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 >

Facial Emotion Recognition (FER) software is an important part of modern software applications. It is used for intelligent user interfaces, diagnostics in psychiatry or psychology, human-computer interaction, and even in surveillance. The recent advancements in the use of deep learning, and the advanced architectures based on them, including Convolutional Neural Networks (CNNs) and transformer models have made the development of FER software much efficient and scalable. This review paper contributes to the existing literature by providing a comprehensive synthesis of Facial Emotion Recognition (FER) systems from a software engineering perspective spanning the period from 2015 to the present.... More >