Abstract
Traffic safety is a significant issue, particularly for motorcyclists, who are significantly more vulnerable to fatal accidents compared to drivers of enclosed vehicles. Advanced Rider Assistance Systems have the potential to improve motorcyclist safety by mitigating critical risks like rear-end collisions. This paper proposes an integrated Advanced Rider Assistance Systems architecture using the RTDETR object detection model to predict rear-end collisions and provide real-time alerts to the riders. Specifically, it aims to mitigate critical risks like rear-end collisions, offering a comprehensive safety solution. The experimental data indicate that the proposed system is suitable for diverse operational environments, supporting the development of advanced sensing and alert systems to improve motorcyclist safety. Our system achieves an average precision (AP) of 0.688 at IoU=0.50 and reduces collision risk by issuing timely warnings. Contributions include the integration of RTDETR for improved detection accuracy, a multi-threshold warning mechanism, and a detailed analysis of system performance under varied conditions.

Abstract
Video capsule endoscopy (VCE) enables high-resolution visualisation of the small bowel but remains constrained by manual review of thousands of frames, which is time-consuming and error-prone under class imbalance. This study investigates deep learning for automatic multiclass lesion classification in VCE, comparing two convolutional networks (ResNet-50, EfficientNet-B3) with two Vision Transformers (Swin, DeiT) on the public Kvasir-Capsule dataset (47,161 images; 11 classes). The pipeline comprises standard preprocessing, class-aware augmentation and adaptive data augmentation, stratified data partitioning, hyperparameter optimisation with Optuna, and evaluation using accuracy, precision, recall, and F1-score. DeiT achieved the best overall performance (accuracy = 0.98; F1 = 0.96), with strong class-wise results in clinically salient categories (e.g., ulcer, fresh blood, angiectasia), indicating effective modelling of long-range dependencies and subtle patterns. We further assess computational feasibility by reporting training configuration and indicative inference time per image, supporting potential integration into assisted reading workflows. Limitations include reliance on a single public dataset, pronounced class imbalance, and the absence of prospective clinical validation, which may affect generalisability. These findings position Transformer-based models as promising candidates for VCE decision support, while underscoring the need for future work on (i) multicentric datasets and external validation, (ii) comprehensive statistical analysis with confidence intervals and robust baselines under imbalance, and (iii) prospective studies quantifying end-to-end impact on reading time and diagnostic safety. © 2025 The Authors. Published by Elsevier B.V.

Abstract
The increasing connectivity of Internet of Medical Things (IoMT) devices has accentuated their susceptibility to cyberattacks. The sensitive data they handle makes them prime targets for information theft and extortion, while outdated and insecure communication protocols further elevate security risks. This paper presents a lightweight and innovative approach that combines Benford's law with statistical distance functions to detect attacks in IoMT devices. The methodology uses Benford's law to analyze digit frequency and classify IoMT devices traffic as benign or malicious, regardless of attack type. It employs distance-based statistical functions like Jensen-Shannon divergence, KullbackLeibler divergence, Pearson correlation, and the Kolmogorov test to detect anomalies. Experimental validation was conducted on the CIC-IoMT-2024 benchmark dataset, comprising 45 features and multiple attack types. The best performance was achieved with the Kolmogorov test (alpha = 0.01), particularly in DoS ICMP attacks, yielding a precision of.99.24%, a recall of.98.73%, an F1 score of.98.97%, and an accuracy of.97.81%. Jensen-Shannon divergence also performed robustly in detecting SYN-based attacks, demonstrating strong detection with minimal computational cost. These findings confirm that Benford's law, when combined with well-chosen statistical distances, offers a viable and efficient alternative to machine learning models for anomaly detection in constrained environments like IoMT.

