Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Aniss Moumen
Ibn Tofaïl University
Computer Science & IT Engineering

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

Found 2 Documents
Search

 1 
Application of artificial neural networks for personality traits prediction based on handwriting Ahmed Remaida; Benyoussef Abdellaoui; Mohamed Amine Lafraxo; Zineb Sabri; Hamza Nouib; Younes El Bouzekri El Idrissi; Aniss Moumen
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1534-1544

Abstract

The automatization of personality traits prediction still brings considerable research these days, especially when the detection could be achieved without administrating personality assessment instruments. With the evolution of computational intelligence, a variety of deep machine learning techniques were developed and proposed for that purpose. Nevertheless, proposing robust and rapid systems to solve this problem remains a challenging task. The process of feature extraction is the main key. This paper presents an effective method for extracting five graphological features from handwriting ensuring the prediction of personality traits based on the big five personality traits model. We started by collecting both handwriting samples and big five questionnaires, then the feature extraction process, after that the data preparation and finished with the application of several popular deep machine learning models to achieve the prediction. Experimental results indicate the remarkable performance of the multi-layer perceptron (MLP) compared to other classifiers, the model was 100% precise and classification accuracy attained 100% for trained data and 72.73% for new tested data. With only 100 participants, we strongly believe that our proposed method is simple and promising, and better results will be attained with a larger dataset.
An intelligent and explainable IoT-Edge-Cloud architecture for real-time water quality monitoring Sara Bouziane; Badraddine Aghoutane; Aniss Moumen; Anas El Ouali; Ali Essahlaoui; Abdellah El Hmaidi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1109-1120

Abstract

Continuous and reliable monitoring of water quality is critical for early detection of environmental deterioration, yet conventional monitoring approaches are often slow and lack timely data availability. This study proposes an intelligent and explainable internet of things (IoT)–Edge–Cloud architecture to monitor water quality in real time, using IoT sensing, edge based artificial intelligence (Edge AI), cloud-stream processing, and explainable artificial intelligence (XAI). The system calculates the water quality index (WQI) directly at the edge and predicts its evolution using a stacking ensemble model trained on physicochemical measurements taken from the Moulouya River Basin in Morocco. An explainability module based on Shapley additive explanations (SHAP) values gives a clearer image of the contribution of various parameters to WQI predictions, providing transparency of the features, which builds trust in the model’s output. The proposed architecture was implemented as an end-to-end prototype and validated using a simulation-based experimental that mimicked realistic sensor dynamics and connectivity interruptions. The experimental results show strong predictive performance (R² =0.945), stable system operations, and reliable interpretability highlighting the potential of the proposed approach for scalable, intelligent, and transparent environmental monitoring.
Co-Authors Abdellah El Hmaidi Ahmed Remaida Ali Essahlaoui Anas El Ouali Badraddine Aghoutane Benyoussef Abdellaoui Hamza Nouib Mohamed Amine Lafraxo Sara Bouziane Younes El Bouzekri El Idrissi Zineb Sabri