Ajay Namdeorao Paithane

@jspmrscoe.edu.in

Professor In E&TC
JAYWANT SHIKSHAN PRASARAK MANDALS RAJARSHI SHAHU COLLEGE OF ENGINEERING PUNE

Ajay Namdeorao Paithane


               Download Compact PDF  
https://researchid.co/ajaypaithane

EDUCATION

PhD in Electronics from PUNE University

RESEARCH INTERESTS

Embedded system, Biomedical Signal and Image Processing

FUTURE PROJECTS

Waste Management and Energy conservation


Applications Invited
Project in Health care
32

Scopus Publications

290

Scholar Citations

9

Scholar h-index

9

Scholar i10-index

Scopus Publications

  • Speech Database Creation for Marathi Impaired Speech
    Lata More, Sakshi Paithane, Ajay Paithane
    Lecture Notes in Networks and Systems, 2026
  • Occluded face recognition using optimum features based on efficient preprocessing and machine learning
    Rajesh H. Khobragade, Dinesh B. Bhoyar, Ajay Paithane, Suresh Kurumbanshi
    E Prime Advances in Electrical Engineering Electronics and Energy, 2025
  • Smart Arrhythmia Detection Using Single Lead ECG Signal and Hybridized Deep Neural Network Model
    Shailesh Hambarde, Ajay Paithane, Poonam Lambhate, Aparna Shailesh Hambarde, Pratima Amol Kalyankar
    Web Intelligence, 2025
    Arrhythmia is an irregular electrical activity of the heart that needs to be treated quickly and promptly to avoid the risk of cardiac failure and stroke. Signal processing utilizing Electrocardiogram (ECG) signals continues to be the gold standard for detecting cardiac abnormalities. However, the low classification accuracy and lack of labeled ECG data might seriously impair the existing algorithm's overall performance. To address the drawbacks of the existing techniques, the proposed research utilizes a deep learning model formulated utilizing the cephalous wolf optimization-based deep neural network model (CWO opt NN) for effective arrhythmia detection. The proposed model leverages the characteristics of a single lead ECG database to retrieve the input data initially. Next, the signal is preprocessed by adopting the window sliding approach to eliminate any potential noise. In addition, the extracted time-domain features, frequency domain features, geometrical features, CSI-CVI features, wavelet features, and statistical features, aid in boosting the accuracy of arrhythmia detection. To accurately identify arrhythmia, the developed model explores the Neural Networks for learning the cardiac cycles effectively. Specifically, cephalous wolf optimization, developed by the typical hybridization of the cephalous wolf and wolf hawk, is essential to the research's relevance since it allows for the successful identification of arrhythmia by fine-tuning the classifier's weights and bias. Considering the achievement rates for arrhythmia identification at training percentage 80, the F1-score is 96.10%, precision is 97.08%, and recall is 95.14% respectively, similarly based on the k-fold 8, F1-score is 96.10%, precision is 96.80%, and recall is 94.86% respectively.
  • AI-Driven Multimodal Stress Detection: A Comparative Study
    Sangita Ajit Patil, Ajay Namdeorao Paithane
    Biomedical and Pharmacology Journal, 2025
    Stress affects mental and physical health, contributing to cardiovascular diseases and cognitive disorders, and early detection plays a crucial role in mitigating these risks. This study enhances stress detection by analyzing electroencephalography (EEG) signals from the DEAP ( A Database using Physiological Signals) data set and electrocardiogram (ECG) signals from the WESAD (Wearable Stress and Affect Detection) data set, with EEG offering a cost-effective solution and ECG providing detailed cardiovascular insights. It compares individual sensor analysis with multi-sensor fusion, demonstrating that fusion improves accuracy, as the ECG model achieves 91.79% accuracy, the EEG model reaches 96.6%, the feature-level fusion model achieves 98.6%, and the score-level fusion model achieves 97.8%. Using the Archimedes Optimization Algorithm (AoA) and Analytical Hierarchical Process (AHP) for feature selection and a hybrid Deep Convolutional Neural Network-Long Short-Term Memory (DCNN-LSTM) model for processing, the study highlights the effectiveness of a multi modal approach for real- time, accurate stress monitoring in clinical and industrial settings. It also integrates additional modalities and refines methods to enhance the system further, positioning AI-driven multimodal systems as powerful tools for early intervention and improved mental health management.
  • Design and analysis of stochastic 5G new radio LDPC decoder using adaptive sparse quantization kernel least mean square algorithm for optical satellite communications
