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28,296 Article Results

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

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

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

Overcoming challenges in managing public schools of novice principals

10.11591/ijere.v14i5.33538
Jayson Ryan T. De Leon , Rich Paulo S. Lim , Justin Vianey M. Embalsado , Jed V. Madlambayan , Chillet G. Credo , Ricardo C. Salunga
A qualitative phenomenological approach was utilized in this study to explore the challenges experienced by novice school principals and how they overcome these challenges in managing their schools in the Division of Mabalacat City during school year 2023-2024. Guided by in-depth one-on-one semi-structured interviews, data was gathered from nine public elementary school principals. With the transcribed data, coding was employed using thematic analysis. Results showed that novice principals’ challenges are categorized into two: i) interpersonal challenges, including keeping the school safe and conducive and engaging with stakeholders, and ii) intrapersonal challenges, which include transitioning to higher roles and responsibilities and catching up with the new knowledge and skills needed to acquire. Moreover, novice principals experienced in overcoming these challenges were also examined. Findings revealed that growing interpersonal skills by establishing a good relationship with stakeholders and building rapport with teachers and growing intrapersonal skills by never stopping learning and having the right attitude would help them cope with their difficulties in managing the school. Finally, a proposed novice principals’ challenges model framework was developed and recommended for use in the Division of Mabalacat City to improve the knowledge, skills, and qualities of beginning and aspiring principals with their new roles in managing their schools.
Volume: 14
Issue: 5
Page: 3686-3701
Publish at: 2025-10-01

Well-being and engagement: its implications for university policy on administrative employee’s wellness program

10.11591/ijere.v14i5.34387
John Michael D. Aquino , Jayson L. de Vera
The well-being and engagement of administrative employees are critical to creating a productive and sustainable work environment. This study investigates causes of university administrative staff well-being and professional involvement. This study examines: i) employee engagement and well-being; ii) administrative employees’ biggest workplace challenges; and iii) how wellness programs promote personal and professional progress. This study used a concurrent triangulation mixed-method research approach. Gallup’s employee engagement survey found that 124 employees have overall favorable attitudes, with a composite mean score of 4.36 demonstrating moderate to high levels of engagement across key workplace indicators. The inconsistent recognition may have an impact on involvement, with the lowest mean of 3.80 and the biggest variability of 1.09. Meanwhile, semi-structured interviews were conducted with 12 administrative employees from a university in region 4A. The findings highlight factors influencing well-being, such as effective communication, work-life balance, positive office environments, and opportunities for promotion. Stress, heavy workloads, and insufficient recognition were seen to be significant challenges, whereas coping strategies including task prioritization, emotional regulation, and peer support were regarded as critical. The results show that well-being boosts commitment and productivity, whereas engagement improves mental health and job happiness. Universities must offer stress management, professional development, and recognition to improve results and staff engagement.
Volume: 14
Issue: 5
Page: 3515-3525
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

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

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

Enhancing diabetes prediction through probability-based correction: a methodological approach

10.11591/ijece.v15i5.pp4933-4941
Aitouhanni Imane , Berqia Amine
Predictive healthcare analytics demands accurate predictions from interpretable models for early diagnosis and intervention on diabetes prognosis, which remains a well-established challenge. This study presents a new probability-based correction method to enhance the performance of a model in diabetes prediction. Initial model comparisons are performed using the PyCaret framework to identify the baseline model. Logistic regression was selected due to its simplicity, interpretability, and its higher accuracy, which outperformed other models. To further facilitate future research in this field, this study was conducted using a noisy dataset without any changes or preprocessing steps other than those available in the dataset from the producer. This intentional decision meant that the new probability-based method could be evaluated in isolation without any additional modifications being applied. The proposed correction method adjusts predictions into borderline probability intervals to obtain more accurate classifications. This approach increased the model accuracy by 6% from 75% to 81%, thus proving successful in resolving the misclassification problem with higher risk. This approach outperforms state-of-the-art methods and demonstrates its generalizability in enhancing the certainty of downstream clinical decisions.
Volume: 15
Issue: 5
Page: 4933-4941
Publish at: 2025-10-01

Identification of factors that influence student satisfaction from the analysis of voice messaging from WhatsApp: a case study

