Article Per Year (5 Year)
p-Index From 2019 - 2024
0.23
P-Index
This Author published in this journals
All Journal JOIV : International Journal on Informatics Visualization
Essaid El Bachari
Cadi Ayyad University, Marrakesh, Morocco
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Main problems and proposed solutions for improving Template Matching Khalid Aznag; Ahmed El Oirrak; Essaid El Bachari
JOIV : International Journal on Informatics Visualization Vol 3, No 2 (2019)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (890.15 KB) | DOI: 10.30630/joiv.3.2.225

Abstract

In this work we discuss the problems of template matching and we propose some solutions.  Those problems are: 1) Template and image of search differ by a scale, 2) Template or image of search is object of rotation, 3) Template or image of search is object of an affinity. The well known method is NCC (Normalized Cross Correlation); this method can not handle scale, rotation, affinity or occlusion. Also the NCC is not preferred for binary image. So we propose here to use index similarity for example Jaccard index.
Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques Outmane Bourkoukou; Essaid El Bachari
JOIV : International Journal on Informatics Visualization Vol 2, No 4 (2018)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1444.661 KB) | DOI: 10.30630/joiv.2.4.158

Abstract

Personalized courseware authoring based on recommender system, which is the process of automatic learning objects selecting and sequencing, is recognized as one of the most interesting research field in intelligent web-based education. Since the learner’s profile of each learner is different from one to another, we must fit learning to the different needs of learners. In fact from the knowledge of the learner’s profile, it is easier to recommend a suitable set of learning objects to enhance the learning process. In this paper we describe a new adaptive learning system-LearnFitII, which can automatically adapt to the dynamic preferences of learners. This system recognizes different patterns of learning style and learners’ habits through testing the psychological model of learners and mining their server logs. Firstly, the device proposed a personalized learning scenario to deal with the cold start problem by using the Felder and Silverman’s model. Next, it analyzes the habits and the preferences of the learners through mining the information about learners’ actions and interactions. Finally, the learning scenario is revisited and updated using hybrid recommender system based on K-Nearest Neighbors and association rule mining algorithms. The results of the system tested in real environments show that considering the learner’s preferences increases learning quality and satisfies the learner.
Co-Authors Ahmed El Oirrak Khalid Aznag Outmane Bourkoukou