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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) Journal of Robotics and Control (JRC) International Journal of Robotics and Control Systems
Sakhi, Abdelhak
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Mechanical Engineering

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Journal : Journal of Robotics and Control (JRC)

 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.
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.
Co-Authors Kzaz, Larbi Mansour, Salah Eddine Mansour, Salah-Eddine Sekkaki, Abderrahim