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30,411 Article Results

Pilot study on the use of art therapy techniques to improve the psycho-emotional state of educational psychologists

10.11591/ijere.v14i5.30603
Tatigul Samuratova , Gulnar Khazhgaliyeva , Oksana Makarova , Nikolay Pronkin
The aim of this study is to investigate the impact of art therapy on the psycho-emotional state of educational psychologists. The issue at hand is the prevalence of depression, anxiety, and emotional burnout among future educational psychologists, which can negatively affect their professional performance. To address this problem, the application of art therapy was proposed as a tool to improve the emotional health of students. The experiment involved 107 students aged 20-22 from the Yelabuga Institute of Kazan Federal University. The assessment of emotional state was conducted using the Beck Depression Inventory, the Spielberger-Hanin Anxiety Scale, and the Schreiner, Rosenberg, and Boyko tests. The results indicated that after three months of art therapy, the average level of depression decreased by 15%, anxiety levels decreased by 20%, and emotional burnout was reduced by 15%. Additionally, students’ stress resistance increased by 20%. Thus, art therapy is an effective means for reducing the emotional burden on students. It is recommended to incorporate art therapy techniques into the curricula of universities, colleges, and secondary schools. Further research is necessary to confirm the effectiveness of art therapy among students of various specializations.
Volume: 14
Issue: 5
Page: 4129-4139
Publish at: 2025-10-01

A memory improved proportionate affine projection algorithm for sparse system identification

10.11591/ijece.v15i5.pp4605-4619
Senthil Murugan Boopalan , Sarojini Raju , Krithiga Sukumaran , Manimegalai Munisamy , Kalphana Ilangovan , Sudha Ramachandran , Janani Munisamy , Bharathiraja Ramamoorthi , Sakthivel Pichaikaran
For cluster sparse system identification, it is known that the cluster sparse improved proportionate affine projection algorithm (CS-IPAPA) outperforms the standard IPAPA. However, since CS-IPAPA does not retain past proportionate factors, its performance can be further improved. In this paper, a modification to CS-IPAPA is proposed by utilizing the past instant proportionate elements based on its projection order. Steady-state performance of the proposed memory cluster sparse improved proportionate affine projection algorithm (MCS-IPAPA) is studied by deriving the condition for mean stability. Different simulation setups show that the proposed algorithm outperforms different versions of IPAPA in terms of convergence rate, normalized misalignment (NM) and tracking, for different types of inputs like colored noise, white noise, and speech signal. By incorporating past proportionate factors, the proposed MCS-IPAPA significantly reduces computational complexity for higher projection orders.
Volume: 15
Issue: 5
Page: 4605-4619
Publish at: 2025-10-01

Comparison of long short-term memory and deep neural network optimized neural networks for maximum power tracking of wind turbines

10.11591/ijece.v15i5.pp4454-4464
Ezzitouni Jarmouni , Ahmed Mouhsen , Mohamed Lamhamdi , Ennajih Elmehdi , Naoual Ajedioui , En-Naoui Ilias
In wind energy conversion systems, maximum power point tracking (MPPT) performance is crucial, as it is directly related to wind speed variability and the characteristics of the equipment used. Maximum power point tracking controllers are essential for optimizing the efficiency of wind power generation. This paper presents the development of three distinct approaches to maximum power point tracking: the classical perturb and observe (P&O) method, and two other techniques based on artificial intelligence, namely long short-term memory (LSTM) networks and deep neural networks (DNNs). Rather than focusing solely on the development of an intelligent neural network-based maximum power point tracking model, our work emphasizes the design of a deep neural network controller with an optimized architecture and a reduced number of layers and neurons per layer, thereby simplifying its implementation in embedded process control units while maintaining high maximum power point tracking performance. The results obtained show that our optimized deep neural network model identifies the point of maximum power more effectively than other techniques, demonstrating remarkable performance in terms of response time, accuracy, and the quality of the generated power.
Volume: 15
Issue: 5
Page: 4454-4464
Publish at: 2025-10-01

Fuzzy clustering optimization based artificial bee colony algorithm for brain magnetic resonance imaging image segmentation

