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31,456 Article Results

Analysis of risk factors associated with subjective fatigue symptoms among coal mining vehicle operators

10.11591/ijphs.v14i4.26601
Robiana Modjo , Desy Sulistiyorini , Eko Rizkianto , Indri Hapsari Susilowati , Didik Triwibowo
Coal mining vehicle operators have negative consequences with health effects such as fatigue. The aim of the study was to analyze the risk factors related to subjective fatigue among coal mining operators. Specifically, it seeks to examine the relationship between work shift, working period, sleep quantity, sleep quality, and personal characteristics, such as age, neck circumference, and body mass index. Data were collected from a coal mining site in South Kalimantan, Indonesia in 2022 with primary questionnaires. A total of 440 workers who worked in the mining and hauling area for at least one year were recruited in this study. This was a cross-sectional study. Quantitative data were obtained to describe the level of subjective fatigue and to analyze the risk factors associated with the work fatigue in coal mining vehicle operators. The fatigue was associated with obesity (OR:1.777, 95% CI:1.0067-2.960), overweight (OR:1.783, 95% CI:1.046-3.040), neck circumference (OR:1.513, 95% CI:1.0983-2.329), sleep quality (OR:4.597, 95% CI:2.762-7.650), and working period (OR:0.545, 95% CI:10.360-0.825) (p<0.05 for all). However, fatigue did not significantly affect from sleep quantity and work shift. Ensuring individual health is essential for preserving the opportunity to sleep and allowing the body to recover from fatigue.
Volume: 14
Issue: 4
Page: 1797-1805
Publish at: 2025-12-01

Navigating ethics: social media challenges in Indonesian dental practice

10.11591/ijphs.v14i4.24582
Aditya Pratama Sarwono , Marie Louisa
Social media has evolved into an important tool for dental practitioners to promote their services and attract patients, necessitating a careful balance between effective promotion and upholding ethical standards to maintain patient trust. This research delves into the challenges and implications associated with using social media in dentistry, emphasizing the ethical considerations, legal frameworks, and the potential impact on public understanding of oral and dental health. By providing a comprehensive discussion on the importance of ethical promotion in the dental profession, the study not only highlights the need for dental practitioners to prioritize ethical standards in their social media activities but also offers effective strategies for attracting patients. These strategies include delivering relevant and informative content, engaging with followers, demonstrating professionalism and expertise, obtaining informed consent, and respecting patient privacy. The research underscores that navigating these complexities is essential for dental practitioners to promote their services ethically, contributing to the success and growth of their practice while safeguarding the integrity of health information and fostering positive relationships with the public.
Volume: 14
Issue: 4
Page: 1897-1908
Publish at: 2025-12-01

Unveiling the emotional labor of overseas Filipino international teachers

10.11591/ijere.v14i6.34579
Leomar O. Baylosis , Ivy F. Amante , Rovy M. Banguis , Aldin Paul S. Genovia , Shem A. Cedeño
Emotional labor at work typically manifests through surface acting and deep acting. This phenomenological study examines the emotional labor experienced by 15 international Filipino teachers working in the United States, Saudi Arabia, and Thailand. Guided by self-determination theory (SDT), the research explores their reasons for teaching abroad, as well as the challenges they face and how they navigate them emotionally. A qualitative design was employed using semi-structured interviews and framed narratives. Each participant engaged in one individual interview and one focus group discussion. Data were analyzed using interpretative phenomenological analysis (IPA) to generate key themes. Findings show that deep acting involves emotional control, display of positive emotions, emotional exhaustion, experience of negative emotions, and emotional indifference toward self. In contrast, surface acting includes masking emotions, projecting artificial feelings, and withdrawal behaviors. The five major themes emerged as contributing factors to emotional labor: cultural adjustment, language barrier, professional challenges, limited support networks, and work-life balance. Coping strategies identified include emotional regulation, positive cognitive response, support from family and peers, and participation in recreational activities. These nuanced findings offer important insights for international teacher preparation, emotional well-being, and future research on cross-cultural educational contexts.
Volume: 14
Issue: 6
Page: 4628-4637
Publish at: 2025-12-01