    R. Krishna Priya, Nitin N. Sakhare, Ajay Paithane, R. Shekhar, M. Sabarimuthu
    Internet Technology Letters, 2025
    A Stochastic Low‐Density Parity‐Check (LDPC) decoder is a type of 5G New Radio standard LDPC decoder that uses stochastic techniques to perform decoding. Stochastic LDPC decoding with 5G NR standard typically uses an iterative process, where messages exchanged among variable nodes (VN), check nodes multiple times. Stochastic LDPC decoders are often used in scenarios where the received signal is subject to varying levels of noise. They will provide improved error correction performance compared to traditional LDPC decoders, especially when dealing with channels with varying signal‐to‐noise ratios in 5G networks. Using the adaptive sparse quantization kernel least mean square algorithm (SLDPC‐ASQ‐KLMSA), this paper proposes an area‐efficient architecture design for a stochastic LDPC decoder. The LDPC code (2048, 1723) is taken from the LOGBASE‐T standard and used in this study. We examine the ASQ‐KLMSA connection effects. Starting with the VN. It makes checking node functioning easier and reduces inter‐connect complexity by capping extrinsic message length at 2 bits. Because of the simplified check node operation in ASQ‐KLMSA, the decoder nodes must exchange messages with a greater degree of accuracy. The 3–3 input grouping sub‐node of the degree‐6 VN was changed with an adder‐based 5–1 input grouping sub‐node for the (2048, 1723) code in order to get more accurate results when the check‐to‐variable messages aren't strong enough. A suggested decoder architecture was determined using a stochastic LDPC decoder developed for TSMC 65 nm process (2048, 1723). Bite error rate, throughput, mean square error, latency, power, and area usage are some of the metrics used to evaluate the effectiveness of the SLDPC‐ASQ‐KLMSA algorithm that has been suggested and implemented in Python. Thus, the proposed approach has attained 34.44%, and 38.39% low mean square error while compared with the existing methods such as higher‐performance stochastic LDPC decoder architecture designed through correlation analysis (HP‐SLDPC‐CA), Higher Throughput and Hardware Efficient Hybrid LDPC Decoder Utilizing Bit‐Serial Stochastic Updating(HLDPC‐BSSU), Flexible FPGA‐Based Stochastic Decoder for 5G LDPC codes (FPGA‐SD‐5G‐LDPC), respectively.
  • METAHEURISTIC-ASSISTED HYBRID RECOGNITION MODEL for BRAIN ACTIVITY DETECTION
    Amruta Jagadish Takawale, Ajay N. Paithane
    Biomedical Engineering Applications Basis and Communications, 2025
    Electroencephalogram (EEG) recordings typically capture the integrals of active brain potentials, which vary in latencies and populations. Anomalies in EEG data, often associated with epilepsy, play a crucial role in identifying conditions such as brain death, encephalopathy, coma, depth of anesthesia, and sleep disturbances. To get early warnings for these diseases, this work intends to propose a novel approach for brain activity detection from EEG signals. A new Hybrid Classification of Combined Coot Blue Monkey Optimization (HC-CCBO) method is proposed in this work. Initially, improved [Formula: see text]-score normalization was used to preprocess the EEG signal. Further, Discrete Wavelet Transform (DWT), improved correlation and statistical features were extracted. After that, we set up hybrid classification, which exploited Bi-Directional Gated Recurrent Unit (Bi-GRU) and Deep Max Out (DMO) models. Further, weights of BI-GRU and DMO were optimized via Combined Coot Blue Monkey Optimization (CCBO) optimization. Finally, we obtain the output scores of the rest, left fist, both fists, right fist, and both feet from the suggested hybrid brain activity recognition model. The effectiveness of the suggested HC-CCBO model is compared to conventional techniques using a variety of metrics. Compared to the existing models, the suggested model obtains a high maximum accuracy of 0.93 at the 90th learning percentage.
  • Efficient VLSI Architecture Integrating Vedic Mathematics for Square Computation
    Journal of VLSI Circuits and Systems, 2025
  • Implementation and Analysis of an Optimized Feature Selection Algorithm for Stress Detection
    16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
  • Low-Cost Monitoring and Control System for Vertical Farming
    Dinesh B. Bhoyar, Swati K. Mohod, Rahul A. Burange, Ajay Paithane
    Aip Conference Proceedings, 2024
  • Combined LinkNet-MBi-LSTM for brain activity recognition with new Stockwell transform features
    Amruta Jagadish Takawale, Ajay N. Paithane
    International Journal of Developmental Neuroscience, 2024
