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28,296 Article Results

Design of a solar-powered electric vehicle charging station

10.11591/ijece.v15i5.pp4465-4476
Emerson Cabanzo Mosquera , Walter Naranjo Lourido , Javier Eduardo Martínez Baquero
This manuscript presents the design of a solar-powered electric vehicle (EV) charging station in Villavicencio, Colombia, aimed at reducing reliance on the utility grid, lowering energy costs, and minimizing environmental impact. The station designed integrates a photovoltaic system to harness renewable energy, ensuring a sustainable and cost-effective charging solution. It accommodates both AC and DC fast charging options to meet diverse vehicle requirements. The design considers available space, energy generation potential, and financial feasibility to maximize efficiency and return on investment. A technical analysis of battery storage, power electronics, and system configuration is provided, along with a cost-benefit assessment. Simulation results confirm the station's ability to deliver stable power under varying conditions. With an estimated payback period of 2.8 years, this project demonstrates the economic and environmental advantages of solar-powered EV infrastructure, supporting the transition to clean transportation in Colombia.
Volume: 15
Issue: 5
Page: 4465-4476
Publish at: 2025-10-01

Efficient fall detection using lightweight network to enhance smart internet of things

10.11591/ijece.v15i5.pp5031-5044
Pinrolinvic D. K. Manembu , Jane Ivonne Litouw , Feisy Diane Kambey , Abdul Haris Junus Ontowirjo , Vecky Canisius Poekoel , Muhamad Dwisnanto Putro
Fall detection automatically recognizes human falls, mainly to monitor and prevent severe injury and potential fatalities. It can be developed by applying deep learning methods to recognize human subjects during fall incidents and implemented in the internet of things (IoT) to monitor patient and elderly individuals’ activity. The development of object detection presents you only look once v8 (YOLOv8) as an influential network, but its efficiency needs to be improved. A modified YOLOv8 architecture is proposed to introduce a novel lightweight network version called YOLOv8-Hypernano (YOLOv8h) that recognizes fall events. The backbone incorporates a combined spatial and channel attention module, which enhances focus on human subjects by concentrating on movement patterns to detect falls more accurately. This work also offers a consecutive selective enhancement (CSE) module to improve efficiency and effectiveness in feature extraction while reducing computational costs. The neck structure is modified by adding a lightweight bottleneck network. The proposed network reconstructs feature maps in depth, paying more attention to accurate human movement patterns and enhancing efficiency and effectiveness in feature extraction. Experimental results of YOLOv8h with the light bottleneck and consecutive selective enhancement modules show giga floating-point operations per seconds (GFLOPS) of 5.6 and 1,194,440 parameters. The model performance is calculated in mean average precision, achieving 0.603 and 0.732 on the Le2i and Fallen datasets, respectively. These results demonstrate that the optimized network improves accuracy performance while maintaining lightweight computing requirements that can run smoothly on IoT devices, achieving comparable speed and efficiency suitable for operation on low-cost computing devices.
Volume: 15
Issue: 5
Page: 5031-5044
Publish at: 2025-10-01

Analysis of partial discharge characteristics in transformer oil insulation media using needle-plane and plane-plane electrode systems

10.11591/ijece.v15i5.pp4445-4453
Teuku Khairul Murad , Abdul Syakur , Iwan Setiawan
Insulation failure is a common issue in electric power transmission. Insulation is necessary to separate two or more live conductors to prevent electrical arcing or sparking between them. Partial discharge (PD) is a phenomenon that can also occur in high-voltage equipment under pre-breakdown conditions. This PD activity can take place in liquid insulation, such as transformer oil, leading to a decrease in the quality and reliability of the transformer. This study aims to detect PD under various conditions and investigate its characteristics. Although various studies have been conducted on PD in liquid insulation, most of them focus on PD characterization under specific conditions without considering variations in electrode configurations that may influence the PD phenomenon. Therefore, this research is necessary to fill this gap by analyzing PD characteristics using a needle-plane and plane-plane electrode system. This study introduces the use of castor oil as an alternative liquid insulating material. In this study, PD testing will be conducted in a laboratory environment, and it is expected to produce reliable data regarding the capability of liquid insulation to withstand PD. The results obtained indicate that the PD phenomenon occurs more quickly in the needle-plane electrode configuration compared to the plane-plane configuration. PD in the needle-plane electrode occurs at an average voltage of 10.96 kV, while PD in the plane-plane electrode occurs at an average voltage of 12.5 kV.
Volume: 15
Issue: 5
Page: 4445-4453
Publish at: 2025-10-01

