*Prof. Girish B. Desale* has done his M.Sc. (Comp. Sci.), SET, MCM, and PG-DAC, and perusing Ph.D. in ML domain.
He is working as an *Asst. Prof. & Head* of department of Computer Science and IT at *JET’s Z. B. Patil College, Dhule* affiliated to KBCNMU, Jalgaon. He has *25 years* in excellent teaching and administration & industry experience (15 years as a HOD).
He has published *3 Indian Patents* and *2 UK Patent* in Data Science & AI Domain.
He has Published Books for academics and contributed multiple papers to esteemed publications and conferences/Journals.
He has worked as an *organizing member* for Conferences & also as a paper reviewer.
He was invited as a *Guest Lecturer* and *Expert Committee Member* for Avishkar events.
He worked as *Chairman & member of LIC & Expert committee* by KBCNMU, Jalgaon.
He is *Chairman of UG/PG Examinations* at University level.
He is working for *placement activities* and students counselling & support.
He attended various conference, symposi
EDUCATION
MSC,SET,MCM,PG-DAC
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Artificial Intelligence
3
Scopus Publications
36
Scholar Citations
4
Scholar h-index
1
Scholar i10-index
Scopus Publications
Deep Learning based Abstractive text Summarization: A Survey G Uday Kiran, Ramakrishna Gandi, M. Lavanya, Girish Bhagwant Desale, Ch. Upendar Rao, B. Veerasekhar Reddy 2024 Parul International Conference on Engineering and Technology Picet 2024, 2024 The explosion in the amount of textual data in the last several years has created an abundant source of resources for data extraction and analysis. It is necessary to summarize this data in order to access relevant information in a fair amount of time. Here we take a look at some of the more current methods for summarizing abstractive texts with deep learning models. A hotspot for research in the field of Natural Language Processing (NLP) is summarization, the process of condensing a document without losing any of its significance. You can classify summarization methods as either “extractive” or “abstractive” depending on whether they use natural language processing to construct new phrases or keep the exact ones from the source text. Extensive research has been conducted on extractive summarization, and the issue has now attained maturity. At this point, the focus of the study is on providing an abstract summary. Abstractive Text Summarization (ATS) is a tough and complicated operation because of the inherent complexity of the natural language content. Researchers can get a thorough overview of Deep Learning based A TS in this work. This paper provides a comprehensive survey of deep learning models employed for abstractive summarization, delving into their architectures and methodologies. Furthermore, it scrutinizes prevalent datasets utilized for training and evaluation purposes, offering insights into their characteristics and suitability for benchmarking. The study evaluates the performance of various abstractive summarization methods on these datasets, shedding light on their effectiveness and limitations. Lastly, emerging themes in the field are discussed, along with unresolved challenges, aiming to foster deeper understanding and advancement in DL-based abstractive text summarization among researchers.
Optimizing Resource Allocation in Cloud Environments using Fruit Fly Optimization and Convolutional Neural Networks Taviti Naidu Gongada, Girish Bhagwant Desale, Shamrao Parashram Ghodake, K. Sridharan, Vuda Sreenivasa Rao, Yousef A.Baker El-Ebiary International Journal of Advanced Computer Science and Applications, 2024 — Cloud computing environments play a crucial role in modern computing infrastructures, offering scalability, flexibility, and cost-efficiency. However, optimizing resource utilization and performance in such dynamic and complex environments remains a significant challenge. This study addresses this challenge by proposing a novel framework that integrates Fruit Fly Optimization (FFO) with Convolutional Neural Networks (CNN) for task scheduling optimization. The background emphasizes the importance of efficient resource allocation and management in cloud computing to meet increasing demands for computational resources while minimizing costs and enhancing overall system performance. The objective of this research is to develop a comprehensive framework that leverages the complementary strengths of FFO and CNN to address the shortcomings of traditional task scheduling approaches. The novelty of the proposed framework lies in its integration of optimization techniques with advanced data analysis methods, enabling dynamic and adaptive task allocation based on real-time workload patterns. The proposed framework is thoroughly evaluated using historical workload data, and results demonstrate significant improvements over traditional methods. Specifically, the FFO-CNN framework achieves average response times ranging from 120 to 180 milliseconds, while maintaining high resource utilization rates ranging from 90% to 98%. These results highlight the effectiveness of the FFO-CNN framework in enhancing resource utilization and performance in cloud computing environments. This research contributes to advancing the state-of-the-art in cloud resource management by introducing a novel approach that combines optimization and data analysis techniques. The proposed framework offers a promising solution to the challenges of resource allocation and task scheduling in cloud computing environments, paving the way for more efficient and sustainable cloud infrastructures in the future.