Smart wearable glove for enhanced human-robot interaction using multi-sensor fusion and machine learning

10.11591/ijece.v15i6.pp5162-5172
Nourdine Herbaz , Hassan El Idrissi , Hamza Sabir , Abdelmajid Badri
Hand gesture recognition (HGR) using flexible sensors (flex-sensor) and the MPU6050 sensor has proved to be a key area of research in human-machine interaction, with major applications in biasing, rehabilitation, and assisted robotics. This paper proposes a wearable intelligent glove designed to operate a robotics arm in real time, relying on multi-sensor fusion and machine learning methods to enhance the system's responsiveness and precision. The proposed system enables the intuitive reproduction of hand movements and precise control of the robotic arm. In the context of Industry 4.0 and internet of things (IoT), the classification of gestures is necessary for maintaining operational efficiency. To guarantee gesture recognition, data signals from the smart glove are collected and trained by a recurrent neural network (RNN), which achieves 98.67% accuracy for real-time classification of seven gestures. Beyond industrial applications, the wearable smart glove can be exploited in a recognized circuit of all systems, including rehabilitation exercises that involve recording the progression of muscular activity for the assessment of motor functions and serve as a tool for patient recovery.
Volume: 15
Issue: 6
Page: 5162-5172
Publish at: 2025-12-01

Adaptive tilt acceleration derivative filter control based artificial pancreas for robust glucose regulation in type-I diabetes mellitus patient

10.11591/ijece.v15i6.pp5297-5313
Smitta Ranjan Dutta , Akshaya Kumar Patra , Alok Kumar Mishra , Ramachandra Agrawal , Dillip Kumar Subudhi , Lalit Mohan Satapathy , Sanjeeb Kumar Kar
This study proposes an Aquila optimization–based tilt acceleration derivative filter (AO-TADF) controller for robust regulation of blood glucose (BG) levels in patients with type-I diabetes mellitus (TIDM) using an artificial pancreas (AP). The primary objective is to develop a controller that ensures normo-glycemia (70–120 mg/dl) while enhancing stability, accuracy, and robustness under physiological uncertainties and external disturbances. The AO algorithm tunes the control gains of the TADF controller to minimize the integral time absolute error (ITAE), ensuring optimal insulin infusion in real time. The AO-TADF controller introduces a filtered structure to improve the dynamic response and noise rejection capability, effectively handling the nonlinear nature of glucose-insulin dynamics. Simulation results demonstrate that the proposed approach achieves a faster settling time (230 minutes), lower peak overshoot (3.9 mg/dl), and reduced noise (1%) compared to conventional proportional integral derivative (PID), fuzzy, sliding mode (SM), linear quadratic gaussian (LQG), and H∞ controllers. The closed-loop system achieves a stable glucose level of 81 mg/dl under varying meal and exercise disturbances, validating the superior performance and robustness of the AO-TADF approach.
Volume: 15
Issue: 6
Page: 5297-5313
Publish at: 2025-12-01

Hardware efficient multiplier design for deep learning processing unit

10.11591/ijece.v15i6.pp5205-5214
Jean Shilpa V. , Anitha R. , Anusooya S. , Jawahar P. K. , Nithesh E. , Sairamsiva S. , Syed Rahaman K.
Deep learning models increasing computational requirements have increased the demand for specialized hardware architectures that can provide high performance while using less energy. Because of their high-power consumption, low throughput, and incapacity to handle real-time processing demands, general-purpose processors frequently fall short. In order to overcome these obstacles, this work introduces a hardware-efficient multiplier design for deep learning processing unit (DPU). To improve performance and energy efficiency, the suggested architecture combines low-power arithmetic circuits, parallel processing units, and optimized dataflow mechanisms. Neural network core operations, such as matrix computations and activation functions, are performed by dedicated hardware blocks. By minimizing data movement, an effective on-chip memory hierarchy lowers latency and power consumption. According to simulation results using industry-standard very large-scale integration (VLSI) tools, compared to traditional processors, there is a 25% decrease in latency, a 40% increase in computational throughput, and a 30% reduction in power consumption. Architecture’s scalability and modularity guarantee compatibility with a variety of deep learning applications, such as edge computing, autonomous systems, and internet of things devices.
Volume: 15
Issue: 6
Page: 5205-5214
Publish at: 2025-12-01

