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31,456 Article Results

Systematic Literature Review on Developing an Integrated STEM Leadership Model for Middle Leaders in School

10.11591/ijere.v14i2.31691
Saifulbahri Abdul Rahman , Abdul Halim Busari , Mohammad Nur Azhar Mazlan , Adawati Suhaili
This systematic literature review investigates the development of anintegrated science, technology, engineering, and mathematics (STEM)leadership model tailored for middle leaders in Malaysian schools. Theintroduction highlights the global emphasis on STEM education to fosterinnovation and economic growth, while acknowledging Malaysia’scommitment to enhancing STEM capabilities within its educational system.The problem statement identifies a gap in effective STEM leadership amongmiddle leaders, which is critical for implementing STEM initiatives andimproving student outcomes. To achieve this, we conducted an extensivesearch of scholarly articles from reputable databases such as Scopus and Webof Science (WoS), focusing on studies published between 2021 and 2024. Theflow of study is based on PRISMA framework. The database found (n=34)final primary data was analyzed. The finding was divided into three themeswhich are i) STEM education policy and implementation; ii) leadership inSTEM educational; iii) professional development in STEM education. Theconclusion emphasizes the need for a specialized leadership model thatincorporates instructional leadership principles, fosters professionaldevelopment, and supports collaborative practices among middle leaders. Thisintegrated model aims to address the unique challenges faced by middleleaders in Malaysian schools, ultimately enhancing STEM education andcontributing to Malaysia’s educational and economic aspirations.
Volume: 14
Issue: 2
Page: 786-796
Publish at: 2025-04-01

Enhancing predictive modelling and interpretability in heart failure prediction: a SHAP-based analysis

10.11591/ijict.v14i1.pp11-19
Niaz Ashraf Khan , Md. Ferdous Bin Hafiz , Md. Aktaruzzaman Pramanik
Predictive modelling plays a crucial role in healthcare, particularly in forecasting mortality due to heart failure. This study focuses on enhancing predictive modelling and interpretability in heart failure prediction through advanced boosting algorithms, ensemble methods, and SHapley Additive exPlanations (SHAP) analysis. Leveraging a dataset of patients diagnosed with cardiovascular diseases (CVD), we employed techniques such as synthetic minority over-sampling technique (SMOTE) and bootstrapping to address class imbalance. Our results demonstrated exceptional predictive performance, with the gradient boosting (GBoost) model achieving the highest accuracy of 91.39%. Ensemble techniques further enhanced performance, with the voting classifier (VC), stacking classifier (SC), and Blending achieving accuracies of 91.00%. SHAP analysis uncovered key features such as time, Serum_creatinine, and Ejection_fraction, significantly impacting mortality prediction. These findings highlight the importance of transparent and interpretable machine learning models in healthcare decision-making processes, facilitating informed interventions and personalized treatment strategies for heart failure patients.
Volume: 14
Issue: 1
Page: 11-19
Publish at: 2025-04-01

Target image validation modeling using deep neural network algorithm

10.11591/ijece.v15i2.pp2042-2054
Naemah Mubarakah , Poltak Sihombing , Syahril Efendi , Fahmi Fahmi
Research on image validation models is an interesting topic. The application of deep learning (DL) for object detection has been demonstrated to effectively and efficiently address the challenges in this field. Deep neural networks (DNN) are deep learning algorithms capable of handling large datasets and effectively solving complex problems due to their robust learning capacity. Despite their ability to address complex problems, DNN encounter challenges related to the necessity for intricate architectures and a large number of hidden layers. The objective of this research is to identify the most effective model for achieving optimal performance in image validation. This study investigates target image validation using DNN algorithms, examining architectures with 3, 4, 5, and 6 hidden layers. This study also evaluates the performance of image validation across various activation functions, batch sizes, and numbers of neurons. The results of the study show that the best performance for image validation is achieved using the Leaky-ReLU and Sigmoid activation functions, with a batch size of 64, and an architecture consisting of 3 hidden layers with neuron sizes of 256, 128, and 64. This model is capable of providing real-time target image validation with an accuracy of up to 94.31%.
Volume: 15
Issue: 2
Page: 2042-2054
Publish at: 2025-04-01

