I'm an Assistant Professor (Visiting Faculty) at Lord Buddha Education Foundation (Asia Pacific University of Technology & Innovation) in Kathmandu, Nepal. I've also held roles as an Assistant Professor and Program Leader (B.Sc. IT) at the same institution and as an Assistant Professor (CSE) at GD-Rungta College of Engineering & Technology (Chhattisgarh Swami Vivekananda Technical University) Bhilai, India. I received an M. Tech in Computer Science & Engineering from Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha in 2016, and a B. Tech in Computer Science & Engineering from Dr. MGR Educational & Research Institute, Maduravoyal, Chennai in 2013. My research interests include Machine Learning and Deep Learning. My research focuses on Deep Learning and Machine Learning.
EDUCATION
2014-2016 M. Tech (Computer Science & Engineering) Kalinga Institute of Industrial Technology, Bhubaneswar-24
2009-2013 B. Tech (Computer Science & Engineering) Dr. MGR Educational and Research Institute, Chennai-95
2009 Intermediate (Science) Central Board of Secondary Education, New Delhi
2006 Matriculation Central Board of Secondary Education, New Delhi
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Computer Engineering, Information Systems, Artificial Intelligence
119
Scopus Publications
5884
Scholar Citations
32
Scholar h-index
84
Scholar i10-index
Scopus Publications
Multi-class eye disease classification using deep learning EfficientNetB0 fusion techniques Uday Kumar Sah, Jyotir Moy Chatterjee, R. Sujatha Scientific Reports, 2026 Eye disease is one of the most conspicuous reasons of optical diminishing and, in certain cases, the prime cause of complete sightlessness. So, there is an urgent need to innovate systems that are capable of spotting eye sicknesses such as glaucoma, cataracts and diabetic retinopathy (DR). To solve this, motivating the necessity for accurate systematized assessment systems, we have advanced a two-backbone deep learning (DL) structure that systematically conglomerates EfficientNetB0 with three harmonizing architectures: ResNet50, InceptionV3, and AlexNet, exercising four different fusion strategies: concatenation, element-wise summation, weighted and majority voting. Using a stable dataset of 4,217 High Resolution retina images across the four symptomatic classes, we have educated and evaluated 12 fusion model arrangements to recognize ideal feature assimilation methodologies. Internal validation exhibited that concatenation fusion attained the great baseline performance, with EfficientNetB0 + ResNet50 (Exp01) triumph 95.26% accuracy (AUROC: 0.993) and EfficientNetB0 + InceptionV3 getting 94.79% accuracy (AUROC: 0.989). External validation on 400 images from Messidor-2 and ODIR datasets disclosed significant performance increases, with accuracies oscillating from 94.99% to 97.99% and AUROC values between 0.991 and 0.999, confirmatory authentic cross-dataset generalization rather than overfitting. Weighted and sum fusion strategies evidenced particularly effective for the external dataset, with EfficientNetB0 + ResNet50 (Exp03) weighted fusion reaching 97.49% accuracy, and EfficientNetB0 + AlexNet (Exp09) sum fusion accomplished an MCC of 0.980, signifying that intelligent feature combination can counterbalance for architectural boundaries and enhance domain robustness. Class-wise heatmap analysis displayed that while DR triumphed near-perfect detection across all configurations, glaucoma detection improved considerably with weighted, sum, and voting fusion, and reduced misclassifications with normal eye images. Explainability and interpretability examination using Score-CAM (disclosed steady anatomical attention patterns across both datasets: glaucoma models concentrated on the optic nerve head and peripapillary area, DR models emphasized macular zones and vascular structures, and cataract models focused on lens denseness all aligning with traditional medical checkups standards and proven that model decisions are based in pathophysiological relevant features rather than spurious connections. Our findings are that the dual-backbone fusion architectures with improved feature integration strategies, remarkably weighted and sum fusion, produce not only higher analytical accuracy but also strong simplification and clear quick decision-making essential for actual clinical implementation across various imaging locations.
