Reliable Automated ECG Arrhythmia Classification Using Reinforced VGG-27 Neural Network Framework Trupti G. Thite, Sonal K. Jagtap International Journal of Adaptive Control and Signal Processing, 2025 Automated categorization of electrocardiogram (ECG) waveforms using deep learning (DL) methods has garnered considerable attention in recent research. However, prevalent DL networks encounter challenges including overfitting, class imbalance, limitations in deeper network training, and high computational demands. To address these issues, this study proposes an Automated ECG Arrhythmia Classification framework employing the Reinforced Visual Geometry Group‐27 (REF‐VGG‐27). Initially, the framework encompasses preprocessing steps such as denoising, R‐peak identification, data balancing, and cross‐validation. For automatic feature extraction and classification, two DL architectures are suggested: a novel hybrid model combining 2D convolutional neural network (2DCNN) with VGG‐16, featuring a deep architecture for extracting morphological characteristics, frequency features related to heart rate variability (HRV), and statistical attributes crucial for identifying atrial fibrillation (AF). Subsequently, to classify arrhythmia patterns, the VGG‐16 Model is employed. Utilizing publicly available ECG image datasets, the proposed model achieved remarkable accuracy benchmarks: 99.61% accuracy, precision of 99.61%, and recall of 99.48%. Comparative analysis with existing approaches substantiates the efficiency and robustness of our model.
Plant disease prediction system using advance computational Technique Mayuresh B. Gulame, Trupti. G. Thite, Kranti D. Patil Journal of Physics Conference Series, 2023 A vital sector of India’s economy is agriculture. Identification of plant infections is crucial to preventing crop damage and further disease. The majority of plants, such as apple, tomato, cherry, and grapes, have leaves that appear to have disease signs. The plant health can be monitored through images to precisely predict the disease and to take early preventative action. The traditional method is to manually inspect the plant leaf to identify the kind of disease, as done by farmers or plant pathologists. In this research, we presented a deep CNN model termed as Decompose, Transfer, and Compose (DTComp) for the classification of plant disease. The deep learning model makes predictions more quickly and precisely than manual plant leaf observation. Out of all the pretrained deep models, the ResNet50 model achieves the highest accuracy for classification. DTComp can handle any anomalies in the images using class decomposition approach to examine the class boundaries. The experimental findings demonstrated DTComp capacity for detecting plant disease instances on dataset gathered from multiple villages using the Kaggel Open Source platform. DTComp can successfully identify plant disease with a high accuracy of 98.30% from images. Additionally, this model can be deployable on real-time systems equipped with a Raspberry Pi and a camera module.
Real-time electrocardiogram monitoring for heart diseases with secured internet of thing protocol Trupti G. Thite, Daulappa G. Bhalke International Journal of Medical Engineering and Informatics, 2022 Real-time effective ECG data collecting, transmitting, and monitoring system with feature extraction is a big challenge in biomedical signal processing. The electrocardiogram is a widely used testing system to measure and analyse coronary heart diseases, i.e., cardiovascular diseases (CVDs). Heart rate remote monitoring under the service provided by hospital equipment is the technology that currently needs to improve. IoT enabled medical device helps to achieve this efficiently. To design such systems energy-efficient communication protocol, data-transfer minimisation, assurance of delivery (security), heterogeneous natures of the environment are necessary considerations. This paper outlines a literature survey of three main important areas; 'real-time ECG monitoring using wearable sensors', 'feature extraction and classification method for real-time ECG monitoring', and 'secured IoT protocol for real-time ECG monitoring'.
