Data Analysis, Artificial Intelligence,Neural Network,Machine Learning,Business Intelligence and Medical Image Analysis
52
Scopus Publications
638
Scholar Citations
11
Scholar h-index
15
Scholar i10-index
Scopus Publications
Signal-aware deep learning–based respiratory motion prediction for lung tumor management Kaushik Pratim Das, Chandra J., Partha Pratim Medhi Frontiers in Oncology, 2026 Introduction Respiratory motion management in radiotherapy for lung cancer patients remains a significant challenge, as it directly affects accurate tumor targeting. Furthermore, unaccounted tumor motion during treatment planning and delivery can lead to imaging artifacts and biased dose distributions, which compromises the accuracy of image-guided radiotherapy. This issue places clinicians in a dilemma between expanding treatment margins, which increases radiation exposure to healthy tissue or risking reduced targeting precision. Methods In this work, a hybrid deep learning model composed of dilated convolutional layers, bidirectional long-short term memory layers, and a generative autoencoder module is proposed to jointly model the spatial and temporal characteristics of respiratory motion, while enabling reconstruction of the physiologically coherent respiratory signals. Each architectural component learns complementary motion-related patterns from respiratory signals to support tumor motion prediction. The model performs motion-range classification, captures abnormal breathing patterns across spatial and temporal domains, reconstructs physiologically coherent respiratory cycles, and predicts tumor motion within an algorithmic validation framework. Results Experimental evaluation demonstrates high motion-range classification performance of 98.37%, including low root-mean square error in motion prediction, while maintaining stable performance across long and complex respiratory signals over multiple breathing cycles. Discussion This study focuses on algorithmic feasibility and establishes a computational foundation for future clinically calibrated and dosimetrically validated models. The findings indicate that the proposed approach can support future motion-aware radiotherapy planning strategies by improving motion characterization at the algorithmic level.
A Deep Learning-Enhanced Adaptive Intrusion Detection Framework for Real-Time Cyber Security Threat Analysis B. Vijay, Chandra J, Nagendra N, G. Shobana, S. Karunya, Saranya K Proceedings of the 2026 International Conference on AI Driven Smart Systems and Ubiquitous Computing Icauc 2026, 2026 The rapidly evolving nature of cyber-attacks significantly reduces the effectiveness of conventional intrusion detection systems (IDS) that rely on static rules and signatures. This work presents an adaptive deep learning– based intrusion detection framework designed to maintain reliable performance in real-time environments affected by concept drift. The proposed approach integrates one-dimensional convolutional neural networks (1D-CNN) for local feature interaction learning with a bidirectional long short-term memory (BiLSTM) network to model sequential network traffic behavior. To address evolving attack patterns, a sliding-window–based incremental learning mechanism is employed, enabling continuous model adaptation to recent traffic characteristics. The model is trained using cross-entropy loss optimized with the Adam optimizer, while dropout regularization is applied to reduce overfitting and ensure fast convergence. To enhance transparency and analyst trust, explainable artificial intelligence techniques are incorporated, including SHAP-based feature attribution and an attention mechanism for interpreting temporal dependencies. Experimental evaluation on labeled network traffic data demonstrates stable convergence, consistent detection accuracy under changing traffic conditions, and improved robustness compared to non-adaptive baseline models. These results confirm the effectiveness and practical applicability of the proposed framework for real-time and interpretable cybersecurity intrusion detection.