Understanding emotion regulation strategies in female adolescents with depressive symptoms: a qualitative study

10.11591/ijere.v14i5.31924
Siti Rashidah Yusoff , Khairul Farhah Khairuddin , Suzana Mohd Hoesni , Nur Afrina Rosharudin , Tuti Iryani Mohd Daud , Noor Azimah Muhammad , Manisah Mohd Ali , Mohamad Omar Ihsan Razman , Dharatun Nissa Puad Mohd Kari , Mohd Pilus Abdullah
In Malaysia, adolescents are at a high risk for depression, with the prevalence rising from 18.3% in 2017 to 26.9% in 2022. Additionally, the proportion of female adolescents affected is significantly higher than male adolescents, with 36.1% of females experiencing depression compared to 17.7% of males. Thus, a qualitative study was conducted to explore the emotion regulation strategies used by female adolescents experiencing depressive symptoms. Semi-structured interviews were performed with 15 female adolescents, aged 14 to 16 years, who had severe depression scores as assessed by the DASS-21. Using purposive sampling, all 15 female adolescents were selected from six public secondary schools in the Klang Valley, Malaysia. The Klang Valley, which includes the two main states of Selangor and Kuala Lumpur, was chosen due to its ranking among the top three states in 2022 with the highest rates of depression symptoms. All responses were recorded and analyzed using a thematic analysis approach. The findings revealed that female adolescents employed five emotion regulation strategies: suppressing expression, pampering themselves, seeking support, reorganizing their thoughts, and engaging in negative actions. This study explores the emotional experiences of female adolescents to design feasible and flexible interventions that address a wide range of individual needs.
Volume: 14
Issue: 5
Page: 3946-3959
Publish at: 2025-10-01

Energy evaluation of dependent malicious nodes detection in Arduino-based internet of things networks

10.11591/ijece.v15i5.pp4983-4992
Moath Alsafasfeh , Abdullah Alhasanat , Samiha Alfalahat
Detection of malicious nodes in the internet of things (IoT) network consumes power, which is one of the main constraints of the IoT network performance. To evaluate the energy-security trade-off for malicious node detection, this paper proposes an Arduino-based system for dependent malicious nodes (DMN) detection. The experimental work using Arduino and radio frequency (RF) modules was implemented to detect dependent malicious nodes in an IoT network. The detection algorithms were evaluated in terms of energy efficiency. The experiment comprises a coordinator node with five sensor nodes and varying malicious nodes. The results assess the detection algorithms in terms of distinguishing between normal and malicious behaviors and their impact on energy efficiency. The experiment demonstrated that the detection system could identify the malicious nodes. Additionally, the effect of increasing the number of sensors or malicious nodes on the suggested detection algorithm’s energy usage is evaluated.
Volume: 15
Issue: 5
Page: 4983-4992
Publish at: 2025-10-01

Classifying the suitability of educational videos for attention deficit hyperactivity disorder students with deep neural networks

10.11591/ijece.v15i5.pp4889-4898
Alshefaa Emam , Eman Karam Elsyed , Mai Kamel Galab
This paper presents a comprehensive deep learning-based system to evaluate the educational videos' suitability for students with attention deficit hyperactivity disorder (ADHD). Current methods frequently ignore important instructional elements that are necessary for improving learning experiences for students with ADHD, such as instructor hand movements, video length, object variety, and audio-visual quality. We emphasize two key issues for how to address these difficulties, first, we present the ADHD online instructor (AOI) dataset, a particular benchmark for assessing instructional hand movement in video suitability to solve the absence of a reference dataset for classifying educational videos relevant to ADHD. Second, the system includes creating an enhanced multitask deep learning model that increases classification accuracy by using task-specific enhancements and optimized architectures. This solves the requirement for a strong model that can distinguish between suitable and unsuitable instructional content. Comprehensive tests using pretrained convolutional neural network (CNN) models indicate that the enhanced VGG16 model outperforms baseline methods by achieving a highest accuracy of 97.84%. The results highlight the value of integrating deep learning methods with structured benchmark datasets, exposing up the path to more resilient and flexible instructional materials designed for students with ADHD.
Volume: 15
Issue: 5
Page: 4889-4898
Publish at: 2025-10-01

Route towards certification: a path analysis on licensure performance of new teacher education curriculum graduates

