Deep Learning applications in automated detection of plant diseases across diverse crops Shwetha K.S., B.R. Ramji, Ujwal U.J., Mayura Tapkire, Sangamesh M Magi, Jayashri Madalgi Journal of Integrated Science and Technology, 2026 In agriculture, detection of plant diseases is a crucial activity that is important for ensuring crop health and increasing productivity. In this project, we provide an in-depth analysis of the use of a Convolutional Neural Network (CNN) for plant disease detection. This paper utilizes a dataset that includes a sizable number of photos showing both healthy plants and plants with various diseases. The dataset is enhanced and pre-processed to improve the ability to be generalized the model. Convolutional layers, pooling layers, and fully connected layers are used in the creation and training of a CNN architecture to extract useful characteristics from the images. The model's parameters are optimized during the training phase using stochastic gradient descent and a properly selected learning rate. Over-fitting is avoided by using regularization techniques like dropout and weight decay. The proposed model demonstrates strong performance, achieving an accuracy of 96.84% in classifying plant diseases from leaf images. The dataset used for training and evaluation was obtained from Kaggle and includes images representing multiple plant disease categories. By leveraging deep learning techniques, the model effectively distinguishes between healthy and diseased leaves, addressing limitations associated with traditional visual inspection methods, which are often time-consuming and prone to human error. The findings highlight the potential of automated disease detection systems in agriculture, enabling early diagnosis and timely intervention to reduce crop losses and enhance overall crop management practices.
Optimized Prediction and Classification of Celiac Disease in Biopsy Images using Transfer Learning 16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
Advancing Dermatological AI: A Unified Deep Learning Pipeline for Skin Lesion Analysis A S Manjunath, Shashidhar R, Shashank M P, Mayura Tapkire, Roopa M, Kruthika S 2025 2nd Asia Pacific Conference on Innovation in Technology Apcit 2025, 2025 Skin cancer is one of the most common types of cancer worldwide, and early and accurate diagnosis is required to avert the advancement of malignant cases. The current dermatology diagnosis system depends on expert human evaluation of dermoscopic images, which produces limited accessibility and inconsistent results. This study developed a comprehensive deep learning framework that combines lesion segmentation with multiple dataset classification, severity assessment, and explainable AI to improve diagnostic performance and clinical confidence. The system uses Attention U-Net for lesion segmentation, which produces a Dice coefficient of 0.9661 and IoU score of 0.9361 on the ISIC 2018 dataset. The classification module used EfficientNet models trained on the ISIC 2018 (seven classes) and PAD-UFES-20 (six classes) datasets and produced 87.68% and 73.33% accuracy, respectively. The severity prediction module, which uses clinical metadata attributes, provides a reliable assessment of lesion urgency. The heatmaps generated by the Grad-CAM visualization demonstrate decision-critical regions in an interpretable manner. The integrated system surpasses the performance of single components, while segmentation-assisted classification provides better results than raw image analysis. AI capabilities are matched to clinical demands through this unified approach.
Early Detection of Heart Blockages: The Power of AI and Feedforward Neural Networks Shashidhar R, Pranav Vasishta G, Adarsh Hegde, Madhura J, Roopa M, et al. 2nd International Conference on Electronics Computing Communication and Control Technology Iceccc 2025, 2025 Cardiovascular disease remains one of the leading causes of death globally, and therefore early detection of arterial blockage is required for effective intervention. The work here proposes an automated diagnosis system based on a feedforward neural network (FNN) for diagnosing ECG signals in addition to angiographic images for early blockage detection. The dataset used in this work was collected using an AD8232 sensor to provide high fidelity in ECG signal acquisition for effective model training. Our approach tries to minimize reliance on lengthy human analysis while reducing the chances of diagnostic errors simultaneously. The proposed FNN is trained on a large database of diverse ECG recordings to identify subtle, clinically meaningful patterns that are generally difficult for experienced clinicians to identify. Experimental data show the network to be 95% sensitive and 97% accurate in all cases, which indicates its clinical worth. These measurements of performance were contrasted with routine diagnostic measurements in a controlled test, which highlighted the FNN's ability to identify true positive cases and reject false positives. The implementation of this neural network in clinical settings has the potential to shorten the time for diagnostic interventions as well as enhance access, especially in remote or low-resource areas. This innovation not only has the potential to improve diagnostic efficiency but also sets the stage for future innovation in precision medicine and prevention. In summary, the system developed here represents a major step forward in the use of artificial intelligence in cardiovascular diagnosis, providing a cost-effective, scalable solution.
