M.E (Computer Engineering) Ph.D in Computer Engineeering)
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Engineering, Computer Networks and Communications, Electrical and Electronic Engineering
5
Scopus Publications
31
Scholar Citations
4
Scholar h-index
Scopus Publications
An Intelligent Ensemble Deep Learning Techniques with Improved Owl Search Algorithm-Aided Optimal Feature Selection for Predicting the Presence of Heart Diseases Deepak Yashwantrao Bhadane, Indrabhan S. Borse International Journal of Image and Graphics, 2026 Cardiovascular disease evaluation and prediction are critical medical duties to ensure precise classification, which allows heart specialists to offer treatment to patients. Heart illness is one of the deadliest diseases, particularly a sudden cardiac arrest, which strikes an individual so suddenly that there is no opportunity to manage it, and this condition is extremely hard to detect. Numerous medical machine learning, as well as data mining methods, have been employed to extract significant information about coronary artery disease prognosis. However, the precision of the intended outcomes is not adequate. An effective deep learning-assisted heart disease prediction model is presented to solve the above difficulties. At first, the data are accumulated from a standard dataset. After this process, the features from the data are optimally selected using the Improved Owl Search Algorithm (IOSA). These features are further transferred to the prediction phase. Here, the heart disease is predicted by the Ensemble Deep Attention with Convolution Network (EDACNet), and this model is composed by the combination of “Long Short-Term Memory (LSTM)”, “Gated Recurrent Unit (GRU)”, “Deep Markov Random Field (Deep MRF)” and “1Dimensional Convolutional Neural Network (1DCNN)”. The prediction scores obtained from each structure are multiplied by an optimized weight, and it is subjected to a weighted averaging process. Here, the weight is optimized by the same IOSA. The final results after the weighted averaging process provide the final predicted outcomes over heart diseases. At last, the validation process is carried out to measure the usefulness of a designed prediction model. The result of the developed model achieves better performance in terms of accuracy and precision. It shows the value of accuracy to be 96 and also the precision attained the value of 97. Therefore, the entire validation developed a model that provides effective performance over existing techniques.
A Novel Hybrid Convolution and Multiscale Dilated EfficientNetB7-Based Plant Disease Detection and Classification with Adaptive Segmentation Procedures Manesh P. Patil, Indrabhan S. Borse International Journal of Image and Graphics, 2026 The economic success of the country is mainly dependent on crop growth. Diseases in crops are the greatest obstacle to food production. Early recognition of plant diseases is critical in the universe. In the standard diagnosis method, biologists directly assess each individual plant via onsite examinations. Nevertheless, physical crop illness examinations have some restrictions due to decreased precision and inadequate employee availability. To resolve these issues, there is a need to create automated systems that can recognize and classify the plant illnesses. New pathogens on the leaves of plants constantly appear because of plant structure and farming practices. Consequently, reliable identification and classification of leaf-related illnesses at their initial phases helps to prevent infection progression and promote plant productivity. The abundance of moderately strong data in the image environment and front, the significant color similarities in the unaffected and infected plant regions, the presence of distortion in sample images, and alterations in the location and dimension of foliage make plant leaf disease detection a challenging task. Therefore, an efficient deep model-based plant disease discovery and classification model is executed in this research work. Initially, the required image is acquired from online databases. The gathered images are then specified as input to the segmentation phase in which the Adaptive and Attention-aided Mask Region-based Convolutional Neural Network (AAM-RCNN) is utilized. For enhanced segmentation performance, the parameters in the AAM-RCNN are optimized with the aid of the Boosted Random Parameter-based Golden Tortoise Beetle Optimizer (BRP-GTBO). The segmented images are taken for detection and classification process takes place with the aid of Hybrid Convolution Two-Dimension/One-Dimension (2D/1D) and Multi-scale Dilated EfficientnetB7 (HC-MDEB7). In the Hybrid Convolution (2D/1D) model, the color and morphological features are provided as input to the 1D convolutional layer, whereas the texture patterns are used by the 2D convolutional layer. Finally, the detected and classified outcome is obtained from the HC-2D/1D-MDEB7 model. Experimental verification is carried out to prove the efficacy of the recommended framework.