A HBMO-based batch beacon adjustment for improving the Fast-RRT

10.11591/ijeecs.v38.i1.pp107-119
Heru Suwoyo , Yingzhong Tian , Andi Adriansyah , Julpri Andika
Fast-RRT improves on the original rapidly-exploring random trees (RRT) by incorporating two main stages: improved-RRT and fast-optimal. The improved-RRT stage enhances the search process through fast-sampling and random steering, while the fast-optimal stage optimizes the path using fusion and path arrangement. However, path fusion can only be optimal when the newly found path is unique and different from previous paths. This uniqueness rarely occurs in cases with narrow corridors, so path fusion only provides suboptimal conditions. To address this, the study explores using honey bee mating optimization (HBMO) to optimize or replace the fusion stage. HBMO helps determine new beacon coordinates, which are nodes between the start and goal points along the path, through a batch beacon adjustment approach. The results show that integrating HBMO into FastRRT improves its optimality, with a 21.85% reduction in path cost and a 5.22% decrease in completion time across environments with varying difficulty levels. This hybrid algorithm outperforms previous methods in terms of both path optimality and convergence rate, demonstrating its effectiveness in enhancing Fast-RRT’s performance.
Volume: 38
Issue: 1
Page: 107-119
Publish at: 2025-04-01

Explainable zero-shot learning and transfer learning for real time Indian healthcare

10.11591/ijict.v14i1.pp91-101
Swati Saigaonkar , Vaibhav Narawade
Clinical note research is globally recognized, but work on real-time data, particularly from India, is still lagging. This study initiated by training models on medical information mart for intensive care (MIMIC) clinical notes, focusing on conditions like chronic kidney disease (CKD), myocardial infarction (MI), and asthma using the structured medical domain bidirectional encoder representations from transformers (SMDBERT) model. Subsequently, these models were applied to an Indian dataset obtained from two hospitals. The key difference between publicly available datasets and real-time data lies in the prevalence of certain diseases. For example, in a real-time setting, tuberculosis may exist, but the MIMIC dataset lacks corresponding clinical notes. Thus, an innovative approach was developed by combining a fine-tuned SMDBERT model with a customized zero-shot learning method to effectively analyze tuberculosis-related clinical notes. Another research gap is the lack of explainability because deep learning (DL) models are inherently black-box. To further strengthen the reliability of the models, local interpretable model-agnostic explanations (LIME) and shapley additive explanations (SHAP) explanations were projected along with narrative explanations which generated explanations in a natural language format. Thus, the research provides a significant contribution with ensemble technique of zero-shot learning and SMDBERT model with an accuracy of 0.92 as against the specialized models like scientific BERT (SCIBERT), biomedical BERT (BIOBERT) and clinical BioBERT.
Volume: 14
Issue: 1
Page: 91-101
Publish at: 2025-04-01

Enhancing engineering education through virtual reality: a systematic study on immersive engineering education practices

10.11591/ijece.v15i2.pp1889-1899
Tarek Riaji , Sanae El Hassani , Fatima Ezzahrae M'hamdi Alaoui
This article explores the integration of virtual reality (VR) and associated technologies in engineering education, focusing on the pedagogical approaches adopted in this integration, which we refer to as immersive engineering education. This study considers the application possibilities and the transformative impact of VR on engineering education. The article addresses the critical collection and analysis of VR applications in engineering education. It covers main VR-related papers published from 2015 to February 2024 and indexed in Scopus, Web of Sciences, or both, and discussing design, development challenges, and collaborative tools. Empirical evidence showcases improved engagement, motivation, and learning outcomes. The findings offer modern insights for educators and researchers on leveraging VR for impactful learning experiences, while also noting the need for further research in this evolving field.
Volume: 15
Issue: 2
Page: 1889-1899
Publish at: 2025-04-01

Classification of brain stroke based on susceptibility-weighted imaging using machine learning

