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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 : International Journal of Robotics and Control Systems

 1 
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.
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 Mansour, Salah Eddine Mansour, Salah-Eddine Sekkaki, Abderrahim