10.11591/ijece.v15i5.pp4916-4932
Chakir Mokhtari , Mohammed Debakla , Boudjelal Meftah
In brain magnetic resonance imaging (MRI) analysis, image clustering is regarded as one of the most crucial tasks. It is frequently employed to estimate and visualize brain anatomical structures, identify pathological regions, and assist in guiding surgical procedures. Fuzzy c-means algorithm (FCM) is widely used in the MRI image segmentation process. However, it has been several weaknesses such as noise sensitivity, stuck in local optimum and issues with parameters initialization. To address these FCM problems, this paper presents a novel fuzzy optimization method that enhances brain MRI image segmentation by integrating the artificial bee colony (ABC) algorithm with FCM clustering techniques. The proposed method seeks to optimize multiple FCM parameters simultaneously, including the objective function, number of clusters, and cluster center values. The method was evaluated on both simulated and clinical brain MR images, with an emphasis on segmenting white matter, grey matter, and cerebrospinal fluid regions. Experimental results demonstrate significant improvements in segmentation accuracy, achieving a Jaccard similarity (JS) of nearly 1, a partition coefficient index (PCI) of 0.92, and a Davies-Bouldin index (DBI) of 0.41, outperforming other stats of the arts methods.
Volume: 15
Issue: 5
Page: 4916-4932
Publish at: 2025-10-01

Systematic review: the application of ChatGPT on Arabic language text processing

10.11591/ijece.v15i5.pp4837-4847
Ali Mousa AlSbou , Fadzli Syed Abdullah , Ashanira Mat Deris
Over 420 million people speak Arabic, and it is the official language of 22 countries. Its complex morphology and dialectal diversity present unique challenges for natural language processing (NLP) models like ChatGPT. This systematic review investigates the application of ChatGPT in Arabic language text processing, examining its potential uses, accuracy, and limitations. Covering literature published between 2021 and 2024, this review synthesizes findings from 21 articles, addressing four key research questions: ChatGPT’s applications in Arabic text processing, its performance in terms of accuracy and reliability, the challenges and limitations encountered, and future directions to enhance its utilization. Results indicate that ChatGPT has potential in several applications, including educational tools, machine translation, text generation, and sentiment analysis. Despite current limitations, ChatGPT's potential in Arabic text processing is promising. While it shows high accuracy in structured tasks, it struggles with dialectal variations and cultural nuances, especially in complex text types. Primary limitations include a lack of high-quality Arabic datasets, difficulty handling dialects, and a need for more nuanced contextual understanding. Future research should focus on improving data quality, expanding dialectal coverage, fine-tuning models for specific linguistic tasks, and integrating AI with human teaching methods. Addressing these areas will enhance ChatGPT's accuracy and reliability for Arabic NLP.
Volume: 15
Issue: 5
Page: 4837-4847
Publish at: 2025-10-01

A hybrid extreme learning machine and sine cosine algorithm model for accurate electricity price forecasting

10.11591/ijece.v15i5.pp4366-4375
Udaiyakumar Sambathkumar , Sangeetha Shanmugam , Kannayeram Ganapathiya Pillai
Electricity demand is continually rising due to the advancement of new technology, the switch to greener energy, and the popularity of electric vehicles over conventional ones. The proliferation of businesses in the generation and distribution sectors has increased competition in the electricity market. Forecasting electricity prices enables consumers to control their monthly electricity bills and consumer-owned distributed generation by knowing the forecasted hourly price. For demand management, generation scheduling, and bidding price quotations, electricity price forecasting is crucial for buyers, generation businesses, and bidders alike. Electricity price data is highly nonlinear and affected by numerous factors because of which EPF models are more complex, highly volatile and slow in convergence. A range of neural network models, training algorithms, and hybrid systems comprising two or more models have been suggested for precise and efficient electricity price forecasting by researchers over the decade. This study involves the development of a hybrid neural network model with two intelligent algorithms sine cosine algorithm (SCA) and extreme learning machine (ELM) to predict electricity price for a particular duration. The newly developed network model is trained and tested with real-time Indian electricity price data from the year 2022. The selected annual price data set is divided into three different sets to explore seasonal variations and all the sets are given as the input to the model for training and testing to obtain the effective price forecasting.
Volume: 15
Issue: 5
Page: 4366-4375
Publish at: 2025-10-01

A computational study of passive cooling of photovoltaic panels using hybrid material heat sink