10.11591/ijece.v15i2.pp1602-1611
Shaarmila Kandaya , Norhashimah Mohd Saad , Abdul Rahim Abdullah , Ezreen Farina Shair , Ahmad Sobri Muda , Muhammad Izzat Ahmad Sabri
Magnetic resonance imaging (MRI) is used to identify brain disorders, particularly strokes. Rapid treatment, often referred to as "time is brain," is emphasized in recent studies, stressing the significance of early intervention within six hours of stroke onset to save lives and enhance outcomes. The traditional manual diagnosis of brain strokes by neuroradiologists is both subjective and time-intensive. To tackle this challenge, this study introduces an automated method for classify brain stroke from MRI images based on pre- and post-stroke patients. The technique employs machine learning, with a focus on susceptibility weighted imaging (SWI) sequences, and involves four stages: preprocessing, segmentation, feature extraction, classification and performance evaluation. The paper proposes classification and performance evaluation to determine stroke region according to three types of categories, those are poor improvement, moderate improvement and good improvement stroke patients based on pre and post patients. Then, performance evaluation is verified using accuracy, sensitivity and specificity. Results indicate that the hybrid support vector machine and bagged tree (SVMBT) yields the best performance for stroke lesion classification, achieving the highest accuracy which is 99% and showing significant improvement for stroke patients. In conclusion, the proposed stroke classification technique demonstrates promising potential for brain stroke diagnosis, offering an efficient and automated tool to assist medical professionals in timely and accurate assessments.
Volume: 15
Issue: 2
Page: 1602-1611
Publish at: 2025-04-01

Opportunities and challenges while conducting field trips to the museum: a narrative review

10.11591/ijere.v14i2.29297
Chatterjee Sahani , G. S. Prakasha
The museum visit field trip engages and motivates the children in various activities. Field trips to the museum provide the students with a constructivist and experiential learning environment as they construct knowledge through observing the artifacts. The present study describes the possible opportunities and challenges for school children while conducting field trips to the museum. The study employed a narrative review technique to address the research question raised. The study selected the literature reviews from 2012-2023, including studies on field trips to the museum for the academic engagement of school children. The data includes 50 peer-reviewed journal articles categorized into five categories: students’ overall development, experiential learning opportunities, the museum as a resource, the role of teachers, the school, and museum authority. Results revealed that the museum is a resource for learning and is perfect for improving students’ cognitive and affective development towards the various school subjects and helping them enhance their participatory learning opportunities. However, teachers’ knowledge, infrastructure, parental consent, and legitimization with the school authority are some challenges in conducting museum visit field trips. Future research may focus on conducting empirical studies, which include school-museum collaboration, to enhance the horizon of school and community knowledge.
Volume: 14
Issue: 2
Page: 871-878
Publish at: 2025-04-01

Identifying challenges of cultivating creativity in product design

10.11591/ijere.v14i2.29568
Salwa Suradin , Mohamad Sattar Rasul , Marlissa Omar
Creativity, a crucial skill in the fourth industrial revolution (IR4.0) is highly demanded in the workforce to drive innovation in product design. Therefore, it is important to cultivate creativity in product design through design and technology (D&T) education. However, many past studies encountered challenges regarding D&T teachers’ teaching creativity which might affect creativity cultivation among students. Thus, this article aims to identify the challenges faced by D&T teachers in cultivating student creativity within product design development. Utilizing the systematic literature review (SLR) method using reporting standards for systematic evidence syntheses (ROSES) in three databases, Web of Science, Scopus, and Google Scholar. Based on thematic analysis, this SLR leads to three themes: i) lack of pedagogical competence; ii) different thinking styles; and iii) lack of motivation. Addressing these challenges highlights the importance for educational institutions to align their curricula with current industry demands, ensuring students are well-prepared to tackle the complexities of contemporary product design. This effort requires collaboration among educators, industry leaders, and policymakers to update teaching methods, incorporate practical experiences, and enhance an environment that cultivates creativity.
Volume: 14
Issue: 2
Page: 1488-1495
Publish at: 2025-04-01