AI-powered assessments for mild cognitive impairment detection: A comprehensive review T Vinesh, MS Lavanya, V Arun, J Juremi, M Tapkire, BM Shashikala, ... AIP Conference Proceedings 3440 (1), 020038 , 2026 2026
Deep Learning applications in automated detection of plant diseases across diverse crops KS Shwetha, BR Ramji, UJ Ujwal, M Tapkire, SM Magi, J Madalgi Journal of Integrated Science and Technology 14 (4), 1553-1553 , 2026 2026
FedProx-Enhanced Federated Transfer Learning for Heterogeneous 3D Medical Image Classification M Naganna, GR Nayaka, N Mahadev, M Tapkire Journal of Computational and Cognitive Engineering , 2026 2026
A PSO-Fuzzy Approximate Reasoning Model for Decision Support in Remote Health Monitoring T Arpitha, R Shashidhar, CN Pruthvi, M Tapkire, J Meghana International Conference on Soft Computing and its Engineering Applications … , 2025 2025
Improved Diabetes Detection Through Integration of External Risk Factors and Machine Learning Techniques M Natesh, HS Ranjan Kumar, K Vinutha, M Tapkire, S Sulthana, ... SN Computer Science 6 (8), 1-18 , 2025 2025 Citations: 1
Advancing Dermatological AI: A Unified Deep Learning Pipeline for Skin Lesion Analysis AS Manjunath, R Shashidhar, MP Shashank, M Tapkire, M Roopa, ... 2025 2nd Asia Pacific Conference on Innovation in Technology (APCIT), 1-7 , 2025 2025
Gluten identification from food images using advanced deep learning and transfer learning methods M Tapkire, V Arun, MS Lavanya, R Shashidhar Journal of Food Science and Technology 62 (6), 1164-1172 , 2025 2025 Citations: 4
Early Detection of Heart Blockages: The Power of AI and Feedforward Neural Networks R Shashidhar, P Vasishta, A Hegde, J Madhura, M Roopa, MD Tapkire 2025 International Conference on Electronics, Computing, Communication and … , 2025 2025 Citations: 1
Transfer learning based facial emotion recognition MS Lavanya, V Arun, M Tapkire, KP Suhaas SN Computer Science 6 (1), 35 , 2024 2024 Citations: 3
A Review on Facial Expression Recognition Approaches, Datasets and Technologies MS Lavanya, V Arun, M Tapkire, Y Shichkina International Conference on Intelligent Systems, 53-68 , 2024 2024
A COMPREHENSIVE SURVEY OF DATA PRE-PROCESSING TECHNIQUES FOR AUDIO, VIDEO, AND TEXT: APPROACHES AND APPLICATIONS MS Vani, H.Y. , Arun, V. , Anusuya, M.A. , ... Tapkire, M.D. , Lavanya African Journal of Biological Sciences 6 (8), 496-519 , 2024 2024
PERSONALIZED HEALTH ASSISTANT USING MACHINE LEARNING Mayura D Tapkire , Dr. Suhaas KP , Anup R , Hemant D , Manas Tiwari ... Journal of Emerging Technologies and Innovative Research 11 (6), f109-f113 , 2024 2024
HML-PCD: A Hybrid Machine Learning Technique for Early Prediction and Classification of Celiac Disease M Tapkire, V Arun, L M. S. International Journal of Intelligent Systems and Applications in Engineering … , 2024 2024
Object Detection Using TensorFlow for Road Navigation: Enhancing Safety for the Visually Impaired V Arun, BM Shashikala, HY Vani, M Tapkire, MA Anusuya, MS Lavanya International Conference summit on Artificial Intelligence, 209-220 , 2024 2024
Application of artificial intelligence to corelate food formulations to disease risk prediction: a comprehensive review MD Tapkire, V Arun Journal of Food Science and Technology, 1-8 , 2022 2022 Citations: 6
A Survey on celiac disease prediction using AI Techniques MD Tapkire, V Arun 2022 Citations: 1
Implementing Arbitrary Precision in JavaScript Runtime P N M, MD Tapkire International Journal of Innovative Research in Computer and Communication … , 2020 2020
VIRTUAL VOICE ASSISTANT R N R, P C2, S Bhandar3, R Kumar4, MD Tapkire International Research Journal of Engineering and Technology 7 (4), 3399-3402 , 2020 2020 Citations: 5
Object Detection and Image Labelling using Machine Learning Technique L P M", PNS Kulkarni, R G3, RK V4, M D T INTERNATIONAL JOURNAL OF RESEARCH IN ELECTRONICS AND COMPUTER ENGINEERING 7 … , 2019 2019
Parallel data processing in the cloud using nephele MD Tapkire, BM Patil, VM Chandode International Journal of Computer Applications 69 (17) , 2013 2013 Citations: 4
