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All Journal International Journal of Advances in Applied Sciences BAREKENG: Jurnal Ilmu Matematika dan Terapan
Mohamed, Mohamad Afendee
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Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Transportation

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Comparative study on fine-tuning deep learning models for fruit and vegetable classification Mamat, Abd Rasid; Mohamed, Mohamad Afendee; Kadir, Mohd Fadzil Abd; Rawi, Norkhairani Abdul; Aziz, Azim Zaliha Abd; Awang, Wan Suryani Wan
International Journal of Advances in Applied Sciences Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i2.pp384-393

Abstract

Fruit and vegetable recognition and classification can be a challenging task due to their diverse nature and have become a focal point in the agricultural sector. In addition to that, the classification of fruits and vegetables increases the cost of labor and time. In recent years, deep learning applications have surged to the forefront, offering promising solutions. Particularly, the classification of fruits using image features has garnered significant attention from researchers, reflecting the growing importance of this area in the agricultural domain. In this work, the focus was on fine-tuning hyperparameters and the evaluation of a state-of-the-art deep convolutional neural network (CNN) for the classification of fruits and vegetables. Among the hyperparameters studied are the number of batch size, number of epochs, type of optimizer, rectified unit, and dropout. The dataset used is the fruit_vegetable dataset which consists of 36 classes and each class contains 1,000 images. The results show that the proposed model based on the batch size=64 and the number of epochs=25, produces the most optimal model with an accuracy value (training) of 99.02%, while the validation is 95.73% and the loss is 6.06% (minimum).
Addition chain heuristics in application to elliptic curve cryptosystems Mohamed, Mohamad Afendee; Shawai, Yahaya Garba; Derahman, Mohd Noor; Mamat, Abd Rasid; Mohd Satar, Siti Dhalila; Amri Abidin, Ahmad Faisal; Abdul Kadir, Mohd Fadzil
International Journal of Advances in Applied Sciences Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i3.pp546-555

Abstract

The idea of an addition chain can be applied to scalar multiplication involving huge number operations in elliptic curve cryptosystems. In this article, initially, we study the taxonomy of the addition chain problem to build up an understanding of the problem. We then examine the mathematics behind an optimal addition chain that includes the theoretical boundary for the upper limit and lower limit which laid the foundation for experimentation hereafter. In the following, we examine different addition chain solutions that were used to increase efficiency in scalar multiplication. To avoid any possible confusion, we intentionally separated the discussion into two modules called integer recoding method and chain generator based on the heuristics method. These methods were developed by considering various aspects such as the space within which the operation is executed, the curve that is selected, the formulation to express the original equation, and the choices of operation and arithmetic, all together to improve operational efficiency.
CHAOS CONTROL IN PERMANENT MAGNET SYNCHRONOUS MOTOR BY SLIDING MODEL CONTROLLER WITH LYAPUNOV OBSERVER UNDER UNKNOWN INPUTS Hamidzadeh, Seyed Mohamad; Aziz, Amiral; Mohamed, Mohamad Afendee; Vaidyanathan, Sundarapandian; Johansyah, Muhamad Deni
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0541-0556

Abstract

The control of chaotic and hyper-chaotic systems represents a crucial area of research in the field of nonlinear dynamic systems. In this study, we focus on applying chaos control techniques to a permanent magnet synchronous motor (PMSM), a system known to exhibit chaotic behavior under certain conditions. To achieve this, a sliding mode control (SMC) strategy integrated with a Lyapunov-based observer is proposed. The core concept involves designing a candidate Lyapunov function that governs the application of the control law, ensuring system stability while effectively suppressing chaotic dynamics. Through numerical simulations, the proposed sliding mode controller demonstrates its effectiveness in rapidly eliminating chaotic behavior and stabilizing the PMSM system toward a predefined reference trajectory. Notably, the system achieves error convergence within approximately 0.7 seconds under full control (four channels). When control channels are reduced to two, the system still maintains stability, showing flexibility and cost efficiency. In a further simulation, the chaotic PMSM is subjected to two unknown external disturbances, and the proposed controller continues to maintain stability with only a slight increase in convergence time. These quantitative results affirm the robustness, accuracy, and practicality of the proposed control method. This research confirms that integrating sliding mode control with a Lyapunov observer is an effective approach for chaos suppression in PMSMs, offering promising insights for the development of advanced control strategies in nonlinear electromechanical systems.
Co-Authors Abdul Kadir, Mohd Fadzil Amiral Aziz Amri Abidin, Ahmad Faisal Awang, Wan Suryani Wan Aziz, Azim Zaliha Abd Derahman, Mohd Noor Hamidzadeh, Seyed Mohamad Kadir, Mohd Fadzil Abd Mamat, Abd Rasid Mohd Satar, Siti Dhalila Muhammad Deni Johansyah Rawi, Norkhairani Abdul Shawai, Yahaya Garba Vaidyanathan, Sundarapandian