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29,939 Article Results

Ensemble model-based arrhythmia classification with local interpretable model-agnostic explanations

10.11591/ijai.v14.i3.pp2012-2025
Md. Rabiul Islam , Tapan Kumar Godder , Ahsan Ul-Ambia , Ferdib Al-Islam , Anindya Nag , Bulbul Ahamed , Nujhut Tanzim , Md. Estiak Ahmed
Arrhythmia can lead to heart failure, stroke, and sudden cardiac arrest. Prompt diagnosis of arrhythmia is crucial for appropriate treatment. This analysis utilized four databases. We utilized seven machine learning (ML) algorithms in our work. These algorithms include logistic regression (LR), decision tree (DT), extreme gradient boosting (XGB), K-nearest neighbors (KNN), naïve Bayes (NB), multilayer perceptron (MLP), AdaBoost, and a bagging ensemble of these approaches. In addition, we conducted an analysis on a stacking ensemble consisting of XGB and bagging XGB. This study examines various arrhythmia detection techniques using both a single base dataset and a composite dataset. The objective is to identify the optimal model for the combined dataset. This study aims to evaluate the efficacy of these models in accurately categorizing normal (N) and abnormal (A) heartbeats as binary classes. The empirical findings demonstrated that the stacking ensemble approach exhibited superior accuracy when used with the combined dataset. Arrhythmia classification models rely on this as a crucial component. The binary classification achieved an accuracy of 98.61%, a recall of 97.66%, and a precision of 97.77%. Subsequently, the local interpretable model-agnostic explanations (LIME) technique is employed to assess the prediction capability of the model.
Volume: 14
Issue: 3
Page: 2012-2025
Publish at: 2025-06-01

The role of family-centered care in enhancing stroke rehabilitation outcomes: an integrative literature review

10.11591/ijphs.v14i2.24847
Fery Agusman Motuho Mendrofa , Dwi Indah Iswanti , I Made Moh. Yanuar Saifudin
Family-centered care (FCC), which emphasizes the involvement of family members as active participants in the care process, represents a significant paradigm within the realm of stroke rehabilitation. This study aimed to locate and synthesize the most recent evidence concerning the advantages, methodologies, and obstacles associated with the integration of FCC in stroke rehabilitation. The approach taken involves conducting an integrative literature review following the guidelines set forth by Whittemore and Knafl. A thorough exploration of four databases including PubMed, CINAHL, Scopus, and PsycINFO, was carried out, focusing on both quantitative and qualitative studies published between January 2012 and December 2022. Inclusion criteria comprised studies involving adult stroke patients undergoing rehabilitation, detailing family-centered interventions, and presenting outcomes for either the patients or their families. Upon the screening process, 25 studies met the inclusion criteria and were included in the analysis. Various strategies have been identified to effectively involve families in the rehabilitation process, such as educational initiatives, collaborative planning for home-based care, and provision of support for caregivers. However, the implementation of FCC faces challenges stemming from factors at the system level, provider level, and patient/family level, in conclusion, the integration of FCC in stroke rehabilitation yields substantial benefits for both patients and caregivers. It is imperative for nurses to engage families as collaborative partners, tailor interventions according to specific requirements, offer assistance to caregivers, and instigate changes at the systemic level.
Volume: 14
Issue: 2
Page: 1031-1039
Publish at: 2025-06-01

Bridging technology and humanity: humanizing online pedagogy in digital environments

