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Our aim in this paper is to improve the efficiency of a learning process by using learners' traces to detect particular needs. The analysis of the semantic path of a learner or group of learners during the learning process can allow detecting those students who are in needs of help as well as identify the insufficiently mastered concepts. We examine the possibility of using a student's browsing path during a learning session, based on his navigation traces, to update the learner model. We assume that the domain concepts examined outside the learning platform but that are related to the course concepts are problematic to the learner. Knowing about these concepts may allow the course's author to adapt the course to the learner's needs regarding these concepts, as well as allow the tutor to help and assist the learner on these problematic concepts. We rely on Web data mining methods to filter, organize, and analyze the student's browsing path. More precisely, we use a domain ontology of the course and the similarities that exist between external documents (visited pages) and the domain concepts (the course keywords). This analysis process makes it possible to detect students' learning difficulties and to adapt the course based on the learner's model.

Algeria's socio-linguistic situation is known as a complex phenomenon involving several historical, cultural and technological factors. However, there are three languages that are mainly spoken in Algeria (Arabic, Tamazight and French) and they can be mixed in the same sentence (code-switching). Moreover, there are several varieties of dialects that differ from one region to another and sometimes within the same region. This paper aims to provide a new multi-purpose parallel corpus (i.e., DZDC12 corpus), which will serve as a testbed for various natural language processing and information retrieval applications. In particular, it can be a useful tool to study Arabic–French code-switching phenomenon, Algerian Romanized Arabic (Arabizi), different Algerian sub-dialects, sentiment analysis, gender writing style, machine translation, abuse detection, etc. To the best of our knowledge, the proposed corpus is the first of its kind, where the texts are written in Latin script and crawled from Facebook. More specifically, this corpus is organised by gender, region and city, and is transliterated into Arabic script and translated into Modern Standard Arabic. In addition, it is annotated for emotion detection and abuse detection, and annotated at the word level. This article focuses in particular on Algeria's socio-linguistic situation and the effect of social media networks. Furthermore, the general guidelines for the design of DZDC12 corpus are described as well as the dialects clustering over the map. [ABSTRACT FROM AUTHOR]

With the increasing amount of medical data available on the Web, looking for health information has become one of the most widely searched topics on the Internet. Patients and people of several backgrounds are now using Web search engines to acquire medical information, including information about a specific disease, medical treatment or professional advice. Nonetheless, due to a lack of medical knowledge, many laypeople have difficulties in forming appropriate queries to articulate their inquiries, which deem their search queries to be imprecise due the use of unclear keywords. The use of these ambiguous and vague queries to describe the patients' needs has resulted in a failure of Web search engines to retrieve accurate and relevant information. One of the most natural and promising method to overcome this drawback is Query Expansion. In this paper, an original approach based on Bat Algorithm is proposed to improve the retrieval effectiveness of query expansion in medical field. In contrast to the existing literature, the proposed approach uses Bat Algorithm to find the best expanded query among a set of expanded query candidates, while maintaining low computational complexity. Moreover, this new approach allows the determination of the length of the expanded query empirically. Numerical results on MEDLINE, the on-line medical information database, show that the proposed approach is more effective and efficient compared to the baseline. [ABSTRACT FROM AUTHOR]

The present work focuses on an ethnobotanical study of medicinal plants in the Belezma National Park (BNP), which is located in Batna city. It was conducted to precise on the medicinal plants in the park and gathers all the information on the therapeutic practice used by the local inhabitants of the study area. Using 300 questionnaire cards, ethnobotanical surveys of BNP were conducted during two campaigns (2017 and 2018). The obtained results allowed us to identify 50 medicinal plants used by the population of the region, which are divided into 27 families and 46genus. The obtained results show both leaves and stems are mostly used for diseases' treatment in the form of a decoction with a rate of 42.34%. For the treated diseases, the digestive disorders occupy the first place with a rate of 34.01%, followed by uro-genital diseases with a rate of 17.56%, 11.20% for respiratory diseases, 11.84% for orthopedic diseases and 6.23% for cardiovascular. The founded results could be a database for research on phytochemistry and pharmacology for the national medicinal flora and the population of the BNP region. Also, they constitute a very valuable source of information. [ABSTRACT FROM AUTHOR]