Ensuring Trust in Blockchain Enabled Business Processes using Smart Contract Audits Rajendra Vasantrao Patil, Indrabhan Supdu Borse, Manesh Prakash Patil, Abhijit H. Khadke, Govind Mohanlal Poddar, Shravani R. Patil Proceedings of 8th International Conference on Inventive Computation Technologies Icict 2025, 2025 In a business network, blockchain is an agreed on, unchangeable ledger that makes it easier to monitor resources and keep track of events. From the trading of digital currencies like Bitcoin and Ethereum to a variety of uses in the private as well as public sectors, Blockchain technology has advanced. In a distributed setting, it facilitates confidence between untrusted organizations without requiring regulatory intervention. Blockchain provides a number of benefits, such as a shared register of records, which enhance company values. One of the most crucial components of blockchain application that enable safe operations without the involvement of outside parties is the smart contract. Over the past few decades, the usage of smart contracts has increased dramatically. The emergence of additional technologies, such as the distributed ledger, decentralized autonomous organizations, distributed finance, Internet of Things and AI is linked to this technology. The use of sophisticated smart contracts is applicable in every aspect of our everyday lives, from the logistics sector, the arts, economics and the Internet of Things. Smart contract is enforceable and self-executing computer program designed to carry out provisions of an agreement independently without involvement of third party. Since blockchain technology has developed rapidly, smart contracts have also exposed to number of security vulnerabilities, and some attacks resulting from these defects have caused significant losses. Any mistake in development of smart contracts might result in significant financial loss. The smart contracts are also susceptible to theft from even a little code mistake. Because of this, businesses recognize the need of smart contract security audits to reduce the risk of frauds and to boost transparency and security. Smart contracts audit is essential to build trust between parties involved in blockchain enabled business processes. This article present an in depth overview of smart contract, blockchain systems, smart contract vulnerabilities and smart contract audit process
A Novel Adaptive Beamforming Model for 5G Millimeter Wave Uplink Communication System Indrabhan Borse, Hitendra D. Patil Proceedings of the 2nd International Conference on Artificial Intelligence and Smart Energy Icais 2022, 2022 Millimeter wave (MMW) communication systems are emerging model for satisfying the increased necessity of high data rate of future generation cellular communications. The MMW frequencies give the prospective of increment in magnitude orders in terms of capacity. Though, MMW network links suffers from faster differentiation in quality and vulnerable to blockage. The main intent of this paper is to develop an adaptive beamforming technique of uplink communication in 5G millimeter wave cellular network. This paper optimizes the weight of narrowband in adaptive beamforming for enhancing the performance of MMW communication. Here, the adaptiveness in the beamforning is also accomplished by modifying the weight updating strategy of adaptive beamforming, which is performed by the hybrid meta-heuristic algorithm called Salp-Bird Swarm optimization (S-BSO) using Shark Smell optimization (SSO) and Bird Swarm Algorithm (BSA). The comparison is done over the existing models to prove the effective performance of the proposed adaptive beamforming.
ADAPTIVE BEAMFORMING MODEL FOR 5G HIGH SPEED NETWORKS USING MILLIMETER WAVE COMMUNICATION IN UPLINK Indrabhan Borse, Hitendra Patil Review of Computer Engineering Research, 2022 Future generation cellular communications will require increased data rates and transmission using millimeter waves (MMWs), which are an emerging concept to meet this need. The MMW frequencies offer the potential for orders of magnitude capacity improvements. However, MMW network connections are more susceptible to blocking, and they suffer from rapid quality differential. The major limitation of offering multiconnectivity in MMWs is the necessity of tracking the direction of every link with its suitable timing and power. Beamforming enables wireless communications, even with higher frequency bands such as the MMW frequency band. The main purpose of this article is to develop an adaptive beamforming approach for 5G millimeter-wave networks. MMW communication efficiency is improved by enhancing the narrowband weights of adaptive beamforming. Here, the Shark Smell Optimization (SSO) and Bird Swarm Algorithm (BSA) are combined to improve the weight update approach of the new Salp-Bird Swarm Optimization (S-BSO) to achieve adaptiveness in beamforming. To demonstrate the effectiveness of the suggested Salp-Bird Swarm Optimization (S-BSO), an experimental comparison is carried out with the current models.
