Faculty Publications
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Item A Boosting-Based Hybrid Feature Selection and Multi-Layer Stacked Ensemble Learning Model to Detect Phishing Websites(Institute of Electrical and Electronics Engineers Inc., 2023) Lakshmana Rao, L.R.; Rao, R.S.; Pais, A.R.; Gabralla, L.A.Phishing is a type of online scam where the attacker tries to trick you into giving away your personal information, such as passwords or credit card details, by posing as a trustworthy entity like a bank, email provider, or social media site. These attacks have been around for a long time and unfortunately, they continue to be a common threat. In this paper, we propose a boosting based multi layer stacked ensemble learning model that uses hybrid feature selection technique to select the relevant features for the classification. The dataset with selected features are sent to various classifiers at different layers where the predictions of lower layers are fed as input to the upper layers for the phishing detection. From the experimental analysis, it is observed that the proposed model achieved an accuracy ranging from 96.16 to 98.95% without feature selection across different datasets and also achieved an accuracy ranging from 96.18 to 98.80% with feature selection. The proposed model is compared with baseline models and it has outperformed the existing models with a significant difference. © 2013 IEEE.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.