Attention-Based Deep Learning Approach for Pedestrian Detection in Self-Driving Cars Wael Ahmad AlZoubi, Girish Bhagwant Desale, Sweety Bakyarani E, Uma Kumari C R, Divya Nimma, K Swetha, B Kiran Bala International Journal of Advanced Computer Science and Applications, 2024 Autonomous vehicle safety relies heavily on the ability to accurately detect pedestrians, as this capability is crucial for preventing accidents and saving lives. Pedestrian recognition is particularly challenging in the dynamic and complex environments of urban areas. Effective pedestrian detection is crucial for ensuring road safety in autonomous vehicles. Current pedestrian identification systems often fall short in capturing the nuances of pedestrian behavior and appearance, potentially leading to dangerous situations. These limitations are mainly due to difficulties in various conditions, such as low-light environments, occlusions, and intricate urban settings. This paper proposes a novel solution to these challenges by integrating an attention-based convolutional bi-GRU model with deep learning techniques for pedestrian recognition. This method leverages deep learning to provide a robust solution for pedestrian detection. Convolutional layers are utilized to extract spatial features, attention mechanisms highlight semantic details, and Bidirectional Gated Recurrent Units (Bi-GRU) capture the temporal context in the proposed model. The process begins with data collection to build a comprehensive pedestrian dataset, followed by preprocessing using min-max normalization. The key components of the model work together to enhance pedestrian detection, ensuring a more accurate and comprehensive understanding of dynamic pedestrian scenarios. The implementation of this unique approach was carried out using Python, employing libraries such as TensorFlow, Keras, and OpenCV. The proposed attention-based convolutional bi-GRU model outperforms previous models by an average of 17.1%, achieving an accuracy rate of 99.4%. The model significantly surpasses Random Forest, Faster R-CNN, and SVM in terms of pedestrian recognition accuracy, which is critical for autonomous vehicle safety.
RECENT SCHOLAR PUBLICATIONS
IKS-Driven Cyber Resilience for Women in the Digital Age DGB Desale National Conference at WKBS Mahila College, Dhule dated on 17th Jan. 2026 , 2026 2026
Digital India and Emerging Technologies (AI, IoT, Block-Chain) DGB Desale National Conference at M.J. College, Jalgaon dated on 9th Jan. 2026 18 (1 … , 2026 2026
SPAM DETECTION FRAMEWORK FOR CELL PHONES REVIEWS USING CDR-GRU SKB Girish B. Desale Journal of Nonlinear Analysis and Optimization: Theory and Applications ISSN … , 2025 2025
Associate Editor: Contemporary Issues in Science, Computer Science, Engineering and Technology, 2024 GB DESALE Contemporary Issues in Science, Computer Science, Engineering and Technology, 38 , 2024 2024
Word Processing With Google Docs (CS-113) MNC Girish B. Desale, Arati B. Patil, Shirish S. Patil Jalgaon. , 2024 2024
Attention-Based Deep Learning Approach for Pedestrian Detection in Self-Driving Cars DBKB Wael Ahmad AlZoubi1 , Prof. Girish Bhagwant Desale2 , Dr. Sweety ... (IJACSA) International Journal of Advanced Computer Science and Applications … , 2024 2024 Citations: 9
AI BASED CYBER SECURITY SYSTEM GBD P. Narimuktu International Conference on Research and Innovation on Products HELD AT “Geh … , 2024 2024
ADVANCED MACHINE LEARNING PROGRAMMING GBD DR. DINESH D. PATIL,P MARIMUKTU APPLICATION OF EDUCATION 43, 104-111 , 2024 2024
Comprehensive Study on Spam Review Detection Model using Linguistic and Behavioral Features (https://www.ijera.com/papers/vol14no5/1405149156.pdf) SKB Girish B. Desale International Journal of Engineering Research and Applications 14 (5), 149-156 , 2024 2024
Deep Learning Based Abstractive Text Summarization: A Survey GU Kiran, R Gandi, M Lavanya, GB Desale, CU Rao, BV Reddy 2024 Parul International Conference on Engineering and Technology (PICET), 1-5 , 2024 2024
Optimizing Resource Allocation in Cloud Environments using Fruit Fly Optimization and Convolutional Neural Networks PTDYABEE Dr. Taviti Naidu Gongada,Prof. Girish Bhagwant Desale,Shamrao ... International Journal of Advanced Computer Science and Applications 15 (5 … , 2024 2024 Citations: 11
MACHINE LEARNING AND IT'S TECHNIQUES SST PROF. GIRISH B. DESALE ISBN 978-93-5762-906-5 , 2024 2024
