Bibliography
Publications
16 peer-reviewed publications — 9 journal articles and 7 conference papers — plus earlier research work. Every entry links to a dedicated page with the abstract, research highlights, a slide summary where available, and ready-to-copy citations.
Journal Articles
9Peer-reviewed articles in international journals and magazines.
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Computers & Security, art. no. 105075, 2026
Impact Factor 6.8Q1Top 1% -
Archives of Computational Methods in Engineering, 2026
Impact Factor 12.9Q1Read Paper Publisher Page (opens in a new tab) DOI (opens in a new tab) PDF (opens in a new tab) View PresentationAbstract
Volumetric medical imaging has redefined modern healthcare, enabling precise diagnosis, prognosis, and treatment planning. During the past decade, the field has undergone a paradigm shift from classical deep learning architectures to multimodal, agent-driven AI systems capable of uncovering rich volumetric biomarkers and utilizing heterogeneous data for predictive and generative modeling. Existing surveys are fragmented, focusing on specific models or tasks instead of offering a unified view of volumetric learning evolution. This study traces the evolution from classical models (Convolutional Neural Networks, Recurrent Neural Networks, and transformers) to generative approaches (Variational Autoencoders, Generative Adversarial Networks, and diffusion models) and finally to foundation models and AI-agents that enable advanced reasoning and adaptive clinical workflows. As the reported performance varies substantially across datasets, imaging modalities, and evaluation protocols, this review emphasizes methodological evolution, representative innovations, and practical implications rather than direct numerical ranking of competing architectures. For each paradigm, we critically assess methodological innovations, strengths, limitations, and comparative performance in segmentation, classification, detection, reconstruction, and report generation. Beyond synthesizing progress, we identify persistent challenges, including data scarcity, generalization between institutions, and clinical trustworthiness, and outline emerging frontiers in multimodal fusion, explainable AI, and human–AI collaboration. This review provides a unified framework for understanding the evolution of volumetric medical imaging and offers actionable insights for researchers, clinicians, and industry practitioners, contributing to the development of reliable, interpretable, and clinically deployable next-generation medical AI systems. To support further research, we provide a GitHub repository that includes popular 3D medical imaging datasets with recent 3D models in our shared GitHub repository (https://github.com/Owais-CodeHub/3D-Medical-Imaging-Review).
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Earth Systems and Environment, 2026
Impact Factor 5.0Q1Abstract
Accurate classification of the seabed defects is crucial in safeguarding the safety, reliability, and maintenance of underwater infrastructure, including pipelines, cables, and offshore platforms. Classification of underwater images remains a challenging task due to issues such as low visibility, scattering, color distortion, and multi-scale variations in underwater environments. To address these challenges, this study proposes IEResViT, a novel fusion-based hybrid architecture that integrates a Depthwise Inception Convolutional Neural Network (CNN) with a modified Vision Transformer (ViT) framework. The proposed model replaces the traditional multilayer perceptron (MLP) block in the transformer encoder with a ResNet-based residual connection (ResMLP) to enhance hierarchical feature extraction and reduce computational complexity. The architecture processes images through parallel CNN and transformer branches to capture both local and global feature representations, followed by feature fusion and classification. The proposed model was evaluated on the Aquatic Defect dataset, AQUA20 dataset and Marine_PULSE dataset, achieving high classification performance with an accuracy of 98.88% 98.65% and 95.65% respectively, outperforming several state-of-the-art deep learning models while using fewer parameters and reduced computational cost. The results demonstrate that the IEResViT model provides an efficient and lightweight solution for reliable underwater defect image classification.
