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

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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.
Leveraging the learning focal point algorithm for emotional intelligence Mansour, Salah Eddine; Sakhi, Abdelhak; Kzaz, Larbi; Sekkaki, Abderrahim
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i3.pp767-773

Abstract

One of the secrets of the success of the education process is taking into account the learner’s feelings. That is, the teacher must be characterized by high emotional intelligence (EI) to understand the student’s feelings in order to facilitate the indoctrination process for him. Within the framework of the project to create a robot teacher, we had to add this feature because of its importance. In this article, we create a computer application that classifies students' emotions based on deep learning and learning focal point (LFP) algorithm by analyzing facial expressions. That is, the robot will be able to know whether the student is happy, excited, or sad in order to deal with him appropriately.
Co-Authors Mansour, Salah Eddine Mansour, Salah-Eddine Sakhi, Abdelhak Sekkaki, Abderrahim