10.11591/ijere.v14i5.27328
Omar Chamorro-Atalaya , Giorgio Aquije-Cardenas , Raymundo Carranza-Noriega , Lilly Moreno-Chinchay , Yurfa Medina-Bedón , Rufino Alejos-Ipanaque , Abel Tasayco-Jala , Susan Gonzales-Saldaña
In these times when there is talk of a return to a new normality in education after what happened due to the pandemic, it is necessary to permanently evaluate the perception of student satisfaction, contributing to the results obtained through traditional methods such as the survey, with methods in which open opinions can be analyzed as in the case of voice analysis. In this sense, this article describes a case study, which aims to identify the factors that influence student satisfaction with respect to teaching performance, based on the analysis of WhatsApp voice messaging. The study has a qualitative approach, exploratory level and non-experimental design. It was possible to identify various factors grouped into five categories: i) planning; ii) didactic strategies; iii) communication; iv) administration of the class session; and v) professional and personal characteristics of the teacher. Therefore, it is concluded that it is possible to close the gaps between the factors that are sensitive and relevant for the university, when a questionnaire with delimited questions is applied to observe only some factors of student satisfaction, with respect to those sensitive factors and relevant to students, by analyzing their comments from the use of voice messaging from mobile applications.
Volume: 14
Issue: 5
Page: 3744-3755
Publish at: 2025-10-01

Analysis of obstacles in teaching and learning nuclear physics: towards a digital approach in secondary education

10.11591/ijere.v14i5.30557
Aziz Taoussi , Ahmida Chikhaoui , Khalid El Khattabi , Hassan Yakkou
The study explores the specific obstacles encountered in the teaching and learning of nuclear physics within qualifying secondary schools in Morocco, particularly in the Fez-Meknes delegation. We identified a lack of specific research on this topic, despite a growing interest in improving science teaching. Using questionnaires administered to 100 teachers and 200 students, we found that the absence of laboratory experiments due to the dangers associated with nuclear physics is a major obstacle. Difficulties in understanding concepts such as radioactive decay, nuclear fusion, and fission were also noted. To overcome these challenges, we propose the development of digital teaching resources adapted to the Moroccan curriculum, including simulations and interactive tutorials. These resources are viewed positively by teachers and students alike, as they facilitate understanding of concepts, increase engagement and enable self-paced learning, promoting autonomy in learning and the development of creativity in students. For successful integration, it is essential to provide adequate training and ongoing support for teachers. The results offer concrete avenues for improving the quality of teaching in physical sciences and learning in a digital environment motivates students in Morocco.
Volume: 14
Issue: 5
Page: 3846-3858
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

School innovation climate as a driver of teachers’ innovative work behavior: the mediating role of self-efficacy

10.11591/ijere.v14i5.32757
Safiek Mokhlis , Abdul Hakim Abdullah
Teachers’ innovative work behavior (IWB) is widely recognized as a driving force behind educational improvement in the complex and demanding conditions of the 21st century. Among a wide range of factors that could affect IWB, innovation climate (IC) has emerged as a crucial determinant. However, research exploring the mechanism that mediate the link between IC and IWB is still limited. Drawing upon social cognitive theory (SCT), the present study proposes that teachers’ self-efficacy (SE) acts as a mediator in the relationship between IC and IWB. The study involved 376 teachers at 12 public schools in Kuala Terengganu, Malaysia, who were determined based on a stratified random sampling technique. Analysis of data was implemented through the use of structural equation modeling (SEM) with AMOS software to test causal relationships. Results confirmed that schools’ IC was positively correlated with IWB and that this relationship was partially mediated by teachers’ SE. These results align with SCT, which emphasizes the interaction between individual behavior, environment (IC), and personal factors (SE). To cultivate a culture of innovation and improve educational outcomes, school leaders should actively foster an IC that enhances teachers’ SE, thereby promoting their IWB.
Volume: 14
Issue: 5
Page: 3735-3743
Publish at: 2025-10-01

Let’s be a chef! The antecedents of chef’s key competencies for vocational school students

10.11591/ijere.v14i5.26708
Badraningsih Lastariwati , Tuatul Mahfud
Chefs are considered a factor in the success of a culinary tourism business. Therefore, mastering the chef’s key competencies (CKC) through vocational high schools is very important. Many studies have examined the competence of chefs. Still, the mechanism for getting key competency chefs involving industry commitment (IC), social support (SS), vocational teaching quality (TQ), and occupational self-efficacy (OSE) of culinary student chefs has not been discussed clearly. This study investigates the antecedents of the mastery of key chef competencies for vocational school students. This study involved 392 culinary students at seven vocational schools in Yogyakarta, Indonesia. Data was collected by proportional random sampling through a questionnaire. Amos 18 software is used for structural equation modeling (SEM) analysis. The study’s results revealed that the mastery of the chef’s critical competencies for students was directly and significantly influenced by IC, quality of vocational teaching, and OSE of chefs. In addition, chef OSE is a mediator on the influence of IC, SS, and quality of vocational teaching on mastering the chef’s critical competencies for culinary students. This study’s findings discuss in depth some of the implications for vocational education practitioners that are proposed for further improvement.
Volume: 14
Issue: 5
Page: 4006-4018
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
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