MetaConstructs Metaconstructs the Evolution of Digital Twins in the Metaverse Era, 2026
Voice-Signal based Prediction of Parkinson's Disease with Machine Learning Models Swati Sharma, Harsh Taneja, Vivek Tomar, Sangeeta Arora, Jyotir Moy Chatterjee, Vedant Desai 6th Biennial International Conference on Nascent Technologies in Engineering Icnte 2026, 2026 One of the chronic diseases, Parkinson’s disease (PD), is a neurological disorder associated with ageing. It is one of the most prevalent diseases among the elderly. The symptoms of Parkinson’s disease, such as essential tremor, are visible with ageing, which is very challenging to diagnose. In this study, machine learning (ML) models are considered for sickness detection, in which we basically take the sound signal digits and graphs as an input for the models. The data set used is from the UCI MLR (Machine Learning Repository), in which 195 voice recordings from 31 individuals are considered. For the purpose, six ML models were implemented that include RF (random forest), SVM (support vector machine), KNN (K-nearest neighbour), DT (decision tree), NB (naïve bayes) and XGBoost. Parameters are chosen using GridSearchCV, and hyperparameter optimization is done using the synthetic minority sampling approach. These methods are employed to reduce overfitting and boost model performance. Among the different models used, the RF, SVM, and K-NN achieved the highest accuracy of 96.6%, RF achieves the highest F1-score of 0.962, RF and XGB show the highest recall of 0.962, and SVM and KNN achieve the best precision of 1.000. The proposed machine learning framework demonstrates high predictive accuracy for Parkinson’s disease using voice signal data. These findings will be helpful in the field of healthcare diagnostic systems for the early detection of this disease, PD, which can significantly improve patient outcomes and health.
Deep Learning for Digital Mental Health: A Cross-Social-Media Analysis using BERT Pramod Mehra, Om Prakash Pal, Pawan Kumar Mishra, Vartika Agarwal, Rahul Kumar, Jyotir Moy Chatterjee Proceedings of 6th International Conference on Expert Clouds and Applications Icoeca 2026, 2026 Mental health has emerged as a pressing global concern, with increasing numbers of individuals using social media platforms to share their emotional experiences and psychological states. This project explores and analyzes mental health-related discourse across Reddit, Tumblr, and YouTube. By leveraging Natural Language Processing (NLP) and transformer-based models such as BERT, we classify user-generated content into various mental health categories such as depression, anxiety, PTSD, and stress. Our system enables cross-platform analysis to assess the emotional tone, severity, and distribution of psychological distress. Results contribute toward scalable, real-time mental health monitoring and digital well-being research.
Deep Learning for Early Skin Disease Detection: A Multi-Model CNN Performance Evaluation Pramod Mehra, Om Prakash Pal, Jyotir Moy Chatterjee, Rohan Verma, Rahul Kumar, Pawan Kumar Mishra Proceedings of 6th International Conference on Expert Clouds and Applications Icoeca 2026, 2026 Many people face skin problems each year, which makes this a real health concern. We chose this project for that reason. A clear diagnosis is hard because mild spots and serious cases can look the same. They often share the same signs. Biopsies and clinic exams take time and cost money. They can also lead to human error. Many people cannot meet a dermatologist when they need one, which slows diagnosis. Deep learning (DL) helps automate skin lesion checks. CNN models give strong and steady results. We used ResNet50, InceptionV3, MobileNet, and VGG16 to sort six lesion types. We applied dropout, batch normalization, and data augmentation to raise performance on the HAM10000 set. Our aim is to give health staff a tool that helps them spot skin issues early. Among the tested models, VGG16 gave the best mix of ease and accuracy. Among these, VGG16 demonstrated superior accuracy and efficiency, emerging as the most effective model for this task. The results We have evaluated the results using standard performance metrics, which are accuracy, precision, and F1 score, which did confirm that VGG16 is robust in skin disease classification and best among all the compared models. This study focuses on the potential of DL models to support healthy dermatological practices, especially in underprivileged areas with limited access to specialised medical care.
