Vipin Kamble
Verified @gmail.com
Scopus Publications
- MCWT-NR-IQA: modified channel weight Taylored regression model for no-reference image quality assessment
Yogita Sachin Gabhane, Tapan Kumar Jain, Vipin M. Kamble
Journal of Modern Optics, 2026 - PPG-AFNet: a lightweight and intelligible network for atrial fibrillation identification using photoplethysmography signals
Preeti P. Ghasad, Bhupendra Tiwari, Vipin Kamble, Prathamesh Kamble
Biomedical Engineering Letters, 2026 - A Lightweight CNN-Based Model for Atrial Fibrillation Detection Using PPG Signal
Bhupendra Tiwari, Saumya Kumari, Preeti Ghasad, Ankit Bhurane, Vipin Kamble
Lecture Notes in Networks and Systems, 2026 - A database of dentition images of Indian breed cattle and estimation of cattle's age using deep learning algorithms
Chinmay Vijay Patil, Ankit Ashokrao Bhurane, Preeti Ghasad, Vipin Kamble, Manish Sharma, Anand Singh, Nareshkumar Nandeshwar, Ru-San Tan, Rajendra Acharya
Engineering Applications of Artificial Intelligence, 2025 - A systematic review of automated prediction of sudden cardiac death using ECG signals
Preeti P Ghasad, Jagath V S Vegivada, Vipin M Kamble, Ankit A Bhurane, Nikhil Santosh, Manish Sharma, Ru-San Tan, U Rajendra Acharya
Physiological Measurement, 2025
Background. Sudden cardiac death (SCD) stands as a life-threatening cardiac event capable of swiftly claiming lives. It ranks prominently among the leading causes of global mortality, contributing to approximately 10% of deaths worldwide. The timely anticipation of SCD holds the promise of immediate life-saving interventions, such as cardiopulmonary resuscitation. However, recent strides in the realms of deep learning (DL), machine learning (ML), and artificial intelligence have ushered in fresh opportunities for the automation of SCD prediction using physiological signals. Researchers have devised numerous models to automatically predict SCD using a combination of diverse feature extraction techniques and classifiers. Methods: We conducted a thorough review of research publications ranging from 2011 to 2023, with a specific focus on the automated prediction of SCD. Traditionally, specialists utilize molecular biomarkers, symptoms, and 12-lead ECG recordings for SCD prediction. However, continuous patient monitoring by experts is impractical, and only a fraction of patients seeks help after experiencing symptoms. However, over the past two decades, ML techniques have emerged and evolved for this purpose. Importantly, since 2021, the studies we have scrutinized delve into a diverse array of ML and DL algorithms, encompassing K-nearest neighbors, support vector machines, decision trees, random forest, Naive Bayes, and convolutional neural networks as classifiers. Results. This literature review presents a comprehensive analysis of ML and DL models employed in predicting SCD. The analysis provided valuable information on the fundamental structure of cardiac fatalities, extracting relevant characteristics from electrocardiogram (ECG) and heart rate variability (HRV) signals, using databases, and evaluating classifier performance. The review offers a succinct yet thorough examination of automated SCD prediction methodologies, emphasizing current constraints and underscoring the necessity for further advancements. It serves as a valuable resource, providing valuable insights and outlining potential research directions for aspiring scholars in the domain of SCD prediction. Conclusions. In recent years, researchers have made substantial strides in the prediction of SCD by leveraging openly accessible databases such as the MIT-BIH SCD Holter and Normal Sinus Rhythm, which contains extensive 24 h recordings of SCD patients. These sophisticated methodologies have previously demonstrated the potential to achieve remarkable accuracy, reaching levels as high as 97%, and can forecast SCD events with a lead time of 30–70 min. Despite these promising outcomes, the quest for even greater accuracy and reliability persists. ML and DL methodologies have shown great promise, their performance is intrinsically linked to the volume of training data available. Most predictive models rely on small-scale databases, raising concerns about their applicability in real-world scenarios. Furthermore, these models predominantly utilize ECG and HRV signals, often overlooking the potential contributions of other physiological signals. Developing real-time, clinically applicable models also represents a critical avenue for further exploration in this field. - Blind image quality assessment using Beltrami filter-based contrast features (BF-bCF) & LSTM network
Yogita Gabhane, Tapan Kumar Jain, Vipin Kamble
Imaging Science Journal, 2025 - Preface
Communications in Computer and Information Science, 2025 - Design of Mental Health Prediction and Analysis System using Machine Learning and IOT
Pankaj H. Chandankhede, Kanchan S. Vaidya, Ankush D. Kadu, Sanjay S. Khonde, Vipin Kamble, Sagar Bahad
2025 Global Conference on Information Technology and Communication Networks Gitcon 2025, 2025
