Abhijeet Anil Urunkar

@walchandsangli.ac.in

Assistant Professor
Walchand College of Engineering Sangli

Abhijeet Anil Urunkar


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https://researchid.co/urunkaraa
Abhijeet Anil Urunkar is a dedicated and accomplished academician with a strong background in computer engineering and information technology. He has served as an Assistant Professor at several esteemed institutions, including Walchand College of Engineering, Sangli, and Annasaheb Dange College of Engineering and Technology, Ashta. Over the course of his career, Abhijeet has taught a diverse range of subjects such as Object-Oriented Modeling and Design, Software Engineering, Cyber Laws, Operating Systems, Internet of Things, and Computer Programming.
Born on April 17, 1986, in Sangli, Maharashtra, Abhijeet pursued his education with a passion for technology. He holds an M.E. in Computer Engineering from Savitribai Phule Pune University, Pune, where he graduated with a First Class. He also earned a B.E. in Information Technology from Shivaji University, Kolhapur, and a Diploma in Computer Engineering from MSBTE, achieving commendable grades throughout his academic journey.

EDUCATION

ME Computer Engineering

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition
9

Scopus Publications

32

Scholar Citations

2

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • RicNet: Enhancing Rice Leaf Disease Detection Through Optimized Convolutional Neural Network Architectures and Dropout Configurations
    Vijay Katkar, Shrihari Khatawkar, Suyash Jadhav, Rohit Malame, Manoj Rathod, Abhijeet Urunkar
    Lecture Notes in Networks and Systems, 2026
  • Log Monitoring System in Kubernetes for Enterprise Cloud
    16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
  • Supervised Learning Techniques for Deepfake Detection: Integrating ResNet50 and LSTM
    Nandinee L. Mudegol, Abhijeet Urunkar
    2025 1st International Conference on Aiml Applications for Engineering and Technology Icaet 2025, 2025
    Deepfake technology has garnered considerable scholarly interest owing to its capacity to generate exceptionally lifelike yet artificially constructed media artifacts, posing serious threats to various aspects of society, including privacy, security, and misinformation. In response to this growing concern, this project aims to develop an effective deepfake detection system using deep learning models. This paper focuses on leveraging supervised learning techniques to distinguish between authentic and deepfake videos. The proposed approach involves collecting a diverse dataset of both authentic and deepfake videos, covering various scenarios and manipulation techniques. Preprocessing techniques are applied to extract relevant features from the videos, preparing them for input to the deep learning models. Several neural network architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and their combinations [13], are explored for their suitability in detecting deepfake content. Transfer learning methodologies are utilized to capitalize on models that have undergone prior training, such as those trained on ImageNet, as feature extractors or initializations for the network. The models are trained on the labeled dataset. The paper aims to detect deepfake in videos by using ResNet50 for feature extraction and training those features on the LSTM Model.
  • Real Time Threat Detection in Public Places Using Dual CNN Models for Violence and Weapon Detection
    Akshat Mahajan, Bhakti More, Shashank Shenoy, Abhijeet Urunkar, Nandinee Mudegol
    2025 International Conference on Future Technologies Icft 2025, 2025
  • Deep Fake Detection using ResNext Convolutional Neural Network (CNN) combined with a Recurrent Neural Network (RNN)
    Sairaje S. Jadhav, Shoyeb S. Tahasildar, Shubhada D. Kamble, Pranit N. Sankpal, Aprupa S. Pawar, Abhijeet A. Urunkar
    2024 5th IEEE Global Conference for Advancement in Technology Gcat 2024, 2024
