Generalized framework using Federated Learning for brain tumor detection from medical images Rajesh Kumar Shrivastava, Amit Kumar Dwivedi, Dibyanarayan Hazra, Jahnavi Nischal, Kanishka Patel, Shristi Rai 2025 2nd International Conference on Computational Intelligence Communication Technology and Networking Cictn 2025, 2025 This paper presents federated learning to identify brain tumors based on MRI scans. A federated learning approach enables a model to be trained on decentralized data sources without requiring direct data exchange. In the field of medical imaging, where privacy rules restrict data exchange, this technique is very beneficial. The study investigates the categorization of brain tumors using EfficientNet, a convolutional neural network design. A federated learning configuration with twenty clients was designed to evaluate model performance. The results show that the federated learning approach using EfficientNet achieved an accuracy of 97.56% in detecting brain tumors. This research found that federated learning is valuable in brain tumor detection systems.
Polycystic Ovary Syndrome (PCOS) Detection Using Deep Learning and Explainable AI Kanishka Patel, Shristi Rai, Jahnavi Nischal, Devanshi Pandey, Rajesh Kumar Shrivastava, Dibyanarayan Hazra 2025 2nd International Conference on Advanced Computing and Emerging Technologies Acet 2025, 2025 Polycystic ovarian syndrome (PCOS) is a common endocrine illness which affects women of reproductive age. Due to its variety of symptoms, PCOS is often misdiagnosed. For automated PCOS identification, this research proposes a robust deep learning-based approach based on clinical and ultrasound data. To improve model generalization and address data imbalance, extensive data augmentation techniques were applied. Following a series of deep learning model evaluations, the optimal model was tuned for high accuracy and sensitivity. Through the provision of interpretable insights into feature importance and decision-making logic, explainable AI (XAI) techniques such as Grad-CAM++ were integrated to enhance model transparency and reliability in clinical settings. The suggested framework is a viable tool for promoting early PCOS diagnosis and supporting therapeutic decision-making, as evidenced by experimental data showing that it delivers high predictive performance while providing meaningful explanations.
Enhancing sports image data classification in federated learning through genetic algorithm-based optimization of base architecture De Sheng Fu, Jie Huang, Dibyanarayan Hazra, Amit Kumar Dwivedi, Suneet Kumar Gupta, Basu Dev Shivahare, Deepak Garg Plos One, 2024 Nowadays, federated learning is one of the most prominent choices for making decisions. A significant benefit of federated learning is that, unlike deep learning, it is not necessary to share data samples with the model owner. The weight of the global model in traditional federated learning is created by averaging the weights of all clients or sites. In the proposed work, a novel method has been discussed to generate an optimized base model without hampering its performance, which is based on a genetic algorithm. Chromosome representation, crossover, and mutation—all the intermediate operations of the genetic algorithm have been illustrated with useful examples. After applying the genetic algorithm, there is a significant improvement in inference time and a huge reduction in storage space. Therefore, the model can be easily deployed on resource-constrained devices. For the experimental work, sports data has been used in balanced and unbalanced scenarios with various numbers of clients in a federated learning environment. In addition, we have used four famous deep learning architectures, such as AlexNet, VGG19, ResNet50, and EfficientNetB3, as the base model. We have achieved 92.34% accuracy with 9 clients in the balanced data set by using EfficientNetB3 as the base model using a GA-based approach. Moreover, after applying the genetic algorithm to optimize EfficientNetB3, there is an improvement in inference time and storage space by 20% and 2.35%, respectively.
Harmonizing Tradition with Innovation: A Deep Learning-Powered Personalized Erhu Teaching Experience Amit Sharma, Ajay Yadav, Honey Singh, Vibhav Ranjan, Neeraj Joshi, Dibyanarayan Hazara 2nd IEEE International Conference on Iot Communication and Automation Technology Icicat 2024, 2024 Academic efforts to enhance erhu instruction reflect a wider educational reform integrating advanced technologies and innovative frameworks to enrich traditional Chinese musical instrument learning. Deep learning’s role in erhu education promises to revolutionize teaching methods through personalized learning paths and adaptive strategies, enhancing student experiences. The study focuses on the construction of a personalized erhu teaching system based on deep learning. It mentions the utilization of deep learning for offering customized learning paths and adaptive teaching frameworks, aiming to improve the quality of music education and provide students with personalized learning experience. The optimal configuration led to a model with 203,338 trainable parameters, achieving an impressive 93.87% accuracy. This high accuracy, demonstrated through detailed training/validation loss and accuracy plots over 150 epochs to prevent over fitting, and a confusion matrix with minimal classifications, underscores the potential of deep learning in enhancing music genre classification methodologies.