Advancing plant leaf disease detection integrating machine learning and deep learning R. Sujatha, Sushil Krishnan, Jyotir Moy Chatterjee, Amir H. Gandomi Scientific Reports, 2025 Conventional techniques for identifying plant leaf diseases can be labor-intensive and complicated. This research uses artificial intelligence (AI) to propose an automated solution that improves plant disease detection accuracy to overcome the difficulty of the conventional methods. Our proposed method uses deep learning (DL) to extract features from photos of plant leaves and machine learning (ML) for further processing. To capture complex illness patterns, convolutional neural networks (CNNs) such as VGG19 and Inception v3 are utilized. Four distinct datasets—Banana Leaf, Custard Apple Leaf and Fruit, Fig Leaf, and Potato Leaf—were used in this investigation. The experimental results we received are as follows: for the Banana Leaf dataset, the combination of Inception v3 with SVM proved good with an Accuracy of 91.9%, Precision of 92.2%, Recall of 91.9%, F1 score of 91.6%, AUC of 99.6% and MCC of 90.4%, FFor the Custard Apple Leaf and Fruit dataset, the combination of VGG19 with kNN with an Accuracy of 99.1%, Precision of 99.1%, Recall of 99.1%, F1 score of 99.1%, AUC of 99.1%, and MCC of 99%, and for the Fig Leaf dataset with Accuracy of 86.5%, Precision of 86.5%, Recall of 86.5%, F1 score of 86.5%, AUC of 93.3%, and MCC of 72.2%. The Potato Leaf dataset displayed the best performance with Inception v3 + SVM by an Accuracy of 62.6%, Precision of 63%, Recall of 62.6%, F1 score of 62.1%, AUC of 89%, and MCC of 54.2%. Our findings explored the versatility of the amalgamation of ML and DL techniques while providing valuable references for practitioners seeking tailored solutions for specific plant diseases.
Preface Cyber Security in Parallel and Distributed Computing Concepts Techniques Applications and Case Studies, 2024
Advanced Technologies for Science and Engineering: (Volume 1): Intelligent Technologies for Automated Electronic Systems Advanced Technologies for Science and Engineering Volume 1 Intelligent Technologies for Automated Electronic Systems, 2024
PREFACE S. Kannadhasan, R. Nagarajan, P. Ashok Advanced Technologies for Science and Engineering Volume 1 Intelligent Technologies for Automated Electronic Systems, 2024
Blockchain technology, Bitcoin, and IoT P. Srinivas Kumar, Jyotir Moy Chatterjee, Abhishek Kumar, Pramod Rathore, R. Sujatha Quality Assessment and Security in Industrial Internet of Things, 2024
Prediction of suitable candidates for covid-19 vaccination R. Sujatha, B. Venkata Siva Krishna, Jyotir Moy Chatterjee, P. Rahul Naidu, NZ Jhanjhi, Challa Charita, Eza Nerin Mariya, Mohammed Baz Intelligent Automation and Soft Computing, 2022
Preface Vishal Jain, Jyotir Moy Chatterjee, Ankita Bansal, A. C. JAIN Semantic Web for Effective Healthcare Systems, 2021
Preface Ontology Based Information Retrieval for Healthcare Systems, 2020
Preface Recommender System with Machine Learning and Artificial Intelligence Practical Tools and Applications in Medical Agricultural and Other Industries, 2020
Fog computing and its security issues Jyotir Moy Chatterjee, Ishaani Priyadarshini, Shankeys, Dac‐Nhuong Le Security Designs for the Cloud Iot and Social Networking, 2019
Design of a simple gas knob: An application of IoT Shankey Garg, Jyotir Moy Chatterjee, Raghvendra KumarAgrawal Proceedings of the 2018 3rd IEEE International Conference on Research in Intelligent and Computing in Engineering Rice 2018, 2018
Cloud Computing and Virtualization Dac-Nhuong Le, Raghvendra Kumar, Gia Nhu Nguyen, Jyotir Moy Chatterjee Cloud Computing and Virtualization, 2018
RECENT SCHOLAR PUBLICATIONS
Deep Learning for Early Skin Disease Detection: A Multi-Model CNN Performance Evaluation P Mehra, OP Pal, JM Chatterjee, R Verma, R Kumar, PK Mishra 2026 6th International Conference on Expert Clouds and Applications (ICOECA … , 2026 2026
