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28,451 Article Results

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

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

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

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

Language model optimization for mental health question answering application

10.11591/ijece.v15i5.pp4829-4836
Fardan Zamakhsyari , Agung Fatwanto
Question answering (QA) is a task in natural language processing (NLP) where the bidirectional encoder representations from transformers (BERT) language model has shown remarkable results. This research focuses on optimizing the IndoBERT and MBERT models for the QA task in the mental health domain, using a translated version of the Amod/mental_health_counseling_conversations dataset on Hugging Face. The optimization process involves fine-tuning IndoBERT and MBERT to enhance their performance, evaluated using BERTScore components: F1, recall, and precision. The results indicate that fine-tuning significantly boosts IndoBERT’s performance, achieving an F1-BERTScore of 91.8%, a recall of 89.9%, and precision of 93.9%, marking a 28% improvement. For the model, M-BERT’s fine-tuning results include an F1-BERTScore of 79.2%, recall of 73.4%, and precision of 86.2%, with only a 5% improvement. These findings underscore the importance of fine-tuning and using language-specific models like IndoBERT for specialized NLP tasks, demonstrating the potential to create more accurate and contextually relevant question-answering systems in the mental health domain.
Volume: 15
Issue: 5
Page: 4829-4836
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

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

Energy yields and performance analysis of vertical and tilted oriented bifacial photovoltaic modules in tropical region

10.11591/ijece.v15i5.pp4508-4519
Rudi Darussalam , Agus Risdiyanto , Ant Ardath Kristi , Agus Junaedi , Noviadi Arief Rachman , Dalmasius Ganjar Subagio , Muhammad Kasim , Udin Komarudin , Ahmad Fudholi
This study experimentally investigates the performance of bifacial photovoltaic (bPV) modules under vertical and tilted orientations in a tropical region. Related studies are reviewed, then performance metrics including solar radiation, module temperature, bifaciality gain, and energy yield were monitored and analyzed over a specified period. The aim is to determine the optimal orientation for maximizing output power generation, temperature module, and understanding the bifaciality factor through real-world conditions. The experimental setup consisted of three different bifacial photovoltaic module configurations: two vertically mounted with facing east-west (E/W) and north-south (N/S) respectively, while the third was tilted 15 facing north. The study findings revealed that the tilted orientation produced the highest energy yield of 1951 Wh, followed by the vertical east-west (E/W) and vertical north-south (N/S) orientations with 1504 Wh and 609 Wh, respectively. While tilted bPV module benefit from higher irradiance, they also experience elevated temperatures (39% above ambient) compared to vertically bPV modules (8-21%). This can negatively affect efficiency, especially during peak solar hours. The results also show that differences in bPV installation orientation affect the bifaciality factor and gain. These findings offer valuable guidance for optimizing bPV system design and deployment in tropical regions with low latitude, supporting sustainable energy solutions.
Volume: 15
Issue: 5
Page: 4508-4519
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

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

Applications of satellite information for rainwater estimation and usage: a comprehensive review

10.11591/ijece.v15i5.pp4671-4681
Laura Valeria Avendaño-García , Yeison Alberto Garcés-Gómez
Global climate change introduces significant uncertainty in water resource availability, making precipitation studies essential for societal sustainability. Satellite precipitation products (SPPs) have emerged as a vital alternative and complement to traditional meteorological station data for hydrological and climate research. This review examines scientific literature on SPP applications for daily, monthly, and annual rainfall estimations globally. Eleven widely used SPPs were identified, with the tropical rainfall measuring mission (TRMM) and climate hazards group infrared precipitation with station data (CHIRPS) standing out due to their frequent usage, high resolution, and extensive data records. A growing trend in research utilizes SPPs for hydrological studies and validates their estimates by contrasting satellite information with ground station measurements using continuous and categorical statistics. TRMM and CHIRPS, in particular, show precipitation accuracies closer to station data, influenced by local topography and climatology. Furthermore, SPP data, combined with geographic information systems (GIS), proves useful for identifying potential rainwater harvesting sites, offering an alternative information source to address water availability crises in drought-prone areas.
Volume: 15
Issue: 5
Page: 4671-4681
Publish at: 2025-10-01