The "Mental Health Using EEG" system is an innovative approach designed to assess an individual’s mental well-being by integrating sophisticated sensor technology with AI-based analysis. This system employs an EEG sensor to monitor brain wave activity, coupled with SPO2 and heartbeat sensors to measure blood oxygen levels and heart rate, creating a holistic profile of physiological indicators. Data is processed through an ESP32 microcontroller and transmitted to cloud storage (Firestore) for AI-powered analysis, aimed at detecting potential mental health conditions such as stress, anxiety, or depression. Real-time updates are displayed on an LCD, supporting continuous monitoring and early identification of mental health concerns. Cloud integration provides scalable data storage and access, while AI analysis offers actionable insights for proactive mental health management, paving the way for personalized, preventative care. - AIM 2025 Challenge on Screen-Content Video Quality Assessment: Methods and Results
Nikolay Safonov, Mikhail Rakhmanov, Dmitry Vatolin, Radu Timofte, Chunyu Wu, Kejing Wu, Kishor Kumar Patro, Pankaj Rathour, Sumohana S. Channappayya, Pravin Pardhi, Vipin Kamble, Kishor Bhurchandi, Biao Liu, Jin Hu, Jinyang Xu, Yang Dayu, Chen Yihua
Proceedings 2025 IEEE Cvf International Conference on Computer Vision Workshops Iccv W 2025, 2025
This paper presents an overview of the AIM 2025 Challenge on Screen Content Video Quality Assessment. The challenge included a set of 150 source videos. To receive distorted versions, the source videos were transmitted through video conferencing applications, introducing real-world distortions such as compression artifacts and frame drops. Distorted versions were labeled by human crowd-sourcing assessors to receive reference subjective scores. The evaluation was based on subjective quality assessment via crowdsourcing, obtaining votes from over 8,000 assessors. The goal of the participants was to develop an algorithm to assess the visual quality of the videos, achieving the highest correlation with the subjective scores. The challenge attracted more than 45 registered teams, 5 of which passed the final phase with source code verification. The outcomes may provide insights into the state of the art in screen-content video quality assessment and highlight emerging trends and effective strategies in this evolving research area. All data, including the processed videos and subjective comparison votes and scores, is made publicly available – https://github.com/msu-video-group/AIM25_SC_Quality_Assessment - Blind Synthetic Image Quality Assessment Using EfficientNet-V2
International Journal of Intelligent Systems and Applications in Engineering, 2024 - IoT Based Irrigation Management System For Smart Farming Applications
Beemala Vinay, P.V.V Nikhilesh, Vishal Satpute, Parul Sahare, Cheggoju Naveen, Vipin Kamble
2nd IEEE International Conference on Innovations in High Speed Communication and Signal Processing Ihcsp 2024, 2024 - Deaf and Mute Sign Language Translator on Static Alphabets Gestures using MobileNet
Venkatesh Kandukuri, Srujal Reddy Gundedi, Vipin Kamble, Vishal Satpute
2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems Machine Learning and Signal Processing Pcems 2023, 2023 - Suspicious Activity Detection Using Deep Learning Approach
Kshitij Barsagade, Sumeet Tabhane, Vishal Satpute, Vipin Kamble
1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing Ihcsp 2023, 2023 - ISLR: Indian Sign Language Recognition
K.Bhanu Prathap, G.Divya Swaroop, B.Praveen Kumar, Vipin Kamble, Mayur Parate
2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems Machine Learning and Signal Processing Pcems 2023, 2023 - Human Fall Detection using Skeleton Features
Manasa Korumilli, Koppula Sai Lasya, Naveen Cheggoju, Vipin Kamble, Vishal R. Satpute
2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems Machine Learning and Signal Processing Pcems 2023, 2023 - One-Shot Face Recognition
Kondapalli Vinay Kumar, Kunigiri Anil Teja, Reddy Teja Bhargav, Vishal Satpute, Cheggoju Naveen, Vipin Kamble
2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems Machine Learning and Signal Processing Pcems 2023, 2023 - Classifying Human Activities using CNN and ConvLSTM in Video Sequences
Reema Gera, Kalyan Ram Ambati, Pallavi Chakole, Naveen Cheggoju, Vipin Kamble, V. R. Satpute
2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems Machine Learning and Signal Processing Pcems 2023, 2023 - Digital Image Noise Estimation Using DWT Coefficients
Varad A. Pimpalkhute, Rutvik Page, Ashwin Kothari, Kishor M. Bhurchandi, Vipin Milind Kamble
IEEE Transactions on Image Processing, 2021 - Image Quality Assessment using Selective Contourlet Coefficients
Agnel Lazar Alappat, Vipin Kamble
2020 11th International Conference on Computing Communication and Networking Technologies Icccnt 2020, 2020 - No reference noise estimation in digital images using random conditional selection and sampling theory
Vipin Milind Kamble, Mayur Rajaram Parate, Kishor M. Bhurchandi
Visual Computer, 2019 - Symmetric Chaos-Based Image Encryption Technique on Image Bit-Planes using SHA-256
Abhilash Ashok Bhadke, Surender Kannaiyan, Vipin Kamble
2018 24th National Conference on Communications Ncc 2018, 2019 - Nonlinear State Estimation Technique Implementation for Human Heart Model
Amit Vijay Waghmare, Pradhnya Arun Priyadarshi, Surender Kannaiyan, Vipin Kamble
2018 24th National Conference on Communications Ncc 2018, 2019 - Noise Estimation and Quality Assessment of Gaussian Noise Corrupted Images
V M Kamble, K Bhurchandi
Iop Conference Series Materials Science and Engineering, 2018 - Performance evaluation of wavelet, ridgelet, curvelet and contourlet transforms based techniques for digital image denoising
Vipin Milind Kamble, Pallavi Parlewar, Avinash G. Keskar, Kishor M. Bhurchandi
Artificial Intelligence Review, 2016 - No-reference image quality assessment algorithms: A survey
Vipin Kamble, K.M. Bhurchandi
Optik, 2015