Abstract
Video Capsule Endoscopy (VCE) is a pivotal technology in modern gastroenterology, offering a non-invasive method to visualize the entire small bowel. However, the clinical application of VCE is hampered by the extensive review time required, as specialists must manually analyze thousands of images from each procedure. This process is not only laborious and costly but also prone to diagnostic errors due to fatigue, subtle abnormalities, and variability in interpretation across clinicians. To address this challenge, deep learning methods have been explored to automate VCE image analysis. However, most existing approaches rely on a single model architecture, which often fails to generalize across the broad visual diversity found in gastrointestinal imagery. This limitation becomes especially pronounced in multiclass classification tasks, where the ability to distinguish between visually similar tissues and lesions is essential. Ensemble-based methods such as Mixture of Experts (MoE) have shown promising results in general computer vision by leveraging multiple specialized models for improved robustness. However, no prior work has investigated MoE or Hierarchical MoE (HMoE) architectures for multiclass classification of VCE or endoscopic images more broadly. To explore this opportunity, we present a comparative framework evaluating three deep learning strategies for VCE image classification: individual models, flat MoE systems, and Hierarchical MoE architectures. Using a subset of the Kvasir-Capsule dataset, which contains 12 gastrointestinal tissue and lesion classes, we first train and evaluate four backbone models (InceptionNeXt, EfficientViT, ConvNeXtV2, and DeiT3) to establish a performance baseline. The two best-performing architectures, ConvNeXtV2 and DeiT3, are then used as expert backbones within both MoE and HMoE systems. In the MoE configuration, a gating network assigns dynamic per-image weights to multiple expert instances. In contrast, the HMoE configuration constructs a learned binary tree that routes samples based on class similarity through increasingly specialized branches. In the HMoE models, ConvNeXtV2 outperformed DeiT3 in accuracy, whereas DeiT3 showed superior routing accuracy. These results indicate that expert-driven ensemble methods not only outperform standalone models but also offer complementary advantages depending on architecture and routing strategy. This study provides new evidence for the clinical potential of MoE and HMoE frameworks in scalable, accurate VCE image analysis.

Abstract
This article presents a real-time object detection and distance estimation system implemented on a low-cost platform. The system uses a Raspberry Pi 5 and two cameras in a stereoscopic configuration to capture pairs of images. Object detection is performed using YOLO neural networks and distance estimation is based on the disparity between the centers of the detected bounding boxes. The system is evaluated in terms of detection performance, inference speed and depth estimation accuracy. Three YOLO models (YOLOv8n, YOLO11n and YOLO12n) are tested at different resolutions. Among them, the YOLO11n with a resolution of 320x320 achieves the best balance between processing speed and detection quality in stereo-scopic operation. The system has a low error in depth estimation at close range, with absolute errors of less than 1.2 cm up to 60 cm. At greater distances, accuracy is affected by the reduction in the size of the bounding box, which limits the reliability of the disparity. Possible improvements include using segmentation-based localization and optimizing the stereo configuration. The proposed system is suitable for short-range applications in controlled environments and serves as a basis for future improvements in embedded vision systems.

Abstract
Background: One of the most common consequences in individuals with diabetes is the diabetic foot, which can cause foot ulcers and even lead to limb amputation. Since an increase of the temperature in the plantar region is directly correlated with an increased risk of ulceration, infrared thermography (IRT) has been used in multiple studies as an automatic tool for detecting problems in diabetic foot. Artificial intelligence-based computer-aided diagnosis systems are being more frequently used to improve decision-making and minimize errors. These technologies are designed to increase examination accuracy, consistency in image interpretation, prognosis evaluation support, and examination accuracy. They also have the ability to offer insightful information and help medical professionals to manage diabetic foot issues successfully.Methods: In this work, 37 papers that used thermography and artificial intelligence (AI) to identify diabetic foot complications and/or predict the risk of developing diabetic foot are analyzed.Results: The results demonstrate the potential of IRT imaging implementation with AI for the identification and prediction of diabetic foot complications.Conclusions: The combination of IRT and AI shows significant potential for diabetic foot assessment; however, the great majority of these studies show that the research is confined to classification of foot thermograms using pre-prepared data sets. In particular, there is limited research on segmentation methods and constraints in the use of deep learning due to the lack of large and diverse datasets.