BHARATI RAJESH DATTATRAYA

@engg.dypvp.edu.in

Associate Professor in Computer Engineering
DR.D.Y.PATIL INSTITUTE OF TECHNOLOGY,PUNE

BHARATI RAJESH DATTATRAYA


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https://researchid.co/rdbharati

EDUCATION

Ph. D (Computer Engineering)

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering
25

Scopus Publications

139

Scholar Citations

7

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • Enhanced Diagnostic Validation of the EfficientXGBoost Model for Accurate COVID-19 Pneumonia Detection
    Bharti Sahu, Bhagwan Phulpagar, Rajesh D Bharati
    2026 International Conference on Emerging Smart Computing and Informatics Esci 2026, 2026
  • Investigating stock price prediction in the Indian electric vehicle sector using machine learning
    Rachna Somkunwar, Sagar Shinde, Amit Pimpalkar, Rajesh Bharati
    International Journal of Intelligent Systems Technologies and Applications, 2026
    Investment in the Indian electric vehicle (EV) market shows substantial potential despite fluctuations in stock prices. Traditional forecasting techniques often fail to accurately capture the complexity and nonlinear patterns of EV stock price data. The techniques for the problem solution, consisting of auto-regressive integrated moving average (ARIMA), seasonal auto-regressive integrated moving average with exogenous (SARIMAX) and a tuned long short-term memory (T-LSTM). The research investigates the predictive power of machine learning models for forecasting the stock prices of Tata Motors, Mahindra and Mahindra, Olectra Electric, and Bajaj Auto, all of which operate in the Indian EV market. The research procedure included data pre-processing of the time series history, followed by the identification of optimal model parameters, which led to the estimation of the ARIMA and SARIMAX models. The proposed a modified T-LSTM model to enhance efficiency in the Indian EV sector. Three well-recognised error metrics, mean squared error (MSE), root mean squared error (RMSE) and mean absolute error (MAE), helped assess the models' performance and measure their capability.
  • Lightweight Transfer Learning Models for Covid-19 Pneumonia Identification Using Chest X-Ray Imaging
    Bharti Sahu, Dr. Bhagwan Phulpagar, Dr. Rajesh D Bharati
    International Academic Journal of Science and Engineering, 2025
    The increasing rate of spreading COVID-19 created a serious need in accurate and extensive diagnostic centers to support the process of clinical diagnosis, particularly in health institutions that are over-allocated with resources. The chest X-ray is still considered as one of the most readily available procedures to assess the lungs; however, the process of the interpretation is tedious and skewed by inter-observer variations. In this paper, automated deep learning methods will be employed to solve the issue of efficiently identifying pneumonia cases using chest X-ray outcomes. The main objective of it is to compare the various deep learning frameworks with the view of determining the most appropriate one to detect pneumonia. The framework of this paper is a comparison framework with convolutional neural network-based architectures along with a baseline CNN, EfficientNet and Lightweight MobileNet, and Lightweight EfficientNet. The models are trained and tested on a selected chest X-ray dataset with regular preprocessing, data augmentation, and equal spread of classes. The experimental findings prove that lightweight architectures outperform standard CNN models in terms of high accuracy and optimal sensitivity. Lightweight EfficientNet model is the most qualified model by the total performance of 92.5% accuracy, 92.4% precision, 90.1% recall and 90.2% F1-score, which means that the classification performance is strong. The results relate to the effectiveness of lightweight DL models in obtaining differentiating lung characteristics related to COVID-19 pneumonia. The proposed comparative study demonstrates that optimized lightweight deep learning models can provide accurate, rapid, and clinically viable results in detecting COVID-19-related pneumonia from chest X-ray images, thereby validating their potential integration into clinical diagnostic protocols.
