Dr. Nilesh Bhosle
Verified @gmail.com
Associate Professor
Dr. Nilesh Bhosle born in 1980. He received his PhD degree in Electronics Telecommunication Engineering from from Swami Ramanand Teerth Marathwada University Nanded, India. He received his B.E. degree in Electronics Engineering and M.Tech. dergree in Electronics Engineering from Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded, Maharashtra, India, in 2003 and 2006 respectively. His research interests include image processing, content-based image retrieval, and pattern recognition.
EDUCATION
2018 Doctor of Philosophy - Electronics & Tele-communication, SRTM University, Nanded
Ph. D Thesis Title Reduction of semantic gap between low-level & high-level features for CBIR
2006 Master of Technology - Electronics, SGGS Institute of Engineering and Technology, Nanded
Project Designing a Neuro Vision Processor
2003 Bachelor of Engineering - Electronics, SGGS Institute of Engineering and Technology, Nanded
RESEARCH, TEACHING, or OTHER INTERESTS
Engineering, Electrical and Electronic Engineering, Engineering, Multidisciplinary
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Mukesh Kumar Tripathi, Donagapure Baswaraj, Shyam Deshmukh, Kapil Misal, Nilesh P. Bhosle, and Sunil Mahadev Sangve
Institute of Advanced Engineering and Science
<p>The challenges of road maintenance, particularly in detecting potholes and cracks, and the proposed method using transfer learning and convolutional neural networks (CNNs) are significant advancements in this domain. Transfer learning is particularly beneficial, as it allows leverage pre-trained models to enhance the performance of the pothole detection system. CNNs, with their ability to capture spatial hierarchies in data, are well-suited for image-based tasks like pothole detection. The potential applications of the suggested method for intelligent transportation systems (ITS) services, such as alerting drivers about real-time potholes, demonstrate we research’s practical implications. This contributes to road safety and aligns with the broader goals of innovative city initiatives and infrastructure management. Achieving a 96% accuracy rate is a significant result, indicating the robustness of the proposed approach. Using this information to assess initial maintenance needs in a road management system is forward-thinking. Overall, we work is a valuable contribution to intelligent transportation and infrastructure management, showcasing the potential of advanced machine-learning techniques for addressing critical issues in road maintenance.</p>
Prashant Bachanna, Palla Hari Sankar, Mukesh Kumar Tripath, Shivendra Shivendra, Kadali Ravi Kumar, and Nilesh Bhosle
Institute of Advanced Engineering and Science
<div align="center">n system-on-a-chip based complex processors has the problem of multithreading and miss-functionality due to their complexity and high-speed operations. In order to minimize these problems, the proposed design has machine learning based algorithms and cryptography systems for security has been incorporated. In the proposed work, the security level has been taken care of in three different stages such as data integrity, data authentication, and private and public keys encryption and decryption. In order to increase throughput with minimal latency, the proposed architecture with advanced high-performance protocol and advanced high-performance and advanced peripheral bus bridge is incorporated between the fabric dynamically re configurable multi-processor and peripherals along with security algorithms using secure hash algorithm (SHA-256) bits and advanced encryption standard (AES). In order to perform machine learning based applications, the proposed system is incorporated double-precision floating point arithmetic operations. The overall proposed architecture is developed in verilog hybrid deep learning (HDL) and quality checking using the LINT tool. The entire design is interfaced with the Zynq processor and software development kit (SDK) tool to verify data transfer between hardware and software. The obtained results are compared with existing state-of-art results and found that 18% improvement in throughput, a 21% improvement in power consumption savings, and a 34% reduction in latency.</div>
Nilesh Bhosle and Manesh Kokare
Inderscience Publishers
Mininath K. Nighot and Nilesh P. Bhosle
Institute of Advanced Scientific Research
Santosh N Randive, Ranjan K Senapati, and Nilesh Bhosle
IEEE
Since last two decades one of the fast advancing and most sensitive research area is observed to be detection of diabetic retinopathy (DR). In fundus images detection of retinal lesions depends on grading of diabetic retinopathy and computer-aided screening which led to development of automatic telemedicine system. The detection accuracy is still a matter of concern even after existence of huge contribution in area of detection. The existing algorithm for classification of DR images is not able to encode the directional information in 2D and 3D plane. The proposed approach encodes in four different directions (0°, 45°, 90° and 135°) from the reference pixel to its surrounding pixel in 3D plane. The proposed model includes preprocessing, feature extraction using spherical directional local ternary pattern (SDLTP) and classification using traditional distance measure and learning based distance measure using artificial neural network (ANN). SDLTP is used for extracting the directional feature in 3D plane and to reduce the feature vector length, a principle component analysis (PCA) technique is adopted. Further, two techniques are used for classification purpose (distance measure and ANN). The proposed method classification accuracy is measured in terms of precision. From experimental analysis, the proposed method give significant improvement in classification accuracy in both unsupervised and supervised domain because feature extraction in implemented considering the directional information.
Nilesh Bhosle and Manesh Kokare
IEEE
The use of content based image retrieval system in real life applications is limited because of the semantic gap between the low level and high level image features, used for the image similarity measure. Relevance feedback has been considered as a competent technique to overcome the semantic gap problem. However most of the relevance feedback based algorithms suffer from the problem of imbalanced dataset, which means that the numbers of irrelevant images are considerably larger than the number of relevant images for training the classifier. This imbalanced dataset problem causes the degradation in the retrieval results. In order to tackle this problem of imbalanced dataset, a long-term learning approach based on random forest classifier ensemble is proposed in this paper. The long-term learning relevance feedback approach collects the user feedback information for gaining the semantic knowledge of the database images. This knowledge is then learned by random forest classifier to improve the retrieval results. In the experimental evaluation it has been observed that there is a significant improvement in classification accuracy as compared to existing technique available in the literature. A precision of 93% has been observed in 9 iterations of the relevance feedback.
RECENT SCHOLAR PUBLICATIONS
MOST CITED SCHOLAR PUBLICATIONS
Publications
Nilesh Bhosle and Manesh Kokare, ``Random forest based active learning for content-based image retrieval,'' International Journal of Intelligent Information and Database Systems (IJIIDS) (Under Review, 2019) (Inderscience Publications)
Vishal Pawar, Nilesh P Bhosale, "SMART local bus transport Management System using IoT", International Journal of Scientific Research in Science and Technology (IJSRST), Volume 3, Issue 8, , November-December-2017
Nilesh Bhosle and Manesh Kokare, “A Heuristic optimization approach for Semantic based Image Retrieval using Relevance Feedback,'' International Journal of applied Pattern Recognition (IJAPR), vol. 3, no. 4, pp. 293-307, 2016, (Inderscience Publications)
GRANT DETAILS
Received Research Grant of Rs. 80,000 under University Research Grant Scheme
Name of Project: Reduction of Semantic Gap in CBIR Using Relevance Feedback
Funding Agency: Shri Savitribai Phule Pune University
Proposal No.: 13ENG000631
Duration: 2 Years. (2013 – 2015)
RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)
Patents Published #1 title: Energy optimized routing protocol using swarm intelligence with mobile sink in heterogeneous wireless sensor network. (Application A, The Patent Office Journal No. 33/2019 Dated 16/08/2019)
#2 title: A novel system and method to find target in an unknown area using Mobile Sensor Node (MSN) equipped with Global Positioning System (GPS). (Application A, The Patent Office Journal No. 33/2019 Dated 16/08/2019)