    Recognizing brain activity from EEG waves is an important field of study in biomedical engineering and neuroscience. Conventional approaches usually begin with signal processing techniques to extract features from the EEG data, and then machine learning algorithms are applied to classify the data. However, the spatial resolution of these EEG signals is low, which makes it difficult to pinpoint the exact location of the neural activity source in the brain. There are ongoing initiatives to use DL‐based brain activity recognition algorithms to overcome these constraints. Therefore, this work presents a novel hybrid framework for brain activity detection using the enhanced Stockwell transform and an EEG signal that is called LinkNet and modified bidirectional–long short‐term memory (LN‐MBi‐LSTM) model. This framework follows a methodical approach that includes stages for feature extraction, brain activity recognition and preprocessing. Firstly, the improved Weiner filtering (IWF) approach is used to preprocess the EEG input signal. The relevant features are then extracted using a feature extraction technique from the preprocessed EEG signal. To identify the brain activity, these recovered feature sets are subsequently processed separately using LinkNet and modified bidirectional–long short‐term memory (MBi‐LSTM). A thorough analysis that takes into account both simulation and experimental calculations is part of the validation process for the LN‐MBi‐LSTM model. Finally, this study demonstrates the therapeutic potential of the LN‐MBi‐LSTM framework by presenting a strong and verified model for brain activity recognition. With the highest precision of 0.997, the LinkNet‐MBi‐LSTM model distinguishes itself from other models and confirms its exceptional capacity to produce accurate positive predictions.
  • Optimized EEG-Based Stress Detection: A Novel Approach
    Biomedical and Pharmacology Journal, 2024
  • DEVELOPMENT AND SIMULATION OF MICROSTRIP PATCH ANTENNAS FOR 5G WIRELESS CONNECTIVITY
    Journal of Theoretical and Applied Information Technology, 2024
  • Advanced Comparative Analysis of Cardiovascular Heart Disease Detection Techniques: Insights from Empirical Results
    Yogeshwari Nikam, Ajay N Paithane
    2024 IEEE International Conference on Smart Power Control and Renewable Energy Icspcre 2024, 2024
  • Advanced Algorithmic Approaches for Accurate Classification and Statistical Analysis of Brain Tumors: Enhancing Diagnostic Precision
    Advances in Nonlinear Variational Inequalities, 2024
  • IMPLEMENTING JERK FREE BLDC POSITION CONTROL USING SOC WITH FOURTH ORDER TRAJECTORY PLANNING
    Journal of Theoretical and Applied Information Technology, 2023
  • Comprehensive Review of EEG- Based Algorithms for Mental Stress Analysis
    Sangita Ajit Patil, Ajay N. Paithane
    2023 7th International Conference on Computing Communication Control and Automation Iccubea 2023, 2023
  • A SENS Score of Rheumatoid Arthritis Detection Using Customized Convolutional Neural Network
    G. S. Mate, A. N. Paithane, N. M. Ranjan
    Lecture Notes in Networks and Systems, 2023
  • Electroencephalogram Signal Analysis Using Wavelet Transform and Support Vector Machine for Human Stress Recognition
    Ajay N Paithane, Mukil Alagirisamy
    Biomedical and Pharmacology Journal, 2022
  • Design of a low-cost welding machine controller with a novel control algorithm for an enhanced HF TIG welding process
    Dnyaneshwar Vitthal Sahane, Pravin M Ghate, Ajay N Paithane
    Engineering Research Express, 2022
  • QRS detection using empirical mode decomposition method for human computer interface
    International Journal of Advanced Science and Technology, 2020
  • Performance analysis of face detection system using HOG and qualHOG features
    International Journal of Engineering and Advanced Technology, 2019
  • Linear collaborative discriminant regression and CEPSTRA features for Hindi speech recognition
    Journal of Engineering Research Kuwait, 2019
  • Face detection on distorted images by using quality HOG features
    J.A. Mahajan, A. N. Paithane
    Proceedings of the International Conference on Inventive Communication and Computational Technologies Icicct 2017, 2017
  • Implementing image compression using transform based approach
    Mugdha Limaye, Ajay Paithane
    Proceedings of the International Conference on Computing Methodologies and Communication Iccmc 2017, 2017
  • Design and develop an algorithm for a diabetic detection using ECG signal
    Reena Musale, A. N. Paithane
    Proceedings of the International Conference on Computing Methodologies and Communication Iccmc 2017, 2017

