Seafood quality inspection is critical for ensuring food safety and minimizing economic losses from spoilage. While traditional methods are slow and labor-intensive, computer vision and machine learning have emerged as efficient automated alternatives. This study presents SFFDNet, a software engineering-driven convolutional neural network featuring a lightweight 19-layer architecture with optimized feature extraction blocks and regularization strategies. With only 2.49 million parameters—significantly fewer than VGG16 (138M) and ResNet50 (25.6M)—our model achieves 98.80% accuracy on the Large-Scale Fish Segmentation and Classification Dataset. SFFDNet outperforms both transfer learning m... More >

The Internet of Things (IoT) is increasingly integrated into modern software-driven systems across consumer, industrial, and healthcare domains. The heterogeneity of IoT devices, combined with their resource constraints, often renders conventional software security mechanisms insufficient, exposing systems to breaches and exploitation. This study examines recent IoT security incidents to illustrate common vulnerabilities in software-intensive IoT ecosystems, highlighting the resulting risks to critical applications. In response, we review emerging machine learning (ML)-driven security modules and deep learning (DL)-based intrusion detection software, positioning them as adaptive components t... More >