MUKESH AHIRRAO
@ghribmjal.raisoni.net
Assistant Professor, Department of Management
G H Raisoni Institute of Engineering and Business Management
I am highlighting my journey as an accomplished professional and researcher in management and academics imparting knowledge and expertise through teaching various courses such as Finance Management, Managerial Economics, Accountancy, Marketing Management, and Marketing Research & Analysis. My scholarly contributions are evident through research papers published in both national and international journals, covering a wide array of management topics.
EDUCATION
Ph.D, MBA, M.Comm, B.Comm
RESEARCH, TEACHING, or OTHER INTERESTS
Business, Management and Accounting, General Business, Management and Accounting, Accounting, Marketing
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Avadhoot Autade, Pratik Adhav, Abhimanyu BabarPatil, Aditya Dhumal, Sushma Vispute, K. Rajeswari, Mukesh Ahirrao, and Snehal Rathi
IEEE
The goal of this study is to create a facial recognition-based automatic attendance tracking system. We suggested a Convolutional Neural Network (CNN) method for this system's real-time recognition and identification of many faces. For face embedding and identification, we use the FaceNet model, which has demonstrated outstanding performance in recent research. The suggested method uses a camera to take pictures of people, then extracts face traits and compares them to the database of registered users. The recognition of registered individuals is used to record attendance. The proposed system's accuracy is 75%. For single faces, the accuracy of Facenet and VGG-16 are 99.20% and 51.30% respectively.The system can be used in a number of places, including companies, colleges, and schools, where keeping track of attendance is crucial for administrative reasons. The automated attendance monitoring process of the suggested system can greatly reduce human error and save time. Furthermore, recognizing unwanted visitors helps improve the facility's security and safety.
K. Rajeswari, S.R. Vispute, Jatin Gupta, Kirti Dhadwad, Rushikesh Ghuge, M. Ashwin, Reena Kharat, and Mukesh Ahirrao
IEEE
This paper offers a thorough overview of the state of the art in image quality assessment, vehicle detection, and number plate extraction. This research would serve as the foundation for building an automated system for vehicle verification in online vehicle Leasing applications. In order to accurately assess vehicle conditions, confirm vehicle identity, and guarantee compliance with rules, these duties are essential in avoiding bottlenecks that could occur during manual verification in vehicle leasing applications. The best approaches for each task are determined through a thorough examination and comparison of the available models, taking into account elements like accuracy, resilience, and real-time processing capabilities. In order to support the creation and application of effective and precise systems for picture quality evaluation, vehicle detection, and number plate extraction, this research paper seeks to serve as a useful resource for professionals working in the auto leasing industry or for other vehicle related purposes. In this Paper, We Use SRGAN(Super Resolution Generative Adversarial Network) for Image Quality Evaluation of Vehicle with Threshold Value Of 0.93 i.e Lower value than Threshold indicated Poor Image Quality And Greater Value than Threshold Indicates Good Image Quality. We also use three different pre-trained models (Inception, Xception and Mobile Net) for Vehicle Detection, where we use a “Vehicle Detection image Dataset” from kaggle containing Vehicle and Non - Vehicle Images. After Training and Testing the data set with all three models, We come into conclusion that Mobile -net give most accurate result with about 99.4% accuracy. For Number Plate Extraction we use LPRNet Algorithm which give us a mean accuracy of about 0.9.
RECENT SCHOLAR PUBLICATIONS
MOST CITED SCHOLAR PUBLICATIONS
RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)
2022-23 Smart Management System for Controlling Medical Robot Beds for Preventing Bedsores Using Artificial Intelligence and Machine Learning
2023-24 Digital Transformation Device for Education Enhancement
INDUSTRY EXPERIENCE
Accounts Office, Jain Irrigation Systems Ltd, Jalgaon