10.11591/ijece.v15i5.pp4487-4499
Dang Van Binh , Pham Quang Vu , Pham Manh-Hai
Photovoltaic panels generate electricity from solar energy based on the photovoltaic effect. The conversion efficiency of photovoltaic panels depends on many factors such as solar radiation, wind speed, dust, orientation, tilt angle, and operating temperature. When the operating temperature increases by 1 C, the conversion efficiency of photovoltaic panels decreases by 0.4% - 0.5%. Heat sink is a device used to cool electrical and electronic equipment, including photovoltaic panels. This paper presents calculating the cooling capability of hybrid heat sink made from two materials in steady state using heat transfer theory. Heat sink base is constructed from aluminum and copper layers, with copper layer thickness is 1 and 2 mm. Under different conditions of radiation intensity, wind speed, and tilt angle of photovoltaic panel, results show that heat sink added copper layers of 1 and 2 mm, the operating temperature decreases by about 0.6 K and 1.2 K compared to the aluminum base. Accordingly, the conversion efficiency of photovoltaic panel increased by 0.1% and 0.2%.
Volume: 15
Issue: 5
Page: 4487-4499
Publish at: 2025-10-01

Optimizing internet of things based gas sensors: deep learning and performance optimization strategies

10.11591/ijece.v15i5.pp4813-4828
Mariam M. Abdellatif , Mehmet Akif Çifçi , Asmaa A. Ibrahim , Hany M. Harb , Abeer S. Desuky
The rapid growth of industrialization and internet of things (IoT) driven advancements in Industry 5.0 necessitates efficient and user-friendly engineering solutions. Gas leakage incidents in coal mines, chemical enterprises, and households pose significant risks to ecosystems and human safety, emphasizing the need for automated and rapid gas-type detection. Traditional detection methods rely on single-source data and focus on isolated spatial or temporal features, limiting accuracy. This paper proposes a multimodal artificial intelligence (AI) fusion technique combining pre-trained convolutional neural networks (CNNs), such as VGG16, with a deep neural network (DNN) model. The particle swarm optimization (PSO) algorithm optimizes CNN hyperparameters, outperforming traditional trial-and-error methods. The system addresses challenges posed by gases being odorless, colorless, and tasteless, which limit conventional human detection methods. By leveraging sensor fusion, the late fusion technique integrates distinct network architectures for unified gas identification. Experimental results demonstrate 95% accuracy using DNN with gas sensor data, 96% with optimized VGG16 using thermal imaging, and 99.5% through multimodal late fusion. This IoT-enhanced solution outperforms single-sensor approaches, offering a robust and reliable gas leakage detection system suitable for industrial and smart city applications.
Volume: 15
Issue: 5
Page: 4813-4828
Publish at: 2025-10-01

Enhancing network resilience and energy efficiency in the El Abiodh Sidi Cheikh grid through load flow analysis

10.11591/ijece.v15i5.pp4774-4784
Ali Abderrazak Tadjeddine , Soumia Djelaila , Ridha Ilyas Bendjillali , Sofiane Mohammed Bendelhoum , Abdelyamine Boukhobza , Salih Lachache
This study investigates the performance of the El Abiodh Sidi Cheikh (ESC) electrical grid, focusing on energy efficiency, system stability, and the integration of renewable energy sources. Numerical methods, including the Newton-Raphson (NR), accelerated Newton-Raphson (ANR), and fast decoupled (FD) load flow methods, were employed to evaluate power flow, voltage stability, and active power losses. Key results reveal that the NR method achieves the lowest power loss, with a minimal value of 2.32 MW, while voltage violations at specific nodes, such as buses 4 and 13, emphasize the necessity for voltage regulation. Analysis of the sun trajectory and temperature profiles highlights correlations between climatic conditions and energy demand, aiding renewable energy optimization. Additionally, photovoltaic (PV) measurements demonstrate diurnal variations in energy output, critical for enhancing renewable energy integration. These findings underscore the importance of advanced power flow analysis and strategic planning to ensure network resilience, energy efficiency, and reliability in the ESC region.
Volume: 15
Issue: 5
Page: 4774-4784
Publish at: 2025-10-01

Enhancing concrete sustainability: a neural networks hybrid optimization approach to predicting compressive strength using supplementary cementitious materials