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Scientific Reports, vol. 15, art. no. 30997, 2025
Impact Factor 3.9Q1Abstract
The timely and precise identification of diseases in plants is essential for efficient disease control and safeguarding of crops. Manual identification of diseases requires expert knowledge in the field, and finding people with domain knowledge is challenging. To overcome the challenge, computer vision-based machine learning techniques have been proposed by the researchers in recent years. Most of these solutions with the standard convolutional neural network (CNN) approaches use uniform background laboratory setup leaf images to identify the diseases. However, only a few works considered real-field images in their work. Therefore, there is a need for a robust CNN architecture that can identify the diseases in plants in both laboratory and real-field conditioned images. In this paper, we have proposed an Inception-Enhanced Vision Transformer (IEViT) architecture to identify diseases in plants. The proposed IEViT architecture extracts local as well as global features, which improves feature learning. The use of multiple filters with different kernel sizes efficiently uses computing resources to extract relevant features without the need for deeper networks. The robustness of the proposed architecture is established by hyper-parameter tuning and comparison with state-of-the-art. In the experiment, we consider five datasets with both laboratory-conditioned and real-field conditioned images. From the experimental results, we see that the proposed model outperforms state-of-the-art deep learning models with fewer parameters. The proposed model achieves an accuracy rate of 99.23% for the apple leaf dataset, 99.70% for the rice dataset, 97.02% for the ibean dataset, 76.51% for the cassava leaf dataset, and 99.41% for the plantvillage dataset.
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Earth Systems and Environment, vol. 10, pp. 5383-5399, 2025
Impact Factor 5.0Q1Abstract
Underwater infrastructures such as pipelines, ship hulls, and offshore platforms are critical to marine operations but are highly vulnerable to biofouling, structural corrosion, and vegetation overgrowth, leading to increased maintenance costs and environmental hazards. However, visual inspection of these structures remains challenging due to low visibility, uneven lighting, and complex textured surfaces that limit the effectiveness of both traditional and purely deep learning-based approaches. In this work, we introduce AquaFusionNet, a hybrid defect classification framework that seamlessly integrates embeddings from four state-of-the-art pre-trained backbones (EfficientNet-B0, ResNet-50, SENet-50, and Vision Transformer) with complementary traditional descriptors including colour histograms, histogram of oriented gradients (HOG), local binary patterns (LBP), edge density, and gradient statistics via a trainable attention module. This attention mechanism dynamically weights each feature channel, allowing the model to emphasise the most informative cues while preserving fine-scale details under variable turbidity and illumination. We evaluate AquaFusionNet on a curated dataset of 2,228 underwater images spanning three defect categories, with 445 images held out for testing. Our model achieves 98.43% accuracy, 98.18% precision, 97.79% recall, a 96.06% intersection-over-union, and a 97.97% F1-score, outperforming eleven strong baselines, including ResNet-152 and EfficientNet-B0, by a substantial margin. These results demonstrate AquaFusionNet's robustness and generalisability, paving the way for real-time, automated underwater inspection systems that can significantly enhance operational safety and reduce maintenance costs across marine industries.
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IEEE Transactions on Services Computing, pp. 1-13, 2024
Impact Factor 5.8Q1 -
Ad Hoc Networks, vol. 156, art. no. 103437, 2024
Impact Factor 5.4Q1 -
IEEE Internet of Things Journal, vol. 10, pp. 16494-16503, 2023
Impact Factor 8.2Q1 -
IEEE Internet of Things Magazine, vol. 5, pp. 174-178, 2022
Impact Factor 5.8Q1
Conference Papers
7Peer-reviewed papers in international conferences and workshops.
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IEEE International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 1387-1392, 2021
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IEEE INFOCOM — IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 1-6, 2022
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IEEE Global Communications Conference (GLOBECOM), pp. 2897-2902, 2022
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IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), pp. 539-544, 2023
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IEEE Wireless Communications and Networking Conference (WCNC), pp. 1-6, 2024
Abstract
In this paper, we propose RedgeX, a meta-learning based approach for generating analytical models in a distributed edge intelligence network. The approach involves training a meta-learning model on a large dataset of edge device information and performance metrics to predict the optimal analytical model for a given task and available resources. An edge controller, which has the status of all the edge devices, can then deploy the optimal model to the most suitable edge devices based on their available resources. The RedgeX improves the efficiency and effectiveness of edge intelligence systems by dynamically generating analytical models based on the specific requirements of each task and the available resources in the edge devices. The performance evaluation of the proposed scheme shows better utilization of resources, improved performance, and reduced latency in edge intelligence systems.
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IEEE Future Networks World Forum (FNWF), pp. 405-410, 2024
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IEEE Guwahati Subsection Conference (GCON), pp. 1-6, 2025
Other Research Works
2Earlier research work carried out before the doctoral programme.
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Early research work — identity-based cryptography, 2018
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Early research work — cryptanalysis of multi-signature schemes, 2017
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