0000-0003-2550-4494 About
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.
Abhijeet's contributions extend beyond teaching; he has actively participated in institutional activities and held several roles of responsibility. He has been a member of committees for events such as Smart India Hackathon and Annual Functions, coordinated placement activities, and contributed to the preparation of accreditation documentation for NBA, NAAC, and UGC.
His research work and publications demonstrate his commitment to advancing knowledge in his field. He has published 6 international conference papers in reputed conferences and 1 international journal paper.
His professional expertise is further showcased through participation in numerous training programs, workshops, and conferences on robotics, artificial intelligence, IoT, and network security.
Employment
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Walchand College of Engineering Assistant Professor2012 - Present
Education
Education history is unavailable.
Projects & Funding
Projects & funding information is unavailable.
Publications (14)
- Anxiety Analyzer: A Deep Learning Approach for Classification of Stress Save
- Real Time Threat Detection in Public Places Using Dual CNN Models for Violence and Weapon Detection Save
- RicNet: Enhancing Rice Leaf Disease Detection Through Optimized Convolutional Neural Network Architectures and Dropout Configurations Save
- Supervised Learning Techniques for Deepfake Detection: Integrating ResNet50 and LSTM Save
- Log Monitoring System in Kubernetes for Enterprise Cloud Save
- RicNet: Enhancing Rice Leaf Disease Detection Through Optimized Convolutional Neural Network Architectures and Dropout Configurations Save
- Deep Fake Detection using ResNext Convolutional Neural Network (CNN) combined with a Recurrent Neural Network (RNN) Save
- High Frequency Forecasting on Stock Market using Machine Learning Save
- Drowsiness Detection System Using OpenCV and Raspberry Pi: An IoT Application Save
- Fraud Detection and Analysis for Insurance Claim using Machine Learning Save