A Novel Preprocessing Model for Multi Modal Brain MRI image Classification for Stroke Prognosis CHRIST University, India, Alwin Joseph, Chandra J, CHRIST University, India Northeast Journal of Complex Systems, 2025 Magnetic Resonance Imaging (MRI) is an imaging technique used for the diagnosis and observing the progression in various neurological disorders. Stroke is one of the prominent neurological disorders that creates significant impacts in the patients. It occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissues from getting oxygen and nutrients. Multimodal data from various modalities help clinicians in proper prognosis of stroke. Ischemic Stroke Lesion Segmentation Challenge (ISLES22) provides data of stroke data for various stroke patients, the dataset consists of three modalities of data – Fluid Attenuated Inversion Recovery (FLAIR), Apparent Diffusion Coefficient (ADC) and Diffusion-Weighted Imaging (DWI). Multimodal data gives a comprehensive understanding of the brain and the stroke lesions. Complex algorithms and processing steps are required to ensure that the data is prepared for further processing. The objective of this experimental research is to create a novel multimodal preprocessing model that can be used for the preprocessing of the multimodal data from various MRI modalities (FLAIR, DWI and ADC). The proposed model supports the automatic removal of artefacts from the multimodal data, by identifying and applying the best preprocessing techniques for Image Registration (Affine or non-rigid transformations), Normalization (Z Score or min-max normalizations), Denoising Techniques (Gaussian, Median, Non-Local Means, or Anisotropic Diffusion filters) and Bias Filed correction. The best technique is been identified using the evaluation techniques of Dice Coefficient, Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Root Mean Squared Error (RMSE). Preprocessing and preparing of the data are critical for better outcome from the subsequent analysis including segmentation. Here, we propose an Enhanced Image Registration and Artefact Correction (EIRAC) model with Best Image Registration Technique (BIRT) and Multiple Orientation Normalization Denoising and Bias filed correction Parallelly (MONDBP) algorithms for the preprocessing of multimodal MRI images to provides better results for the segmentation of stroke lesions through Machine Learning models.
Personalized Explainable Transformer Models for Student Performance Prediction Jishnu Plavinchottil Jayaraj, Ashok Immanuel V, Chandra J Proceedings 2025 International Conference on Transformative Computing Technologies Ictct 2025, 2025 The research presents a unified framework for forecasting student academic achievement using a transformer-based architecture supported by Explainable Artificial Intelligence techniques. The research is motivated by the need to combine predictive accuracy of transformer models with interpretability in student performance prediction. The framework applies an adapted Feature Tokenizer Transformer to the UCI Student Performance dataset and integrates SHAP and LIME methods to generate instance level, human readable explanations for each prediction. These explanations can help educators design targeted interventions. A Random Forest regressor is included as a baseline for comparison. The experiment results showed that the Random Forest performed slightly better than the Feature Tokenizer Transformer, which could be due to the small size of the dataset and certain features having a strong impact on the results. Nevertheless, the results show that modern deep learning models combined with personalized explainability offer a practical foundation for scalable solutions in more complex educational datasets, which helps connect high performing prediction models with actionable insights, contributing to the development of interpretable, data driven student support systems.
AI Driven Air Quality Analysis for Health: An Experimental Review Samyuktaa S, Chandra J, Ashok Immanuel Proceedings of the 2025 International Conference on Emerging Techniques in Computational Intelligence Icetci 2025, 2025 Air pollution, both indoor and outdoor, was linked to 6.7 million premature deaths in 2020, including over 237,000 children under the age of 5, according to WHO. Indoor Air Pollution (IAP) is a crisis of public health that affects billions of people by exposing them to IAP pollutants like particulate matter (PM2.5), volatile organic compounds(VOCs), polycyclic aromatic hydrocarbons (PAHs), and carbon monoxide (CO). The most common cause of IAP varies from incense burning and biomass fuel to ventilation, leading to a horrific human health effect by causing respiratory disease, cardiovascular disease, sick building syndrome, and mental impairment. This review brings together evidence from various studies on the effects of indoor air quality on the environment, health, and productivity. Apart from pollutant exposure, determinants of well-being, i.e., thermal, acoustic, and visual comfort, are the subject of this article. Developments in artificial intelligence (AI), the Internet of Things (IoT), and computational modeling have revolutionized Indoor Air Quality monitoring to detect pollutants and exposures in real-time. All these technologies have the potential to intervene effectively but are intimidating through the prism of high cost, sensor calibration, and the need for large-scale epidemiological studies. To restrict indoor air pollution risks, inter-disciplinary studies need to be adopted to combine effective ventilation technologies and advanced pollutant control systems. Large-scale applications of clean fuel like solar, biogas, electricity, liquefied petroleum gas (LPG), and efficient biomass stoves need to be employed to restrict home air pollution. The present review calls for an emergent public campaign and policy intervention to enhance indoor air quality, health, and well-being.
