Insider threats are difficult to detect because malicious activity is embedded in otherwise legitimate user behavior, and analysts need interpretable justifications as well as accurate alerts. This paper proposes XFT, an explainable transformer framework operating on tokenized behavioral evidence from heterogeneous ent...
Arafat Al-dhaqm, D. M. Bamasoud, Rahmah Brnawy et al.· Engineering, Technology &...· 0 citations
A deep learning model designed to automatically detect grape leaf diseases based on images, using a pretrained ResNet50 which is trained on ImageNet as feature extractor and a Convolutional Block Attention Module to boost its discriminative capacity is introduced.
Maajid Bashir, A. Reshi, Shabana Shafi et al.· International Journal of Mac...· 0 citations
This work introduces a hybrid residual U-Net-bidirectional long short-term memory with attention (ResUNet-BiLSTM-Attention) channel estimator that learns a nonlinear mapping from noisy pilot observations to MIMO channel coefficients and consistently outperforms both conventional and learning-based baselines.
Mohammad Zubair Khan, Ibrahim Aljubayri, C. Prabha et al.· Scientific Reports· 0 citations
Two hybrid architectures, namely HABSA and AAC-HABSA, centered on a novel Adaptive Aspect Conditioning Layer (AACL) that injects aspect information prior to transformer-based contextual encoding are proposed, providing a computationally efficient, mathematically coherent, and interpretable solution for fine-grained sen...
Mahander Kumar, Lal Khan, Mohammad Zubair Khan et al.· Computer Modeling in Enginee...· 0 citations
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