  • DDoS Attacks: Detection Techniques, Challenges, and Modern Practices
    Ramesh Redekar, Rajesh Bharati
    2025 IEEE International Students Conference on Electrical Electronics and Computer Science Sceecs 2025, 2025
    Internet, one of the essential components of today's civilization, serves numerous purposes for individuals, businesses, and society. However, its extensive use has sparked concerns, especially regarding privacy and cybersecurity. Furthermore, cyber dangers are becoming more dangerous, intense, and complicated. Distributed Denial of Service (DDoS) attacks have evolved as a prevalent and substantial danger to cybersecurity that may disable the network infrastructures of targeted companies and providers. To guard from DDoS assaults, a many security measures are used, like firewalls and intrusion detection systems. Improving the protective capabilities of IDS frameworks using machine and deep learning, other associated technology, is a popular topic currently. Nevertheless, regardless of considerable improvements, identifying DDoS assaults using machine and deep learning, other associated techs remain a difficulty, particularly when dealing with new DDoS attack. Consequently, this review aims to comprehensively discuss about DDoS attacks by going through the contemporary efforts made in the literature to counter the danger due to the DDoS attacks. First, we investigate certain DDoS attack-related solutions suggested by today’s investigators. Finally, we delve deeper by identifying the domains wherein the DDoS attacks are prone to take place; common challenges in recognizing the DDoS attacks in the IoT circumstance; advantages of Software-Defined Networking (SDN); state-of-the-art practices in the academic community to counter DDoS attack attempts in any networks of IoT or SDN or web-connected devices.
  • Advancing Peer Review Integrity: Automated Reviewer Assignment Techniques with a Focus on Deep Learning Applications
    Bhumika Bhaisare, Rajesh Bharati
    Communications in Computer and Information Science, 2025
  • CareerQuest - A Comprehensive Career Guidance Using Machine Learning and Natural Language Processing Techniques
    W.P. Rahane, Sarthak Sulakhe, Utkarsha Todkar, Sanskruti Sokande, Rajesh Bharati
    2025 9th International Conference on Computing Communication Control and Automation Icccbea 2025, 2025
    The absence of personalised data-driven support services in career guidance creates difficulties for modern students in making career choices, thus leading to inappropriate job choices and increased student dropout rates. Career counselling practitioners typically implement generic recommendations that fail to consider particular client talents and personality traits. The research paper presents CareerQuest - A Comprehensive Career Guidance, which employs Machine Learning and Natural Language Processing approaches to recommend suitable career paths aligned with students' requirements for proper educational choices. The design of CareerQuest relies on XGBoost and BERT to deliver personalised professional guidance that matches users' professional abilities against their future goals. The system integrates the MBTI (Myers-Briggs Type Indicator) and RIASEC models to assess career compatibility between user skills and industry preferences for informed decisions. The platform delivers personalised recommendations and advice to all users through predictive analytics that use ML features to track career environment changes. Through its development process, CareerQuest provides its users access to make specific career choices in the adaptable career counseling system. The data processing system of CareerQuest builds educational-professional connections to deliver enduring career satisfaction along with professional growth to students on their complete career path.
  • An intelligent framework of groundnut plant disease classification using heuristic approach-aided multilevel K-means clustering and attention-based hybrid dilated residual network