Deep Learning for Digital Mental Health: A Cross-Social-Media Analysis using BERT P Mehra, OP Pal, PK Mishra, V Agarwal, R Kumar, JM Chatterjee 2026 6th International Conference on Expert Clouds and Applications (ICOECA … , 2026 2026
Map movement using hand gestures by machine learning P Mehra, AS Negi, PK Mishra, JM Chatterjee, H Kumar, S Roka AIP Conference Proceedings 3365 (1), 030072 , 2026 2026
Human versus AI generated text classification using deep learning and transformers I Priyadarshini, JM Chatterjee, P Rawat Neural Computing and Applications 38 (5), 143 , 2026 2026 Citations: 1
Multi-class eye disease classification using deep learning EfficientNetB0 fusion techniques UK Sah, JM Chatterjee, R Sujatha Scientific Reports 16 (1), 6368 , 2026 2026 Citations: 1
Voice-Signal based Prediction of Parkinson’s Disease with Machine Learning Models S Sharma, H Taneja, V Tomar, S Arora, JM Chatterjee, V Desai 2026 6th Biennial International Conference on Nascent Technologies in … , 2026 2026
Impact of Online Education: an Artificial Intelligence-Based analysis R Sujatha, G Yaswanth, JM Chatterjee, I Priyadarshini Proceedings of the National Academy of Sciences, India Section A: Physical … , 2026 2026
A Sequential DL-Based Approach for MRI-Based Brain Tumor Classification With Metaverse R Sujatha, JM Chatterjee, AA Souayeh Democratizing the Metaverse: Accessibility, Inclusivity, and Human-Centric … , 2026 2026
Heart disease prediction using rivalry-based ensemble learning: A weighted rivalry-based ensemble learning approach PV Diviya, R Rathipriya, JM Chatterjee Metaverse and AI in Healthcare, 57-70 , 2026 2026
Artificial Intelligence and Deep Learning in Healthcare H Taneja, G Arora, R Agarwal, JM Chatterjee Artificial Intelligence and Blockchain in Precision Medicine and Virology, 3-26 , 2025 2025
Building Trust Through AI: AI Approaches to Medical Virology in Future Healthcare Systems MK Yogi, BSS Javvadhi, Y Jayababu, JM Chatterjee Artificial Intelligence and Blockchain in Precision Medicine and Virology … , 2025 2025
Artificial Intelligence and Blockchain in Precision Medicine and Virology JM Chatterjee, SK Saxena Springer , 2025 2025 Citations: 2
Federated Learning in Metaverse Healthcare: Personalized Medicine and Wellness S Mahajan, JM Chatterjee Academic Press , 2025 2025
Enhancing English accent identification in automatic speech recognition using spectral features and hybrid CNN-BiLSTM model G Ahmed, AA Lawaye, V Jain, JM Chatterjee, S Mahajan Multimedia Tools and Applications 84 (30), 37401-37428 , 2025 2025 Citations: 11
Automatic emotion recognition using deep neural network R Sujatha, JM Chatterjee, B Pathy, YC Hu Multimedia Tools and Applications 84 (28), 33633-33662 , 2025 2025 Citations: 19
Advancing plant leaf disease detection integrating machine learning and deep learning R Sujatha, S Krishnan, JM Chatterjee, AH Gandomi Scientific Reports 15 (1), 11552 , 2025 2025 Citations: 69
Ensemble based brain tumor classification technique from MRI based on K fold validation approach S Jain, V Jain, JM Chatterjee Journal of Integrated Science and Technology 13 (5), 1114-1114 , 2025 2025 Citations: 9
Transforming healthcare: the synergy of telemedicine, telehealth, and artificial intelligence JM Chatterjee, R Sujatha Role of artificial intelligence, telehealth, and telemedicine in medical … , 2025 2025 Citations: 16
Age-based Data Analysis and Prediction of Autism Spectrum Disorder R Sujatha, SL Aarthy, J NZ International Journal of Computer Science & Network Security, 95-104 , 2025 2025
Strength Optimized Weight Balancing for Traffic Management in Vehicular Ad-hoc Networks M Rath, JM Chatterjee International Journal of Business Data Communications and Networking (IJBDCN … , 2025 2025 Citations: 2
MOST CITED SCHOLAR PUBLICATIONS
