Sickle cell anemia is a genetic disease characterized by a genuine alteration of hemoglobin that leads to the emergence of sickle-shaped red blood cells. The clinical diagnosis of sickle cell disease is based on blood analysis tests such as electrophoresis, chromatography and isoelectric focusing, blood count scanning or peripheral blood smears. These analyses aim to identify the presence of the hemoglobin S (HbS) in the sickle cell patient's blood. Nonetheless, these approaches face limitations linked to processing delays, substantial expenses, the need for specialized expertise, and inaccessibility in developing countries, where the prevalence of sickle cell disease is substantial. Thus, artificial intelligence techniques, including medical image segmentation and machine learning algorithms, offer innovative solutions to address these challenges. This paper gives an overview of recent advances related to image segmentation, feature extraction as well as classification approaches used in the detection and diagnosis of sickle cell disease. We introduced both computer vision techniques, machine learning and convolutional neural networks for sickle cell detection in microscopic images of blood smear. We discuss specific challenges related to segmentation methods while processing microscopic images containing overlapping cells. In addition, we examined challenges related to the robustness of convolutional neural network models used for feature extraction and then classification of sickle cells images. To conclude this paper, research prospects that can be explored in the future related to sickle cell detection in medical concerns are given.

Main objective of Web Scraping is to extract information from one or many websites and process it into simple structures such as spreadsheets, database or CSV file. However, in addition to be a very complicated task, Web Scraping is resource and time consuming, mainly when it is carried out manually. Previous studies have developed several automated solutions. The purpose of this article is to revisit the different existing Web Scraping approaches, categories, and tools, but also its areas of application.

Nowadays, the study of online press has become an issue of phenomenal research. From articles collections and merging, opinion mining, artificial intelligence or automatic classification to fact-checking; researches are developing and opening new perspectives. The main objective is to pave the way for the journalistic consumption of the future. However, though the literature review mentions previous studies on online press articles classification, no work on automatic classification of press articles based on a theme has been carried out yet. In this article, we set out a supervised classifier of journalistic articles applied on Senegalese online press.

In recent years, automatic natural language processing (NLP) has made considerable progress in terms of performance. Nevertheless, to undertake a linguistic analysis of the facts in French remains a real problem today. On the one hand, current taggers do not match the definition of fact in fact-checking and, on the other hand, the complexity of the French language considerably decreases their performance. In this paper, we propose a tool for the automatic segmentation and tagging of simple facts in French language speeches. It takes a speech as an input and generates the labelled facts as an output in table format.