Journal of King Saud University - Computer and Information Sciences

The stringent energy constraints of sensor nodes in the Industrial Internet of Things (IIoT) pose a critical challenge to network sustainability. While clustering routing protocols are effective in mitigating energy dissipation, optimal Cluster Head (CH) selection remains an NP-hard optimization problem. Existing metaheuristic approaches often suffer from premature convergence and imbalanced expl…

Abstract Facial expression recognition (FER) has a variety of applications in advanced intelligent fields such as human–computer interaction, cognitive psychology, and intelligent driving. However, FER in wild scenarios faces multiple challenges, including occlusion, pose variations, and subtle differences, which make current models unable to address these issues effectively. To tackle these chal…

Abstract Defocus image deblurring aims to reconstruct sharp images from defocused inputs. Although deep learning-based methods have achieved significant progress, existing algorithms still struggle to effectively extract critical details from highly redundant feature representations and to achieve clear structural and textural reconstruction in complex texture regions. To address these issues, we…

In recent years, incomplete multi-view partial multi-label classification has attracted growing attention due to its practical relevance. However, many existing methods rely on equal-weight (average) fusion and thus overlook sample-wise reliability differences across views, making the fused representations vulnerable to noise and missing views. To address this issue, we propose the Structure-Awar…

Detecting malicious accounts on social media is becoming increasingly challenging as adversaries evolve from simple spammers to sophisticated actors capable of camouflaging their network topology. Existing unsupervised detection methods predominantly rely on explicit follower/followee structures. However, these topological signals are often manipulated by attackers (e.g., through link farming), c…

Abstract Automated analysis of UAV imagery is crucial for remote sensing but faces challenges like extreme scale variations, complex background clutter, and the vanishing of fine-grained details. To address these limitations, we propose MMAF-YOLO, a novel object detection network based on multi-scale mutual aggregation and fine-grained feature modulation. First, we design a Multi-Scale Mutual Agg…

Driving facial expressions is critical for digital animation, yet resource-constrained systems still rely heavily on linear blendshape models. Existing methods struggle with feature redundancy in high-dimensional landmark prediction and numerical instability in extreme expression regions. To address these, we propose Attention-Gate ContextNet (AtG-ContextNet),a regression framework that integrate…

Determining shelter sites in conflict-affected areas poses a major challenge for humanitarian agencies, faced with time limitations, limited data, and uncertainty. This study presents a Hybrid Scenario-Aware Decision Support Framework that integrates Multi-Criteria Decision-Making (MCDM) and Machine Learning (ML), offering transparent, scalable, and data-driven shelter evaluation. At first, the T…

Endoscopy-based lesion segmentation is essential for the early detection of neoplastic lesions. Framed as a dense-prediction task on high-resolution mucosal panoramas, its automated and precise contouring alleviates clinicians’ real-time cognitive burden, while yielding quantitative biomarkers to stratify patient risk and guide subsequent therapeutic strategies. However, three key challenges pers…

Underwater images often suffer from severe visual degradation due to the complex imaging and transmission conditions in underwater environments. In resource-constrained underwater systems, image enhancement and transmission must jointly consider restoration quality, communication efficiency, and energy consumption. In particular, autonomous underwater vehicle (AUV) swarms require lightweight solu…

Knowledge graphs are increasingly used to model complex multi-relational dependency structures in domains such as economic supply chains, where firms, suppliers, products, facilities, and locations interact through heterogeneous relations. Multi-hop reasoning over such graphs can uncover implicit dependencies and risk exposures, but remains challenging because knowledge graphs are often incomplet…

Accurate multi-class skin cancer classification is clinically valuable only when models perform reliably across both common and rare diagnostic categories. To address persistent long-tail failures, we systematically evaluate three enhanced vision-transformer architectures, GC-ViT Small, CoAt-Lite, and FocalNet, augmented with two novel attention modules: Transformer Blocks for global context inte…

Unmanned aerial vehicles (UAVs) are increasingly deployed in ultra-wideband (UWB) environments to support applications that demand high data rates and reliable connectivity. However, effective communication between UAVs and ground terminals is challenged by six-dimensional (6D) posture-induced signal variation, non-stationary channel behavior, and dynamic resource constraints. Existing models are…

Traditional Byzantine Fault Tolerance (BFT) consensus algorithms effectively tolerate node behavioral faults but lack the ability to verify the quality of input data. This makes them vulnerable to security risks from low-quality or “compliant yet malicious” data in data-driven applications. To address this gap, we propose a Data-Quality-Driven Byzantine Fault Tolerance algorithm based on Zero-Kno…

Abstract Federated Learning (FL) enables collaborative model training without exposing sensitive data, making it a cornerstone for privacy-aware AI. However, bringing FL from theory to practice in multi-tier architectures that contain hierarchical edge-fog-cloud systems remains difficult, challenged by security vulnerabilities, resource constraints, and system heterogeneity. This paper reviews se…