Electroencephalography (EEG) underpins two complementary lines of research: the clinical diagnosis of neurological disorders and the design of Brain–Computer Interfaces (BCIs) for communication and control. Despite shared signal–processing foundations, the two fields impose very different technical and validation requirements. This narrative review synthesises recent advances (2018–2025) across both domains, distinguishing laboratory performance from clinical readiness throughout. For clinical diagnosis, deep-learning models (convolutional, recurrent, graph-based, and transformer architectures) have reported offline, subject-dependent accuracies above 90% for Alzheimer's disease, Parkinson's disease, and epilepsy. We interpret these figures with caution: most were obtained on small, curated datasets under offline protocols, and should therefore be viewed as candidate computational biomarkers rather than clinically validated ones. For BCIs, we discuss realistic ranges of motor-imagery accuracy (typically 70–85% cross-subject, offline) and SSVEP information transfer rates (up to approximately 100 bits/min under controlled laboratory conditions).We compare CNNs, LSTMs, Transformers, and Graph Neural Networks in terms of data requirements, interpretability, robustness, and suitability for clinical deployment, separating conceptual similarity between diagnostic and BCI pipelines from genuine operational transferability. Human factors that AI alone cannot resolve — BCI illiteracy (15-30% of users), calibration burden, inter-subject variability, and cognitive fatigue — are discussed as fundamental limitations. Finally, we review emerging directions (hybrid BCIs, explainable AI, federated learning) and regulatory frameworks such as the EU AI Act and GDPR as components of a realistic pathway toward trustworthy EEG systems, recognising that clinical deployment remains a long-term goal requiring independent validation, regulatory approval, and prospective clinical trials.
Arabic invoice processing requires not only recognition of generic entities, but also interpretation of their invoice-specific roles and relations. This article extends a YOLOv11–AraBERT active-learning pipeline with a layout-aware structural layer for field classification, key-value linking, and product line-item reconstruction. YOLOv11 localizes text regions in a custom Arabic invoice dataset, OCR converts the detected regions to text, and AraBERT performs sequence labeling with an entity inventory that includes PRODUCT. Layout-AraBERT then combines AraBERT representations, predicted entity types, and normalized two-dimensional bounding-box features to distinguish fields such as invoice date, tax amount, subtotal, total amount, product name, quantity, unit price, and line total. On the authors’ custom dataset, YOLOv11 reaches 93.42% mAP@0.5:0.95 and the final AraBERT active-learning batch reaches 89.36% entity-level F1. PRODUCT extraction reaches 87.77% F1. For the structural stage, Layout-AraBERT reaches 91.2% field accuracy and 88.7% key-value linking F1 under clean OCR conditions; at a 20% synthetic OCR-noise level these values decrease to 81.6% and 78.2%, respectively. Evaluation also includes cross-template generalization, ablation analysis, and annotation-efficiency measurements. The resulting framework provides a reference baseline for layout-aware Arabic invoice understanding on the authors’ custom dataset.
The Internet of Medical Things (IoMT) is transforming healthcare by enabling smart devices to generate vast amounts of sensitive data. Federated learning (FL) allows collaborative model training on distributed IoMT data while preserving privacy, but conventional FL methods face critical challenges in this setting. IoMT devices are often bandwidth-constrained and heterogeneous, making communication efficiency a top priority and the presence of faulty client updates can severely degrade global model performance while coping with highly non-IID data. This work proposes SEFA Sparse & Efficient Federated Aggregation which marries client-side dual compression with server-side two-stage robust aggregation. On the client side, SEFA applies top-k gradient sparsification and an adaptive 8-bit quantization scheme to drastically reduce uplink communication per round while incurring minimal accuracy loss. On the server side, SEFA incorporates a two-stage robust aggregation: first, a Multi-Krum with neighbor selection identifies a subset of updates that are closest to the majority second, a coordinate-wise median is computed over the selected updates to produce a fault-tolerant global model update. This design allows SEFA to achieves comparable accuracy to standard FL in IID settings while maintaining high accuracy under non-IID data with 30% faulty clients, outperforming baseline methods including FedAvg, coordinate-median (FedMedian), single-stage Multi-Krum and a recent IoMT FL framework by a modest margin. SEFA is evaluated on three representative IoMT healthcare datasets Pima Indians Diabetes, Body Performance and Maternal Health Risk using lightweight neural network models demonstrating that SEFA offers a practical and robust solution for deploying federated learning in IoMT environments, with strong fault tolerance, efficiency and minimal performance trade-offs.
Inverse kinematics (IK) is a fundamental problem in robotics that involves computing the joint parameters necessary to achieve the desired position and orientation of a robotic manipulator's end-effector. Traditional methods for solving IK such as Jacobian-based solvers, pseudo-inverse methods, and numerical solvers such as Levenberg-Marquardt and Newton-Raphson are faced with serious challenges ranging from local minimum, singularity, computational cost, and the inability to handle highly redundant systems effectively. To address some of these problems, we proposed Multi-layered Graph Convolutional Neural Network (MGCNN) for 7-DOF (Degree-Of-Freedom) redundant manipulators. The architecture consists of 7 GCN (Graph Convolutional Network) blocks each block consists of a GCN layer, normalization, and activation functions followed by 2 fully connected layers for joint regression. A skip-connection mechanism was used to pool and concatenate outputs from all GCN blocks before being fed into the fully- connected (FC) layers of the network which ensures optimal feature utilization across all layers. The model was trained on mixed data (preplanned trajectories and random poses), where each pose of the robotic arm is represented as a graph with joints and kinematic links representing nodes and edges of the graph respectively. Node features consist of the global end-effector poses and local Denavit-Hartenberg (DH) parameters, enabling the network to learn both global and joint-specific relationships. Experimental results demonstrate strong generalization, a narrow confidence interval relative to Mean Absolute Error (MAE), low error susceptibility, and fast inference. The model achieves an MAE below 0.001 compared to ground truth, proving its robustness and efficiency.
Pedestrian trajectory prediction plays a pivotal role in real-world applications such as autonomous driving, unmanned delivery, and intelligent surveillance. However, existing deep learning approaches still face critical challenges, including mode collapse and the generation of unrealistic trajectories in complex environments. To address these limitations, we propose Phase Fusion Network (PFNet), a novel trajectory prediction framework designed to enhance prediction accuracy in intricate digital media scenarios. PFNet introduces an innovative Graph Encoder (GE) that incorporates a probabilistic modeling strategy to better capture spatial features and pedestrian interactions. To mitigate mode collapse, a common limitation in GAN-based methods, PFNet employs a dual-discriminator mechanism that improves both the realism and diversity of predicted trajectories. Additionally, PFNet adopts a two-phase architecture, where the generation phase strengthens spatial representation and the prediction phase refines temporal consistency. Extensive experiments on standard benchmarks, including ETH, UCY, and the Stanford Drone datasets, demonstrate that PFNet consistently outperforms state-of-the-art methods in terms of both Average Displacement Error (ADE) and Final Displacement Error (FDE).