A Generalized Federated Learning Approach for Robust Crop Disease Classification Across Diverse Farms Rajesh Kumar Shrivastava, Amit Kumar Dwivedi, Suneet Kumar Gupta, Dibyanarayan Hazra, Ayush Maurya, Ankita Gupta, Brijendra Pratap Singh International Symposium on Advanced Networks and Telecommunication Systems Ants, 2024 In the current scenario, one of the major problems faced by developing countries like India is food security, as the world’s largest population lives here. Plant-based foods are the primary source of food, yet their susceptibility to diseases further compromises the requirements for food security. Although early detection of plant diseases might help in resolving the diseases in crops, farmers may not be able to identify them due to a lack of knowledge and facilities. Deep learning presents a potential solution that identifies diseases using crop images. Although deep learning is a promising solution, it may compromise data privacy because it may require image data from multiple farms. In this work, we produce a federated learning-based privacy-preserving crop disease classification solution where each farm classifies its own data using the Vision Transformer model and only shares model weights with the centralized server. We evaluated the experiment and found that the Vision Transformer, as a base model, provides $\mathbf{9 2 . 0 \%}$ accuracy in disease detection, which we further enhanced to $\mathbf{9 7 . 0 \%}$ with 20 clients. We find this solution promising for AI in agriculture, as it preserves farm data privacy and improves the accuracy of disease classification compared to existing work.
Generalized framework using Federated Learning for tomato disease classification over unbalanced dataset Dibyanarayan Hazra, Suneet Kumar Gupta, Umesh Gupta, Mohit Agarwal ACM International Conference Proceeding Series, 2023 Each cuisine required tomato in their kitchen for various food items and this makes tomato most popular crop worldwide and India is in second rank in terms production of tomato. Now a days, production of tomato goes down because of various diseases and to treat these diseases farmer needs to have extensive prior knowledge about the pathogen and along with various factor which promote the disease in the tomato. Due to lack of knowledge, the disease spreads rapidly and destroys all crops. To fill this gap, deep learning (DL) has been playing an important role, and there is much research on DL, how it can be used in medical industry and the agriculture industry for the identification of disease using images. There is a limitation for DL model that it does not work well with small dataset and huge amount of samples are required to train the model. Moreover, the data are not shared openly for security or for any other reason. Therefore, to overcome this challenge a Federated Learning (FL) based approach has been presented in the article. In FL, a deep learning model is shared with organizations which having the data and train the model. After training, the model information is shared with a centralized server which designs a generalized model. After getting the generalized model, it is shared with all other sites. The process is repeated until a generalized model is not designed and well-suited with all the sites. In our study, we tested our model on a tomato leaf disease data set using FL methodology with 10 clients and achieved the best precision with 88. 01%.
Internet of Things (IoT) Enabled Image Segmentation Model For Lung Disease Classification: An Approach Based On Particle Swarm Optimization Suneet Kumar Gupta, Dibyanarayan Hazra, Mohit Agarwal, Simar Preet Singh, Rahul Dass, Deepika Pantola 2023 2nd International Conference on Smart Technologies for Smart Nation Smarttechcon 2023, 2023 In the last decades, the domain of IoT has been explored by research community due to its vast real time applications. A combination of deep learning and IoT is well accepted worldwide as using deep learning, IoT devices can be easily converted into intelligence devices. Moreover, these devices are capable enough to take the decision based on real-time data. However, deployment of deep learning model is not so easy in IoT devices as these devices are constraint with limited computational power and storage space. Generally, deep learning architectures are large in terms of storage space, and due to the complication of model, it required resources to generate the output. To overcome the storage space and the large resource barrier, we proposed the method based on the particle swarm optimization technique for compression of the UNet architecture for its easy deployment on IoT devices for semantic segmentation usages. In this paper, all the intermediate steps involved for this compression of UNet using PSO is well explained with suitable examples. Experimentally, it has been proven that the proposed algorithm compresses the UNet architecture in the chest radiograph data set by 77% after 0. 68% decrease in accuracy with an improvement in the inference time by 2.23X.
