Dr. Khushal D. Khairnar
PG Diploma Program Coordinator
pgdsai.comp@coeptech.ac.in
Wellesely Rd, Shivajinagar, Pune - 411 005. Maharashtra, India.
Phone: 7387999508
Qualification
PhD. (Computer Science and Engineering)
ME (Computer Engineering)
BE (Information Technology)
Experience
February 2024- Present Coordinator – PG Diploma in Data Science and AI Course
January 2023- January 24 Data Analyst (Assistant Manager), Lohia Mechatronik, Pune
October 2020- Aug 2022 Adjunct Faculty, COEP, Pune
June 2015- December 2020 Assistant Professor, MMCOE, SPPU, Pune
Selected Publications
1. Khushal Khairnar, Ajinkya Dhainje, “Automatic Attendance Monitoring System using Neural Network”, 2022, Dizhen Dizhi Journal, ISSN-253-4967.
2. Khushal Khairnar, Manish Bhavsar, Pradeep Deshmukh, “Machine Learning and 5G Charging Function with Network Analytics Function for Network Slice as a Service”, IEEE ICAC3N-22.
3. Khushal Khairnar, Sajidullah Khan “Automatic Early Leaf Spot Disease Segmentation on Cotton Plant Leaf”, International Journal of Recent Technology and Engineering (IJRTE), ISSN: 2277-3878, Volume-9 Issue-2, July2020
4. Khushal Khairnar, Nitin Goje, “Image Processing Based Approach for Diseases Detection and Diagnosison Cotton Plant Leaf”,Techno-Societal 2018, Springer.
5. Khairnar Khushal, Disease Detection and Diagnosis on Plant using Image Processing-A review, International Journal of Computer Application, 2013, Volume 208, ISSN : 0975- 888, PageNo:36-38.
6. Khushal Khairnar, Image Processing Based Approach for Disease Detection and Diagnosis on otton Plant Leaf, CPGCO-2015,2015,March.
7. Khairnar Khushal, Application Offloading Over Mobile Cloud, International Journal of Engineering Science and Computing Journal, 2016,June, Volume 6 Issue 5, ISSN: 2321 3361, PageNo:5534-5537.
8. Khairnar Khushal, Furniture Layout Application Based on Marker Detection and Using Augmented Reality, International Journal of Engineering Science and Computing Journal,2016,Feb,Volume06Issue4,ISSN:2321336,PageNo:4201-4204.
Industry Project
1. Web Inspection system(Defect Detection) using AI (January 2023- November 2023):
Image processing-based application where defects detection and classification are on the different web (coated and uncoated) with the help of the Yolo7 deep learning algorithm.
Tools used: CVAT, Triton server, Training PC
2. Defect Analyzer Software (January 2023- October 2023):
This tool is a Python-based application designed to analyze and manage defects. It stores various data related to defect analysis, including defect summaries, defect details, defect statistics and roll summaries.
Tools used: QT, MySQL and Python
