Advancements in intelligent vehicular networks and computing systems have created new possibilities for innovative approaches that enhance traffic safety, comfort, and transportation performance. Machine Learning (ML) has become widely employed for boosting conventional data-driven methodologies in various scientific study domains. The integration of a Vehicle-to-Everything (V2X) system with ML enables the acquisition of knowledge from multiple places, enhances the operator’s awareness, and predicts future crashes to prevent them. The information serves multiple functions, such as determining the most efficient route, increasing the driver’s knowledge, forecasting movement strategy to avoid risky circumstances, and eventually improving user convenience, security, and overall highway experiences. This article thoroughly examines Artificial Intelligence (AI) and ML methods that are now investigated through different study endeavors in vehicular ad hoc networks (VANETs). Furthermore, it examines the benefits and drawbacks accompanying such intelligent methods in the context of the VANETs system and simulation tools. Ultimately, this study pinpoints prospective domains for vehicular network development that can utilize the capabilities of AI and ML.

Skin cancer is one of the most common types of cancer, and early identification is key to better patient outcomes. An optimized YOLOv8, method of detecting skin cancer utilizing the CLEO (Chimpanzee Leader Election Optimization) optimizer to enhance performance is presented in this paper. YOLOv8 incorporates the CLEO optimizer to fine-tune the model's hyperparameters, enhancing the model's efficacy and precision to identify skin lesions. By leveraging YOLOv8's advanced object detection capabilities and CLEO's optimization strengths, this approach offers a highly effective solution in real-time, for automated skin cancer screening, potentially aiding dermatologists in early diagnosis and treatment planning. Extensive experimentation on publicly available ISIC (International Skin Imaging Collaboration), skin cancer dataset demonstrates that the proposed model improves the precision, accuracy, recall, and mAP (mean Average Precision) rates by 2.1%, 2.1%, 6.5%, and 1.1% as compared with the unoptimized algorithm. Further, YOLOv8 integrated with CLEO optimizer attains superior detection accuracy and quicker inference times in comparison to the other cutting-edge methods for skin cancer detection. The analysis can set the stage for practical use.