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29,922 Article Results

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

Research skills and digital competence in Huancavelica students during COVID-19

10.11591/ijere.v14i5.32702
Daker Riveros-Anccasi , Lizeth Karina Riveros-Terrazo , Ubaldo Cayllahua-Yarasca , Charapaqui Anccasi-Juan , Angel Epifanio Rojas-Quispe , Christian Luis Torres-Acevedo , Carlos Laurente-Chahuayo
This study addresses the challenge of developing research skills among teacher training students at the National University of Huancavelica during the COVID-19 pandemic, focusing on the role of digital competencies. Using a quantitative, descriptive correlational design, data were collected from 180 students across four professional education programs in the VII and IX cycles. Two questionnaires, comprising 40 and 45 questions respectively, assessed digital competencies and research skills. The data, analyzed using SPSS (version 25) with a 5% margin of error, revealed a strong positive correlation (Rho=0.808) between digital competencies and research skills. Students with higher digital literacy, particularly in information and data literacy and communication and collaboration, demonstrated better proficiency in research tasks such as designing methodologies, data analysis, and presenting findings. The study emphasizes the importance of integrating digital skills into teacher education to enhance research capabilities, especially in post-pandemic educational contexts. Notably, 60% of students with “excellent” digital competency levels achieved “excellent” research skills, compared to only 10% with “good” competencies. These findings underscore the need to prioritize digital literacy in teacher education programs to support the development of essential research skills.
Volume: 14
Issue: 5
Page: 4019-4028
Publish at: 2025-10-01

Decomposition and multi-scale analysis of surface electromyographic signal for finger movements

10.11591/ijece.v15i5.pp4593-4604
Afroza Sultana , Md. Tawhid Islam Opu , Md. Shafiul Alam , Farruk Ahmed
Decomposition of the surface electromyography (sEMG) signal is vital for separating the composite, complex, noisy signals recorded from muscles into their integral motor unit action potentials (MUAPs). By precisely identifying each motor unit’s activity, this method offers greater insights into the functioning of the neuromuscular system, which helps isolate each motor unit's contribution, making it essential for understanding muscle coordination and diagnosing neuromuscular disorders. In this study, we employ the maximal overlapping discrete wavelet transform (MODWT), which is well-suited for analyzing signals in the time-frequency domain. The study decomposed the sEMG signal into six levels to identify the neural activity of finger movements and analyzed the motor unit action potential (MUAP). In the frequency range of 30.2 and 64.6 Hz, the signal exhibits the highest MUAP which is independent of movement. Using inverse MODWT, it was rebuilt from the decomposed levels. With 95.8% accuracy, the similarity between the reassembled signal and the original signal was determined using correlation analysis to assess the efficacy of the method.
Volume: 15
Issue: 5
Page: 4593-4604
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

An efficient direction oriented block-based video inpainting using morphological operations and adaptively dimensioned search region with direction-oriented block-based inpainting

10.11591/ijece.v15i5.pp4705-4713
Shyni Shajahan , Y. Jacob Vetha Raj
Video inpainting is a technique in computer vision used to remove unwanted objects from video sequences while preserving visual consistency, so that modifications remain unnoticeable to the human eye. This paper presents an accurate video inpainting model based on the adaptively dimensioned search region with direction-oriented block-based inpainting (ADSR-DOBI) algorithm. The model operates in five main phases: preprocessing, background separation, morphological operations, object removal, and video inpainting. Initially, the input video is converted into frames, followed by preprocessing steps such as deionizing and resizing. These frames are then processed using a background subtraction module, where object localization and foreground detection are performed using the binomially distributed foreground segmentation network (BDFgSegNet) and morphological techniques. This results in segmented foreground objects tracked across frames. The object removal phase eliminates the identified foreground objects and defines the missing regions (holes) to be filled. The ADSR-DOBI algorithm is then applied to inpaint these regions seamlessly. Experimental results demonstrate that this approach outperforms existing state-of-the-art methods in both accuracy and efficiency.
Volume: 15
Issue: 5
Page: 4705-4713
Publish at: 2025-10-01

Students’ attitude towards assessment, its relation with their academic achievement, and factors that shape their attitude

10.11591/ijere.v14i5.30154
Ahmad Ullah Al Azad , Sumera Ahsan , Shafi Md. Mahdi
Student assessment is a crucial part of the teaching-learning process that shapes students’ learning. Students’ attitudes towards assessment play an important role in determining how actively they will participate in assessment activities. It also involves exploring the complex interplay between students’ expectations and experiences regarding assessment. Therefore, this study aims to explore Bangladeshi tertiary level students’ attitude towards assessment, its relation with their academic achievement, and factors shaping their attitude. A sequential mixed-method design was employed. A total of 162 graduate and undergraduate students were surveyed with a scale to measure their attitude. Based on the attitude score, six students were interviewed for deeper insights. The findings show that students generally have a neutral attitude toward their institution’s assessment system. Among different types of assessments, students showed the most positive attitude towards classroom assessment. There is a positive but weak relationship between attitude towards assessment and students’ academic achievement. The interviews revealed that because of the subjective experience of assessment, the same assessment activities could yield different attitudes towards assessment among students. The study will provide insights into how students’ experiences with assessment contribute to developing their attitude towards assessment in an educational setting. This study may also help policy makers and educators to develop a student friendly assessment system in higher education.
Volume: 14
Issue: 5
Page: 4076-4087
Publish at: 2025-10-01