Polycystic Ovary Syndrome (PCOS) Detection Using Deep Learning and Explainable AI K Patel, S Rai, J Nischal, D Pandey, RK Shrivastava, D Hazra 2025 2nd International Conference on Advanced Computing and Emerging … , 2025 2025.0
Generalized framework using Federated Learning for brain tumor detection from medical images RK Shrivastava, AK Dwivedi, D Hazra, J Nischal, K Patel, S Rai 2025 2nd International Conference on Computational Intelligence … , 2025 2025.0
Development of iot enabled deep learning model for indian food classification: An approach based on differential evaluation M Agarwal, AK Dwivedi, D Hazra, SK Gupta, D Garg Food Analytical Methods 18 (2), 172-189 , 2025 2025.0 Citations: 4
A generalized federated learning approach for robust crop disease classification across diverse farms RK Shrivastava, AK Dwivedi, SK Gupta, D Hazra, A Maurya, A Gupta, ... 2024 IEEE International conference on advanced networks and … , 2024 2024.0 Citations: 5
The efficient classification of breast cancer on low-power iot devices: A study on genetically evolved u-net M Agarwal, AK Dwivedi, D Hazra, P Sharma, SK Gupta, D Garg Computers in Biology and Medicine 183, 109296 , 2024 2024.0 Citations: 4
Enhancing sports image data classification in federated learning through genetic algorithm-based optimization of base architecture DS Fu, J Huang, D Hazra, AK Dwivedi, SK Gupta, BD Shivahare, D Garg Plos one 19 (7), e0303462 , 2024 2024.0 Citations: 9
Compressed Deep Learning Model for Detecting COVID-19 Disease: A Genetic Algorithm based approach A Sharma, SK Gupta, D Kumari, STR Dwarampudi, GBP Reddy, D Hazra 2023 Second International Conference On Smart Technologies For Smart Nation … , 2023 2023.0
Internet of Things (IoT) Enabled Image Segmentation Model For Lung Disease Classification: An Approach Based On Particle Swarm Optimization SK Gupta, D Hazra, M Agarwal, SP Singh, R Dass, D Pantola 2023 Second International Conference On Smart Technologies For Smart Nation … , 2023 2023.0
Generalized framework using federated learning for tomato disease classification over unbalanced dataset D Hazra, SK Gupta, U Gupta, M Agarwal Proceedings of the 2023 9th International Conference on Computer Technology … , 2023 2023.0 Citations: 6
Boosting Intelligent Farming: Federated Learning for Dispersed AI D Hazra, SK Gupta Rawat Prakashan , 2023 2023.0
Evolution of Weapon Systems and Rise of Intelligent Warfare N Jain, K Agrawal, DN Hazra, M Niranjan Robotics in Weaponry using Machine Learning and Engineering, 314-338 , 0
MOST CITED SCHOLAR PUBLICATIONS
Enhancing sports image data classification in federated learning through genetic algorithm-based optimization of base architecture DS Fu, J Huang, D Hazra, AK Dwivedi, SK Gupta, BD Shivahare, D Garg Plos one 19 (7), e0303462 , 2024 2024.0 Citations: 9
Generalized framework using federated learning for tomato disease classification over unbalanced dataset D Hazra, SK Gupta, U Gupta, M Agarwal Proceedings of the 2023 9th International Conference on Computer Technology … , 2023 2023.0 Citations: 6
A generalized federated learning approach for robust crop disease classification across diverse farms RK Shrivastava, AK Dwivedi, SK Gupta, D Hazra, A Maurya, A Gupta, ... 2024 IEEE International conference on advanced networks and … , 2024 2024.0 Citations: 5
Development of iot enabled deep learning model for indian food classification: An approach based on differential evaluation M Agarwal, AK Dwivedi, D Hazra, SK Gupta, D Garg Food Analytical Methods 18 (2), 172-189 , 2025 2025.0 Citations: 4
The efficient classification of breast cancer on low-power iot devices: A study on genetically evolved u-net M Agarwal, AK Dwivedi, D Hazra, P Sharma, SK Gupta, D Garg Computers in Biology and Medicine 183, 109296 , 2024 2024.0 Citations: 4
Polycystic Ovary Syndrome (PCOS) Detection Using Deep Learning and Explainable AI K Patel, S Rai, J Nischal, D Pandey, RK Shrivastava, D Hazra 2025 2nd International Conference on Advanced Computing and Emerging … , 2025 2025.0
Generalized framework using Federated Learning for brain tumor detection from medical images RK Shrivastava, AK Dwivedi, D Hazra, J Nischal, K Patel, S Rai 2025 2nd International Conference on Computational Intelligence … , 2025 2025.0
Compressed Deep Learning Model for Detecting COVID-19 Disease: A Genetic Algorithm based approach A Sharma, SK Gupta, D Kumari, STR Dwarampudi, GBP Reddy, D Hazra 2023 Second International Conference On Smart Technologies For Smart Nation … , 2023 2023.0
Internet of Things (IoT) Enabled Image Segmentation Model For Lung Disease Classification: An Approach Based On Particle Swarm Optimization SK Gupta, D Hazra, M Agarwal, SP Singh, R Dass, D Pantola 2023 Second International Conference On Smart Technologies For Smart Nation … , 2023 2023.0
Boosting Intelligent Farming: Federated Learning for Dispersed AI D Hazra, SK Gupta Rawat Prakashan , 2023 2023.0
Evolution of Weapon Systems and Rise of Intelligent Warfare N Jain, K Agrawal, DN Hazra, M Niranjan Robotics in Weaponry using Machine Learning and Engineering, 314-338 , 0