10.11591/ijere.v14i3.31937
Nor Asiah Razak , Che Zalina Zulkifli , Yusri Abdullah , Ahmad Zulfadhli Khairuddin , Aervina Misron , Piriya Somasundram , Azizova Gulnora Shakirdjanovna
Comprehensive analyses on incorporating the intersection of online education, humanizing teaching approaches, and digital tools remain scarce. To the best of the authors' knowledge, limited comprehensive studies integrate online pedagogy and digital tools to humanize teaching methods, enabling students to become engaged and personalized learners, while fostering empathy among educators. A systematic literature review (SLR) was conducted, utilizing databases from the Scopus, Web of Science (WoS), and Google Scholar. The study employed content and comparative analysis and advocated a grounded theory approach to inductively analyses and navigate the articles’ data for addressing three research questions. Based on a set of criteria for inclusion and exclusion, 34 research articles written in English between 2010 and 2024 were reviewed. Results indicated the community of inquiry (CoI) framework has been prominent over the past two decades and is considered suitable for integration with any digital tools when investigating pedagogical strategies at all education levels, aiming to make online learning student-centered or human-centered with the principle of ‘no child left behind'. The review offers significant implications for humanizing online learning to the educational technology community, particularly for policymakers and practitioners, to strategies, reflect on, and, if necessary, improve their practices for future sustainable education and efficient pedagogical performance. 
Volume: 14
Issue: 3
Page: 2207-2223
Publish at: 2025-06-01

Enhancing Alzheimer’s disease diagnosis through metaheuristic feature selection and advanced classification techniques

10.11591/ijece.v15i3.pp3382-3395
Arar Al-Tawil , Worood Al-Muhtaseb , Laiali Almazaydeh , Hanaa Fathi
A diverse array of diagnostic and detection methods has been developed as a result of the advent of Alzheimer’s disease (AD) as a significant global health issue. This study employs bio-inspired algorithms, such as the parrot optimization algorithm (POA), grey wolf optimizer (GWO), and differential evolution (DE), to identify the most effective feature selection techniques for AD diagnosis. The predictive accuracy of these algorithms was improved by the simple keywords: Alzheimer’s disease optimization classification machine learning metaheuristic mentation of the Alzheimer’s disease Dataset. This was achieved by integrating a personalized fitness function and optimizing parameter settings with decision tree classifiers. To evaluate the algorithms’ effectiveness in machine learning models with population sizes of 30 and 60, precision, recall, accuracy, and F1-score were evaluated at 5, 15, and 30 iterations. The gradient boosting and XGBoost classifiers consistently obtained the highest results, while DE, GWO, and parrot optimization (PO) achieved maximal accuracy rates of 0.94, 0.93, and 0.94, respectively. These findings underscore the efficacy of integrating metaheuristic algorithms with robust classifiers to enhance the predictive accuracy of AD diagnosis. Furthermore, they illustrate that artificial intelligence (AI) algorithms that are operated by biological processes can accurately forecast AD, with the success rates and stability of the proposed methods serving as metrics for evaluating their efficacy.
Volume: 15
Issue: 3
Page: 3382-3395
Publish at: 2025-06-01

The application of fuzzy Delphi method for the development of STEM teaching model

10.11591/ijere.v14i3.29780
Zhaofeng Zeng , Xin Li , Siew Wei Tho
In order to improve the ability of physics student teachers (PSTs) to teach using the science, technology, engineering, and mathematics (STEM) teaching model, this study applied the fuzzy Delphi method (FDM) to determine the constituent items that need to be included in the process of constructing the STEM teaching model according to the characteristics of PSTs. A questionnaire through literature review and expert advice was prepared, which contained 17 items in three constructs, including eight items for cultivating students’ abilities, four for teaching strategy design, and five for the expected outcomes. Then, the questionnaire was distributed to 16 experts to collect opinions and suggestions, which were analyzed and ranked using the FDM. The findings showed that all 17 items passed the expert consensus, all the specialist consensus values above 75%, the threshold values (d) ≤0.2, and the fuzzy scores (A) ≥α-cut value=0.5. Within the framework of the study and based on expert consensus, it is necessary for the newly developed STEM teaching model for PSTs to incorporate all 17 items across three constructs. This would optimally enhance the PSTs’ ability to employ the STEM teaching model in their teaching instruction.
Volume: 14
Issue: 3
Page: 2061-2069
Publish at: 2025-06-01

Hybrid semantic model based on machine learning for sentiment classification of consumer reviews