Performance of deep learning vs machine learning in plant leaf disease detection R Sujatha, JM Chatterjee, NZ Jhanjhi, SN Brohi Microprocessors and Microsystems 80, 103615 , 2021 2021 Citations: 865
COVID-19 patient health prediction using boosted random forest algorithm C Iwendi, AK Bashir, A Peshkar, R Sujatha, JM Chatterjee, S Pasupuleti, ... Frontiers in public health 8, 357 , 2020 2020 Citations: 617
A hybrid CNN-SVM threshold segmentation approach for tumor detection and classification of MRI brain images MO Khairandish, M Sharma, V Jain, JM Chatterjee, NZ Jhanjhi Irbm 43 (4), 290-299 , 2022 2022 Citations: 592
A machine learning forecasting model for COVID-19 pandemic in India RAA Sujath, JM Chatterjee, AE Hassanien Stochastic Environmental Research and Risk Assessment 34 (7), 959-972 , 2020 2020 Citations: 461
Energy-efficient cluster head selection through relay approach for WSN PS Rathore, JM Chatterjee, A Kumar, R Sujatha The Journal of Supercomputing 77 (7), 7649-7675 , 2021 2021 Citations: 191
Hybrid machine learning approaches for landslide susceptibility modeling VV Nguyen, BT Pham, BT Vu, I Prakash, S Jha, H Shahabi, A Shirzadi, ... Forests 10 (2), 157 , 2019 2019 Citations: 181
A novel hybrid approach of SVM combined with NLP and probabilistic neural network for email phishing A Kumar, JM Chatterjee, VG Díaz International Journal of Electrical and Computer Engineering 10 (1), 486 , 2020 2020 Citations: 147
ARM–AMO: An efficient association rule mining algorithm based on animal migration optimization F Chiclana, R Kumar, M Mittal, M Khari, JM Chatterjee, SW Baik Knowledge-Based Systems 154, 68-80 , 2018 2018 Citations: 130
Collaborative handshaking approaches between internet of computing and internet of things towards a smart world: a review from 2009–2017 S Jha, R Kumar, JM Chatterjee, M Khari Telecommunication Systems 70 (4), 617-634 , 2019 2019 Citations: 128
A machine learning way to classify autism spectrum disorder R Sujatha, SL Aarthy, J NZ International Journal of Emerging Technologies in Learning (iJET) 16 (6 … , 2021 2021 Citations: 123
COVID-19 mortality rate prediction for India using statistical neural network models S Dhamodharavadhani, R Rathipriya, JM Chatterjee Frontiers in public health 8, 441 , 2020 2020 Citations: 112
Wheat seed classification: utilizing ensemble machine learning approach A Khatri, S Agrawal, JM Chatterjee Scientific programming 2022 (1), 2626868 , 2022 2022 Citations: 92
A novel finetuned YOLOv6 transfer learning model for real-time object detection C Gupta, NS Gill, P Gulia, JM Chatterjee Journal of Real-Time Image Processing 20 (3), 42 , 2023 2023 Citations: 91
Machine learning with health care perspective V Jain, JM Chatterjee Cham: Springer, 1-415 , 2020 2020 Citations: 81
Cyber security in parallel and distributed computing: Concepts, techniques, applications and case studies DN Le, R Kumar, BK Mishra, JM Chatterjee, M Khari John Wiley & Sons , 2019 2019 Citations: 78
Neutrosophic soft set decision making for stock trending analysis S Jha, R Kumar, LH Son, JM Chatterjee, M Khari, N Yadav, ... Evolving Systems 10 (4), 621-627 , 2019 2019 Citations: 77
Cloud computing and virtualization DN Le, R Kumar, GN Nguyen, JM Chatterjee John Wiley & Sons , 2018 2018 Citations: 74
Advancing plant leaf disease detection integrating machine learning and deep learning R Sujatha, S Krishnan, JM Chatterjee, AH Gandomi Scientific Reports 15 (1), 11552 , 2025 2025 Citations: 69
Internet of Things based system for Smart Kitchen JM Chatterjee, R Kumar, M Khari, DT Hung, DN Le International Journal of Engineering and Manufacturing 8 (4), 29 , 2018 2018 Citations: 55
An Ensemble‐Based Multiclass Classifier for Intrusion Detection Using Internet of Things D Rani, NS Gill, P Gulia, JM Chatterjee Computational Intelligence and Neuroscience 2022 (1), 1668676 , 2022 2022 Citations: 54
