Article Per Year (5 Year)
p-Index From 2021 - 2026
0.817
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Khalifa Mansouri
Hassan II University of Casablanca
Computer Science & IT Electrical & Electronics Engineering

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
A design of a multi-agent recommendation system using ontologies and rule-based reasoning: pandemic context Amina Ouatiq; Kamal ElGuemmat; Khalifa Mansouri; Mohammed Qbadou
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp515-523

Abstract

Learners attend their courses in remote or hybrid systems find it difficult to follow one size fits all courses. These difficulties have increased with the pandemic, lockdown, and the stress they cause. Hence, the role of adaptive systems to recommend personalized learning resources according to the learner's profile. The purpose of this paper is to design a system for recommending learning objects according learner's condition, including his mental state, his COVID-19 history, as well as his social situation and ability to connect to the e-learning system on a regular basis. In this article, we present an architecture of a recommendation system for personalized learning objects based on ontologies and on rule-based reasoning, and we will also describe the inference rules required for the adaptation of the educational content to the needs of the learners, taking into account the learner’s health and mental state, as well as his social situation. The system designed, and validated using the unified modeling language (UML). It additionally allows teachers to have a holistic view of learners’ progress and situations.
Towards smart modeling of mechanical properties of a bio composite based on a machine learning Aziz Moumen; Abdelghani Lakhdar; Zineb Laabid; Khalifa Mansouri
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3138-3145

Abstract

The main interest in many research problems in polymer bio composites and machine learning (ML) is the development of predictive models to one or several variables of interest by the use of suitable independent inputs or variables. Nevertheless, these fields have generally adopted several approaches, while bio composite behavior modeling is usually based on phenomenological theories and physical models. These latter are more robust and precise, but they are generally under the restricted predictive ability due to the particular set of conditions. On the other hand, Machine learning models can be highly efficient in the modeling phase by allowing the management of high and massive dimensional sets of data to predict the best behavior of bio composites. In this situation, biomaterial scientists would like to benefit from the comprehension and implementation of the powerful ML models to characterize or predict the bio composites. In this study, we implement a smart methodology employing supervised neural network models to predict the bio composites properties presenting more significant environmental and economic advantages than composites reinforced by synthetic fibers.
Towards developing a pocket therapist: an intelligent adaptive psychological support chatbot against mental health disorders in a pandemic situation Intissar Salhi; Kamal El Guemmat; Mohammed Qbadou; Khalifa Mansouri
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp1200-1211

Abstract

Nowadays with COVID-19 ongoing epidemic outbreak, containment for weeks was one of the most effective measures adopted to deal with the spread of the virus until a vaccine could be efficient. Over that period, increased anxiety, depression, suicide attempts and post-traumatic stress disorder are accumulated. Several studies referred to the need of using chatbots, which recognizes human emotions in such pandemic contexts. More recently, numerous research papers improved the ability of artificial intelligence methods to recognize human emotion. However, they are still limited. The aim of this paper is the development of a chatbot against the disturbing psychic consequences of the pandemic, taking human emotion recognition into account. The object is to help people; especially students; suffering from mental disorders, by progressively understanding the reasonsbehind them. This innovative chatbot was developed by using the natural language processing model of deep learning. An advanced model of deep learning has been elaborated the intention for people and that to help them to regulate their mood and to reduce distortion of negative thoughts, that why a collection of a new database was done. The sequence-to-sequence model encoder and decoder consist of Long short-term memory cells and it is defined with the bi-directional dynamic recurrent neural network packets.
Dysgraphia detection based on convolutional neural networks and child-robot interaction Soukaina Gouraguine; Mustapha Riad; Mohammed Qbadou; Khalifa Mansouri
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2999-3009

Abstract

Dysgraphia is a disorder of expression with the writing of letters, words, and numbers. Dysgraphia is one of the learning disabilities attributed to the educational sector, which has a strong impact on the academic, motor, and emotional aspects of the individual. The purpose of this study is to identify dysgraphia in children by creating an engaging robot-mediated activity, to collect a new dataset of Latin digits written exclusively by children aged 6 to 12 years. An interactive scenario that explains and demonstrates the steps involved in handwriting digits is created using the verbal and non-verbal behaviors of the social humanoid robot Nao. Therefore, we have collected a dataset that contains 11,347 characters written by 174 participants with and without dysgraphia. And through the advent of deep learning technologies and their success in various fields, we have developed an approach based on these methods. The proposed approach was tested on the generated database. We performed a classification with a convolutional neural network (CNN) to identify dysgraphia in children. The results show that the performance of our model is promising, reaching an accuracy of 91%.
Co-Authors Abdelghani Lakhdar Amina Ouatiq Aziz Moumen Intissar Salhi Kamal El Guemmat Kamal ElGuemmat Mohammed Qbadou Mustapha Riad Soukaina Gouraguine Zineb Laabid