10.11591/ijai.v14.i3.pp2001-2011
Palaniraj Rajidurai Parvathy , Nagarajan Mohankumar , Rajendran Shobiga , Gour Sundar Mitra Thakur , Mamatha Bandaru , Velusamy Sujatha , Shanmugam Sujatha
Digital information is regularly produced from a variety of sources, including social media and customer service reviews. For the purpose of increasing customer happiness, this written data must be processed to extract user comments. Consumers typically share comments and thoughts about consumable items, technological goods, and services supplied for payment in the modern period of consumerism with simple access to social networking globe. Each object has a plethora of remarks or thoughts that demand special attention due to their sentimental worth, especially in the written portions. The goal of the current project is to do sentiment prediction on the Amazon Electronics, Kindle, and Gift Card datasets. In order to predict sentiment and evaluate utilizing many executions evaluates admitting accuracy, recall, and F1-score, a hybrid soft voting ensemble method that combines lexical and ensemble methodologies is proposed in this study. In addition to calculating a subjectivity score and sentiment score, this study also suggests a non-interpretive sentiment class label that may be used to assess the sign of the evaluations applying suggested method for sentiment categorization. The effectiveness of our suggested ensemble model is examined using datasets from Amazon customer product reviews, and we found an improvement of 2-5% in accuracy compared to the current state-of-the-art ensemble method.
Volume: 14
Issue: 3
Page: 2001-2011
Publish at: 2025-06-01

Application of artificial intelligence and machine learning in expert systems for the mining industry: modern methods and technologies

10.11591/ijece.v15i3.pp3291-3308
Natalya Mutovina , Margulan Nurtay , Alexey Kalinin , Aleksandr Tomilov , Nadezhda Tomilova
The mining industry has changed significantly in recent decades with the introduction of advanced technologies such as artificial intelligence (AI) and machine learning (ML). These innovations contribute to the creation of expert systems that help in optimizing processes, increasing the safety and sustainability of operations. This article is a literature review of modern AI and ML methods and technologies used in the mining industry. Discusses various intelligent and expert systems used to improve productivity, reduce operating costs, improve occupational safety, environmental sustainability, machine automation, predictive analytics, quality monitoring and control, and inventory and logistics management. The advantages and disadvantages of different approaches are analyzed, as well as their potential impact on the future of the mining industry. The review highlights the importance of integrating AI and ML into mining processes to achieve more efficient and safer solutions.
Volume: 15
Issue: 3
Page: 3291-3308
Publish at: 2025-06-01

Transforming university education: a systematic review of mathematical modeling in learning

10.11591/ijere.v14i3.32523
Ines Miryam Acero Apaza , Hugo Walter Zamata Choque , Anibal Javier Cutipa Laqui
This article is focused on analyzing the impact of mathematical modeling on the learning of university students. The starting point is the complexity of the learning process in technological careers, since cognitive, emotional, social and pedagogical elements are involved in this process, therefore, a multidisciplinary approach is required to allow a holistic understanding of educational phenomena. For this purpose, a systematic review was applied following the PRISMA guidelines and 30 research studies available in Scopus, PubMed, and Web of Science were selected. These studies highlight the importance of mathematical modeling in improving education. The results highlight a higher frequency of use for models with structural equations, models related to adaptive profiles and virtual mathematical models. It is concluded that mathematical modeling represents a valuable resource in higher education, which enriches the learning experience and prepares students to face academic and professional challenges. It is impact is manifested in the improvement of conceptual understanding, the strengthening of problem-solving skills and the close linkage between theory and practice.
Volume: 14
Issue: 3
Page: 2118-2131
Publish at: 2025-06-01

Analyzing the impact of motorcycle traffic on road congestion and vehicle flow

10.11591/ijece.v15i3.pp2928-2937
Ayoub Charef , Mustapha Riad
Urban traffic systems are increasingly burdened by the rising prevalence of motorcycles, particularly in cities like Marrakech where they significantly influence traffic dynamics and congestion. This paper investigates the impact of motorcycle positioning on start-up lost time at signalized intersections, employing a comprehensive methodology that integrates real-world data collection and advanced simulation techniques. Using mobile phone cameras, traffic data were captured at key intersections, and the positioning and movements of motorcycles were analyzed using the YOLOv10 deep learning algorithm. These empirical data informed simulations carried out with the simulation of urban mobility (SUMO) tool to explore various motorcycle positioning strategies. The study reveals that motorcycles positioned close to cars exacerbate congestion, extending travel times and increasing queue lengths. Conversely, scenarios with dedicated motorcycle lanes demonstrate reduced congestion and smoother traffic flows. These findings highlight the critical role of strategic motorcycle positioning in enhancing urban traffic efficiency and suggest that dedicated motorcycle lanes could significantly improve overall traffic management.
Volume: 15
Issue: 3
Page: 2928-2937
Publish at: 2025-06-01