    Kapil Netaji Vhatkar, Vinod V. Kimbahune, Rachna K. Somkunwar, Rajesh Bharti, Jayesh Mohanrao Sarwade, Atul B. Kathole
    Australian Journal of Electrical and Electronics Engineering, 2025
  • Real-Time Deep Learning-Driven Surveillance with Spatiotemporal Feature Extraction for Detection of Anomalous Human Behavior Across Dynamic Environments
    Madhuri Pangavhane, Rahul Patil, Rajesh Bharati, Deepak Gupta, Prashant Ahire, Pramod Patil, Wasudeo Rahane, Deepak Dharrao
    International Journal of Safety and Security Engineering, 2025
  • An adaptive methodology based on predictive deep learning and context aware clustering for electricity power usage mining and optimization at different granularity levels
    Pramod D. Patil, Rahul Patil, Prashant Ahire, Rajesh Bharati, Yashwant Dongre
    E Prime Advances in Electrical Engineering Electronics and Energy, 2024
    Smart metering in electricity power grid is an optimistic trend at global level. All smart devices and appliances based on Internet of Things (IoT) are now playing very significant role in household. These days’ electric power usage mining and optimization is possible down to meter level only. However, it is very challenging and significant to go down to different granularity levels such as appliances, various sensors and activities etc. The shifting of the electric power usage to low price electricity is also significant and possible by mining and optimizing electric power usage behaviour at low level. All smart appliances and activities are needs to be customized to when you use them. This paper proposes an adaptive methodology based on predictive deep learning and context aware clustering to discover new ways for mining and optimization of electric power usage at different granularity levels and make optimal decisions for shifting electric power usage to low cost. Here we have considered households and business meters approximately 2000 with unique id of each meter. The data of three months is used for user preference of starting appliance. The predictive accuracy of proposed methodology for usage mining and optimization is improved by average 4%. Different input data features are used to form clusters of meters with similar power consumption behaviour for household occupancy. The clustering accuracy for household occupancy is improved from 0.68 to 0.91. The impact of accurate household occupancy detection and appliance usage mining and optimization is in reduction of electric power cost. The consumer can see how electric power efficiency and time-of-use shift makes a difference using experimental setup.
  • A modified time adaptive self-organizing map with stochastic gradient descent optimizer for automated food recognition system
    Jameer Gulab Kotwal, Shweta Koparde, Chaya Jadhav, Rajesh Bharati, Rachna Somkunwar, Vinod kimbahune
    Journal of Stored Products Research, 2024
    Numerous decades of study have been devoted to associating artificial intelligence and culinary type recognition. Automated food identification systems are significant in many disciplines, comprising dietary valuation, menu analysis, and nutritional tracking. In the past, traditional image analysis algorithms caused in poor classification accuracy, but deep learning methods have enabled the identification of food types and its constituents. This study proposed a novel method to develop food recognition competence and accuracy by connecting a Stochastic Gradient Descent (SGD) optimizer to a Modified Time Adaptive Self-Organizing Map (MTA-SOM). Food arrival differences subsequent from lighting, changing perspectives, and occlusions sometimes provide challenges to traditional food recognition algorithms. In this research, propose an MTA-SOM that learns and adapts to changing food item appearances by dynamically changing its topology over time. This research leverages the self-organizing possessions of SOMs and the fine-tuning properties of SGD by relating the MTA-SOM and the SGD optimizer, thereby maximizing the advantages of both techniques. The research method includes collecting a large number of food images from a difference of cuisines and presentation styles in order to assess the effectiveness of the proposed method. This proposed method performs an extensive test and connect MTA-SOM and SGD to present approaches of food recognition. Important advances in precision and robustness are produced as the system learns to recognize food items more precisely and adapts to changes in food appearance. By automating food detection with high precision and adaptability, our method could revolutionize our capability to interact with food-related data and offer important insights into dietary practices and nutritious decisions.