    The utilization of digital video alterations has been observable for an extended period thanks to skillful deployment of visual enhancements; however, the latest progressions in deep learning have led to a significant rise in the credibility of artificial material and its availability. These AI-generated media, commonly known as DeepFake (DF), have become increasingly prevalent. Although creating DeepFake content using artificially intelligent tools is a relatively straightforward task, the challenge lies in the detection of such manipulations. Training algorithms to effectively identify DeepFake (DF) content is a complex endeavor due to the intricacies involved. In our pursuit, we have made notable progress in the identification of DeepFake material through the utilization of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) architectures. The framework we have developed involves the application of a CNN for the extraction of features on a frame-by-frame basis, which are subsequently utilized in the training of an RNN. Through this process, the RNN is able to acquire the ability to determine whether a video has been subjected to manipulation, demonstrating its proficiency in recognizing temporal anomalies introduced by tools used in the creation of DeepFake content. Our projected results will be verified using a large dataset of fabricated videos obtained from established datasets. Our goal is to illustrate the competitive effectiveness of our system in identifying DeepFake material, emphasizing the straightforwardness of our framework while achieving reliable outcomes in this arduous undertaking.
  • Drowsiness Detection System Using OpenCV and Raspberry Pi: An IoT Application
    Abhijeet A. Urunkar, Aditi D. Shinde, Amruta Khot
    Lecture Notes in Electrical Engineering, 2022
  • Fraud Detection and Analysis for Insurance Claim using Machine Learning
    Abhijeet Urunkar, Amruta Khot, Rashmi Bhat, Nandinee Mudegol
    Spices 2022 IEEE International Conference on Signal Processing Informatics Communication and Energy Systems, 2022
    Insurance Company working as commercial enterprise from last few years have been experiencing fraud cases for all type of claims. Amount claimed by fraudulent is significantly huge that may causes serious problems, hence along with government, different organization also working to detect and reduce such activities. Such frauds occurred in all areas of insurance claim with high severity such as insurance claimed towards auto sector is fraud that widely claimed and prominent type, which can be done by fake accident claim. So, we aim to develop a project that work on insurance claim data set to detect fraud and fake claims amount. The project implement machine learning algorithms to build model to label and classify claim. Also, to study comparative study of all machine learning algorithms used for classification using confusion matrix in term soft accuracy, precision, recall etc. For fraudulent transaction validation, machine learning model is built using PySpark Python Library.
  • High Frequency Forecasting on Stock Market using Machine Learning
    Amruta Khot, Rashmi Bhat, Abhijeet Urunkar, Nandinee Mudegol
    2022 3rd International Conference for Emerging Technology Incet 2022, 2022
    Intraday stock trading has become a popular trend in US, Europe, and Indian markets and forecasting these rapid market movements have become an important topic in finance. With the emergence of technology and computing power machine learning and neural network, methodologies are becoming a reality and efficient way to solve problems. The trend of intraday trading has made the researchers and developers explore neural networks approach to forecast the high frequency changing data. We focused on gathering minute to minute changing data of 80 stocks belonging to 8 different sectors from S&P 500 and developed a custom double layer neural network which efficiently captures the dependencies in the stock market and allows us to predict the returns of a specific stock at the end of next 5th minute time interval.
  • Authenticity of System Users via Mouse Handling Method
    Kiran Kamble, Nandinee Mudegol, Pooja Mundada, Abhijeet Urunkar
    2021 7th IEEE International Conference on Advances in Computing Communication and Control Icac3 2021, 2021
    The proposed work narrate a behavioural bio-metric approach to confirm authenticated users dynamically based on their mouse motion. A self–generated mouse data [9] was used to extract features to categorize the user’s mouse handling pattern which is different from other users. The model built is trained using the Gaussian Naive Bayes Classifier for quick and accurate classification of data. The proposed model performs better than previously used models in all evaluation metrics including, accuracy, false accept rate, false reject rate.

