Modelling Driver Behaviour at Urban Signalised Intersections Using Logistic Regression and Machine Learning

This study investigated several factors that may influence driver actions throughout the yellow interval at urban signalised intersections. The selected samples include 2,168 observations. Almost 33% of drivers stopped ahead of the stop line, 60% passed the intersection through the yellow interval, and 7% passed after the yellow interval was complete (red light running, RLR violations). Binary logistic regression models showed that the chance of passing went up as vehicle speed went up and down as the gap between the vehicle and the traffic light and green interval went up. The movement type and vehicle position influenced the passing probability, but the vehicle type did not. Moreover, multinomial logistic regression models showed that the legal passing probability declined with the growth in the green time and vehicle distance to the traffic signal. It also increased with the growth in the speed of approaching vehicles. Also, movement type directly affected the chance of legally passing, but vehicle position and type did not. Furthermore, the driver’s performance during the yellow phase was studied using the k-nearest neighbours algorithm (KNN), support vector machines (SVM), random forest (RF) and AdaBoost machine learning techniques. The driver’s action run prediction was the most accurate, and the run-on-red camera was the least accurate

Comparison of two methods for quantitative assessment of mandibular asymmetry using cone beam computed tomography image volumes

The aim of this study was to compare two methods of measuring mandibular asymmetry. The first method uses mirroring of the mandible in the midsagittal plane; the second uses mirroring of the mandible and registration on the cranial base

Advancing Roadway Sign Detection with YOLO Models and Transfer Learning

Roadway signs detection and recognition is an essential element in the Advanced Driving Assistant Systems (ADAS). Several artificial intelligence methods have been used widely among of them YOLOv5 and YOLOv8. In this paper, we used a modified YOLOv5 and YOLOv8 to detect and classify different roadway signs under different illumination conditions. Experimental results indicated that for the YOLOv8 model, varying the number of epochs and batch size yields consistent MAP50 scores, ranging from 94.6% to 97.1% on the testing set. The YOLOv5 model demonstrates competitive performance, with MAP50 scores ranging from 92.4% to 96.9%. These results suggest that both models perform well across different training setups, with YOLOv8 generally achieving slightly higher MAP50 scores. These findings suggest that both models can perform well under different training setups, offering valuable insights for practitioners seeking reliable and adaptable solutions in object detection applications

Automated Pavement Cracks Detection and Classification Using Deep Learning

Monitoring asset conditions is a crucial factor in building efficient transportation asset management. Because of substantial advances in image processing, traditional manual classification has been largely replaced by semi-automatic/automatic techniques. As a result, automated asset detection and classification techniques are required. This paper proposes a methodology to detect and classify roadway pavement cracks using the well-known You Only Look Once (YOLO) version five (YOLOv5) and version 8 (YOLOv8) algorithms. Experimental results indicated that the precision of pavement crack detection reaches up to 67.3% under different illumination conditions and image sizes. The findings of this study can assist highway agencies in accurately detecting and classifying asset conditions under different illumination conditions. This will reduce the cost and time that are associated with manual inspection, which can greatly reduce the cost of highway asset maintenance

Assessment and Management of Atopic Dermatitis in Primary Care Settings

An increasingly common chronic inflammatory skin condition is atopic dermatitis (AD). It exhibits severe itching as well as recurring eczematous lesions. New difficulties for treatment selection and approach occur with the expansion of available therapy alternatives for healthcare professionals and patients.  The article highlights recent developments in scientific research on atopic dermatitis diagnosis and assessment that have led to the identification of novel therapeutic targets and the development of targeted therapies, both of which have the potential to completely change the way AD is treated, particularly in a primary care setting

Challenges and Risks Involved in Deploying 6G and NextGen Networks

Необходимо быть в курсе проблем, ожидающих нас в сетях следующего поколения (NextGen), чтобы предпринять надлежащие шаги для минимизации или устранения проблем по мере их возникновения. Этой цели послужит внедрение искусственного интеллекта в сетях NextGen для политики конфиденциальности и безопасности. Важно быть в курсе этих новых технологий и приложений, чтобы поддерживать безопасную связь в будущем.Проблемы и риски, связанные с развертыванием сетей 6G и NextGen исследуются стратегии проектирования и развертывания более защищенных и ориентированных на пользователя сетей NextGen с помощью искусственного интеллекта для улучшения пользовательского опыта. В нем дополнительно исследуются политические, социальные и географические проблемы, связанные с реализацией этих сетей 6G, и рассматриваются способы повышения безопасности будущих потенциальных приложений, а также защиты пользовательских данных от незаконного доступа. Этот крупный справочный труд, охватывающий такие темы, как алгоритмы глубокого обучения, свИспользуемые программы Adobe AcrobatThere is a need to be aware of the challenges awaiting us in next generation (NextGen) networks in order to take the proper steps to either minimize or eliminate issues as they present themselves. Incorporating artificial intelligence in NextGen networks for privacy and security policies will serve this purpose. It is essential to stay current with these emerging technologies and applications in order to maintain safe and secure communications in the future.Challenges and Risks Involved in Deploying 6G and NextGen Networks explores strategies for the design and deployment of more secured and user-centered NextGen networks through artificial intelligence to enrich user experience. It further investigates the political, social, and geographical challenges involved in realizing these 6G networks and explores ways to improve the security of future potential applications as well as protect user data from illegal access. Covering topics such as deep learning algorithms, aerial network communication, and edge comp