10.11591/ijere.v14i5.33552
Tedric Dave E. Senosa , Jr., Roberto G. Sagge
The board licensure examination for professional teachers (BLEPT) is a critical assessment for aspiring educators in the Philippines. Despite its vital importance, limited research has explored the comprehensive influence of the education graduates’ demographic background, psychological state, and achievement in the institutional parameters on the BLEPT performance. This study examined these influences on the licensure performance among 101 bachelor of secondary education (BSEd) mathematics and science graduates under the new teacher education curriculum. The researchers collected data using validated researcher-made questionnaires and educational metrics. Using structural equation modeling (SEM), results showed that the path model highlights the multifaceted nature of BLEPT performance, which shows that an intrinsic commitment towards the teaching profession and a supportive network create a cycle of positive experiences that fuels the graduates’ academic performance and self-efficacy, leading to a notable licensure performance. Likewise, the model emphasizes the vital effect of graduates’ education-related employment on their licensure examination performance. Taking these factors into account, teacher education institutions (TEIs) and key educational stakeholder should create targeted interventions, investigate unforeseen factors, and restructure curricula implementation to address the shortage of competent Filipino educators in these critical educational disciplines which are mathematics and science education.
Volume: 14
Issue: 5
Page: 3379-3389
Publish at: 2025-10-01

Maintenance management of physical infrastructure in educational institutions: a systematic review

10.11591/ijere.v14i5.33130
Julisa del Rosario Quispe Vilca , Dennys Geovanni Calderón Paniagua , Grisely Rosalie Quispe Vilca , Isabel Evelyna Choque Siguairo , Alexander Nicolás Vilcanqui Alarcón
The physical infrastructure of education in Latin America (LATAM) requires actions to ensure its conservation and maintenance in the different systems and levels. This is due to the absence of a maintenance programmed proposed by the State and the lack of trained personnel to implement it. The objective of this study was to analyze the importance of maintenance management of physical infrastructure in educational institutions. A systematic review was conducted following the guidelines of the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. The search process was carried out in the Scopus, ERIC, and Web of Science (WoS) databases, and eligibility criteria were established. The review covered the time interval between 2015 and 2023, and 16 English-language papers were selected. The results indicate that the lack of adequate and sustained investment, together with the lack of scheduled maintenance of educational infrastructure and the absence of structured maintenance plans, have a negative impact on student achievement. It is necessary for national and local governments to develop public policies focused on the conservation and improvement of educational infrastructure, incorporating modern management tools to facilitate this process.
Volume: 14
Issue: 5
Page: 3490-3501
Publish at: 2025-10-01

Predicting student performance and identifying learning behaviors using decision trees and K-means clustering

10.11591/ijere.v14i5.33815
Md. Mahadhi Hasan , Md Nakibul Islam , Md Ikramul Haque Nirjon , Md Sharif Uddin , Md. Muntasir Mamun , Zaheed Alam Munna , Al Mahmud Rumman
The insufficiency of a strong mechanism to measure student performance and learning behavior has been pointed out as a result of the expansion of higher education in Bangladesh. The objectives of the study are to predict students’ performance and recognize unique learning behaviors in the Bangladeshi higher education contexts by applying decision trees and K-means clustering methods. Validity and reliability of the results are ensured by following methods: 10-fold cross-validation for the decision tree model and Silhouette score assessment for the K-means clustering model, thus improving the predictive accuracy and differentiation of clusters. The study is based on a dataset of student records numbering 1,200, researching factors such as attendance (91.22%), exam results (mean 83.54%), completed assignments (mean 80.54%), and age (mean 23.47). Learning analytics theory is used since it is crucial to apply data to enhance the understanding and effectiveness of learning processes. The decision tree model showed excellent performance with high rates in precision, recall, and F1-scores, which were all at 0.99 for the evaluated performance measures, hence increasing its good predictive power. K-means clustering analysis grouped the students into three distinct groups: active learners, passive learners, and at-risk students. This research urges the adaptation of data mining methodologies within the framework of higher education and strongly emphasizes the important role that an early identification of at-risk students can play. This research is a contribution to the learning analytics area, and it further proves the applicability of data mining methods in predicting academic performance and improving education outcomes in developing contexts.
Volume: 14
Issue: 5
Page: 3872-3881
Publish at: 2025-10-01

