Faculty Publications
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Item Essential Requirements of IoT’s Cryptographic Algorithms: Case Study(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2021) Kumar, S.; Lone, Z.A.; Chandavarkar, B.R.Internet of Things (IoT) devices are increasing rapidly in today’s world, but the security of devices remains a major concern due to the unavailability of the memory and processing power in these devices, which is because of their smaller size. The trade-off lies between security and performance, i.e. if security is increased, which will come with high complexity and hence would deter the performance. On the other hand, if performance has to be increased, it would come with a cost in terms of security. Also, IoT devices can be used as bots as they are globally accessible without much of a security. The most secure cryptographic algorithms use a lot of resources, and in case of IoT, resources are not available on that scale, so there is a need to design a secure algorithm (lightweight cryptography) that would use less resources and hence won’t affect the performance either. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Salient Object Detection in Hyperspectral Images Using Felzenswalb’s Segmentation Algorithm(Springer Science and Business Media Deutschland GmbH, 2024) Lone, Z.A.; Pais, A.R.Salient object detection has been explored extensively in low dimensional images like RGB, grayscale, etc., but have been explored very little in high dimensional images like Hyperspectral images (HSI) etc. In HSI, few studies have used low-level features to perform salient object detection. In this paper, we propose a high-level feature-based salient object detection algorithm. The manifold ranking is applied on the self-supervised CNN features learned by an unsupervised segmentation task. The training of the model continues until the clustering loss or saliency map converges to a defined error. We found out that the proposed algorithm performed better than state-of-the-art in terms of precision. © Springer Nature Switzerland AG 2024.Item An Illumination Invariant Approach to Salient Object Detection in Hyperspectral Images(Institute of Electrical and Electronics Engineers Inc., 2025) Lone, Z.A.; Pais, A.R.; Murali Krishna, M.M.; Mhala, N.C.Salient Object Detection (SOD) in Hyperspectral Images (HSI) has traditionally relied on low-level features, with limited exploration of high-level features. Low-level features lack semantic context, making them ineffective for detecting complex or subtle salient objects. Moreover, the inherent sensitivity of HSI to uneven illumination poses additional challenges, particularly when there is uneven illumination on the objects. To address these challenges, a novel SOD algorithm is proposed, that employs manifold ranking on high-level features learned through a deep segmentation network. Unlike existing methods that use raw HSI as input, the proposed approach utilizes spectral gradient as input, mitigating the effects of spectral variation. The learned features are ranked using a graph-based manifold ranking approach, which enhances the robustness of the detection process. Experimental evaluation on the HS-SOD dataset demonstrates that the proposed approach performs better, achieving superior results compared to state-of-the-art methods, with a precision of 0.63, an f1 score of 0.51, and a Mean Absolute Error (MAE) of 0.08. © 2025 IEEE.Item Object detection in hyperspectral images(Elsevier Inc., 2022) Lone, Z.A.; Pais, A.R.Object Detection is a task of estimating and locating an object precisely in an image. It is a fundamental problem in computer vision and has been studied extensively in low dimensional images like RGB, grayscale, etc. High dimensional images like Hyperspectral images (HSI) contain ample information and are very powerful in enhancing the fine spectral differences between different objects. The advancement in spectral sensor technologies is making hyperspectral data more readily available, making it a promising technology for image analysis tasks. HSI has been explored in the fields of remote sensing, biomedical imaging, mineral classification, goods quality assessment, and object detection etc. The research concerning object detection in HSI has been gathering pace in recent times. This survey paper is an attempt to create a resource for researchers in the field. This paper provides a comprehensive review of both Supervised and Salient object detection. Moreover, a collection of important datasets is mentioned. We conclude the paper by mentioning research challenges and the future directions for the research in the field. © 2022 Elsevier Inc.Item Salient object detection in HSI using MEV-SFS and saliency optimization(Springer Science and Business Media Deutschland GmbH, 2025) Lone, Z.A.; Pais, A.R.The existing methods in salient object detection (SOD) in hyperspectral images (HSI) have used different priors like center prior, boundary prior to procure cues to find the salient object. These methods fail, if the salient object is slightly touching the boundary. So, we extrapolate boundary connectivity, a measure to check if the object touches the boundary. The salient object is obtained by using background and foreground cues, which are calculated using boundary connectivity and contrast map, respectively. Also, to reduce the information redundancy and hence time complexity, we select top three most informative bands using different feature selection and feature extraction algorithms. The proposed algorithm is tested on HS-SOD dataset. It is observed that the proposed algorithm performs better than the state-of-the-art techniques in almost all the metrics, such as Precision (0.57), Recall (0.46), f1 score (0.51), CC (0.43), NSS (2.13), and MAE (0.09). In addition, we performed a comparative analysis of four different feature selection (MEV-SFS, OPBS) and feature extraction (PCA, MNF) algorithms in the context of SOD in HSI. We observed that feature selection algorithms are computationally efficient with OPBS and MEV-SFS taking about 7.98 and 8.34 s on average to reduce the feature space, respectively. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.