  • Efficient Heuristic Management of Cloud Data Lakes for Enhanced Big Data Handling in Cloud Computing Environments
    Wasudeo Purushottam Rahane
    Panamerican Mathematical Journal, 2024
  • SVM-based Sarcasm Detection System: NLP Using Heuristic Approach
    Rajesh Bharati, Wasudeo Rahane, P.D. Patil, Shubham Tapkeer, Vijay Waghmare, Jaydeep Patil, Simran Desai
    2024 8th International Conference on Computing Communication Control and Automation Iccubea 2024, 2024
  • Gender and Age Detection Using Multimodal Deep Neural Network
    Prashant G. Ahire, Rahul A. Patil, Rajesh D. Bharati, Mayuri Sakalkar, Parinitha Samaga, Abhinav Ramteke, Sarthak Shelar
    2024 8th International Conference on Computing Communication Control and Automation Iccubea 2024, 2024
  • Document Generation and Validation using Blockchain
    Rajesh Bharati, Deepika Jaiswal, Priyanka Jadhav, Pranav Patil, Sarthak Joshi, Venkatesh Lashkare, Hrushikesh Patil, Prashant Ahire
    2024 8th International Conference on Computing Communication Control and Automation Iccubea 2024, 2024
  • Hybrid graph partitioning with olb approach in distributed transactions
    Rajesh Bharati, Vahida Attar
    Intelligent Automation and Soft Computing, 2023
  • Examining Social Media Posts for Identification of Anxiety and Depression Utilizing Machine Learning Techniques
    Babasaheb S. Satpute, Wasudeo P. Rahane, Rajesh Bharati
    Proceedings International Conference on Technological Advancements in Computational Sciences Ictacs 2023, 2023
  • Convolutional Neural Network Based Alzheimer's Disease Detection Using OIASIS Dataset
    Babasaheb S. Satpute, Wasudeo P. Rahane, Rajesh Bharati
    Proceedings International Conference on Technological Advancements in Computational Sciences Ictacs 2023, 2023
  • Predictive Modeling of Vehicle CO2 Emissions Using Machine Learning Techniques: A Comprehensive Analysis of Automotive Attributes
    Babasaheb S. Satpute, Rajesh Bharati, Wasudeo P. Rahane
    Proceedings International Conference on Technological Advancements in Computational Sciences Ictacs 2023, 2023
  • EzLang: A C Based Programming Language
    Puravasu Jaideep Sesha, Siddhi Anil Bairagi, K Abhishek, DineshKumar Yadav, Rajesh Bharati
    2023 7th International Conference on Computing Communication Control and Automation Iccubea 2023, 2023
  • Performance Analysis of Scalable Transactions in Distributed Data Store
    R. D. Bharati, V. Z. Attar
    Smart Innovation Systems and Technologies, 2022
  • A Comprehensive Survey on Distributed Transactions Based Data Partitioning
    R.D. Bharati, V.Z. Attar
    Proceedings 2018 4th International Conference on Computing Communication Control and Automation Iccubea 2018, 2018
  • Analysis of job scheduling algorithms and studying dynamic job ordering to optimize MapReduce
    Ahmed Qasim Mohammed, Rajesh Bharati
    Advances in Intelligent Systems and Computing, 2018
  • An efficient technique to improve resources utilization for hadoop MapReduce in heterogeneous system
    Ahmed Qasim Mohammed, Rajesh Bharati
    ICCT 2017 International Conference on Intelligent Communication and Computational Techniques, 2017
  • An effective Computation Offloading in pervasive devices to cloud
    Jaya A. Suradkar, R. D. Bharati
    International Conference on Computing Analytics and Security Trends Cast 2016, 2017
  • Cloud iDedup: History aware in-line Deduplication for cloud storage to reduce fragmentation by utilizing Cache Knowledge
    Reshma A. Fegade, R.D. Bharati
    International Conference on Computing Analytics and Security Trends Cast 2016, 2017