AI Powered Interactive English Learning Board DRSV DR. VINOTH KUMAR CHOKKALIGAM, DR. RESHU GUPTA SINGH, GIRISH BHAGWANT DESALE GB Patent 6,359,678 , 2024 2024
Portable AI Data Enhancing and Analysis Device MMNB Mr. Perumal Marimuktu, Mr. Girish Bhagwant Desale, Mrs. Rupali Sinchan ... GB Patent 6,358,026 , 2024 2024 Citations: 8
INTRODUCTION TO DATABASE MANAGEMENT AND SYSTEMS PRAB PROF. GIRISH B. DESALE ISBN 978-93-6132-090-3 , 2024 2024
BIOMETRIC ENABLED MAILING DEVICE MBSP Mr. P. Marimuktu,Mr.Girish Bhagwant Desale IN Patent 200,799 , 2024 2024
Dynamic Ensemble Learning System for Adaptive Predictive Modeling in Data Science MRDN Mr. P. Marimuktu, Mrs. Arati Tushar Patil, Mr. Dewendra Onkar Bharambe ... IN Patent 202,341,090,159 , 2024 2024
FUNDAMENTALS OF IOT AND IT'S APPLICATIONS PGBD DR. VIJAY KUMAR SALVIA ISBN 978-93-6132-187-0 , 2024 2024
Impact of Smart Classrooms in Teaching Learning Effectiveness in Higher Education: A Quantitative Investigation DCSS Dr. Pranjali Madhur, Girish B. Desale, Dr. Vamseedhar Annam Journal of Informatics Education and Research 4 (2), 8 , 2024 2024 Citations: 7
MOST CITED SCHOLAR PUBLICATIONS
Optimizing Resource Allocation in Cloud Environments using Fruit Fly Optimization and Convolutional Neural Networks PTDYABEE Dr. Taviti Naidu Gongada,Prof. Girish Bhagwant Desale,Shamrao ... International Journal of Advanced Computer Science and Applications 15 (5 … , 2024 2024 Citations: 11
Attention-Based Deep Learning Approach for Pedestrian Detection in Self-Driving Cars DBKB Wael Ahmad AlZoubi1 , Prof. Girish Bhagwant Desale2 , Dr. Sweety ... (IJACSA) International Journal of Advanced Computer Science and Applications … , 2024 2024 Citations: 9
Portable AI Data Enhancing and Analysis Device MMNB Mr. Perumal Marimuktu, Mr. Girish Bhagwant Desale, Mrs. Rupali Sinchan ... GB Patent 6,358,026 , 2024 2024 Citations: 8
Impact of Smart Classrooms in Teaching Learning Effectiveness in Higher Education: A Quantitative Investigation DCSS Dr. Pranjali Madhur, Girish B. Desale, Dr. Vamseedhar Annam Journal of Informatics Education and Research 4 (2), 8 , 2024 2024 Citations: 7
Big Data and Big Data Management (BDM) with current Technologies–Review GB Desale, AS Patil, SP Patil IJERA 7 (04), 42-44 , 2017 2017 Citations: 1
IKS-Driven Cyber Resilience for Women in the Digital Age DGB Desale National Conference at WKBS Mahila College, Dhule dated on 17th Jan. 2026 , 2026 2026
Digital India and Emerging Technologies (AI, IoT, Block-Chain) DGB Desale National Conference at M.J. College, Jalgaon dated on 9th Jan. 2026 18 (1 … , 2026 2026
SPAM DETECTION FRAMEWORK FOR CELL PHONES REVIEWS USING CDR-GRU SKB Girish B. Desale Journal of Nonlinear Analysis and Optimization: Theory and Applications ISSN … , 2025 2025
Associate Editor: Contemporary Issues in Science, Computer Science, Engineering and Technology, 2024 GB DESALE Contemporary Issues in Science, Computer Science, Engineering and Technology, 38 , 2024 2024
Word Processing With Google Docs (CS-113) MNC Girish B. Desale, Arati B. Patil, Shirish S. Patil Jalgaon. , 2024 2024
AI BASED CYBER SECURITY SYSTEM GBD P. Narimuktu International Conference on Research and Innovation on Products HELD AT “Geh … , 2024 2024
ADVANCED MACHINE LEARNING PROGRAMMING GBD DR. DINESH D. PATIL,P MARIMUKTU APPLICATION OF EDUCATION 43, 104-111 , 2024 2024
Comprehensive Study on Spam Review Detection Model using Linguistic and Behavioral Features (https://www.ijera.com/papers/vol14no5/1405149156.pdf) SKB Girish B. Desale International Journal of Engineering Research and Applications 14 (5), 149-156 , 2024 2024
Deep Learning Based Abstractive Text Summarization: A Survey GU Kiran, R Gandi, M Lavanya, GB Desale, CU Rao, BV Reddy 2024 Parul International Conference on Engineering and Technology (PICET), 1-5 , 2024 2024
MACHINE LEARNING AND IT'S TECHNIQUES SST PROF. GIRISH B. DESALE ISBN 978-93-5762-906-5 , 2024 2024
AI Powered Interactive English Learning Board DRSV DR. VINOTH KUMAR CHOKKALIGAM, DR. RESHU GUPTA SINGH, GIRISH BHAGWANT DESALE GB Patent 6,359,678 , 2024 2024
INTRODUCTION TO DATABASE MANAGEMENT AND SYSTEMS PRAB PROF. GIRISH B. DESALE ISBN 978-93-6132-090-3 , 2024 2024
BIOMETRIC ENABLED MAILING DEVICE MBSP Mr. P. Marimuktu,Mr.Girish Bhagwant Desale IN Patent 200,799 , 2024 2024
Dynamic Ensemble Learning System for Adaptive Predictive Modeling in Data Science MRDN Mr. P. Marimuktu, Mrs. Arati Tushar Patil, Mr. Dewendra Onkar Bharambe ... IN Patent 202,341,090,159 , 2024 2024