10.11591/ijece.v15i5.pp4965-4982
Esra’a Alhenawi , Ayat Mahmoud Al-Hinawi , Zaher Salah , Omar Alidmat , Esraa Abu Elsoud , Raed Alazaidah , Bashar Rizik AlSayyed
This research evaluates the implementation of advanced machine learning methodologies for concrete mix design to achieve better predictive models and sustainable outcomes. This study develops a hybrid optimization approach by combining dung beetle optimizer (DBOA) and firefly algorithm (FLA) to optimize hyperparameters for convolutional-recurrent neural networks in order to correctly predict concrete compressive strength when using supplementary cementitious materials (SCMs). Shapley additive explanations (SHAP) provide feature significance analysis, which ensures that the model produces understandable conclusions supported by empirical findings. The findings demonstrate that this method enhances the predictive accuracy of strength analysis, along with offering critical insights about SCM usage in order to improve sustainable construction methods. The model proves suitable for integration into actual concrete mix design and quality control systems because it achieves both computational speed and passes validation tests on distinct datasets. The research creates foundations for upcoming studies about multimodal learning enrichment and deals with ethical concerns in construction site safety when using machine learning systems.
Volume: 15
Issue: 5
Page: 4965-4982
Publish at: 2025-10-01

Optimized fractional-order direct torque control with space vector modulation strategy for two-wheel-drive electric vehicles

10.11591/ijece.v15i5.pp4409-4420
Touhami Nawal , Ouled-Ali Omar , Mansouri Smail , Benhammou Aissa
Electric vehicles (EVs) are a sustainable and efficient transportation choice, offering zero emissions, lower operating costs, and advanced performance features like instant torque and regenerative braking. They promote energy independence, improve urban livability, and support the global shift toward cleaner, renewable energy-powered mobility, making them a future-proof investment. The electric motor is a critical component in electric vehicles (EVs), the importance of which lies in its high efficiency, instant torque delivery, and smooth operation, which enhances performance and energy use. This paper focuses on a two-wheel drive electric vehicle (TWD EV) configuration powered by an energy storage battery system (ESBS), driven by two permanent magnet synchronous motors (PMSMs), and controlled using direct torque control with space vector modulation (DTC-SVM). fractional-order proportional integral derivative (FOPID) controllers, optimized via the grey wolf optimizer (GWO) algorithm, are implemented for precise speed control of the PMSMs. An electronic differential (ED) is incorporated to ensure vehicle stability, safety, and performance. The simulation results show that the proposed GWO-FOPID controller gave super results by reducing electromagnetic torque overshoot by 33%, improves torque settling time by 55%, and achieves the lowest electromagnetic torque ripple of approximately ±1 Nm compared to conventional DTC-SVM and GWO-PID approaches. Additionally, it optimized speed overshoot and undershoot by 44%, significantly enhancing system performance, responsiveness, and drive smoothness. This novel combination of fractional-order control, metaheuristic optimization, and electronic differential integration marks a meaningful advancement in high-precision and efficient control for 2WD EVs.
Volume: 15
Issue: 5
Page: 4409-4420
Publish at: 2025-10-01

A novel approach for recommendation using optimized bidirectional gated recurrent unit

10.11591/ijece.v15i5.pp5019-5030
Prakash Pandharinath Rokade , Swati Babasaheb Bhonde , Prashant Laxmanrao Paikrao , Umesh Baburao Pawar
In today's world, every one of us refreshes our mood and gets energy through entertainment and enjoyment. Human nature is to provide feedback through ratings or comments for products used, services received, or films viewed. The recommendation system serves the user with recommendations based on historical stored information of user preferences. These systems amass information about the user in order to provide personalized experiences. These systems put efforts into delivering personalized experiences by accumulating information about the user. Hybrid algorithms are necessary to address the issues recommendation systems confront, which include low prediction accuracy, output that exceeds range, and inadequate convergence speed. This study suggests building a movie recommendation system using the remora optimization algorithm (ROA) and the bidirectional gated recurrent unit (BiGRU), the most recent version of the recursive neural network (RNN). The proposed method's results are compared with those of the genetic algorithm (GA), feed forward neural network (FFNN), and multimodal deep learning (MMDL). In terms of movie recommendation, BiGRU with ROA performs better than GA, MMDL, and FFNN.
Volume: 15
Issue: 5
Page: 5019-5030
Publish at: 2025-10-01

