It is recognized that the performance of any prediction model is a function of several factors. One of the most significant factors is the adopted preprocessing techniques. In other words, preprocessing is an essential process to generate an effective and efficient classification model. This paper investigates the impact of the most widely used preprocessing techniques, with respect to numerical features, on the performance of classification algorithms. The effect of combining various normalization techniques and handling missing values strategies is assessed on eighteen benchmark datasets using two well-known classification algorithms and adopting different performance evaluation metrics and statistical significance tests. According to the reported experimental results, the impact of the adopted preprocessing techniques varies from one classification algorithm to another. In addition, a statistically significant difference between the considered data preprocessing techniques is demonstrated.

Teaching and exam proctoring represent key pillars of the education system. Human proctoring, which involves visually monitoring examinees throughout exams, is an important part of assessing the academic process. The capacity to proctor examinations is a critical component of educational scalability. However, such approaches are time-consuming and expensive. In this paper, we present a new framework for the learning and classification of cheating video sequences. This kind of study aids in the early detection of students’ cheating. Furthermore, we introduce a new dataset, “actions of student cheating in paper-based exams”. The dataset consists of suspicious actions in an exam environment. Five classes of cheating were performed by eight different actors. Each pair of subjects conducted five distinct cheating activities. To evaluate the performance of the proposed framework, we conducted experiments on action recognition tasks at the frame level using five types of well-known features. The findings from the experiments on the framework were impressive and substantial.

Semantic-enhanced recommendation systems are promising approaches to overcome the sparsity and cold-start problems, which are hard to handle using the conventional collaborative filtering (CF) approaches. Further research is needed to effectively integrate ontologies into collaborative filtering recommender systems. This paper proposes an ontology-based semantic similarity measure to evaluate similarities between items and eventually generate accurate recommendations. The proposed semantic similarity measure termed fusion-based semantic similarity takes into account the semantics of ontological instances (i.e. items) inferred from a specific domain ontology, which is determined by analyzing the hierarchical relationships among the instances, as well as the features of the instances and their relationships to other instances. The new measure comprehensively captures the semantic knowledge associated with instances by exploiting all possible shared semantics between instances in a given domain ontology. Furthermore, this paper proposes a new semantic-enhanced hybrid recommendation approach as a result of combining the new semantic similarity measure with the standard item-based CF to enhance the quality of generated recommendations. In order to assess the effectiveness of our semantic-enhanced hybrid collaborative filtering method, a series of experiments were conducted to compare the performance of the proposed approach against well-established benchmark techniques. The reported experimental results consistently emphasize its superiority, demonstrating enhanced predictive abilities and a notable improvement in the quality of recommendations. More specifically, the proposed approach achieved notable 6% reduction in Mean Absolute Error (MAE) in certain cases, outperforming other benchmark techniques. Additionally, this study highlights the potential of using semantic-based similarity to enhance the performance of recommendation systems. Such enhancements address challenges within collaborative filtering, potentially leading to advancements in recommendation system design and optimization.

Connected and Autonomous Vehicles are promising for advancing traffic safety and efficiency. However, the increased connectivity makes these vehicles vulnerable to a broad array of cyber threats. This paper presents a novel hybrid approach for intrusion detection in in-vehicle networks, specifically focusing on the Controller Area Network bus. Ensemble learning techniques are combined with sophisticated optimization techniques and dynamic adaptation mechanisms to develop a robust, accurate, and computationally efficient intrusion detection system. The proposed system is evaluated on real-world automotive network datasets that include various attack types (e.g., Denial of Service, fuzzy, and spoofing attacks). With these results, the proposed hybrid adaptive system achieves an unprecedented accuracy of 99.995% with a 0.00001% false positive rate, which is significantly more accurate than traditional methods. In addition, the system is very robust to novel attack patterns and is tolerant to varying computational constraints and is suitable for deployment on a real-time basis in various automotive platforms. As this research represents a significant advancement in automotive cybersecurity, a scalable and proactive defense mechanism is necessary to safely operate next-generation vehicles.