Dr. From Nantes Central School, Toumi Bouchentouf is a professor of computer science and artificial intelligence at the ENSAO of the Mohamed Premier University of Oujda (UMP); head of the house of artificial intelligence within the University of Mohammed the First (UMP); member of the commission of the P2E project (Student-Entrepreneur program) at the level of the University Mohammed Premier within the framework of the UMP-Region- CGEM convention, to select student startup projects. 2021-2023 Dr. Bouchentouf has been leading or being a member of several CNRST research projects since 2020, such as the INKAD-AI-Covid-19 project: intelligent and ethical platform for research and decision-making support (2020-2021) and in collaboration with the MASCIR foundation and within the framework of the AL-KHAWARIZMI Program is supervising a thesis within the project Developments of speech processing algorithms for a medical assistance robot for seniors (2020-2023). Dr. Bouchentouf has been directing theses in Artificial Intelligence in Visio for the past few years, such as the detection and classification of illegal fishing boats by an autonomous drone using deep learning as well as the improvement of drones through the use of computer vision using CNN-type neural networks (convolutions) and the optimization of trajectories. In Natural Language Processing (NLP) Dr. Bouchentouf directed theses on the transformation of natural language into code, SQL cases using deep algorithms learning and created an intelligent search engine for biomedical research articles and a Q/A-type biomedical data set in French. He also directed theses on the modeling and detection of machine learning attacks to improve the evaluation of intrusion detection systems. Analysis of public opinion in social networks by application of deep -type algorithms learning about tweets. Also on cross- platform application development approaches, on the object-oriented transformation of requirements engineering and on software process management and classification of metrics for software quality.