The convergence of Vehicular Ad Hoc Networks (VANETs) and the Internet of Things (IoT) is giving rise to the Internet of Vehicles (IoV), a key enabler of next-generation intelligent transportation systems. This survey provides a comprehensive analysis of the architectural, communication, and computing foundations that support VANET–IoT integration. We examine the roles of cloud, edge, and in-vehicle computing, and compare major V2X and IoT communication technologies, including DSRC, C-V2X, MQTT, and CoAP. The survey highlights how sensing, communication, and distributed intelligence interact to support applications such as collision avoidance, cooperative perception, and smart traffic management. We identify four central challenges—security, scalability, interoperability, and energy constraints—and discuss how these issues shape system design across the network stack. In addition, we review emerging directions including 6G-enabled joint communication and sensing, reconfigurable surfaces, digital twins, and quantum-assisted optimization. The survey concludes by outlining open research questions and providing guidance for the development of reliable, efficient, and secure VANET–IoT systems capable of supporting future transportation networks.

Computing advances in data storage are leading to rapid growth in large-scale datasets. Using all features increases temporal/spatial complexity and negatively influences performance. Feature selection is a fundamental stage in data preprocessing, removing redundant and irrelevant features to minimize the number of features and enhance the performance of classification accuracy. Numerous optimization algorithms were employed to handle feature selection (FS) problems, and they outperform conventional FS techniques. However, there is no metaheuristic FS method that outperforms other optimization algorithms in many datasets. This motivated our study to incorporate the advantages of various optimization techniques to obtain a powerful technique that outperforms other methods in many datasets from different domains. In this article, a novel combined method GASI is developed using swarm intelligence (SI) based feature selection techniques and genetic algorithms (GA) that uses a multi-objective fitness function to seek the optimal subset of features. To assess the performance of the proposed approach, seven datasets have been collected from the UCI repository and exploited to test the newly established feature selection technique. The experimental results demonstrate that the suggested method GASI outperforms many powerful SI-based feature selection techniques studied. GASI obtains a better average fitness value and improves classification performance.

Assessment is fundamental to programming education; however, it is a labour-intensive and complicated process, especially in extensive learning contexts where it relies significantly on human teachers. This paper presents an automated grading methodology designed to assess Python programming exercises, producing both continuous and discrete grades. The methodology incorporates GPT-4-Turbo, a robust large language model, and machine learning models selected by PyCaret’s automated process. The Extra Trees Regressor demonstrated superior performance in continuous grade prediction, with a Mean Absolute Error (MAE) of 4.43 out of 100 and an R² score of 0.83. The Random Forest Classifier attained the highest scores for discrete grade classification, achieving an accuracy of 91% and a Quadratic Weighted Kappa of 0.84, indicating substantial concordance with human-assigned categories. These findings underscore the promise of integrating LLMs and automated model selection to facilitate scalable, consistent, and equitable assessment in programming education, while substantially alleviating the workload on human evaluators.

Error-correcting codes and machine learning algorithms are two fundamental pillars in ensuring the reliability and accuracy of data transmission and storage systems. Error-correcting codes have long been instrumental in detecting and correcting errors during data communication and retrieval, while machine learning techniques have emerged as powerful tools for data analysis and pattern recognition. Integrating machine learning into error correction processes presents exciting opportunities to enhance error detection and correction performance, especially in complex and dynamic environments. In this article, we provide an in-depth overview of error-correcting codes, including traditional techniques like Hamming codes, Reed-Solomon codes, Turbo codes, and Convolutional codes. We explore the challenges faced by traditional error correction methods in handling modern data complexities. Subsequently, we delve into the integration of machine learning algorithms, such as neural networks, support vector machines, and decision trees, for error correction tasks. We discuss how machine learning enables adaptive, efficient, and dynamic error correction strategies. Furthermore, we examine real-world applications of machine learning-based error correction in communication systems, data storage, DNA sequencing, and quantum computing. Additionally, we highlight the challenges that researchers and engineers need to overcome, such as computational complexity, real-time processing, and adversarial attacks. Ultimately, the collaboration between error-correcting codes and machine learning offers exciting prospects for a future where data transmission and storage systems are more secure, efficient, and adaptable. By addressing challenges and embracing innovations, we can pave the way for error-free data management in an ever-evolving data-driven world.

With the rapid evolution of online learning environments, the ability to predict students' academic performance has become crucial for personalizing and enhancing the educational experience. In this article, we present a predictive model based on machine learning techniques, designed to be integrated into online learning platforms using the competency-based approach. This model leverages features from four key dimensions: demographic, social, emotional, and cognitive, to accurately predict learners' academic performance. We detail the methodology for collecting and processing learning traces, distinguishing between explicit traces, such as demographic data, and implicit traces, which capture learners' interactions and behaviors during their learning process. The analysis of these data not only improves the accuracy of performance predictions but also provides valuable insights into skill acquisition and learners' personal development. The results of this study demonstrate the potential of this model to transform online education by making it more adaptive and focused on individual learners' needs.