A non-destructive approach for estimation of Hb, HCT and red blood cells using reflectance spectroscopic technique

10.11591/ijece.v15i5.pp4569-4580
P. Divyabharathi , Neelamegam Devarasu
Paediatric haematology involves the use of non-invasive methods and technologies to evaluate haematological parameters in children. These techniques attempt to offer precise measurements of blood constituents without the necessity of intrusive procedures such as venipuncture or blood draws, which can be difficult and unpleasant for paediatric patients. The data gathered from the elbow will be given priority for further investigations to find haematological profiles. Estimates of haemoglobin, haematocrit, and red blood cell count were done and compared against the values obtained using conventional methods. This method achieves an accuracy of 75.56% with high precision and specificity which makes the method particularly beneficial for paediatric applications, potentially due to physiological differences or enhanced calibration for younger populations. The sensitivity varies with red blood cells (RBC) showing the lowest true positive detection rate. Future work could focus on improving the sensitivity of these parameters to enhance the accuracy. Conventional techniques cannot monitor continuously and remotely, which is crucial for a point-of-care screening device in the current era. The proposed non-destructive technique offers the benefits of infection control, pain reduction, and minimal operational cum maintenance expenses, all while being portable and child friendly.
Volume: 15
Issue: 5
Page: 4569-4580
Publish at: 2025-10-01

Performance evaluation of a high-gain 50 W DC-DC flyback boost converter for variable input voltage low-power photovoltaic applications

10.11591/ijece.v15i5.pp4520-4530
Muhammad Hafeez Mohamed Hariri , Lim Kean Boon , Tole Sutikno , Nor Azizah Mohd Yusoff
DC-DC boost converters are essential for stabilizing the voltage output of photovoltaic (PV) modules. This paper analyzes a unique 50 W high-gain DC-DC flyback boost converter for various input voltage PV applications. Scientific analysis was employed to determine suitable parameters for critical circuit components. Simulations were conducted to evaluate the proposed high-gain DC-DC boost converter's performance. Subsequently, a prototype of the high-gain DC boost converter was fabricated with a printed circuit board (PCB) size of 100×100 mm. The proposed prototype's performance is compared to that of conventional boost converters based on criteria such as input voltage, output voltage, component count, voltage stress, voltage gain, efficiency, and rated power. The results indicate that the proposed converter can achieve a 300 V output voltage with a 50 W power rating from variable input voltages ranging between 12 V and 36 V. The highest gain achieved was 25 with a 12 V input voltage, though at a lower power rating of 15 W. A peak efficiency of 84.30% was measured with a 24 V DC input voltage. The proposed converter's features, particularly its high step-up voltage gain, make it suitable for industrial and renewable energy applications.
Volume: 15
Issue: 5
Page: 4520-4530
Publish at: 2025-10-01

Efficient mask region-based convolutional neural network-based architecture for COVID-19 detection from computed tomography data

10.11591/ijece.v15i5.pp4751-4761
Nader Mahmoud , Ashraf B. El-Sisi
The worldwide effect of the coronavirus disease (COVID-19) pandemic has been catastrophic, leading to a significant number of fatalities worldwide. In response to the outbreak, health care institutions have proposed the use of chest computed tomography (CT) as an important diagnosis tool for rapid diagnosis, leveraging deep learning approaches for disease detection. This paper aims to progress a robust methodology towards accurate diagnosis of COVID-19 based on deep learning approaches with chest CT images. We propose a mask region-based convolutional neural network (Mask R-CNN) model architecture that is well-trained and used to discriminate between COVID-19-infected and uninfected cases. In order to improve feature extraction, the proposed model incorporates a fuzzy color enhancement preprocessing technique that reduces image fuzziness and increases contrast. A publicly available chest CT dataset is considered for quantitative evaluation of the proposed architecture model, which includes various frontal image views of COVID-19 and non-COVID-19 cases. The proposed approach yielded an accuracy of 98.8% with 98.4% precision and 98.5% recall. Additionally, the proposed model architecture has been quantitatively evaluated in comparison with benchmark approaches, yielding superior performance in terms of conventional evaluation metrics.
Volume: 15
Issue: 5
Page: 4751-4761
Publish at: 2025-10-01
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