Smartphone use and its association with academic performance among university students in Bangladesh

10.11591/ijere.v14i6.34651
Md. Biplob Hossain , Noyon Ali , Ahmed Al Sabbir , Faysal Ahmed Imran , Md Shahjahan
Smartphone use has become integral to daily life, particularly among university students. While smartphones provide educational benefits, their overuse and addiction may negatively impact academic performance. This study investigates the prevalence of smartphone use, addiction levels, and their associations with academic performance among undergraduate students in Bangladesh. A cross-sectional survey was conducted on 615 undergraduate students from seven universities. The smartphone addiction scale-short version (SAS-SV) measured addiction levels, while academic performance was assessed via self-reported cumulative grade point average (CGPA). Descriptive statistics and logistic regression analyses were performed to explore associations. Among respondents, 29.1% were categorized as smartphone-addicted based on SAS-SV thresholds. Key predictors of higher academic performance (CGPA>3.0) included male gender (adjusted odds ratio (AOR): 3.71, 95% confidence interval (CI): 2.47–5.59, p<0.01), rural background (AOR: 1.64, 95% CI: 1.11–2.43, p=0.01), and attending private universities (AOR: 1.85, 95% CI: 1.28–2.74, p<0.01). Smartphone use for educational purposes was positively associated with better academic outcomes (AOR: 1.48, 95% CI: 0.95–2.30, p<0.01). Although smartphones are widely used for academic purposes, excessive use for stress relief or non-educational activities may harm academic outcomes. Interventions promoting responsible use and raising awareness about smartphone addiction are crucial for enhancing academic performance among university students.
Volume: 14
Issue: 6
Page: 4854-4863
Publish at: 2025-12-01

Greenhouse gas reduction system for engines using electrolyte technology

10.11591/ijece.v15i6.pp5524-5534
Bopit Chainok , Boonthong Wasuri , Piyamas Chainok
This research focuses on developing a system to reduce greenhouse gas emissions in internal combustion vehicle engines using electrolyte technology and embedded programming on an electronic board via the OBI protocol. The main objectives are to create a prototype, apply it in real-world scenarios, evaluate its efficiency, and facilitate technology transfer. The system, designed to reduce greenhouse gases from vehicles, consists of a Bluetooth on-board diagnostics (OBD) scanner connected to the electronic control unit (ECU). This scanner transmits data to an embedded microcontroller through a Bluetooth module. The microcontroller, which includes software for controlling oxygen measurement and production, operates to decrease greenhouse gas emissions. The results show that the electronic device, IC ELM327, decodes OBD into RS232, processes the oxygen output from the exhaust pipe using embedded programming on the Arduino Uno-R3 microprocessor, and controls the oxygen production unit with electrolyte technology. The system adds 9.82% oxygen to the exhaust and reduces carbon monoxide by 21.04% and carbon dioxide by 13.86%. Additionally, the technology transfer received high satisfaction with a mean score of 4.61, indicating efficient technology dissemination.
Volume: 15
Issue: 6
Page: 5524-5534
Publish at: 2025-12-01

Machine learning model for accurate prediction of coronary artery disease by incorporating error reduction methodologies

10.11591/ijece.v15i6.pp5655-5666
Santhosh Gupta Dogiparthi , Jayanthi K. , Ajith Ananthakrishna Pillai , K. Nakkeeran
Coronary artery disease (CAD) remains a leading cause of mortality worldwide, with an especially high burden in developing countries such as India. In light of increasing patient loads and limited medical resources, there is an urgent need for accurate and reliable diagnostic support systems. This study introduces a machine learning (ML) framework that aims to enhance CAD prediction accuracy by specifically addressing the reduction of false negatives (FN), which are critical in medical diagnostics. Utilizing a stacked ensemble model comprising five base classifiers and a meta-classifier, the framework integrates cost-sensitive learning, classification threshold tuning, engineered features, and manual weighting strategies. The model was developed using a clinically acquired dataset from the Jawaharlal Institute of postgraduate medical education and research (JIPMER), consisting of 428 patient records with 36 original features. Evaluation metrics show that the proposed model achieved an accuracy of 92.19%, sensitivity of 98%, and an F1-score of 95.15%. These improvements are significant in a clinical context, potentially reducing missed diagnoses and improving patient outcomes. The model is intended for deployment in cardiology outpatient settings and demonstrates a scalable, adaptable approach to medical diagnostics.
Volume: 15
Issue: 6
Page: 5655-5666
Publish at: 2025-12-01