MOST CITED SCHOLAR PUBLICATIONS
Application of artificial intelligence to corelate food formulations to disease risk prediction: a comprehensive review MD Tapkire, V Arun Journal of Food Science and Technology, 1-8 , 2022 2022 Citations: 6
VIRTUAL VOICE ASSISTANT R N R, P C2, S Bhandar3, R Kumar4, MD Tapkire International Research Journal of Engineering and Technology 7 (4), 3399-3402 , 2020 2020 Citations: 5
Gluten identification from food images using advanced deep learning and transfer learning methods M Tapkire, V Arun, MS Lavanya, R Shashidhar Journal of Food Science and Technology 62 (6), 1164-1172 , 2025 2025 Citations: 4
Parallel data processing in the cloud using nephele MD Tapkire, BM Patil, VM Chandode International Journal of Computer Applications 69 (17) , 2013 2013 Citations: 4
Transfer learning based facial emotion recognition MS Lavanya, V Arun, M Tapkire, KP Suhaas SN Computer Science 6 (1), 35 , 2024 2024 Citations: 3
Improved Diabetes Detection Through Integration of External Risk Factors and Machine Learning Techniques M Natesh, HS Ranjan Kumar, K Vinutha, M Tapkire, S Sulthana, ... SN Computer Science 6 (8), 1-18 , 2025 2025 Citations: 1
Early Detection of Heart Blockages: The Power of AI and Feedforward Neural Networks R Shashidhar, P Vasishta, A Hegde, J Madhura, M Roopa, MD Tapkire 2025 International Conference on Electronics, Computing, Communication and … , 2025 2025 Citations: 1
A Survey on celiac disease prediction using AI Techniques MD Tapkire, V Arun 2022 Citations: 1
AI-powered assessments for mild cognitive impairment detection: A comprehensive review T Vinesh, MS Lavanya, V Arun, J Juremi, M Tapkire, BM Shashikala, ... AIP Conference Proceedings 3440 (1), 020038 , 2026 2026
Deep Learning applications in automated detection of plant diseases across diverse crops KS Shwetha, BR Ramji, UJ Ujwal, M Tapkire, SM Magi, J Madalgi Journal of Integrated Science and Technology 14 (4), 1553-1553 , 2026 2026
FedProx-Enhanced Federated Transfer Learning for Heterogeneous 3D Medical Image Classification M Naganna, GR Nayaka, N Mahadev, M Tapkire Journal of Computational and Cognitive Engineering , 2026 2026
A PSO-Fuzzy Approximate Reasoning Model for Decision Support in Remote Health Monitoring T Arpitha, R Shashidhar, CN Pruthvi, M Tapkire, J Meghana International Conference on Soft Computing and its Engineering Applications … , 2025 2025
Advancing Dermatological AI: A Unified Deep Learning Pipeline for Skin Lesion Analysis AS Manjunath, R Shashidhar, MP Shashank, M Tapkire, M Roopa, ... 2025 2nd Asia Pacific Conference on Innovation in Technology (APCIT), 1-7 , 2025 2025
A Review on Facial Expression Recognition Approaches, Datasets and Technologies MS Lavanya, V Arun, M Tapkire, Y Shichkina International Conference on Intelligent Systems, 53-68 , 2024 2024
A COMPREHENSIVE SURVEY OF DATA PRE-PROCESSING TECHNIQUES FOR AUDIO, VIDEO, AND TEXT: APPROACHES AND APPLICATIONS MS Vani, H.Y. , Arun, V. , Anusuya, M.A. , ... Tapkire, M.D. , Lavanya African Journal of Biological Sciences 6 (8), 496-519 , 2024 2024
PERSONALIZED HEALTH ASSISTANT USING MACHINE LEARNING Mayura D Tapkire , Dr. Suhaas KP , Anup R , Hemant D , Manas Tiwari ... Journal of Emerging Technologies and Innovative Research 11 (6), f109-f113 , 2024 2024
HML-PCD: A Hybrid Machine Learning Technique for Early Prediction and Classification of Celiac Disease M Tapkire, V Arun, L M. S. International Journal of Intelligent Systems and Applications in Engineering … , 2024 2024
Object Detection Using TensorFlow for Road Navigation: Enhancing Safety for the Visually Impaired V Arun, BM Shashikala, HY Vani, M Tapkire, MA Anusuya, MS Lavanya International Conference summit on Artificial Intelligence, 209-220 , 2024 2024
Implementing Arbitrary Precision in JavaScript Runtime P N M, MD Tapkire International Journal of Innovative Research in Computer and Communication … , 2020 2020
Object Detection and Image Labelling using Machine Learning Technique L P M", PNS Kulkarni, R G3, RK V4, M D T INTERNATIONAL JOURNAL OF RESEARCH IN ELECTRONICS AND COMPUTER ENGINEERING 7 … , 2019 2019