RECENT SCHOLAR PUBLICATIONS
CNN Based Deep Learning Framework for detection of ROI in Breast Cancer Images Y Satish, I Borse International Conference on Emerging Trends in Engineering & Sciences , 2025 2025
Deep Learning-Based Crack Detection in Ancient Paintings for Digital Art Conservation VD Suryawanshi, IS Borse, RV Patil 2025
Ensuring Trust in Blockchain Enabled Business Processes using Smart Contract Audits RV Patil, I Borse the 8th International Conference in Inventive Computation Technologies , 2025 2025 Citations: 8
ENHANCING NETWORK INTRUSION DETECTION WITH ENSEMBLE DEEP LEARNING TECHNIQUES HK Verma, IS Borse 2025
ENHANCING E-COMMERCE MARKETING STRATEGIES THROUGH CONSUMER ENGAGEMENT INSIGHTS AND PREDICTIVE ANALYTICS M Gupta, IS Borse 2025 Citations: 3
Study of Adaptive Spoofing Attack Detection in Connected Vehicles with Dilated and Attention Driven Neural Network V Deshmukh, I Borse International Conference on Recent Advances in Engineering, Science and … , 2025 2025
Plant Disease Detection Using Image Segmentation Methods M Patil, I Borse International Conference on Recent Advances in Engineering, Science and … , 2025 2025
An Efficient Prediction Method for Coronary Heart Disease Using Ensemble Deep Learning Techniques RV Patil, I Borse International Conference on Recent Advances in Engineering, Science and … , 2025 2025
A Comprehensive Deep Learning Framework for Breast Cancer Detection and Classification Using Multiple Convolutional Neural Network Architectures S Yedage, I Borse Journal of Information Systems Engineering and Management 10 (5), 584-601 , 2025 2025
A novel hybrid convolution and multiscale dilated EfficientNetB7-based plant disease detection and classification with adaptive segmentation procedures MP Patil, IS Borse International Journal of Image and Graphics, 2750020 , 2025 2025 Citations: 4
An Intelligent Ensemble Deep Learning Techniques with Improved Owl Search Algorithm-Aided Optimal Feature Selection for Predicting the Presence of Heart Diseases DY Bhadane, IS Borse International Journal of Image and Graphics, 2750021 , 2025 2025 Citations: 4
DATA IMBALANCE HANDLING TECHNIQUES IN DISEASE PREDICTION MODELS DY Bhadane, IS Borse 2025
PLANT DISEASE IDENTIFICATION AND CLASSIFICATION USING ADAPTIVE SEGMENTATION TECHNIQUE MP Patil, IS Borse 2025 Citations: 2
Enhancing E-Commerce Strategy Through Predictive Modelling of User Engagement Metrics M Gupta, I Borse International Journal for Innovative Engineering and Management Research 13 … , 2024 2024
Enhancing Network Intrusion Detection with An Optimized Deep Learning Ensemble Model HK Verma, I Borse International Journal for Innovative Engineering and Management Research 13 … , 2024 2024
Comprehensive Study on Plant Disease Detection by using Hybrid Convolution Techniques M Patil, I Borse International Journal of Innovations in Engineering & Science 9 (4), 116-122 , 2024 2024
Comprehensive Review of Breast Cancer Prediction Using Machine Learning S Yedage, I Borse Annals of the Bhandarkar Oriental Research Institute 105 (6), 73-84 , 2024 2024
Enhancing Security in Vehicular Networks: A Study of Controller Area Network Intrusion Detection Systems V Deshmukh, I Borse International Journal of Innovations in Engineering & Science 9 (2), 82-85 , 2024 2024
A Research on Ensemble Deep Learning Techniques for Predicting Heart Diseases D Bhadane, I Borse International Journal of Innovations in Engineering & Science 9 (1), 13-17 , 2024 2024
Comprehensive Review on Plant diseases detection and identification M Patil, I Borse Journal of Electrical System 20 (3), 732-746 , 2024 2024 Citations: 6
MOST CITED SCHOLAR PUBLICATIONS