Semantic based medical visual question answering with explainable artificial intelligence

10.11591/ijai.v14.i3.pp2169-2177
Sheerin Sitara Noor Mohamed , Kavitha Srinivasan , Raghuraman Gopalsamy
The medical visual question answering (MVQA) system takes the advantage of both computer vision (CV) and natural language processing (NLP) to accept the medical image and corresponding question as input and generates the respective answer as output. One step further, the MVQA system capable of generating the answer based on the semantics has a distinct place and hence semantic based medical visual question answering (SMVQA) system is proposed in this research. In SMVQA, the semantics for input image and question are generated using layerwise relevance propagation explainable artificial intelligence (LRP XAI) technique and the answer is derived using deductive reasoning method. For this, seven MVQA datasets are used for model creation, testing and validation. The training phase of the SMVQA system is implemented using VGGNet, long short-term memory (LSTM), LRP XAI, ResNet and bidirectional encoder representations from transformers (BERT) to generate a model file. Then the inference is derived in the testing phase based on the generated model file for the test set. Finally, the answer is derived from the inference using natural language toolkit (NLTK) library, term frequency-inverse document frequency (TF-IDF), cosine similarity, best match25 (BM25) techniques along with deductive reasoning. As a result, the proposed SMVQA system gives improved performance then the existing MVQA system especially for abnormality type samples.
Volume: 14
Issue: 3
Page: 2169-2177
Publish at: 2025-06-01

Impedance matching and power recovery in response to coil misalignment in wireless power transmission

10.11591/ijece.v15i3.pp2556-2566
Lunde Ardhenta , Ichijo Hodaka , Kazuya Yamaguchi , Takuya Hirata
The improper alignment of the coils between the transmitter and receiver has a significant impact on wireless power transfer. If designers carefully calculate the parameters of inductance, capacitance, coupling coefficient, and working frequency and precisely implement these parameters into actual components, the system can optimize power transfer. However, it is evident that such a precise realization is often unachievable. This paper proposes a symbolic condition to maintain significant power despite the misalignment of transmitter and receiver coils. These symbolic conditions constrain parameters by simplifying some variables. This matching condition develops in the inequality of coupling coefficient, working frequency and quality factor, which are a crucial reference for maintaining power transfer. This condition is considered an additional one to the well-known impedance matching condition.
Volume: 15
Issue: 3
Page: 2556-2566
Publish at: 2025-06-01

Development and validation of the principals’ digital leadership instrument using Rasch measurement model

10.11591/ijere.v14i3.32214
Peng Yuanyuan , Bity Salwana Alias , Azlin Norhaini Mansor , Mohd Rashid Ab Hamid
This study addresses the critical need for robust measurement tools in digital leadership (DL) within educational settings—a topic of increasing relevance but limited research. Using the Rasch model measurement analysis, the study aims to develop and validate an instrument tailored to assess principals’ digital leadership (PDL) in China. The questionnaire, based on the five dimensions of the International Society for Technology in Education (ISTE) for education leaders—equity and citizenship advocate (ECA), visionary planner (VP), empowering leader (EL), systems designer (SD), and connected learner (CL)—was adapted to reflect Chinese cultural contexts. Following expert validation, the 33-item instrument was piloted with 188 teachers from higher vocational and technical colleges in Sichuan Province. The Rasch analysis, performed using Winsteps 3.72.3, assessed item fit, unidimensionality, local independence, reliability, separation index, and item-person mapping. The findings revealed that 26 items met all assumptions, demonstrating the strong reliability, validity, and psychometric robustness of the instrument. In conclusion, the validated PDL instrument is a reliable tool for assessing the DL of principals within the Chinese educational context, offering insights into professional development, and sets the stage for future research and policy development in the field of educational leadership.
Volume: 14
Issue: 3
Page: 1577-1589
Publish at: 2025-06-01