Design-thinking integration in cultural project planning: bridging theory and practice

10.11591/ijere.v14i5.33730
Shiyong Zhang , Poonsri Vate-U-Lan , Panneepa Sivapirunthep
This qualitative study aimed to bridge the gap between theoretical knowledge and practical skills by developing a design thinking-based innovative pedagogy for creative planning of cultural projects at Shanxi University of Finance and Economics (SUFE). The discover, define, design, develop, and deploy (5Ds) model, an innovative pedagogy grounded in design thinking, was designed with five phases. After evaluation by five cultural project planning experts, the model was refined to align closely with pedagogical objectives. Three primary research instruments were employed: a theoretically derived framework, a meticulously designed lesson plan, and an achievement test. Contextually tailored to Chinese culture, each phase was symbolized by a Chinese lantern, signifying prosperity and the intended positive outcomes of the pedagogy. The phases comprised: i) discover, where consumer needs were analyzed; ii) define, identifying problems and setting objectives; iii) design, focusing on idea generation; iv) develop, involving prototyping, implementation, and revisions; and v) deploy, for final product testing and market introduction. This culturally resonant pedagogical framework supports a comprehensive approach to teaching cultural project planning, fostering innovation, and practical application. Through symbolic integration, the 5Ds model aligns with educational goals while encouraging Chinese students to embrace innovation. Outcomes indicate enhanced teaching quality and instructional effectiveness, addressing existing educational challenges.
Volume: 14
Issue: 5
Page: 4149-4163
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

Bone-Net: a parallel deep convolutional neural network-based bone fracture recognition

10.11591/ijece.v15i5.pp4692-4704
Md. Hasan Imam Bijoy , Nusrat Islam Kohinoor , Syeda Zarin Tasnim , Md Saidur Rahman Kohinoor
Many people suffer from bone fractures, which can result from minor accidents, forceful blows, or even diseases like osteoporosis or bone cancer. In the medical realm, accurately identifying bone fractures from X-ray images is paramount for effective diagnosis and treatment. To address this, a comparative study is conducted utilizing three distinct models: a traditional convolutional neural network (CNN), MobileNet-V2, and a newly developed parallel deep convolutional neural network (PDCNN). The primary aim is to evaluate and contrast these models in terms of precision, sensitivity, and specificity for diagnosing bone fractures. X-ray images of fractured and non-fractured bones are sourced from Kaggle and subjected to various image processing techniques to rectify anomalies. Techniques such as cropping, resizing, contrast enhancement, filtering, and augmentation are applied, culminating in canny edge detection. These processed images are then used to train and test models. The results showcased the superior performance of the newly developed PDCNN model, achieving an impressive accuracy of 92.89%, surpassing both the traditional CNN and pretrained MobileNet-V2 models. A series of ablation studies are conducted to fine-tune the hyperparameters of the PDCNN model, further validating its efficacy. Throughout the investigation, PDCNN consistently outperformed MobileNet-V2 and traditional CNN, underscoring its potential as an advanced tool for streamlining bone fracture identification.
Volume: 15
Issue: 5
Page: 4692-4704
Publish at: 2025-10-01

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

Deep learning-based stacking ensemble for malaria parasite classification in blood smear images

10.11591/ijeecs.v40.i1.pp508-517
Komal Kumar Napa , Kalyan Kumar Angati , Senthil Murugan Janakiraman , Balamurugan Amoor Gopikrishnan , Bindu Kolappa Pillai Vijayammal , Vattikuti Charan Sri Manikanta Sai
Malaria remains a significant global health challenge, necessitating accurate and efficient diagnostic tools. Deep learning models have emerged as promising solutions for automated malaria detection using microscopic blood smear images. This study evaluates the performance of various convolutional neural network (CNN) architectures, including VGG16, ResNet50, MobileNetV2, and EfficientNet, in classifying infected and uninfected cells. Individual model performances were assessed based on accuracy, precision, recall, and F1-score, with EfficientNet achieving the highest standalone accuracy of 88.0%. To enhance classification performance, a stacking ensemble approach was implemented, using a logistic regression meta-classifier to integrate outputs from multiple models for improved decision-making. The stacking model outperformed individual networks, achieving an accuracy of 89.4%, with precision, recall, and F1- scores surpassing those of standalone models. Challenges in malaria parasite classification—such as high inter-class similarity, variations in staining quality, and class imbalance were addressed through data augmentation and model tuning. These findings highlight the potential of ensemble learning in medical image analysis, paving the way for more accurate and scalable malaria detection systems.
Volume: 40
Issue: 1
Page: 508-517
Publish at: 2025-10-01

Boosting algebra mastery through activity-based learning in an indigenous peoples education secondary school

