Article Per Year (5 Year)
p-Index From 2021 - 2026
0.92
P-Index
This Author published in this journals
All Journal Journal of Robotics and Control (JRC) International Journal of Robotics and Control Systems
Mansour, Salah-Eddine
Unknown Affiliation
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Enhancing Security Mechanisms for IoT-Fog Networks Mansour, Salah-Eddine; Sakhi, Abdelhak; Kzaz, Larbi; Sekkaki, Abderrahim
Journal of Robotics and Control (JRC) Vol 5, No 1 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i1.20745

Abstract

This study contributes to improving Morocco's fish canning industry by integrating artificial intelligence (AI). The primary objective involves developing an AI and image processing-based system to monitor and guarantee canning process quality in the facility. It commenced with an IoT-enabled device capable of capturing and processing images, leading to the creation of an AI-driven system adept at accurately categorizing improperly crimped cans. Further advancements focused on reinforcing communication between IoT devices and servers housing individual client's neural network weights. These weights are vital, ensuring the functionality of our IoT device. The efficiency of the IoT device in categorizing cans relies on updated neural network weights from the Fog server, crucial for continual refinement and adaptation to diverse can shapes. Securing communication integrity between devices and the server is imperative to avoid disruptions in can classification, emphasizing the need for secure channels. In this paper, our key scientific contribution revolves around devising a security protocol founded on HMAC. This protocol guarantees authentication and preserves the integrity of neural network weights exchanged between Fog computing nodes and IoT devices. The innovative addition of a comprehensive dictionary within the Fog server significantly bolsters security measures, enhancing the overall safety between these interconnected entities.
Using Learning Focal Point Algorithm to Classify Emotional Intelligence Sakhi, Abdelhak; Mansour, Salah-Eddine; Sekkaki, Abderrahim
Journal of Robotics and Control (JRC) Vol 5, No 1 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i1.20895

Abstract

Recognizing the fundamental role of learners' emotions in the educational process, this study aims to enhance educational experiences by incorporating emotional intelligence (EI) into teacher robots through artificial intelligence and image processing technologies. The primary hurdle addressed is the inadequacy of conventional methods, particularly convolutional neural networks (CNNs) with pooling layers, in imbuing robots with emotional intelligence. To surmount this challenge, the research proposes an innovative solution—introducing a novel learning focal point (LFP) layer to replace pooling layers, resulting in significant enhancements in accuracy and other vital parameters. The distinctive contribution of this research lies in the creation and application of the LFP algorithm, providing a novel approach to emotion classification for teacher robots. The results showcase the LFP algorithm's superior performance compared to traditional CNN approaches. In conclusion, the study highlights the transformative impact of the LFP algorithm on the accuracy of classification models and, consequently, on emotionally intelligent teacher robots. This research contributes valuable insights to the convergence of artificial intelligence and education, with implications for future advancements in the field.
Detection of Sealing Defects in Canned Sardines Using Local Binary Pattern and Perceptron Techniques for Enhanced Quality Control Mansour, Salah-Eddine; Sakhi, Abdelhak
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1737

Abstract

In the canned sardine production industry, sealing issues often arise due to various factors, such as the quantity of fish in the can or improper calibration of the sealing machine. These sealing defects can result in poorly sealed cans that may explode and contaminate an entire production batch, leading to significant financial losses and damage to the company's reputation. This study proposes an advanced and reliable method for classifying fish can images to detect potential defects, such as sealing issues, which are critical to maintaining quality standards in the canning industry. Our classification method utilizes the Local Binary Patterns (LBP) algorithm for feature extraction across the entire dataset of images. The extracted features are then processed using a Perceptron classifier to identify poorly sealed cans. This approach achieved a precision score of 0.85, demonstrating its effectiveness. Additionally, our analysis revealed that LBP significantly contributes to improving classification accuracy. By automating and enhancing the quality assurance process, this method provides the canning industry with a robust tool for ensuring high product standards, minimizing errors, and increasing efficiency in production lines.
Leveraging LFP Architecture for Pneumothorax Detection in Chest X-rays Mansour, Salah-Eddine; Sakhi, Abdelhak
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i1.25107

Abstract

The frequency of pneumothorax diagnoses has risen since the COVID-19 pandemic, leading to an increase in related research. This study presents a novel approach for pneumothorax detection using the Learning Focal Point (LFP) architecture, which is based on the LFP algorithm. The LFP architecture segments chest X-ray images into multiple zones, allowing for the effective extraction of critical regions associated with pneumothorax. By focusing on these essential zones, the method aims to enhance the accuracy and reliability of detection, optimizing both training and testing processes. Unlike traditional methods that process the entire image, the LFP architecture prioritizes the most relevant areas, improving the efficiency of the model. Our results demonstrate a significant improvement in detection accuracy, achieving an impressive score of 0.87. This advancement holds promise for aiding clinicians in making more accurate diagnoses and providing timely interventions for patients suffering from pneumothorax. The proposed LFP-based method can be a valuable tool in medical imaging, particularly in the context of emergency care, where rapid and reliable diagnosis is crucial. Overall, the study highlights the potential of the LFP architecture to improve pneumothorax detection and contribute to the advancement of medical diagnostic technologies.
Optimizing Virtual Classrooms: Real-Time Emotion Recognition with AI and Facial Features Sakhi, Abdelhak; Mansour, Salah-Eddine
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1827

Abstract

Online education, especially post-COVID, faces the challenge of maintaining student engagement, particularly at the college level. A key factor in effective learning is understanding students’ emotional states, as they influence comprehension and participation. To address this, we propose an intelligent system that classifies students’ emotions by analyzing facial expressions, allowing teachers to adapt their methods in real-time. Our system utilizes the Learning Focal Point algorithm to improve emotion classification accuracy, focusing on key facial regions related to emotional expressions. The methodology involves preprocessing facial images, extracting features, and classifying emotions using the algorithm. Trained on a diverse dataset, the system performs well under various conditions, with a classification accuracy of 94% based on a well-known database. Although the system shows significant improvements over traditional methods, factors like image quality and internet connection can impact accuracy in realworld applications. Ultimately, our approach enhances remote learning by providing real-time emotional feedback, fostering a more responsive and student-centered environment.
Co-Authors Kzaz, Larbi Sakhi, Abdelhak Sekkaki, Abderrahim