Ensuring Trust in Blockchain Enabled Business Processes using Smart Contract Audits RV Patil, I Borse the 8th International Conference in Inventive Computation Technologies , 2025 2025 Citations: 8
Comprehensive Review on Plant diseases detection and identification M Patil, I Borse Journal of Electrical System 20 (3), 732-746 , 2024 2024 Citations: 6
A novel hybrid convolution and multiscale dilated EfficientNetB7-based plant disease detection and classification with adaptive segmentation procedures MP Patil, IS Borse International Journal of Image and Graphics, 2750020 , 2025 2025 Citations: 4
An Intelligent Ensemble Deep Learning Techniques with Improved Owl Search Algorithm-Aided Optimal Feature Selection for Predicting the Presence of Heart Diseases DY Bhadane, IS Borse International Journal of Image and Graphics, 2750021 , 2025 2025 Citations: 4
ENHANCING E-COMMERCE MARKETING STRATEGIES THROUGH CONSUMER ENGAGEMENT INSIGHTS AND PREDICTIVE ANALYTICS M Gupta, IS Borse 2025 Citations: 3
A Novel Adaptive Beamforming Model for 5G Millimeter Wave Uplink Communication System I Borse, HD Patil 2022 Second International Conference on Artificial Intelligence and Smart … , 2022 2022 Citations: 3
PLANT DISEASE IDENTIFICATION AND CLASSIFICATION USING ADAPTIVE SEGMENTATION TECHNIQUE MP Patil, IS Borse 2025 Citations: 2
Adaptive Beamforming Model for 5G High Speed Networks using Millimeter Wave Communication in Uplink I Borse, H Patil Review of Computer Engineering Research 9 (4), 209-221 , 2022 2022 Citations: 1
CNN Based Deep Learning Framework for detection of ROI in Breast Cancer Images Y Satish, I Borse International Conference on Emerging Trends in Engineering & Sciences , 2025 2025
Deep Learning-Based Crack Detection in Ancient Paintings for Digital Art Conservation VD Suryawanshi, IS Borse, RV Patil 2025
ENHANCING NETWORK INTRUSION DETECTION WITH ENSEMBLE DEEP LEARNING TECHNIQUES HK Verma, IS Borse 2025
Study of Adaptive Spoofing Attack Detection in Connected Vehicles with Dilated and Attention Driven Neural Network V Deshmukh, I Borse International Conference on Recent Advances in Engineering, Science and … , 2025 2025
Plant Disease Detection Using Image Segmentation Methods M Patil, I Borse International Conference on Recent Advances in Engineering, Science and … , 2025 2025
An Efficient Prediction Method for Coronary Heart Disease Using Ensemble Deep Learning Techniques RV Patil, I Borse International Conference on Recent Advances in Engineering, Science and … , 2025 2025
A Comprehensive Deep Learning Framework for Breast Cancer Detection and Classification Using Multiple Convolutional Neural Network Architectures S Yedage, I Borse Journal of Information Systems Engineering and Management 10 (5), 584-601 , 2025 2025
DATA IMBALANCE HANDLING TECHNIQUES IN DISEASE PREDICTION MODELS DY Bhadane, IS Borse 2025
Enhancing E-Commerce Strategy Through Predictive Modelling of User Engagement Metrics M Gupta, I Borse International Journal for Innovative Engineering and Management Research 13 … , 2024 2024
Enhancing Network Intrusion Detection with An Optimized Deep Learning Ensemble Model HK Verma, I Borse International Journal for Innovative Engineering and Management Research 13 … , 2024 2024
Comprehensive Study on Plant Disease Detection by using Hybrid Convolution Techniques M Patil, I Borse International Journal of Innovations in Engineering & Science 9 (4), 116-122 , 2024 2024
Comprehensive Review of Breast Cancer Prediction Using Machine Learning S Yedage, I Borse Annals of the Bhandarkar Oriental Research Institute 105 (6), 73-84 , 2024 2024
Publications
1. A Novel Adaptive Beamforming Model for 5G Millimeter Wave Uplink Communication System
2. Holographic Beam forming for High Speed Network in 5G Communication
3. A Survey of 5G network using Millimeter wave