RECENT SCHOLAR PUBLICATIONS

  • Explainable Federated Multimodal Deep Learning Framework for Early Alzheimer’s Disease Detection: Integrating MRI, Clinical Data, and Expert-Guided Few-Shot Learning with …
    B Satpute, W Rahane, R Bharati, S NN
    2026
  • ARTEMIS: Adaptive Reliable Task Execution with Multi-agent Intelligence and Self-verification
    B Satpute, R Bharati, W Rahane, S NN
    2026
  • Investigating stock price prediction in the Indian electric vehicle sector using machine learning
    R Somkunwar, S Shinde, A Pimpalkar, R Bharati
    International Journal of Intelligent Systems Technologies and Applications … , 2026
    2026
  • CareerQuest-A Comprehensive Career Guidance Using Machine Learning and Natural Language Processing Techniques
    WP Rahane, S Sulakhe, U Todkar, S Sokande, R Bharati
    2025 9th International Conference on Computing, Communication, Control and … , 2025
    2025
  • DDoS Attacks: Detection Techniques, Challenges, and Modern Practices
    R Redekar, R Bharati
    2025 IEEE International Students' Conference on Electrical, Electronics and … , 2025
    2025
    Citations: 2
  • Real-Time Deep Learning-Driven Surveillance with Spatiotemporal Feature Extraction for Detection of Anomalous Human Behavior Across Dynamic Environments.
    M Pangavhane, R Patil, R Bharati, D Gupta, P Ahire, P Patil, W Rahane, ...
    International Journal of Safety & Security Engineering 15 (1) , 2025
    2025
    Citations: 7
  • SVM-based Sarcasm Detection System: NLP Using Heuristic Approach
    R Bharati, W Rahane, PD Patil, S Tapkeer, V Waghmare, J Patil, S Desai
    2024 8th International Conference on Computing, Communication, Control and … , 2024
    2024
    Citations: 4
  • Document generation and validation using blockchain
    R Bharati, D Jaiswal, P Jadhav, P Patil, S Joshi, V Lashkare, H Patil, ...
    2024 8th International Conference on Computing, Communication, Control and … , 2024
    2024
    Citations: 3
  • Gender and Age Detection Using Multimodal Deep Neural Network
    PG Ahire, RA Patil, RD Bharati, M Sakalkar, P Samaga, A Ramteke, ...
    2024 8th International Conference on Computing, Communication, Control and … , 2024
    2024
    Citations: 2
  • An adaptive methodology based on predictive deep learning and context aware clustering for electricity power usage mining and optimization at different granularity levels
    PD Patil, R Patil, P Ahire, R Bharati, Y Dongre
    e-Prime-Advances in Electrical Engineering, Electronics and Energy 8, 100628 , 2024
    2024
    Citations: 9
  • A modified time adaptive self-organizing map with stochastic gradient descent optimizer for automated food recognition system
    JG Kotwal, S Koparde, C Jadhav, R Bharati, R Somkunwar
    Journal of Stored Products Research 107, 102314 , 2024
    2024
    Citations: 22
  • Advancing peer review integrity: Automated reviewer assignment techniques with a focus on deep learning applications
    B Bhaisare, R Bharati
    International conference on computation of artificial intelligence & machine … , 2024
    2024
    Citations: 5
  • Examine Heuristic Data Lake Management Using AWS: A Big Data Handling Approach
    WP Rahane, PD Patil, RD Bharti
    Journal of Electrical Systems 20 (1s), 875-880 , 2024
    2024
  • Examining social media posts for identification of anxiety and depression utilizing machine learning techniques
    BS Satpute, WP Rahane, R Bharati
    2023 3rd International Conference on Technological Advancements in … , 2023
    2023
    Citations: 2
  • Convolutional Neural Network Based Alzheimer's Disease Detection Using OIASIS Dataset
    BS Satpute, WP Rahane, R Bharati
    2023 3rd International Conference on Technological Advancements in … , 2023
    2023
    Citations: 1
  • Predictive Modeling of Vehicle CO 2 Emissions Using Machine Learning Techniques: A Comprehensive Analysis of Automotive Attributes
    BS Satpute, R Bharati, WP Rahane
    2023 3rd International Conference on Technological Advancements in … , 2023
    2023
    Citations: 8
  • EzLang: AC Based Programming Language
    PJ Sesha, SA Bairagi, K Abhishek, DK Yadav, R Bharati
    2023 7th International Conference On Computing, Communication, Control And … , 2023
    2023
    Citations: 1
  • Hybrid Graph Partitioning with OLB Approach in Distributed Transactions.
    R Bharati, V Attar
    Intelligent Automation & Soft Computing 37 (1) , 2023
    2023
    Citations: 5
  • Performance analysis of scalable transactions in distributed data store
    RD Bharati, VZ Attar
    International Conference on Computing in Engineering & Technology, 542-548 , 2022
    2022
    Citations: 6
  • Data Handling Requirements in Cloud Storage Systems
    S Pratale, R Bharati
    NOVYI MIR 6 (7), 65-70 , 2021
    2021

