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p-Index From 2020 - 2025
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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Electronics, Electromedical Engineering, and Medical Informatics Indonesian Journal of Electrical Engineering and Computer Science Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Mzili, Toufik
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering Transportation

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Hybrid long short-term memory and decision tree model for optimizing patient volume predictions in emergency departments Abatal, Ahmed; Mzili, Mourad; Benlalia, Zakaria; Khallouki, Hajar; Mzili, Toufik; Billah, Mohammed El Kaim; Abualigah, Laith
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp669-676

Abstract

In this study, we address critical operational inefficiencies in emergency departments (EDs) by developing a hybrid predictive model that integrates long short-term memory (LSTM) networks with decision trees (DT). This model significantly enhances the prediction of patient volumes, a key factor in reducing wait times, optimizing resource allocation, and improving overall service quality in hospitals. By accurately forecasting the number of incoming patients, our model facilitates the efficient distribution of both human and material resources, tailored specifically to anticipated demand. Furthermore, this predictive accuracy ensures that EDs can maintain high service standards even during peak times, ultimately leading to better patient outcomes and more effective use of healthcare facilities. This paper demonstrates how advanced data analytics can be leveraged to solve some of the most pressing challenges faced by emergency medical services today.
A Comprehensive Evaluation of Machine Learning Techniques for Forecasting Student Academic Success Abatal, Ahmed; Korchi, Adil; Mzili, Mourad; Mzili, Toufik; Khalouki, Hajar; Billah, Mohammed El Kaim
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.489

Abstract

Improving academic outcomes relies on accurately anticipating student outcomes within a course or program. This predictive capability empowers instructional leaders to optimize the allocation of resources and tailor instruction to meet individual student needs more effectively. In this study, we endeavor to delineate the attributes of machine learning algorithms that excel in forecasting student grades. Leveraging a comprehensive dataset encompassing both personal student information and corresponding grades, we embark on a rigorous evaluation of various regression algorithms. Our analysis encompasses a range of widely used technniques, Incorporating various machine learning algorithms like XGBoost, Linear Regression, K-Nearest Neighbor, Decision Tree, Random Forest, and Deep Neural Network. By conducting thorough comparisons using metrics such as Root Mean Squared Error, determination coefficient, Mean Average Error and Mean Squared Error. Our aim is to pinpoint the algorithm that exhibits superior predictive ability. Notably, our experimental findings unveil the deep neural network as the standout performer among the evaluated algorithms. Having an outstanding coefficient of determination of 99.95% and Minimal error margins, the DNN emerges as a potent tool for accurately forecasting student grades. This discovery not only underscores the efficacy of advanced machine learning methodologies but also underscores the transformative potential they hold in shaping educational practices and optimizing student outcomes.
Enhanced hippopotamus optimization algorithm for power system stabilizers Aribowo, Widi; Mzili, Toufik; Sabo, Aliyu
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp22-31

Abstract

This article presents techniques for modifying the power system stabilizer's (PSS) parameters. An enhanced version of the hippocampal optimization algorithm (HO) is presented here. HO represents a novel approach in metaheuristic methodology, having been inspired by the observed clinging behavior in hippos. The notion of the HO is defined using a trinary-phase model that includes their position updates in rivers or ponds, defensive techniques against predators, and mathematically described evasive methods. To confirm the efficacy of the recommended approach, this article provides comparison simulations of the PSS objective function and transient response. This study employs validation through a comparison between Original HO and conventional methods. Simulation results demonstrate that, when compared to competing algorithms, the suggested approach yields optimal results and, in some cases, exhibits fast convergence. It is known that, in comparison to the original HO approach, the recommended way can lower the average undershoot of the rotor angel and speed by 12.049% and 26.97%, respectively.
Hybrid horned lizard optimization algorithm-aquila optimizer for DC motor Aribowo, Widi; Abualigah, Laith; Oliva, Diego; Mzili, Toufik; Sabo, Aliyu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1673-1682

Abstract

This research presents a modification of the horned lizard optimization (HLO) algorithm to optimize proportional integral derivative (PID) parameters in direct current (DC) motor control. This hybrid method is called horned lizard optimization algorithm-aquila optimizer (HLAO). The HLO algorithm models various escape tactics, including blood spraying, skin lightening or darkening, crypsis, and cellular defense systems, using mathematical techniques. HLO enhancement by modifying additional functions of aquila optimizer improves HLO performance. This research validates the performance of HLAO using performance tests on the CEC2017 benchmark function and DC motors. From the CEC2017 benchmark function simulation, it is known that HLAO's performance has promising capabilities. By simulating using 3 types of benchmark functions, HLOA has the best value. Tests on DC motors showed that the HLAO-PID method had the best integrated of time-weighted squared error (ITSE) value. The ITSE value of HLOA is 89.25 and 5.7143% better than PID and HLO-PID.
Frilled Lizard Optimization to optimize parameters Proportional Integral Derivative of DC Motor aribowo, widi; Abualigah, Laith; Oliva, Diego; Mzili, Toufik; Sabo, Aliyu; A. Shehadeh, Hisham
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 1 No. 1 (2024)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v1i1.33973

Abstract

This paper presents a Proportional-Integral-Derivative (PID) parameter optimization method for direct current (dc) motors. The method utilizes a metaheuristic technique known as Frilled Lizard Optimization (FLO), which is inspired by natural processes. FLO draws inspiration from the lizard's hunting method of employing a sit-and-wait approach with great patience. The method is divided into two distinct phases: the exploration phase, which simulates a swift predator attack by a lizard, and the exploitation phase, which imitates the lizard's return to the treetop after feeding. This study confirms the effectiveness of FLO by conducting performance tests on the CEC2017 benchmark function and a DC motor. Through the simulations conducted on the CEC2017 benchmark function, it has been determined that FLO has superior exploration and exploitation capabilities. When testing a DC motor, it was discovered that the PID-FLO approach is effective in reducing overshoot and achieving optimal performance
Co-Authors A. Shehadeh, Hisham Abatal, Ahmed Abualigah, Laith Benlalia, Zakaria Billah, Mohammed El Kaim Khallouki, Hajar Khalouki, Hajar Korchi, Adil Mzili, Mourad Oliva, Diego Sabo, Aliyu Widi Aribowo