Viral hepatitis morbidity and mortality data in major urban cities in the Philippines

10.11591/ijphs.v14i2.24577
Rael S. Manriquez , Mark Anthony J. Torres , Cesar G. Demayo
This study investigates the transmission, impact, and prevention of viral hepatitis A (HAV), hepatitis B (HBV), hepatitis C (HCV), hepatitis D (HDV), and hepatitis E (HEV) in the National Capital Region (NCR) and Region 7, Philippines, from 1960 to 2020. These infections significantly contribute to liver complications, including cirrhosis and hepatocellular carcinoma, affecting mental well-being and posing risks to pregnant women. Although hepatitis mortality is notable, complete treatment can mitigate the risk. Transmission occurs through various routes, such as blood products, body secretions, and perinatal routes. The study underscores the importance of understanding transmission and implementing screening and prevention measures. Vaccination, particularly for Hepatitis A and B, is crucial, reshaping disease epidemiology through universal infant immunization. Challenges like low vaccination coverage persist, especially among children and healthcare workers. Analyzing mortality data reveals a significant recent decrease attributed to government efforts and vaccination programs since 1995. Despite regional variations, mortality remains relatively low. The study recommends prioritizing and expanding vaccination programs, raising awareness, improving healthcare accessibility, and strengthening surveillance systems. Coupled with community engagement, these measures promise sustained success against viral hepatitis, reinforcing the observed trend in mortality reduction.
Volume: 14
Issue: 2
Page: 1015-1021
Publish at: 2025-06-01

Augmented reality learning media for electrical motor: case study in electrical engineering education

10.11591/ijece.v15i3.pp2545-2555
Bima Mustaqim , Abdul Muin Sibuea , Sahat Siagian , Agus Junaidi , Muhammad Amin , Sriadhi Sriadhi , Wan Ahmad Jaafar Wan Yahaya , Harun Sitompul
The impact of augmented reality (AR) learning tools and students' critical thinking abilities on learning outcomes in electrical engineering education is the focus of this study. The study explores the ways in which these factors, both independently and in combination, influence student performance. Findings reveal that AR-based learning materials significantly enhance understanding and retention across self-directed and guided learning models. Critical thinking skills emerge as a key determinant of success, with students exhibiting strong critical thinking consistently outperforming peers with lower-level skills, regardless of the instructional model. The study also highlights variations in AR tools' effectiveness depending on the learning model and students’ critical thinking abilities. Guided learning with AR tools benefits all students, while self-directed AR tools prove most effective for those with advanced critical thinking skills. Students with lower critical thinking abilities face challenges in navigating less structured AR environments. These results underscore the importance of fostering critical thinking and adopting tailored strategies when integrating AR technology into engineering education. By considering both the learning model and critical thinking levels, educators can optimize AR’s potential to enhance student learning outcomes in technical fields.
Volume: 15
Issue: 3
Page: 2545-2555
Publish at: 2025-06-01

Employability of Latin honor graduate in a state university in the Philippines

10.11591/ijere.v14i3.32164
Emelyn Rico-Villanueva , Kim Jemar F. Falo , Annabelle Fampo-Ida , Ma. Levi R. Punla , Rose L. Sumanting
This study examines the employability of Latin-honor graduates from Romblon State University (RSU) between 2015 and 2022, addressing the factors that influence their transition into the workforce. The research investigates how academic performance, board examination results, and advanced education impact employment outcomes. Using a mixed-method approach, including surveys and binary logistic regression analysis, the study identifies key predictors of employability. Results indicate that achieving academic honors, passing licensure exams, and pursuing further education significantly enhance graduates’ chances of securing permanent employment and advancing in their careers. These findings highlight the need for aligning RSU’s curriculum with labor market demands and underscore the importance of continuous professional development. The study offers practical recommendations to improve graduate employability and informs policy initiatives aimed at supporting career progression for Latin-honor graduates.
Volume: 14
Issue: 3
Page: 1882-1903
Publish at: 2025-06-01
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