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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Buletin Ilmiah Sarjana Teknik Elektro International Journal of Robotics and Control Systems Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Abualigah, Laith
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering Transportation

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Journal : International Journal of Electrical and Computer Engineering

 1 
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 hybrid adaptive neuro-fuzzy inference system and reptile search algorithm model for wind power forecasting Al-Widyan, Mohamad I.; Abualigah, Laith; Jaradat, Ghaith M.; Alsmadi, Mutasem Khalil
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2857-2873

Abstract

Estimating the number of wind ranches generated in the upcoming minutes, hours, or days is the focus of wind power forecasting. Deep learning has garnered a lot of interest in wind control estimation because of how well they perform classification, grouping, and recurrence. The adaptive neuro-fuzzy inference system was successfully applied in wind power forecasting. However, its performance relies on optimal selection of hyperparameters. This study introduces a novel predictive model by incorporating the reptile search algorithm with adaptive neuro-fuzzy inference system (ANFIS) for short-term wind power forecasting. It employs reptile search algorithm (RSA), known for adjustable parameters, disentangled search, and consistent outcomes, to optimize ANFIS’s hyperparameters. Additionally, via exploitation during training, RSA performs a selection of best features in the dataset that contributes to the classification accuracy of ANFIS. This aims to enhance precision of the anticipated yield. Employing authentic wind power data from Jordan is undertaken to evaluate efficiency. The performance is compared with alternative techniques, including artificial neural networks, random forests, and support vector machines. Findings showed that ANFIS-RSA performs competitively for the well-known Chinese benchmark dataset (99.9% accuracy; 0.99 R2; 10.54 MAE; 11.62 RMSE) and is more robustly accurate than others over the Jordanian dataset (0.84.6% accuracy; 0.96 R2; 0.098 MAE; 0.203 RMSE).
Chaotic red-tailed hawk algorithm to optimize parameter power system stabilizer Aribowo, Widi; Abualigah, Laith; Oliva, Diego; Aljohani, Abeer; Sabo, Aliyu
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3536-3545

Abstract

This article introduces a recently created adaptation of the red-tailed hawk (RTH) algorithm. The proposed approach is a modified version of the original RTH algorithm, incorporating chaotic elements to enhance its integrity and performance. The RTH algorithm emulates the hunting behavior of the red-tailed hawk. This article demonstrates the adjustment of the power system stabilizer using the suggested technique in a case study involving a single-machine system. The suggested method was validated by benchmarking against known functions and evaluating its performance on a single-machine system in terms of transient responsiveness. The essay employs the original RTH algorithm as a means of comparison. The simulation results demonstrate that the proposed technique exhibits promising performance.
Co-Authors A. Shehadeh, Hisham Abatal, Ahmed Aditya Prapanca Al-Widyan, Mohamad I. Aljohani, Abeer Almomani, Mohammad H. Alomari, Saleh Ali Alsmadi, Mutasem Khalil Amaliah, Fithrotul Irda As’ad Shidqy Aziz Ayusta Lukita Wardani B, Nur Vidia Laksmi Benlalia, Zakaria Billah, Mohammed El Kaim Eker, Erdal Ekinci, Serdar Elsayed Abd Elaziz, Mohamed Hussein, Ahmad MohdAziz Izci, Davut Jaradat, Ghaith M. Khallouki, Hajar Mahendra Widyartono Migdady, Hazem Mzili, Mourad Mzili, Toufik Oliva, Diego Rahmadian, Reza Sabo, Aliyu Smerat, Aseel Snasel, Vaclav Soleimanian Gharehchopogh, Farhad Widi Aribowo Zangana, Hewa Majeed Zitar, Raed Abu