages.
Abstract
DESIGN AND INTEGRATION OF A CIRCULAR SMALL LOOP ANTENNA WITH LORA-BASED LIVESTOCK TRACKING SYSTEM USING METHOD OF MOMENTS IN CROSS RIVER STATE
Ofem U. O, Tawo Godwin Ajuo, Osahon Okoro
DOI: 10.17148/IJIREEICE.2025.13821
Abstract: This paper presents the design and analysis of a circular small loop antenna for livestock tracking applications in Cross River State, Nigeria, with an integrated LoRa RA-02 transceiver system. The study investigates the impact of two critical parameters—the number of wire turns and the radius of the loop—on the radiation resistance and efficiency of the antenna. These parameters were varied across ten stages, and their effects were analyzed using the Method of Moments and simulated with MobatLab. The investigation was conducted in three phases: simultaneous variation of radius and turns, variation of turns with fixed radius (0.623 cm), and variation of radius with fixed turns (2 turns). Results showed that radiation resistance is proportional to the square of the number of turns and inversely proportional to the loop radius, with an antenna efficiency of 87.33%. To demonstrate practical application, the optimized antenna was integrated into a LoRa-based transmitter and receiver system using the RA-02 (SX1278) module. The transmitter unit, mounted on livestock, collects GPS data and transmits it wirelessly to a fixed receiver station. Field tests confirmed reliable long-range communication and effective real-time tracking, validating the suitability of circular loop antennas for rural IoT deployments. This hybrid approach combines theoretical antenna modeling with real-world wireless communication, offering a scalable solution for smart agriculture and livestock monitoring.
Keywords: Circular loop antenna, Radiation resistance, Radiation efficiency, LoRa RA-02 transceiver, Livestock tracking, IoT communication, Method of Moments, GPS telemetry, Smart agriculture, Low-power wireless system
Keywords: Circular loop antenna, Radiation resistance, Radiation efficiency, LoRa RA-02 transceiver, Livestock tracking, IoT communication, Method of Moments, GPS telemetry, Smart agriculture, Low-power wireless system
Abstract
Smart Shop Cart
Prof Vanishree H V, Khushi V, Pooja D Rao, Uzma Naaz, Varun H S
DOI: 10.17148/IJIREEICE.2025.13822
Abstract: Today, retail shopping demands increased convenience and efficiency to meet customer expectations. This paper presents a Smart Shop Cart system that integrates Arduino-based control, RFID technology, and sensor-driven human-following capabilities to automate and enhance the shopping experience. Utilizing RFID tags and readers, the system enables automatic product identification and real-time billing, minimizing manual effort and checkout time.
The human-following mechanism, based on ultrasonic and infrared sensors, allows the cart to autonomously track the shopper, providing hands-free mobility. Additionally, a voice assistant and a mobile application offer interactive control and real-time purchase monitoring. Experimental results demonstrate that the proposed system significantly improves shopping convenience and operational efficiency, making it a promising solution for smart retail environments.
Keywords: Smart Shop Cart, Arduino, RFID, Human Following System, Voice Assistant, Automated Billing, Embedded Systems, Sensor Integration
The human-following mechanism, based on ultrasonic and infrared sensors, allows the cart to autonomously track the shopper, providing hands-free mobility. Additionally, a voice assistant and a mobile application offer interactive control and real-time purchase monitoring. Experimental results demonstrate that the proposed system significantly improves shopping convenience and operational efficiency, making it a promising solution for smart retail environments.
Keywords: Smart Shop Cart, Arduino, RFID, Human Following System, Voice Assistant, Automated Billing, Embedded Systems, Sensor Integration
Abstract
POWER CONSUMPTION COMPARISON OF COVENTIONAL AND SINGLE-ENDED 6T SRAM CELL USING CMOS
G.S. Sunitha, Bhoomika E, Bhoomika H, Chandan D S, Laxmi B Daddi
DOI: 10.17148/IJIREEICE.2025.13823
Abstract: This project focusses on analysing memory systems' power usage utilising both single-ended and typical 6T SRAM cells. SRAM, or static random-access memory, is crucial for contemporary computer systems where power efficiency is a top priority. The traditional 6T SRAM cell uses a differential read method, which increases power consumption while offering great stability. The single-ended 6T SRAM cell, on the other hand, has a simpler read operation, which drastically lowers layout complexity and power consumption. The power dissipation of the two systems is assessed and compared in this study using simulation-based techniques. The results emphasise the limitations in power efficiency, showing that traditional 6T SRAM may be more appropriate for situations where power consumption is less crucial, while single-ended 6T SRAM cells are more suited for power-sensitive applications like low-power Internet of Things devices.