Detection of tuberculosis using convolutional neural network with transfer learning Journal of Advanced Research in Dynamical and Control Systems, 2017
TrustX: An Explainable and Cryptographically Verifiable Deep Learning Framework for Multimodal Manipulation Detection B Vijay, J Chandra, N Nagendra, RS Shanmugasundaram, K Saranya, ... 2026 International Conference on Data Science, Agents and Artificial … , 2026 2026
Signal-aware deep learning–based respiratory motion prediction for lung tumor management KP Das, PP Medhi Frontiers in Oncology 16, 1735140 , 2026 2026
A Deep Learning–Enhanced Adaptive Intrusion Detection Framework for Real-Time Cyber Security Threat Analysis B Vijay, J Chandra, N Nagendra, G Shobana, S Karunya, K Saranya 2026 International Conference on AI-Driven Smart Systems and Ubiquitous … , 2026 2026
Impact of fine-tuning large language model in society: a comprehensive study J Chandra, A Joseph, J Joseph, P Upadhyay, S Kumar Challenges and Applications of Generative Large Language Models, 23-45 , 2026 2026
Applications and future directions in multimodal large language model: opportunities and challenges J Chandra, M Malviya, S Sabu, RK Rajendran, A Joseph Challenges and Applications of Generative Large Language Models, 219-241 , 2026 2026
for Emotional Intelligence Using Peripheral Signals PY Preema, J Chandra, CS Angel Proceedings of Fourth International Conference on Computing and … , 2025 2025
Intelligence Using Peripheral Signals PY Preema, J Chandra, CS Angel Data Science and Applications: Proceedings of ICDSA 2024, Volume 6 6, 185 , 2025 2025
KMSBOT: enhancing educational institutions with an AI-powered semantic search engine and graph database: DV Subramanian et al. DV Subramanian, J Chandra, VA Immanuel, V Rohini Soft Computing 29 (1), 1-15 , 2025 2025 Citations: 11
Feature Fusion Classification for Emotional Intelligence Using Peripheral Signals PY Preema, J Chandra, CS Angel International Conference on Computing and Communication Networks, 197-209 , 2024 2024
Bimodal Classification for Emotional Intelligence Using Peripheral Signals PY Preema, J Chandra, CS Angel International Conference on Data Science and Applications, 185-198 , 2024 2024
Hybrid approach for multi-classification of news documents using artificial intelligence N Nagendra, J Chandra 2024 5th International Conference on Intelligent Communication Technologies … , 2024 2024 Citations: 4
Role of Artificial Intelligence in Neuroimaging for Cognitive Research M Malviya, A Joseph, J Chandra, V Pooja Machine Learning and Deep Learning in Neuroimaging Data Analysis, 91-108 , 2024 2024 Citations: 1
Applications of Machine Learning and Deep Learning Models in Brain Imaging Analysis A Joseph, J Chandra, B Banerjee, M Rangaswamy, KJ Reddy Machine Learning and Deep Learning in Neuroimaging Data Analysis, 43-56 , 2024 2024 Citations: 1
Spatio-Temporal analysis of temperature in Indian States J Chandra, A Singhal, A Joseph AIP Conference Proceedings 2909 (1), 030004 , 2023 2023
A comprehensive study on detection of emotions using human body movements: Machine learning approach PY Preema, J Chandra AIP Conference Proceedings 2909 (1), 030012 , 2023 2023
Research advancements in autism spectrum disorder using neuroimaging M Meenakshi, J Chandra AIP Conference Proceedings 2909 (1), 030014 , 2023 2023 Citations: 1
Machine Learning Strategies for Understanding Autistic Neuro Images M Malviya, J Chandra Engineering, Science, and Sustainability, 84-88 , 2023 2023
A Review on Multi-Modal Classification for Emotional Intelligence PY Preema, J Chandra, A Joseph Engineering, Science, and Sustainability, 118-122 , 2023 2023
Effect of Chemo-mechanical Polishing on the Surface and Superconducting Properties of Niobium Coupons: A Comparative Study J Chandra, PN Rao, S Rai, M Manekar Journal of Superconductivity and Novel Magnetism 36 (3), 777-791 , 2023 2023
A survey on artificial intelligence for reducing the climate footprint in healthcare KP Das, J Chandra Energy Nexus 9, 100167 , 2023 2023 Citations: 71
