Brain stroke is the second leading cause of death worldwide, following ischemic heart disease. Ischemic stroke occurs when blood vessels are obstructed by a thrombus or other blockages. Prompt and accurate diagnosis of ischemic stroke is critical for patient survival. This study proposes a novel approach for ischemic stroke detection from computed tomography (CT) images, utilizing a hybrid feature extraction technique combined with a convolutional block attention module (CBAM). The hybrid feature extraction leverages the strengths of two pre-trained models, DenseNet121 and MobileNetV3, through feature fusion to provide a comprehensive representation of brain CT images. The CBAM module is integrated to enhance the most relevant features by focusing on both channel and spatial attention mechanisms, significantly improving the model’s ability to detect ischemic strokes with high accuracy. The proposed approach was developed and evaluated on a unique first-hand dataset (Dataset 1) collected from the Specialized private Hospital in Palestine. To further demonstrate the robustness and generalizability of the method, it was also tested on a public dataset (Dataset 2). The results show that the proposed model achieved outstanding performance across all metrics: on Dataset 1, it reached an accuracy of 99.21%, precision of 99.17%, recall of 99.32%, and an F1-score of 99.24%. On Dataset 2, it achieved an accuracy of 98.73%, precision of 98.71%, recall of 98.74%, and an F1-score of 98.88%. The exceptional results are attributed to the power of feature fusion, which creates a robust and comprehensive feature representation. In addition to the CBAM, which refines the feature maps by selectively focusing on the most important channels and spatial regions in brain CT images. This has led to substantial improvement in the detection of ischemic stroke areas.

Background/Objectives: Tuberculosis remains a significant global health problem, particularly in resource-limited environments. Its mortality and spread can be considerably decreased by early and precise detection via chest X-ray imaging. This study introduces a novel approach based on hybrid deep learning for Tuberculosis detection from chest X-ray images. Methods: The introduced approach combines GhostNet, a lightweight convolutional neural network tuned for computational efficiency, and MobileViT, a transformer-based model that can capture both local spatial patterns and global contextual dependencies. Through such integration, the model attains a balanced trade-off between classification accuracy and computational efficiency. The architecture employs feature fusion, where spatial features from GhostNet and contextual representations from MobileViT are globally pooled and concatenated, which allows the model to learn discriminative and robust feature representations. Results: The suggested model was assessed on two publicly available chest X-ray datasets and contrasted against several cutting-edge convolutional neural network architectures. Findings showed that the introduced hybrid model surpasses individual baselines, attaining 99.52% accuracy on dataset 1 and 99.17% on dataset 2, while keeping low computational cost (7.73M parameters, 282.11M Floating Point Operations). Conclusions: These outcomes verify the efficacy of feature-level fusion between a convolutional neural network and transformer branches, allowing robust tuberculosis detection with low inference overhead. The model is ideal for clinical deployment and resource-constrained contexts due to its high accuracy and lightweight design.

Software-defined networking (SDN) is an advanced network architecture to transform the conventional network to meet evolving requirements. Unlike traditional networks, SDN breaks down the control and data plane, making it efficient to monitor, configure, and optimize the network resources. However, due to its centralized nature, SDN is prone to several attack vectors. Among them, distributed denial of service (DDoS) attacks are the most potent threats to the SDN controller. This paper presents a novel approach to detect DDoS attacks on SDN controllers using a novel detection model based on a parallel one-dimensional convolutional neural network-gated recurrent unit (1D CNN-GRU) architecture equipped with a novel hybrid feature selection approach. The suggested hybrid feature selection combines the filter feature methods (mutual information, chi-squared, and ANOVA) with a self-attention mechanism to effectively detect the most vital features for DDoS attack detection in SDN environments. The proposed detection model 1D CNN-GRU leverages the strengths of both a convolutional neural network (CNN) and a gated recurrent unit (GRU). The CNN captures spatial and local patterns, while the GRU focuses on temporal dependencies within the data. The proposed approach showcased high levels of accuracy (97.882%), precision of (98.078%), and F1-measure of (97.276%). Additionally, it exhibited a low false positive rate (FPR) of 1.208% and a short detection time of 1.206 seconds.