10.11591/ijere.v14i5.33969
Rolly Najial Apdo , Rachel Basañez Apdo
Algebra is a fundamental area of mathematics, yet many students, particularly indigenous learners, struggle with its concepts and procedures. This study examines the impact of activity-based learning on the conceptual understanding and procedural skills of junior high school students in an indigenous peoples education (IPEd) school. Using a mixed-methods approach, 105 indigenous students from grades 7 to 9 at Daan Taligaman Integrated Secondary School (DTISS), Philippines, participated. Pre-test and post-test scores were analyzed using a paired-samples t-test, while thematic analysis explored students’ learning experiences. The results revealed significant improvements in both conceptual understanding and procedural skills, with grade 7 scores increasing from 41.08% to 80.38% (conceptual) and 34.83% to 74.13% (procedural). A similar trend was apparent for the grades 8 and 9 students. Key themes identified were engagement and enjoyment, increased confidence, and improved understanding. The study highlights the effectiveness of interactive, culturally responsive learning strategies in enhancing algebra mastery among indigenous students and calls for their integration into mathematics education.
Volume: 14
Issue: 5
Page: 4029-4039
Publish at: 2025-10-01

Pre-service teachers’ demographics, cultural competence, and culturally responsive teaching practices

10.11591/ijere.v14i5.33064
Edilberto Z. Andal , Albert Andry E. Panergayo
This study examines the influence of cultural competence (CC) on culturally responsive teaching (CRT) practices among pre-service teachers (PSTs) in a state university in Laguna, Philippines. Despite the emphasis on education, the impact of cultural awareness, knowledge, and skills on CRT remains underexplored. Using a cross-sectional quantitative research design, data from 633 PSTs were collected through a validated web-based survey. Multiple regression analysis showed that cultural knowledge (B=0.373, p<0.05) and cultural skills (B=0.511, p<0.05) significantly predict CRT practices, while cultural awareness (B=0.003, p>0.05) does not. Demographic factors such as age, gender, and year level do not moderate this relationship. These findings highlight the need for prioritizing cultural knowledge and skills in teacher education curricula. Institutions should integrate structured training to equip PSTs for diverse classrooms. Future research should explore the long-term effects of these competencies in an actual teaching environment and assess targeted training interventions. Strengthening CC through curriculum enhancement can better prepare educators to meet the challenges of an increasingly diverse educational landscape.
Volume: 14
Issue: 5
Page: 3526-3533
Publish at: 2025-10-01

Improving breast cancer prediction through explainable artificial intelligence - A transdisciplinary approach

10.11591/ijeecs.v40.i1.pp288-296
Reena Lokare , Jyoti Sunil More , Vaishali V. Sarbhukan , Mansing Rathod , Sarita Rathod , Sunita Patil
Artificial intelligence (AI) technology has shown tremendous contributions in various applications like speech recognition, expert systems, computer vision, robotics, and gaming. machine learning (ML) and deep learning (DL) algorithms under AI address problems such as prediction, classification, and regression. AI has touched many domains. The results or the predictions generated by these algorithms are not easily accepted by the user. Especially, the Healthcare domain is facing a great challenge in accepting the results or the predictions with the concern, Are AI results reliable, correct, and ethical? Doctors or medical practitioners are not ready to treat patients based on results or suggestions generated by AI algorithms. Hence, a technology that can explain how the results returned by AI algorithms are trustworthy, transparent, and interpretable was strongly needed. This need has given rise to the latest technology-explainable artificial intelligence (XAI). With the use of XAI, all the predictions, classifications made by AI algorithms are explainable, auditable, comprehensive, validating, and socially acceptable. This paper discusses explaining the results of breast cancer prediction as a case study. The results show that such an explanation will build trust in the doctors and hence will increase the acceptance of the AI-based systems.
Volume: 40
Issue: 1
Page: 288-296
Publish at: 2025-10-01

Team assisted individualization: enhancing students’ conjecturing skills in statistics and probability

10.11591/ijere.v14i5.30882
Kheigy Axl Ross Galapon Francisco , Danica Mae Esguerra Brillo , Liezl Joy Lazaro Quilang
Many students struggle to understand mathematical concepts, leading to inadequate conjecturing skills. This study investigates the effectiveness of team assisted individualization (TAI), a cooperative learning approach, in improving these skills in mathematics. Utilizing a pre-test-post-test control group design, the research involved matched groups from two senior high school sections. Data were collected through tests and interviews conducted before and after the intervention, and analyzed using means, standard deviations, paired samples t-tests, analysis of covariance (ANCOVA), and content analysis. The findings indicate a significant enhancement in students’ conjecturing abilities following the implementation of TAI, even when controlling for their numerical reasoning skills. These results demonstrate that TAI is more effective than traditional lecture-discussion methods (LDM) in promoting conjecturing skills. Consequently, this study encourages educators to explore and adopt TAI strategies to better facilitate the development of students’ mathematical reasoning and conjecturing capabilities.
Volume: 14
Issue: 5
Page: 3983-3993
Publish at: 2025-10-01
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