MOST CITED SCHOLAR PUBLICATIONS

  • A modified time adaptive self-organizing map with stochastic gradient descent optimizer for automated food recognition system
    JG Kotwal, S Koparde, C Jadhav, R Bharati, R Somkunwar
    Journal of Stored Products Research 107, 102314 , 2024
    2024
    Citations: 22
  • Computation offloading: overview, frameworks and challenges
    JA Suradkar, RD Bharati
    International Journal of Computer Applications 134 (6), 28-31 , 2016
    2016
    Citations: 10
  • An adaptive methodology based on predictive deep learning and context aware clustering for electricity power usage mining and optimization at different granularity levels
    PD Patil, R Patil, P Ahire, R Bharati, Y Dongre
    e-Prime-Advances in Electrical Engineering, Electronics and Energy 8, 100628 , 2024
    2024
    Citations: 9
  • Load balancing algorithm for dht based structured peer to peer system
    C Taank, R Bharati
    International Journal of Emerging Technology and Advanced Engineering 3 (1 … , 2013
    2013
    Citations: 9
  • Predictive Modeling of Vehicle CO 2 Emissions Using Machine Learning Techniques: A Comprehensive Analysis of Automotive Attributes
    BS Satpute, R Bharati, WP Rahane
    2023 3rd International Conference on Technological Advancements in … , 2023
    2023
    Citations: 8
  • A comprehensive survey on distributed transactions based data partitioning
    RD Bharati, VZ Attar
    2018 Fourth International Conference on Computing Communication Control and … , 2018
    2018
    Citations: 8
  • Task Allocation for Maximizing Reliability of Distributed Computing Systems using Dynamic Greedy Heuristic
    RD Bharati, VN Jagtap, OC Gupta, SS Landge
    International Journal of Advanced Research in Computer and Communication … , 2013
    2013
    Citations: 8
  • Real-Time Deep Learning-Driven Surveillance with Spatiotemporal Feature Extraction for Detection of Anomalous Human Behavior Across Dynamic Environments.
    M Pangavhane, R Patil, R Bharati, D Gupta, P Ahire, P Patil, W Rahane, ...
    International Journal of Safety & Security Engineering 15 (1) , 2025
    2025
    Citations: 7
  • Performance analysis of scalable transactions in distributed data store
    RD Bharati, VZ Attar
    International Conference on Computing in Engineering & Technology, 542-548 , 2022
    2022
    Citations: 6
  • An enhanced client-server assignment for internet distributed systems
    R Bharati, I Naidu, A Kiran, K Khune, C Vyas
    International Journal of Engineering Trends and Technology 10 (4), 198-201 , 2014
    2014
    Citations: 6
  • Advancing peer review integrity: Automated reviewer assignment techniques with a focus on deep learning applications
    B Bhaisare, R Bharati
    International conference on computation of artificial intelligence & machine … , 2024
    2024
    Citations: 5
  • Hybrid Graph Partitioning with OLB Approach in Distributed Transactions.
    R Bharati, V Attar
    Intelligent Automation & Soft Computing 37 (1) , 2023
    2023
    Citations: 5
  • An efficient technique to improve resources utilization for hadoop MapReduce in heterogeneous system
    AQ Mohammed, R Bharati
    2017 International Conference on Intelligent Communication and Computational … , 2017
    2017
    Citations: 5
  • SVM-based Sarcasm Detection System: NLP Using Heuristic Approach
    R Bharati, W Rahane, PD Patil, S Tapkeer, V Waghmare, J Patil, S Desai
    2024 8th International Conference on Computing, Communication, Control and … , 2024
    2024
    Citations: 4
  • Document generation and validation using blockchain
    R Bharati, D Jaiswal, P Jadhav, P Patil, S Joshi, V Lashkare, H Patil, ...
    2024 8th International Conference on Computing, Communication, Control and … , 2024
    2024
    Citations: 3
  • Workload-Driven Transactional Partitioning for Distributed Databases
    RD Bharati, VZ Attar
    Data Intelligence and Cognitive Informatics: Proceedings of ICDICI 2020, 389-396 , 2021
    2021
    Citations: 3
  • Function as a service in cloud computing: A survey
    R Mukundand, R Bharati
    International Journal of Future Generation Communication and Networking 13 … , 2020
    2020
    Citations: 3
  • Task Allocation Policy in Distributed Computing Using Refined Heuristics
    M Shahakar, R Mahajan
    International Journal of Emerging Technology and Advanced Engineering 4 (6 … , 2014
    2014
    Citations: 3
  • DDoS Attacks: Detection Techniques, Challenges, and Modern Practices
    R Redekar, R Bharati
    2025 IEEE International Students' Conference on Electrical, Electronics and … , 2025
    2025
    Citations: 2
  • Gender and Age Detection Using Multimodal Deep Neural Network
    PG Ahire, RA Patil, RD Bharati, M Sakalkar, P Samaga, A Ramteke, ...
    2024 8th International Conference on Computing, Communication, Control and … , 2024
    2024
    Citations: 2