Detecting anomalous events in videos is one of the most popular computer vision topics. It is considered a challenging task in video analysis due to its definition, which is subjective or context-dependent. Various approaches have been proposed to address the anomaly detection problems. These approaches vary from hand-crafted to deep learning. Many researchers have gone into determining the best approach for effectively detecting anomalies in video streams while maintaining a low false alarm rate. The results proved that approaches based on deep learning offer very interesting results in this field. In this paper, we review a family of video anomaly detection approaches based on deep learning techniques, which are compared in terms of their algorithms and models. Moreover, we have grouped state-of-the-art methods into different categories based on the approach adopted to differentiate between normal and abnormal events, and the underlying assumptions. Furthermore, we also present publicly available datasets and evaluation metrics used in existing works. Finally, we provide a comparison and discussion on the results of various approaches according to different datasets. This paper can be a good starting point for such researchers to understand this field and review existing work related to this topic.

The computation of network reliability for a system with many states is an NP-hard issue. Finding all the minimum path vectors (d-MPs) lower boundary points for each level d is one of the few approaches for computing such dependability. This research proposed enhancements to the technique described in Chen's "Searching for d-MPs with rapid enumeration" paper. We propose additional adjustments to the method that creates the flow vector F in this enhancement. This decreases the number of required steps and the temporal complexity of the method. Comparing the newly suggested approach to the old algorithm reveals that the adjustment has increased the enumeration's efficiency and degree of complexity.

Learner engagement is a critical concept that can lead to satisfaction, motivation, and success in e-learning courses. It covers contextual, emotional, behavioral, cognitive, and social aspects. The instructors have difficulties identifying who is involved in the courses and the lack of face-to-face interaction with a learning resource to act upon and reduce the dropout rate. This paper presents a novel approach that aims to predict learner engagement in online courses and quantify the relationship between the learners’ success and their engagement. For this purpose, we used the traces gathered from 1 356 learners’ reactions in e-learning courses during the winters of 2020, 2021, and 2022, to implement this approach. To model the learning engagement, a variety of features were considered such as the total number of posts made in forums and the total time spent on the e-learning platform. This study uses the BiLSTM method with FastText word embedding to detect learners’ emotions from the forum discussions. Then, an unsupervised clustering technique based on the new dataset was used to cluster learners into groups according to their engagement level. Several supervised classification algorithms were trained and their performance was evaluated using cross-validation techniques and diverse precision metrics. The findings indicate that the decision tree rule model is more relevant than others, with an accuracy of 98% and an AUC score of 0.97. The conclusions of this research reveal that most learners are observers and that there is a nonlinear correlation between learning success and learning engagement.

In recent years, the vehicular ad-hoc network (VANET), which is an ad-hoc network used by connected autonomous vehicles (CAV) for information processing, has attracted the interest of researchers in order to meet the needs created by the accelerating development of autonomous vehicle technology. The enormous amount of information and the high speed of the vehicles require us to have a very reliable communication protocol. The objective of this paper is to determine a topology-based routing protocol that improves network performance and guarantees information traffic over VANET. This comparative study was carried out using the simulation of urban mobility (SUMO) and network simulator (NS-3). Through the results obtained, we will show that the choice of the type of protocol to use depends on the size of the network and also on the metrics to be optimized.

The learning management system (LMS) is an e-learning software that raised the interest of disparate learners’ groups. However, learners have difficulties in finding learning resources tailored to their preferences in the best way at the right time. Making the learning process more efficient and pleasant for learners can be achieved by using context and learning styles such as customizing aspects. This study proposes a new data-driven approach to retrieve learners' characteristics using traces of their activities based on the Felder-Silverman Learning Style Model (FSLSM). In this research, the traces of 714 learners who enrolled in three agronomy courses taught at IAV HASSAN II (winter session 2019, 2020, and 2021) were analyzed. Learners are categorized into clusters by their preference level for global/sequential learning styles, using an unsupervised clustering method. Then a classifier model tailored to our requirements was trained and based on the learner's learning style and their current context, a learning object recommendation list is proposed for them. The results revealed that the k-means algorithm performed well in identifying learning styles (LS) and the use of context features defined from the learners' adaptive close environments improved learning performance with an accuracy of over 96% given that most of the learners preferred a global learning style.

Over the course of the previous two decades, there has been a rise in the quantity of text documents stored digitally. The ability to organize and categorize those documents in an automated mechanism, is known as text categorization which is used to classify them into a set of predefined categories so they may be preserved and sorted more efficiently. Identifying appropriate structures, architectures, and methods for text classification presents a challenge for researchers. This is due to the significant impact this concept has on content management, contextual search, opinion mining, product review analysis, spam filtering, and text sentiment mining. This study analyzes the generic categorization strategy and examines supervised machine learning approaches and their ability to comprehend complex models and nonlinear data interactions. Among these methods are k-nearest neighbors (KNN), support vector machine (SVM), and ensemble learning algorithms employing various evaluation techniques. Thereafter, an evaluation is conducted on the constraints of every technique and how they can be applied to real-life situations.