RECENT SCHOLAR PUBLICATIONS

  • Anxiety Analyzer: A Deep Learning Approach for Classification of Stress
    NM Abhijeet Urunkar
    Journal of Harbin Engineering University (JHEU) 47 (01), 83-91 , 2026
    2026.0
  • Real Time Threat Detection in Public Places Using Dual CNN Models for Violence and Weapon Detection
    A Mahajan, B More, S Shenoy, A Urunkar, N Mudegol
    2025 International Conference on Future Technologies (ICFT), 1-7 , 2025
    2025.0
  • Log Monitoring System in Kubernetes for Enterprise Cloud
    A Urunkar, N Mudegol
    GRENZE International Journal of Engineering and Technology 11 (2), 7 , 2025
    2025.0
  • RicNet: Enhancing Rice Leaf Disease Detection Through Optimized Convolutional Neural Network Architectures and Dropout Configurations
    V Katkar, S Khatawkar, S Jadhav, R Malame, M Rathod, A Urunkar
    International Conference on Machine Learning, IoT and Big Data, 49-59 , 2025
    2025.0
  • Supervised Learning Techniques for Deepfake Detection: Integrating ResNet50 and LSTM
    NL Mudegol, A Urunkar
    2025 1st International Conference on AIML-Applications for Engineering … , 2025
    2025.0
    Citations: 3
  • Deep Fake Detection using ResNext Convolutional Neural Network (CNN) combined with a Recurrent Neural Network (RNN)
    SS Jadhav, SS Tahasildar, SD Kamble, PN Sankpal, AS Pawar, ...
    2024 5th IEEE Global Conference for Advancement in Technology (GCAT) , 2025
    2025.0
    Citations: 1
  • High Frequency Forecasting on Stock Market using Machine Learning
    A Khot, R Bhat, A Urunkar, N Mudegol
    2022 3rd International Conference for Emerging Technology (INCET), 1-6 , 2022
    2022.0
    Citations: 1
  • Fraud Detection and Analysis for Insurance Claim using Machine Learning
    NM Abhijeet A. Urunkar, Amruta Khot, Rashmi Bhat
    2022 IEEE International Conference on Signal Processing, Informatics … , 2022
    2022.0
    Citations: 26
  • Drowsiness detection system using OpenCV and raspberry pi: an IoT application
    AA Urunkar, AD Shinde, A Khot
    International Conference on Artificial Intelligence and Sustainable … , 2022
    2022.0
    Citations: 1
  • Authenticity of System Users via Mouse Handling Method
    K Kamble, N Mudegol, P Mundada, A Urunkar
    2021 International Conference on Advances in Computing, Communication, and … , 2021
    2021.0
  • From Fault Detection to RUL Prediction: A Review of Machine Learning Techniques in Motor Health Monitoring
    S Khatawkar, R Khatri, R Tangadi, A Urunkar
    JOURNAL OF TECHNICAL EDUCATION, 44 , 0
  • Survey on fingerprint distortion detection & rectification
    AA Urunkar

MOST CITED SCHOLAR PUBLICATIONS

  • Fraud Detection and Analysis for Insurance Claim using Machine Learning
    NM Abhijeet A. Urunkar, Amruta Khot, Rashmi Bhat
    2022 IEEE International Conference on Signal Processing, Informatics … , 2022
    2022.0
    Citations: 26
  • Supervised Learning Techniques for Deepfake Detection: Integrating ResNet50 and LSTM
    NL Mudegol, A Urunkar
    2025 1st International Conference on AIML-Applications for Engineering … , 2025
    2025.0
    Citations: 3
  • Deep Fake Detection using ResNext Convolutional Neural Network (CNN) combined with a Recurrent Neural Network (RNN)
    SS Jadhav, SS Tahasildar, SD Kamble, PN Sankpal, AS Pawar, ...
    2024 5th IEEE Global Conference for Advancement in Technology (GCAT) , 2025
    2025.0
    Citations: 1
  • High Frequency Forecasting on Stock Market using Machine Learning
    A Khot, R Bhat, A Urunkar, N Mudegol
    2022 3rd International Conference for Emerging Technology (INCET), 1-6 , 2022
    2022.0
    Citations: 1
  • Drowsiness detection system using OpenCV and raspberry pi: an IoT application
    AA Urunkar, AD Shinde, A Khot
    International Conference on Artificial Intelligence and Sustainable … , 2022
    2022.0
    Citations: 1
  • Anxiety Analyzer: A Deep Learning Approach for Classification of Stress
    NM Abhijeet Urunkar
    Journal of Harbin Engineering University (JHEU) 47 (01), 83-91 , 2026
    2026.0
  • Real Time Threat Detection in Public Places Using Dual CNN Models for Violence and Weapon Detection
    A Mahajan, B More, S Shenoy, A Urunkar, N Mudegol
    2025 International Conference on Future Technologies (ICFT), 1-7 , 2025
    2025.0
  • Log Monitoring System in Kubernetes for Enterprise Cloud
    A Urunkar, N Mudegol
    GRENZE International Journal of Engineering and Technology 11 (2), 7 , 2025
    2025.0
  • RicNet: Enhancing Rice Leaf Disease Detection Through Optimized Convolutional Neural Network Architectures and Dropout Configurations
    V Katkar, S Khatawkar, S Jadhav, R Malame, M Rathod, A Urunkar
    International Conference on Machine Learning, IoT and Big Data, 49-59 , 2025
    2025.0
  • Authenticity of System Users via Mouse Handling Method
    K Kamble, N Mudegol, P Mundada, A Urunkar
    2021 International Conference on Advances in Computing, Communication, and … , 2021
    2021.0
  • From Fault Detection to RUL Prediction: A Review of Machine Learning Techniques in Motor Health Monitoring
    S Khatawkar, R Khatri, R Tangadi, A Urunkar
    JOURNAL OF TECHNICAL EDUCATION, 44 , 0
  • Survey on fingerprint distortion detection & rectification
    AA Urunkar