Connected caregiving: investigating mothers in the era of digital access

10.11591/ijict.v14i1.pp347-354
Anissa Saidi , Wong Yee Von , Tirzah Zubeidah Zachariah@ Omar , Lim Seong Pek , Rita Wong Mee Mee , Khoo Kim Leng
Mothers have embraced and utilized digital access for nurturing and personal use to enhance their roles while balancing newfound demands. The Internet has provided mothers access to information on various topics, including pregnancy, childbirth, and infant care. Social media tools and platforms have also provided mothers with a space to connect with other mothers, share experiences, and seek support. This scoping review aims to identify the relationship of the focus skills among mothers in utilizing digital access. Four databases, including Scopus, web of science (WOS), education resources information centre (ERIC), and ScienceDirect, were used in this research, which found 36 articles for eligibility. Only 16 articles are eligible for analysis and reference after the exclusion and inclusion process for data collection. Based on the 16 publications examining mothers’ use of internet access, four essential skills have been identified. These included social, digital, cultural, and problem-solving skills and are acknowledged as being related to digital access mothering. The findings show these skills are offered to mothers through digital access, fostering diverse skill sets, contributing to their empowerment, and supporting sustainable development goal 5: gender equality, aiming to enhance women’s roles and ensure equal opportunities through digital inclusion.
Volume: 14
Issue: 1
Page: 347-354
Publish at: 2025-04-01

Vehicle side control of a wireless power transfer charger using optimized artificial neural network

10.11591/ijece.v15i2.pp1487-1498
Marouane El Ancary , Abdellah Lassioui , Hassan El Fadil , Anwar Hasni , Yassine El Asri , Zakariae El Idrissi
This paper investigates a new approach to control a wireless power transfer (WPT) charger for electric vehicles (EVs) employing an optimized artificial neural network (ANN). Enhancing the efficiency and robustness of such systems is crucial, and integrating artificial intelligence (AI)-based solutions has introduced innovative approaches in this field. The proposed method enables precise regulation of battery charging voltage even under challenging conditions, such as coil misalignment or shared grounding assemblies for multiple EVs. To assess the stability and robustness of the proposed controller, its performance was evaluated under scenarios of coil misalignment and shared grounding assemblies for EVs with varying battery voltages. The controller effectively eliminated overshoot and significantly reduced residual output voltage ripple by 4.33% compared to a conventional proportional-integral (PI) controller, demonstrating the superior performance and reliability of the ANN-based control approach.
Volume: 15
Issue: 2
Page: 1487-1498
Publish at: 2025-04-01

Challenges and solutions in organizing geological field trips for Moroccan secondary school life and earth sciences teachers

10.11591/ijere.v14i2.29883
Aâtika Eddif , Sabah Selmaoui , Hanaa Chalak , Radouan Chakour
Teaching and learning earth sciences (ES), notably geology, are conducted across various environments, encompassing the classroom, laboratory, and, most significantly, the field. The field trip is recognized as a key component of practical training and the overall success of the educational process in ES. This paper elucidates the findings of a survey aimed at understanding the challenges faced by Moroccan secondary school teachers of life and earth sciences (LES) in organizing geological field trips. The survey sought to identify the difficulties these teachers encounter and propose potential support mechanisms to mitigate these challenges. The study revealed that teachers often do not arrange geological field trips due to challenges related to administration, personal factors, pedagogy, and didactics. In an effort to address this situation, the study underscores the importance of integrating practical work into teacher training and student education. It also emphasizes the need for the provision of more adequate teaching materials and logistical support to facilitate geological excursions, thereby enriching the appreciation of earth sciences disciplines among both teachers and students. Finally, the establishment of electronic collaboration between local Moroccan universities and secondary schools is recommended as a crucial step towards further improvement.
Volume: 14
Issue: 2
Page: 1459-1467
Publish at: 2025-04-01

Abstractive summarization using multilingual text-to-text transfer transformer for the Turkish text