Keyword: SRAM Power Analysis, 6T SRAM Cell, Single-Ended SRAM, Low-Power Memory Design.
Keyword: SRAM Power Analysis, 6T SRAM Cell, Single-Ended SRAM, Low-Power Memory Design.
Abstract
“Smart ATM Security System”
Mahendrachri, Abhishek S Shingadi, Bhoomika N, Chethan Kumar K H,Keshava P M
DOI: 10.17148/IJIREEICE.2025.13824
Abstract: The increasing frequency of ATM-related crimes, including theft, vandalism, and unauthorized access, necessitates the development of advanced security solutions. This paper proposes an IoT-enabled Smart ATM Security System that integrates an ESP32 microcontroller, GSM module, IR sensors, touch sensor, motor driver with DC motor, LCD display, buzzer, and battery backup. The system employs multi-layered security: IR sensors monitor human presence near the ATM entrance, the touch sensor detects tampering attempts, and the GSM module provides real-time SMS alerts to bank authorities. A motorized door mechanism ensures restricted access, while an LCD display communicates system status. The design was implemented using Arduino IDE and Embedded C programming. Experimental testing under various scenarios demonstrated rapid response time, high reliability, and uninterrupted operation during power failures. The proposed system offers a cost-effective, scalable, and efficient solution for ATM security.
Keyword: Smart ATM Security System, IoT, ESP32, GSM, IR Sensor, Touch Sensor, Motor Driver, Real-Time Alerts.
Keyword: Smart ATM Security System, IoT, ESP32, GSM, IR Sensor, Touch Sensor, Motor Driver, Real-Time Alerts.
Abstract
SOLAR POWERED SMART GRASS CUTTER
Prakash K M, Nisarga B K, Pavitra S A, Adifa Hussian
DOI: 10.17148/IJIREEICE.2025.13825
Abstract: Manual lawn maintenance is a repetitive, time-consuming, and often physically demanding task. This paper presents the design and development of a low-cost, semi-autonomous robotic grass cutter that can be remotely operated via a smartphone. The system is architected around the versatile ESP32 microcontroller, which provides both powerful processing capabilities and integrated Bluetooth connectivity. The robot's movement and the cutter's operation are controlled by DC motors managed through a motor driver module. A key feature of the system is its sustainable power management, incorporating a lithium-ion battery pack protected by a Battery Management System (BMS) and supplemented by a solar panel for on-the-go trickle charging. User control is achieved through a standard serial Bluetooth application on a smartphone, which sends commands to the ESP32 to navigate the mower and activate the cutting mechanism. This project demonstrates a practical, safe, and eco-friendlier alternative to traditional gasoline-powered lawn mowers.
Keyword: Robotic Lawn Mower, Solar Charging, Battery Management System (BMS).
Keyword: Robotic Lawn Mower, Solar Charging, Battery Management System (BMS).
Abstract
SMART VEHICLE IGNITION SYSTEM
Prakash K M, Bhagya D B, Rani C O, Sindhu B S, Srushti S G
DOI: 10.17148/IJIREEICE.2025.13826
Abstract: The Smart Vehicle Ignition System is a multi-layered system that integrates RFID authentication, facial recognition, and fingerprint sensing to enhance vehicle security and safety. It activates upon successful verification, providing a robust defence against unauthorized access. The system also initiates alcohol detection and tilt sensing mechanisms to ensure driver sobriety and monitor vehicle orientation for potential accidents or rollovers. The system aims to enhance vehicular security, driver responsibility, and real-time safety monitoring, making it suitable for personal vehicles, public transport, and fleet management.
Keyword: fingerprint sensing, facial recognition, vehicle.
Keyword: fingerprint sensing, facial recognition, vehicle.
Abstract
A Hybrid Deep Learning Framework for Personalized Women’s Nutrition Recommendation Based on Menstrual and Health Parameters
Mr. Abhinav N D, Ms. Charishma R, Ms. Kruthi R
DOI: 10.17148/IJIREEICE.2025.13827
Abstract: This research proposes a comprehensive framework for recommending personalized nutritional supplements for women, focusing on menstrual health and individual physiological parameters. The system begins with input data comprising menstrual symptoms (such as mood swings, fatigue, and cramps) and individual health indicators (such as age, BMI, and stress levels). Pre-processing techniques like missing value imputation, Z-score normalization, and one- hot encoding are applied to prepare the data. Feature extraction is carried out using a Layered Sparse Autoencoder Network, capturing complex patterns in the input. These features are then fed into a Hybrid Attention-based Bidirectional Convolutional Greylag Goose Gated Recurrent Network, which predicts nutrition recommendations. The model's performance is optimized using the Greylag Goose Optimization Algorithm. The final output suggests appropriate levels (High/Medium/Low) of essential nutrients such as Magnesium, Iron, Calcium, and Vitamin D, offering a data-driven and adaptive solution to women’s nutritional health.