MOST CITED SCHOLAR PUBLICATIONS
Nanoparticles and convergence of artificial intelligence for targeted drug delivery for cancer therapy: Current progress and challenges KP Das Frontiers in medical technology 4, 1067144 , 2023 2023 Citations: 230
A survey on artificial intelligence for reducing the climate footprint in healthcare KP Das, J Chandra Energy Nexus 9, 100167 , 2023 2023 Citations: 71
Classification of myocardial ischemia in delayed contrast enhancement using machine learning R Merjulah, J Chandra Intelligent data analysis for biomedical applications, 209-235 , 2019 2019 Citations: 44
Segmentation technique for medical image processing: A survey R Merjulah, J Chandra 2017 international conference on inventive computing and informatics (ICICI … , 2017 2017 Citations: 31
Smart Street light Using IR Sensors CJ Sindhu.A.M, Jerin George , Sumit Roy IOSR Journal of Mobile Computing & Application (IOSR - JMCA) 3 (2), 39 - 44 , 2016 2016 Citations: 28
Convolutional neural network for brain tumor analysis using mri images S Hanwat, J Chandra Int. J. Eng. Technol 11 (1), 67-77 , 2019 2019 Citations: 24
IOT Based Green House Monitoring System. TA Singh, J Chandra J. Comput. Sci. 14 (5), 639-644 , 2018 2018 Citations: 23
Random forest application on cognitive level classification of E-learning content B Thomas, J Chandra International Journal of Electrical and Computer Engineering 10 (4), 4372 , 2020 2020 Citations: 19
Applications of artificial intelligence to neurological disorders: current technologies and open problems J Chandra, M Rangaswamy, B Banerjee, A Prajapati, Z Akhtar, K Sakauye, ... Augmenting Neurological Disorder Prediction and Rehabilitation Using … , 2022 2022 Citations: 18
Multimodal classification on PET/CT image fusion for lung cancer: a comprehensive survey KP Das Electrochemical Society Transactions 107 (1), 3649-3673 , 2022 2022 Citations: 13
The effect of bloom’s taxonomy on random forest classifier for cognitive level identification of e-content B Thomas, J Chandra 2020 International Conference on Emerging Trends in Information Technology … , 2020 2020 Citations: 13
KMSBOT: enhancing educational institutions with an AI-powered semantic search engine and graph database: DV Subramanian et al. DV Subramanian, J Chandra, VA Immanuel, V Rohini Soft Computing 29 (1), 1-15 , 2025 2025 Citations: 11
Genome analysis for precision agriculture using artificial intelligence: A survey A Joseph, J Chandra, S Siddharthan Data Science and Security: Proceedings of IDSCS 2020, 221-226 , 2020 2020 Citations: 11
A review on preprocessing techniques for noise reduction in PET-CT images for lung cancer KP Das, J Chandra Congress on Intelligent Systems: Proceedings of CIS 2021, Volume 2, 455-475 , 2022 2022 Citations: 10
Nanoparticles and convergence of artificial intelligence for targeted drug delivery for cancer therapy: Current progress and challenges. Front. Med. Technol. 4, 1067144 (2023) KP Das, J Chandra Artificial Intelligence, and the Future of Manufacturing 331, 2015-2019 , 2022 2022 Citations: 10
Sentiment analysis on social media data using intelligent techniques K Panguila, C Jayaraman International Journal of Engineering Research and Technology 12 (3), 440-445 , 2019 2019 Citations: 8
Intelligent data analysis for biomedical applications R Merjulah, J Chandra, DJ Hemanth, D Gupta, VE Balas Elsevier, Amsterdam , 2019 2019 Citations: 7
A systematic review on features extraction techniques for aspect based text classification using artificial intelligence N Nagendra, J Chandra ECS Transactions 107 (1), 2503 , 2022 2022 Citations: 6
A review of algorithms for mental stress analysis using EEG signal S Maria, J Chandra, B Banerjee, M Rangaswamy IOT with Smart Systems: Proceedings of ICTIS 2021, Volume 2, 561-568 , 2022 2022 Citations: 6
Vortex Matter in Highly Strained Nb Zr : Analogy with Viscous Flow of Disordered Solids J Chandra, M Manekar, VK Sharma, P Mondal, P Tiwari, SB Roy Journal of Low Temperature Physics 186 (1), 21-43 , 2017 2017 Citations: 6