10.11591/ijai.v14.i2.pp1587-1596
Neda Alipour , Serdar Aydın
Today, with the increase in text data, the application of automatic techniques such as automatic text summarization, which is one of the most critical natural language processing (NLP) tasks, has attracted even more attention and led to more research in this area. Nowadays, with the developments in deep learning, pre-trained sequence-to-sequence (text-to-text transfer converter (T5) and bidirectional encoder representations from transformers (BERT) algorithm) encoder-decoder models are used to obtain the most advanced results. However, most of the studies were done in the English language. With the help of the recently emerging monolingual BERT model and multilingual pre-trained sequence-to-sequence models, it has led to the use of state-of-the-art models in languages with fewer resources and studies, such as Turkish. This article used two datasets for Turkish text summarization. First, Google multilingual text-to-text transfer transformer (mT5)-small model was applied on multilingual summarization (MLSUM), which is a large-scale Turkish news dataset, and success was examined. Then, success was evaluated by first applying BERT extractive summarization and then abstractive summarization on 1010 articles collected on the Dergipark site. Rouge measures were used for performance evaluation. This study is one of the first examples in the Turkish language and it is considered to provide a basis for future studies with good results.
Volume: 14
Issue: 2
Page: 1587-1596
Publish at: 2025-04-01

A unique YOLO-based gated attention deep convolution network-Lichtenberg optimization algorithm model for a precise breast cancer segmentation and classification

10.11591/ijece.v15i2.pp1670-1685
Vinoth Rathinam , Sasireka Rajendran , Valarmathi Krishnasamy
A novel you only look once (YOLO)-based gated attention deep convolution network (GADCN) classification algorithm is developed and utilized in this present study for the detection of breast cancer. In this framework, contrast enhancement-based histogram equalization is applied initially to produce the normalized breast image with reduced noise artifacts. Then, the breast region is accurately segmented from the preprocessed images with low complexity and segmentation error using the YOLO-based attention network model. To diagnose breast cancer with better accuracy, the GADCN model is used to predict the exact class of image (i.e., benign or malignant). During classification, the activation function is optimally computed with the use of the Lichtenberg optimization algorithm (LOA). It aids in achieving improved classification performance with little complexity in training and assessment. The significance of the present study includes the use of a unique, YOLO-based GADCN-LOA model that helps in the prediction of breast cancer with higher accuracy. It was observed that the model exhibited 99% accuracy for the datasets utilized. In addition, the selected model outperforms well with sensitivity, specificity, precision, and F1-score. Hence the proposed model could be exploited for the diagnosis of breast cancer at an early stage to enable preventive care.
Volume: 15
Issue: 2
Page: 1670-1685
Publish at: 2025-04-01

Android-based smart digital marketplace application on agricultural commodities using a new variant recommendation system

10.11591/ijece.v15i2.pp1968-1977
Subiyanto Subiyanto , Sucihatiningsih Dian Wisika Prajanti , Nur Azis Salim , Setya Budi Arif Prabowo , Deyndrawan Sutrisno , Andika Anantyo , Dewi Anggriani
In the marketing of agricultural products, addressing the challenges associated with extensive distribution chains is essential, as these directly affect sellers. Additionally, the vast array of available product options often overwhelms customers, complicating their efforts to identify and purchase items that align with their preferences. This work aims to develop a smart e-commerce application for agribusiness, specifically designed for agricultural products on the Android platform. The application integrates a recommendation system that utilizes geolocation-aware neural graph collaborative filtering (GA-NGCF), which facilitates product marketing for farmers and streamlines the product search and selection process for users based on personalized preferences. The development process encompassed various stages, from planning to rigorous testing. The application’s recommendation system, which implements GA-NGCF, operates based on three primary elements: the creation of a geolocation graph of user-item data, the integration of information between neighboring nodes, and the prediction of user preferences. The resulting smart agribusiness e-commerce application, enhanced by GA-NGCF, demonstrated marked improvements in recommendation accuracy and overall application performance during testing. Empirical results indicated substantial enhancements in recommendation metrics, with GA-NGCF achieving a recall of 0.34, a precision of 0.36, and normalized discounted cumulative gain of 0.37, thereby outperforming existing models.
Volume: 15
Issue: 2
Page: 1968-1977
Publish at: 2025-04-01
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