Text classification supervised algorithms with term frequency inverse document frequency and global vectors for word representation: a comparative study

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

Comparative study of proactive and reactive routing protocols in vehicular ad-hoc network

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

Severe Neonatal Presentation of Progressive Familial Intrahepatic Cholestasis Type 4 in an Omani Infant

Progressive familial intrahepatic cholestasis type 4 (PFIC4) is a relatively newly described autosomal recessive disorder caused by biallelic mutations in the gene encoding tight junction protein 2 (TJP2) which is located in chromosome 9q21. PFIC4 is characterized by cholestasis with or without other extrahepatic manifestations. Bleeding tendency due to vitamin k deficiency is a well-known complication of cholestasis. We present a neonate who presented with cholestasis and multiple intracranial bleeds. He was found to have severe coagulopathy and his genetic work up revealed a homozygous variant mutation in TJP2 gene causing PFIC4. He had persistent cholestasis that necessitated an internal biliary diversion with some clinical improvement. Keywords: Jaundice; Intracranial haemorrhage; Progressive Familial Intrahepatic Cholestasis type 4

Reconstituer l’environnement des entreprises : des parties prenantes aux actants parties prenantes ? L’exemple de la station touristique d’Essaouira

The theory of Stakeholders, “Stakeholder Theory” made stakeholder identification the central theme of its work. Despite multiple approaches, unanimity has not been reached on the actors considered as stakeholders, restricted only to the actors influencing economic performance. Given the importance of adopting a more inclusive approach, some authors have called for experimentation with other theoretical frameworks, even from other disciplines. The objective of this contribution is to test the capacity of the sociological theory of the actor-network in the identification of a wider network of stakeholders. This approach has been carried out in the tourist field of Essaouira in view of the large number of relationships that are established between its actors and has led to the new concept of "Actants-Stakeholders"

Interactive ROI Segmentation using Graph Cuts

International audienceEnergy minimization by graph cuts is increasingly used in computer vision. One of the most attractive aspects of this approach is that it allows, under certain conditions, to find global minima of functions, avoiding the pitfall of classical local minima. In this paper, we describe our semisupervised approach of visual objects segmentation from a priori information, reducing the space of research. Then, we present different applications and we discuss the application of this method on color images and we evaluate a number of functions of penalties used for the construction of the graph

Moving Objects Tracking in Video by Graph Cuts and Parameter Motion Model

Published by Foundation of Computer Science, New York, USAInternational audienceThe tracking of moving objects in a video sequence is an important task in different domains such as video compression, video surveillance and object recognition. In this paper, we propose an approach for integrated tracking and segmentation of moving objects from image sequences where the camera is in movement. This approach is based on the calculation of minimal cost of a cut in a graph ―Graph Cuts‖ and the 2D parametric motion models estimated between successive images. The algorithm takes advantage of smooth optical flow which is modeled by affine motion and graph cuts in order to reach maximum precision and overcome inherent problems of conventional optical flow algorithms. Our method is simple to implement and effective. Experimental results show the good performance and robustness of the proposed approach

Context and Learning Style Aware Recommender System for Improving the E-Learning Environment

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

New Automatic Hybrid Approach for Tracking Learner Comprehension Progress in the LMS

Learning style is a significant learner-difference factor. Each learner has a preferred learning style and a different way of processing and understanding the novelty. In this paper, a new approach that automatically identify learners learning styles based on their interaction with the Learning Management System (LMS) is introduced. To implement this approach, the traces of 920 enrolled learners in three agronomy courses were exploited using an unsupervised clustering method to group learners according to their degree of engagement. The decision tree classification algorithm relies on the decision rules construction, which is widely adopted to identify the accurate learning style. As missing good decision rules would lead to learning style misclassification, the Felder-Silverman Learning Style Model (FSLSM) is used as it is among the most adopted models in the technology of quality improvement process. The results of this research highlight that most learners prefer the global learning style