IoT System for monitoring diabetic patients with cardiovascular disease. MSKJ Mrs. Trupti G. Thite , Ms. Supriya S. Saste International Multidisciplinary Conference on Research, Technology … , 2022 2022.0
Multi-Language Real Time Home Appliances Controlling System using Google Assistance TT Dhanshe Miran , Khan Shoaiba , Kazi Taherim Journal of Signal Processing,Vol 7, 7 (No 1), January-April, 2021 , 2022 2022.0
IoT System for monitoring diabetic patients with cardiovascular disease MSKJ Mrs. Trupti G. Thite 1, Ms. Supriya S. Saste 2 International Multidisciplinary Conference on Research, Technology & Engineering , 2022 2022.0
Wearable Electrocardiogram Feature Extraction for Real Time Monitoring Applications TG Thite, DG Bhalke ICCCE 2021: Proceedings of the 4th International Conference on … , 2022 2022.0
4 Recent advancements and challenges of artificial intelligence and IoT in agriculture N Uke, T Thite, S Saste Internet of Things and Machine Learning in Agriculture: Technological … , 2021 2021.0
Cloud Ready Ultrasonic Sensor (HC-SR04) and DHT11 Using Raspberry-pi and ThingSpeak GM Thite Trupti , Saste Supriya Journal of Network Security Computer Networks 5 (1), 7 , 2019 2019.0
Cluster of Image Quality Measures for Image Enhancement SSS Gulame Mayuresh B , Thite Trupti. G Journal of Research in Image and Signal Processing 3 (3), 6 , 2018 2018.0
“A HIGH CAPACITY STEGANOGRAPHY SCHEME FOR JPEG2000 USING SPIHT ALGORITHM” PKKRKSK Trupti S. I J N T 3 (2), 6 , 2012 2012.0
Review on binary image steganography and watermarking GJ Chhajed, KV Deshmukh, TS Kulkarni International Journal on Computer Science and Engineering 3 (11), 3645 , 2011 2011.0 Citations: 26
Self Propelled 2D Plotter Implementation A Choudhary, M Patanwala, S Shaikh
A Portable IoT-cloud ECG Monitoring System for Healthcare N Bhujbal, P Yadav, P Choramale, MTG Thite
MOST CITED SCHOLAR PUBLICATIONS
Review on binary image steganography and watermarking GJ Chhajed, KV Deshmukh, TS Kulkarni International Journal on Computer Science and Engineering 3 (11), 3645 , 2011 2011.0 Citations: 26
IoT System for monitoring diabetic patients with cardiovascular disease. MSKJ Mrs. Trupti G. Thite , Ms. Supriya S. Saste International Multidisciplinary Conference on Research, Technology … , 2022 2022.0
Multi-Language Real Time Home Appliances Controlling System using Google Assistance TT Dhanshe Miran , Khan Shoaiba , Kazi Taherim Journal of Signal Processing,Vol 7, 7 (No 1), January-April, 2021 , 2022 2022.0
IoT System for monitoring diabetic patients with cardiovascular disease MSKJ Mrs. Trupti G. Thite 1, Ms. Supriya S. Saste 2 International Multidisciplinary Conference on Research, Technology & Engineering , 2022 2022.0
Wearable Electrocardiogram Feature Extraction for Real Time Monitoring Applications TG Thite, DG Bhalke ICCCE 2021: Proceedings of the 4th International Conference on … , 2022 2022.0
4 Recent advancements and challenges of artificial intelligence and IoT in agriculture N Uke, T Thite, S Saste Internet of Things and Machine Learning in Agriculture: Technological … , 2021 2021.0
Cloud Ready Ultrasonic Sensor (HC-SR04) and DHT11 Using Raspberry-pi and ThingSpeak GM Thite Trupti , Saste Supriya Journal of Network Security Computer Networks 5 (1), 7 , 2019 2019.0
Cluster of Image Quality Measures for Image Enhancement SSS Gulame Mayuresh B , Thite Trupti. G Journal of Research in Image and Signal Processing 3 (3), 6 , 2018 2018.0
“A HIGH CAPACITY STEGANOGRAPHY SCHEME FOR JPEG2000 USING SPIHT ALGORITHM” PKKRKSK Trupti S. I J N T 3 (2), 6 , 2012 2012.0
Self Propelled 2D Plotter Implementation A Choudhary, M Patanwala, S Shaikh
A Portable IoT-cloud ECG Monitoring System for Healthcare N Bhujbal, P Yadav, P Choramale, MTG Thite