Detecting autism with Vietnamese child facial images using deep learning

10.11591/ijece.v15i5.pp4762-4773
Tran Van Thanh , Lam Thanh Hien , Do Nang Khoa , Le Anh Tu , Ha Manh Toan , Do Nang Toan
Deep learning techniques created a significant increase in intelligent systems, especially in the medical field. Among mental problems, autism is a dangerous neurodevelopmental disorder and it needs to be diagnosed early because of the malleability of child brain development. In our study, we focused on autism detection by using the Vietnamese facial child image and studied the role of international data and Vietnamese data when applying deep learning approach to diagnose autism. To do that, we proposed different strategies based on our hypothesis about factors of the transfer learning and training set types. To conduct the experiment, we prepared a Vietnamese facial child image set from several kindergartens in Ho Chi Minh City, Vietnam and we applied different deep architectures such as ResNet, DenseNet, and AlexNet in the autism classification experiment with both Vietnamese and international facial child images. We analyzed important factors from the experiment results with area under the curve (AUC), accuracy, sensitivity, and specificity, including applying transfer learning and the appearance of Vietnamese data in the training set. Besides, we also discussed the difference of international and Vietnamese data domains. The exposure of data distribution differences in the proposed strategies also highlights the importance of collecting facial data of Vietnamese children.
Volume: 15
Issue: 5
Page: 4762-4773
Publish at: 2025-10-01

Anomaly-based intrusion detection leveraging optimized firewall log analysis: a real-time machine learning solution

10.11591/ijece.v15i5.pp4785-4802
Tran Cong Hung , Dam Minh Linh , Han Minh Chau , Ngo Xuan Thoai , Thai Duc Phuong , Huynh De Thu
Firewall logs play a vital role in cybersecurity by recording network traffic and flagging potential threats. This study evaluates five machine learning algorithms-decision tree (DT), random forest (RF), extra trees (ET), CatBoost (CB), and AdaBoost (AB)-on a dataset of 65,532 firewall log entries. Models were assessed using accuracy, precision, recall, training/prediction time, and Pearson correlation for feature selection, across multiple train-test splits. The DT model achieved the best performance, reaching 99.45% test accuracy, 97.457% precision, and 93.389% recall at a 7:3 split, along with the fastest training time (0.20642s). We propose real-time flow-level intrusion detection (RT-FLID), novel, lightweight, real-time intrusion detection system that leverages multithreaded processing and flow-level analysis to boost detection speed and scalability. Unlike existing approaches that rely heavily on deep packet inspection or computationally intensive processing, RT-FLID requires minimal resources while maintaining high detection accuracy. The architecture efficiently handles large traffic volumes and dynamically identifies anomalies such as distributed denial-of-service (DDoS) and port scans. Validated on real-world logs, the system maintained high accuracy in critical classes like “deny” and “reset-both.” These findings highlight RT-FLID’s novelty and practical advantages, demonstrating its potential for deployment in high-throughput, low-latency network environments.
Volume: 15
Issue: 5
Page: 4785-4802
Publish at: 2025-10-01

Optimal sizing and performance evaluation of hybrid photovoltaic-wind-battery system for reliable electricity supply

10.11591/ijece.v15i5.pp4341-4354
Youssef El Baqqal , Mohammed Ferfra , Reda Rabeh
Given the advantages of hybrid renewable energy systems over single-source systems, this study proposes the optimal sizing and performance evaluation of a hybrid photovoltaic-wind battery system to meet the electricity demand of an isolated community in Dakhla, Morocco. The objective is to achieve an economical approach to electricity generation. Particle swarm optimization (PSO) and grey wolf optimizer (GWO) techniques were used to determine the optimal configuration of system components, including photovoltaic (PV) panels, wind turbines, and battery storage. The annual system cost (ACS) is minimized as the optimization objective, and the levelized cost of electricity (LCOE) is used for economic comparison. MATLAB serves as the platform for implementation and evaluation. Results demonstrate the convergence and effectiveness of PSO and GWO in delivering high-quality solutions. PSO, however, achieves superior system reliability with a lower loss of power supply probability (LPSP) during peak demand. The optimal configuration achieves a minimal LCOE of 0.1065 USD/kWh, representing a 33.44% reduction compared to the applicable rate. These findings highlight the potential of advanced optimization techniques to improve the economic and operational performance of hybrid renewable energy systems, making them a viable solution for rural electrification in regions with limited grid access.
Volume: 15
Issue: 5
Page: 4341-4354
Publish at: 2025-10-01
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