Development of the Mongolian school climate inventory

10.11591/ijere.v14i5.30433
Davaanyam Tumenbayar , Amartuvshin Amarzaya , Lkhagvasuren Ganbat , Sandag Gendenjamts , Navchaa Tserendorj
The school climate is an essential aspect of educational practices and policies. This study aims to investigate Mongolian secondary school teachers’ perceptions of school climate and develop a measurement tool. The study involved 686 randomly selected teachers, and research data were collected online from the Mongolian National Educational Evaluation Centre. Statistical analysis was conducted using SPSS-21 software. This study was conducted in three phases: item generation, a pilot study, and a main study. Firstly, 77 items were developed on a 5-point Likert scale based on a literature review. Before the main survey, a pilot test was carried out with 200 teachers from the southern province of the country. Finally, an exploratory factor analysis (EFA) with Promax rotation was used to explore the content validity of the survey. Cronbach’s alpha was applied to assess the reliability of each factor. The statistical analysis revealed a 14-factor structure based on the data. The reliability analysis results indicated that internal consistency for all factors is at an acceptable level. The study’s overall results suggest that the proposed inventory is a validated measurement tool to examine teachers’ perceptions of the school climate in Mongolian secondary schools.
Volume: 14
Issue: 5
Page: 3702-3711
Publish at: 2025-10-01

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

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

On design of a small-sized arrays for direction-of-arrival-estimation taking into account antenna gains

10.11591/ijece.v15i5.pp4642-4652
Ilia Peshkov , Natalia Fortunova , Irina Zaitseva
In the paper a technique for designing antenna arrays composed of directional elements for direction-of-arrival (DOA) estimation is proposed. Especially this approach is applied for developing hybrid antenna arrays with increased accuracy which features digital spatial spectral estimation after preliminary analog beamforming. The earlier obtained explicit formula for calculating the Cramér–Rao lower bound (CRLB) which determines the relationship between the variance of the DOA-estimation and antenna elements' radiation patterns, array geometry, has been used. Main idea of the proposed technique is that it takes into account spatial pattern and gain of the antenna elements. The high gain unlike the number of the antenna elements or interelement distance is the most important factor which allows reducing the value of the DOA-estimation errors. A couple of the examples of calculating radiation patterns of antenna elements improving accuracy of DOA-estimation with super-resolution are provided in the paper. Proposed antenna arrays are modeled according to the method of moments (MoM). The values of the root mean square error after the DOA-estimation are obtained. It is shown that the resulting hybrid systems can reduce the error value in DOA-estimation with super-resolution.
Volume: 15
Issue: 5
Page: 4642-4652
Publish at: 2025-10-01

Exploring ensemble learning for classifying geometric patterns: insights from quaternion cartesian fractional Hahn moments

10.11591/ijece.v15i5.pp4630-4641
Zouhair Ouazene , Aziz Khamjane
The classification of geometric patterns, particularly in Islamic art, presents a compelling challenge for the field of computer vision due to its intricate symmetry and scale invariance. This study proposes an ensemble learning framework to classify geometric patterns, leveraging the novel quaternion cartesian fractional Hahn moments (QCFrHMs) as a robust feature extraction method. QCFrHMs integrate the fractional Hahn polynomial and quaternion algebra to provide compact, invariant descriptors for geometric patterns. Combined with Zernike Moments, this dual-feature approach ensures resilience against rotation, scaling, and noise variations. The extracted features were evaluated using support vector machines (SVM), random forest, and a soft-voting ensemble classifier. Experiments were conducted on a dataset comprising 1,204 geometric images categorized into two symmetry groups (p4m and p6m). Results demonstrated that the ensemble classifier outperformed standalone models, achieving a classification accuracy of 82.15%. The integration of QCFrHMs significantly enhanced the system's robustness compared to traditional Zernike-only approaches, which aligns with findings in prior studies. This research contributes to the fields of image processing and pattern recognition by introducing an efficient feature extraction technique combined with ensemble learning for precise and scalable geometric pattern classification. The implications extend to art preservation, architectural analysis, and automated indexing of cultural heritage imagery.
Volume: 15
Issue: 5
Page: 4630-4641
Publish at: 2025-10-01

Strategic integration of social media in information technology sector communication: designing effective practices

10.11591/ijece.v15i5.pp4653-4661
Benu Kesar , Shaji Joseph
This paper explores the transformative role of social media in enhancing communication and workflow efficiency within the information technology (IT) sector. We have introduced the adaptive social media for information technology collaboration (ASMIT) framework. Its goal is to provide a holistic strategy for digital transformation in the IT sector. Employing a mixed method approach, the research combines a systematic literature review with case study of HCL Technologies. Thematic analysis categorizes findings under five core pillars of the ASMIT framework. Results indicate that AI-driven tools, when embedded within collaborative social media platforms, significantly enhance organizational agility, project coordination, and security. The study contributes to IT scholarship by bridging technological integration with human-centered collaboration strategies.
Volume: 15
Issue: 5
Page: 4653-4661
Publish at: 2025-10-01
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