RECENT SCHOLAR PUBLICATIONS

  • Occluded face recognition using optimum features based on efficient preprocessing and machine learning
    RH Khobragade, DB Bhoyar, A Paithane, S Kurumbanshi
    e-Prime-Advances in Electrical Engineering, Electronics and Energy 12, 101015 , 2025
    2025
    Citations: 4
  • Efficient VLSI Architecture Integrating Vedic Mathematics for Square Computation
    UG Patil, P Jagtap, K Adake, S Dhanbhar, A Paithane
    Journal of VLSI Circuits and Systems 7 (1), 66-74 , 2025
    2025
    Citations: 1
  • Advanced stress detection with optimized feature selection and hybrid neural networks
    SA Patil, AN Paithane
    Int. J. Electr. Comput. Eng.(IJECE) 15, 1647-1655 , 2025
    2025
    Citations: 2
  • Metaheuristic-assisted hybrid recognition model for brain activity detection
    AJ Takawale, AN Paithane
    Biomedical Engineering: Applications, Basis and Communications 37 (01), 2450039 , 2025
    2025
    Citations: 1
  • AI-Driven Multimodal Stress Detection: A Comparative Study
    SA Patil, AN Paithane
    Biomed. Pharmacol. J. 18 (March Spl Edition), 245-255 , 2025
    2025
    Citations: 4
  • Implementation and Analysis of an Optimized Feature Selection Algorithm for Stress Detection.
    SA Patil, AN Paithane
    Grenze International Journal of Engineering & Technology (GIJET) 11 , 2025
    2025
  • Short Review on Brain Activity Recognition
    AJ Takawale, AN Paithane
    Proceedings of International Conference on Intelligent Vision and Computing … , 2024
    2024
  • Low-cost monitoring and control system for vertical farming
    DB Bhoyar, SK Mohod, RA Burange, A Paithane
    AIP Conference Proceedings 3188 (1), 090006 , 2024
    2024
  • Combined LinkNet‐MBi‐LSTM for brain activity recognition with new Stockwell transform features
    AJ Takawale, AN Paithane
    International Journal of Developmental Neuroscience 84 (8), 943-962 , 2024
    2024
  • Speech Database Creation for Marathi Impaired Speech
    L More, S Paithane, A Paithane
    International Conference on Emergent Converging Technologies and Biomedical … , 2024
    2024
  • DEVELOPMENT AND SIMULATION OF MICROSTRIP PATCH ANTENNAS FOR 5G WIRELESS CONNECTIVITY
    N MALIK, S VASHIST, AN PAITHANE, M ALGIRISAMY
    Journal of Theoretical and Applied Information Technology 102 (3) , 2024
    2024
    Citations: 1
  • Optimized eeg-based stress detection: A novel approach
    S Patil, A Paithane
    Biomed. Pharmacol. J 17 (4), 2607-2616 , 2024
    2024
    Citations: 5
  • Short Review on Brain Activity Recognition via EEG Signal
    AJ Takawale, AN Paithane
    International Conference on Intelligent Vision and Computing, 213-224 , 2023
    2023
  • Comprehensive review of EEG-based algorithms for mental stress analysis
    SA Patil, AN Paithane
    2023 7th International Conference On Computing, Communication, Control And … , 2023
    2023
    Citations: 3
  • IMPLEMENTING JERK FREE BLDC POSITION CONTROL USING SOC WITH FOURTH ORDER TRAJECTORY PLANNING
    A PAITHANE, S PAITHANE, S DAMBHARE, M THIGALE, ...
    Journal of Theoretical and Applied Information Technology 101 (14) , 2023
    2023
  • A SENS Score of Rheumatoid Arthritis Detection Using Customized Convolutional Neural Network
    GSMANPN Ranjan
    communication Intelligent Systems Lecture notes 689, 643-652 , 2023
    2023
  • Electroencephalogram signal analysis using wavelet transform and support vector machine for human stress recognition
    AN Paithane, M Alagirisamy
    Biomedical and Pharmacology Journal 15 (3), 1349-1360 , 2022
    2022
    Citations: 10
  • Design of a low-cost welding machine controller with a novel control algorithm for an enhanced HF TIG welding process
    DV Sahane, PM Ghate, AN Paithane
    Engineering Research Express 4 (2), 025025 , 2022
    2022
    Citations: 3
  • Design and Development of an Algorithm for Vehicle Speed Control from Speed Limit Sign using Deep Learning
    AP Prajakta Jagtap,Sakshi Paithane
    Journal of Huazhong University of Science and Technology 50 (8) , 2021
    2021
  • Priority Based Power Scheduling Algorithm for Residential Loads
    ANP Nivedita Patil, Dr. U. G. Patil
    Journal of Huazhong University of Science and Technology 50 (8), 1-13 , 2021
    2021