Utilization of innovative educational applications for developing the spiritual and creative potential of future educators

10.11591/ijere.v14i6.32125
Elvira Yeshenkulova , Kulimkhan Arymbayeva , Gulzhan Maulenberdieva , Dinara Yeshenkulova , Ulzharkyn Abdigapbarova , Kristina Kolesnik
Contemporary training of future educators necessitates the development of not only professional but also spiritual and creative qualities. This study aims to evaluate the effectiveness of an online course utilizing a chatbot to enhance the spiritual and creative potential of students in pedagogical disciplines. The research involved 534 of 4thyear students from two universities in Kazakhstan, divided into an experimental group (n=268) and a control group (n=266). The experimental group completed six modules covering creative thinking, spiritual development, public speaking, and other aspects of self-development. Pre- and post-course surveys assessed five indicators of spiritual health: vitality, resilience, values, meaning, and openness to change. The intervention resulted in a significant improvement in three key indicators: resilience, values, and meaning. These enhancements are likely to contribute to better preparation of future educators by strengthening their resilience to professional challenges, helping them understand their values and goals, and enhancing their awareness of the significance of education for society.
Volume: 14
Issue: 6
Page: 5129-5139
Publish at: 2025-12-01

Beat the cheat: tendencies of academic misconduct and teachers’ interventions

10.11591/ijere.v14i6.34757
Lauro Santiago Aspiras , Emma Daynos Aspiras
This research employed an explanatory sequential mixed-methods approach, integrating quantitative and qualitative techniques across two phases to explore the prevalence of academic misconduct (AM) and the interventions implemented by educators. Data were gathered from randomly selected students and purposively chosen teachers using structured and semi-structured questionnaires, based on specific criteria. The findings revealed significant occurrences of AM among students, including practices such as plagiarism, copying from websites, and sharing quiz answers via social media. Teachers also reported these behaviors, highlighting the multifaceted motivations behind students’ engagement in AM. In response to these challenges, educators implemented a range of interventions. These included confirmatory communication through phone calls and text messages, the introduction of gamified learning activities, constructive feedback on plagiarized work, and supportive outreach via private messages. In some cases, home visits were conducted to reinforce accountability. The study identified four key outcomes of these interventions: student compliance with academic integrity measures, acceptance of the consequences of misconduct, receptivity to feedback, and the phenomenon known as “Ningas Kugon” mentality, which describes inconsistent or transient changes in student behavior. Hence, this inquiry provides valuable insights into the dynamics of AM and the effectiveness of various educator interventions.
Volume: 14
Issue: 6
Page: 4329-4339
Publish at: 2025-12-01

Detection of breast cancer with ensemble learning using magnetic resonance imaging

10.11591/ijece.v15i6.pp5371-5379
Swati Nadkarni , Kevin Noronha
Despite notable progress in medicine along with technology, the deaths due to breast cancer are increasing steadily. This paper proposes a framework to aid the early detection of lesions in breast with magnetic resonance imaging (MRI). The work has been carried out using diffusion weighted imaging (DWI) and dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI). Data augmentation has been incorporated to enlarge the data set collected from a reputed hospital. Deep learning has been implemented using the ensemble of convolutional neural network (CNN). Amongst the individual CNN models, the you only look once (YOLO) CNN yielded the highest performance with an accuracy of 93.4%, sensitivity of 93.44%, specificity of 93.33%, and F1-score of 93.44%. Using Hungarian optimization, appropriate selection of individual CNN architectures to form the ensemble of CNN was possible. The ensemble model enhanced performance with 95.87% accuracy, 95.08% sensitivity, 96.67% specificity, and F1-score of 95.87%.
Volume: 15
Issue: 6
Page: 5371-5379
Publish at: 2025-12-01