Keywords: BMI, Z-score normalization, one-hot encoding, GGO, RNN, CNN, RNU
Keywords: BMI, Z-score normalization, one-hot encoding, GGO, RNN, CNN, RNU
Abstract
HUMANOID ROBOT
Shubha V Patel, Prajwal S, Raghavendra V Revankar, Renukaradhya M S
DOI: 10.17148/IJIREEICE.2025.13828
Abstract: This project focused on the design, development, and implementation of a humanoid robot. The primary objective is to create an autonomous or semi-autonomous robotic system capable of performing human-like tasks and interacting with its environment in a naturalistic manner. The project addresses key challenges in robotics, including robust locomotion (bipedalism), advanced perception (computer vision and sensor fusion), sophisticated manipulation, and intelligent decision-making. The proposed humanoid robot integrates a mechanical structure for stability, sensors for awareness, actuators for movement, and onboard computing for real-time processing. Emphasis is on object recognition, grasping, and human-robot interaction. Applications include healthcare, service robotics, industrial automation, education, and entertainment
Keywords: Humanoid Robot, Robotics, Artificial Intelligence, Human-Robot Interaction, UV-C Disinfection
Keywords: Humanoid Robot, Robotics, Artificial Intelligence, Human-Robot Interaction, UV-C Disinfection
Abstract
Voice Assistant Smart Navigation Stick for Blind Using IoT
Avinash K G, Manasa S M, Roopa N A, Shabrien Taj T, Meghana V
DOI: 10.17148/IJIREEICE.2025.13829
Abstract: The Voice Assistant Smart Navigation Stick for Blind using IoT is a smart device that helps visually impaired people move safely. It uses sensors to detect obstacles, GPS for navigation, and voice assistance to give real-time guidance. With IoT features, it also enables location tracking and improves independence and safety for users.
Keywords: Smart Walking Stick, Obstacle Detection, IoT.
Keywords: Smart Walking Stick, Obstacle Detection, IoT.
Abstract
TIQ Based Flash ADC
Leela G H, Kruthika N, Kruthika S G, Nandini S, Parvathi R Andanur
DOI: 10.17148/IJIREEICE.2025.13830
Abstract: This project presents the design and analysis of a Threshold Inverter Quantization (TIQ)-based Flash Analog-to-Digital Converter (ADC), aiming to address the limitations of conventional flash ADCs in terms of power consumption and chip area. Traditional flash ADCs, while offering high-speed conversion due to their parallel architecture, suffer from exponential growth in the number of comparators with increasing resolution, resulting in high power usage and large die size. The proposed TIQ Flash ADC replaces conventional comparators with cascaded CMOS inverters, leveraging transistor sizing to set internal reference voltages. This approach eliminates the need for a resistor ladder and reduces power consumption and complexity. The design also applies techniques such as gain boosting for sharper voltage thresholds and efficient encoding of the thermometer code to binary format. Simulation and analysis demonstrate that the TIQ-based ADC achieves comparable speed and resolution.
Keywords: VLSI, TIQ, ADC, Cadence Virtuoso.
Keywords: VLSI, TIQ, ADC, Cadence Virtuoso.
Abstract
An inclusive survey on Sentiment analysis: Approaches, Challenges and Trends
Amandeep Kaur
DOI: 10.17148/IJIREEICE.2025.13831
Abstract: Sentiment analysis, often known as opinion mining, is the process of determining and analyzing people's feelings and viewpoints toward goods, subjects, services, or occasions. Organizations may improve their services and products and create a feedback loop that continuously enhances user experiences by turning such information into relevant knowledge. For sentiment analysis applications, social media platforms and e-commerce websites are crucial since they are significant sources of data that are rich in opinions. Around the world, businesses, governments, and scholars use sentiment analysis to gather commercial insights, assess how the public views policies, and aid in well- informed decision-making. In order to introduce readers to this potent technology and promote additional contributions to the area, this paper provides a thorough description of the tasks, current trends, methodology, and challenges of sentiment analysis.
Keywords: Sentiment Analysis, Opinion Mining, Natural Language Processing (NLP), Machine Learning, Deep Learning, Social Media Analysis, Business Intelligence.
Keywords: Sentiment Analysis, Opinion Mining, Natural Language Processing (NLP), Machine Learning, Deep Learning, Social Media Analysis, Business Intelligence.