MOST CITED SCHOLAR PUBLICATIONS

  • Novel approach for stress recognition using EEG signal by SVM classifier
    P Gaikwad, AN Paithane
    2017 International Conference on Computing Methodologies and Communication … , 2017
    2017
    Citations: 42
  • Electrocardiogram signal analysis using empirical mode decomposition and Hilbert spectrum
    AN Paithane, DS Bormane
    2015 International Conference on Pervasive Computing (ICPC), 1-4 , 2015
    2015
    Citations: 36
  • Automatic Speech Recognition of isolated words in Hindi language using MFCC
    UG Patil, SD Shirbahadurkar, AN Paithane
    2016 International Conference on Computing, Analytics and Security Trends … , 2016
    2016
    Citations: 32
  • Analysis of Nonlinear and Non-Stationary Signal to Extract the Features Using Hilbert Huang Transform
    AN Paithane, DS Bormane
    IEEE , 2014
    2014
    Citations: 29
  • Human emotion recognition using non linear and non stationary EEG signal
    PS Ghare, AN Paithane
    2016 International Conference on Automatic Control and Dynamic Optimization … , 2016
    2016
    Citations: 22
  • Face detection on distorted images by using quality HOG features
    JA Mahajan, AN Paithane
    2017 International Conference on Inventive Communication and Computational … , 2017
    2017
    Citations: 20
  • Human Emotion Recognition using Electrocardiogram Signals
    AN Paithane, DS Bormane, S Dinde
    2014
    Citations: 18
  • Novel algorithm for feature extraction and feature selection from electrocardiogram signal
    AN Paithane, DS Bormane, U Patil
    International Journal of Computer Applications 134 (9), 6-9 , 2016
    2016
    Citations: 16
  • Electroencephalogram signal analysis using wavelet transform and support vector machine for human stress recognition
    AN Paithane, M Alagirisamy
    Biomedical and Pharmacology Journal 15 (3), 1349-1360 , 2022
    2022
    Citations: 10
  • Human disposition detection using EEG signals
    AR Bhagwat, AN Paithane
    2016 International conference on computing, analytics and security trends … , 2016
    2016
    Citations: 7
  • Automatic speech recognition models: A characteristic and performance review
    UG Patil, SD Shirbahadurkar, AN Paithane
    2016 International Conference on Computing Communication Control and … , 2016
    2016
    Citations: 7
  • Optimized eeg-based stress detection: A novel approach
    S Patil, A Paithane
    Biomed. Pharmacol. J 17 (4), 2607-2616 , 2024
    2024
    Citations: 5
  • Design and develop an algorithm for a diabetic detection using ECG signal
    R Musale, AN Paithane
    2017 International Conference on Computing Methodologies and Communication … , 2017
    2017
    Citations: 5
  • Occluded face recognition using optimum features based on efficient preprocessing and machine learning
    RH Khobragade, DB Bhoyar, A Paithane, S Kurumbanshi
    e-Prime-Advances in Electrical Engineering, Electronics and Energy 12, 101015 , 2025
    2025
    Citations: 4
  • AI-Driven Multimodal Stress Detection: A Comparative Study
    SA Patil, AN Paithane
    Biomed. Pharmacol. J. 18 (March Spl Edition), 245-255 , 2025
    2025
    Citations: 4
  • Linear collaborative discriminant regression and Cepstra features for Hindi speech recognition
    UG Patil, SD Shirbahadurkar, AN Paithane
    Journal of Engineering Research 7 (4), 96-114 , 2019
    2019
    Citations: 4
  • Implementing image compression using transform based approach
    M Limaye, A Paithane
    2017 International Conference on Computing Methodologies and Communication … , 2017
    2017
    Citations: 4
  • Facial Emotion Detection using Eigenfaces
    AN Paithane, S Hullyalkar, G Behera, N Sonakul, A Manmode
    International Journal Of Engineering And Computer Science 3 (05), 92 , 2014
    2014
    Citations: 4
  • Comprehensive review of EEG-based algorithms for mental stress analysis
    SA Patil, AN Paithane
    2023 7th International Conference On Computing, Communication, Control And … , 2023
    2023
    Citations: 3
  • Design of a low-cost welding machine controller with a novel control algorithm for an enhanced HF TIG welding process
    DV Sahane, PM Ghate, AN Paithane
    Engineering Research Express 4 (2), 025025 , 2022
    2022
    Citations: 3