Classroom climate and absenteeism: modeling English achievement mediated by engagement

10.11591/ijere.v14i6.35795
Palash Majumder , Nimisha Beri , Salil Biswas
This study investigates the intricate relationships between positive classroom climate, student engagement, English as a second language (ESL) achievement, and absenteeism among 240 secondary school students in West Bengal, India. Addressing a significant research gap within the multilingual Indian ESL context, the study proposes and tests a novel multiple mediation structural model using structural equation modeling (SEM). Findings reveal that a positive classroom climate significantly predicts higher student engagement (β=.598, p<0.001) and ESL achievement (β=0.476, p<0.001). Crucially, both engagement (β=-0.250, p=0.002) and ESL achievement (β=-0.186, p=0.015) are negatively associated with absenteeism, with classroom climate exerting substantial indirect effects on attendance (total indirect effect β=-0.238, p<0.001). This research, aligned with sustainable development goal 4 (SDG 4): quality and inclusive education, provides empirical evidence for fostering supportive learning environments and engagement practices to simultaneously improve academic outcomes and reduce absenteeism, particularly for ESL learners in underserved contexts.
Volume: 14
Issue: 6
Page: 4432-4442
Publish at: 2025-12-01

Development of research competences in undergraduate students

10.11591/ijere.v14i6.33724
Carmen Lily Winchez Aylas , Patricia Bejarano Alvarez
This study addresses the limited involvement of university students in research activities, which reflects a significant gap in the development of key research competencies for their academic and professional advancement. Lack of involvement restricts essential skills such as problem formulation, rigorous data analysis and communication of findings, preventing students from generating relevant knowledge and meeting the challenges of their future careers. Through a systematic review of the literature on the development of research competencies in undergraduate students, using reliable sources such as Scielo, Dialnet and other academic journals, this study identified effective methodologies to foster such competencies. Rigorous exclusion criteria were applied to ensure the relevance and timeliness of the selected studies. In conclusion, educational strategies such as flipped learning and research workshops were found to have a positive impact on the development of research competences, enhancing both technical and transversal skills, such as innovation and critical thinking. These methodologies prepare students for academic and professional challenges and foster a research culture in educational institutions. This article explicitly identifies and synthesizes innovative educational strategies (flipped learning, Lean startup, and research workshops) that have a proven impact on strengthening both technical and transversal research competences in undergraduates in Latin America. This systematic approach and regional focus are not found in prior reviews.
Volume: 14
Issue: 6
Page: 4928-4936
Publish at: 2025-12-01

Cultivating words: integrating immersive technologies and translation in agricultural settings

10.11591/ijere.v14i6.35451
Alma Karasaliu , Ilir Palla
This study explores an innovative pedagogical approach that integrates translation, educational technology, and virtual reality (VR) to enhance domain-specific language acquisition and digital engagement among undergraduate students in agricultural education. The 18 first-year students enrolled in “Agricultural Business Management” at Fan S. Noli University participated in a study combining immersive virtual experiences, translation tasks, and artificial intelligence (AI) tools. Students used platforms like Google Earth VR and 360Cities to describe agricultural environments in Albanian, and translated these texts into English using human and machine translation (Google Translate). The translated texts were then visualized through DALL·E to stimulate reflection on the semantic representation of the original scenes. Mozilla Hubs supported collaborative analysis of translation quality and peer feedback. Statistical analysis using R software (version 4.3.1) included the Welch two-sample t-test and Wilcoxon signed-rank test to compare translation outcomes. The results indicated significant improvement in technical vocabulary retention and increased students’ awareness of translation complexities. Moreover, students reported heightened motivation and positive attitudes toward technology-enhanced language learning. The study highlights the pedagogical value of immersive, interdisciplinary strategies in English for specific purposes (ESP), particularly in technical disciplines like agriculture.
Volume: 14
Issue: 6
Page: 4785-4795
Publish at: 2025-12-01
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