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29,758 Article Results

Enhancing sleep disorder diagnosis through ensemble ML models: a comprehensive study on insomnia and sleep apnea

10.11591/ijaas.v15.i1.pp29-41
Satyaprakash Swain , Binod Kumar Pattanayak , Mihir Narayan Mohanty , Amiya Kumar Sahoo , Suvendra Kumar Jayasingh
Sleep disorders are common and can significantly harm human health, with insomnia and sleep apnea being the most prevalent conditions. These disorders are often difficult to detect and treat accurately. Although machine learning (ML) techniques have shown promise in improving diagnostic precision and personalized treatment, most existing studies rely on single source data or conventional ML models, which limit their robustness and generalizability across diverse populations. To address this research gap, this study integrates multi-modal data and ensemble learning techniques to enhance accuracy, interpretability, and real-time applicability in diagnosing insomnia and sleep apnea. A dataset of 400 samples was collected through manual methods and internet of things (IoT) devices from multiple sources. Statistical techniques were applied for data cleaning, followed by principal component analysis (PCA) to reduce dimensionality and improve training efficiency. Four base ML models: decision tree (DT), support vector machine (SVM), naive Bayes (NB), and random forest (RF) were initially trained and evaluated. Subsequently, a boosting-based ensemble model was implemented to further improve performance. The proposed gradient boosting model with RF as the base learner achieved the highest diagnostic accuracy of 96.01%. The results demonstrate that ensemble ML models combined with multi-modal data significantly enhance the accuracy of insomnia and sleep apnea diagnosis.
Volume: 15
Issue: 1
Page: 29-41
Publish at: 2026-03-01

Application of fuzzy logic for the evaluation of student academic performance in biomedical subjects

10.11591/ijaas.v15.i1.pp236-244
Elda Maraj , Anila Peposhi , Aida Bendo
Conventional educational systems primarily use rigid assessment models that narrowly define student achievement through examination scores, categorizing outcomes into success or failure. Fuzzy logic, a mathematical approach derived from set theory, provides a more flexible framework capable of capturing uncertainty and gradations in performance. Initially applied in engineering and artificial intelligence, fuzzy logic has shown significant promise in educational contexts where nuanced evaluation is essential. This study applies a fuzzy logic-based methodology to the evaluation of biomedical course performance at the Sports University of Tirana, Faculty of Rehabilitation Sciences. Data were collected from fifty students enrolled in biomedical subjects and analyzed through both classical examination grading and fuzzy logic evaluation. Comparative analysis revealed that while classical assessment remains constrained by static calculations, fuzzy logic introduces dynamic adaptability. The findings highlight the superiority of fuzzy logic over traditional methods in providing a multidimensional picture of academic achievement. This approach not only refines evaluation accuracy but also supports fairer and more individualized assessment practices. Consequently, fuzzy logic emerges as a powerful tool for modernizing educational evaluation systems, particularly in biomedical disciplines where learning outcomes often extend beyond conventional metrics.
Volume: 15
Issue: 1
Page: 236-244
Publish at: 2026-03-01

Hydrothermal synthesis of ZnFe2O4@g-C3N4 for enhanced adsorption-photocatalytic degradation of ciprofloxacin

10.11591/ijaas.v15.i1.pp313-321
Medya Ayunda Fitri , Muchammad Tamyiz , Eko Prasetyo Kuncoro , Mamlu’atul Nihaya , Muhammad Abdul Basith Thom Thom , Cindy Dwi Cahyani , Bahauddin Alqostolani
The persistence of antibiotic contaminants such as ciprofloxacin (CIP) in aquatic environments poses significant environmental and health risks, necessitating the development of efficient removal strategies. In this work, a zinc ferrite-anchored two-dimensional carbon nitride nanocomposite (ZF@2DCN) was synthesized via a simple calcination and hydrothermal approach to achieve synergistic adsorption–photocatalytic degradation of CIP under visible light. Structural and optical characterizations confirmed the successful formation of a ZF–2DCN heterojunction with high crystallinity, strong interfacial interactions, and enhanced visible-light absorption. The incorporation of ZF reduced the bandgap of 2DCN from 2.8 to 2.6 eV, promoting improved charge separation. Adsorption studies revealed rapid equilibrium within 30 min and multilayer adsorption on heterogeneous active sites, with a maximum adsorption capacity of 11.7 mg g-1. Under visible-light irradiation, ZF@2DCN achieved up to 81% CIP degradation within 60 min, exhibiting an apparent reaction rate approximately 2.5 times higher than that of pristine 2DCN. The enhanced performance is attributed to the strong synergy between adsorption-driven pollutant enrichment and photocatalytic degradation. Overall, ZF@2DCN shows strong potential as an efficient material for antibiotic removal in wastewater treatment.
Volume: 15
Issue: 1
Page: 313-321
Publish at: 2026-03-01

Hybrid energy storage systems as a sustainable energy source

10.11591/ijaas.v15.i1.pp219-226
Muhammad Adam , Suwarno Suwarno , Catra Indra Cahyadi
The use of fossil fuel power plants will contribute to emissions and environmental pollution, which has an impact on air and environmental pollution. Applying hybrid energy systems can help reduce the emission footprint and improve the stability of local electricity networks, especially in services with high energy consumption. Hybrid optimization of multiple energy resources (HOMER) is a simulator that simulates using renewable energy with the hybrid renewable energy systems (HRES). The simulation produces a system with the most appropriate combination of photovoltaic (PV), wind power (WP), and converter. The combination of PV-WP produces an economical choice for providing electrical energy in a particular location. The hybrid PV-WP model can save about 40.8% less than the current condition. The investment can be returned in 10.11 years, which is recommended for similar conditions in other areas. This positive impact can provide incentives for policymakers in the implementation of a hybrid system that can neutralize emissions and environmental pollution.
Volume: 15
Issue: 1
Page: 219-226
Publish at: 2026-03-01

Google Play review analysis on Sharia pawnshop applications in Indonesia

10.11591/ijaas.v15.i1.pp86-98
Azhar Alam , Adityo Wiwit Kurniawan , Muhammad Sholahuddin
Digital transformation opens opportunities for Sharia pawnshops to develop innovative application-based services following Sharia principles. This study analyzes the perception and experience of users of the Sharia pawnshop application on the Google Play Store using a netnography approach. It collects and analyzes 395 user reviews between June and December 2024, which consist of 219 positive reviews and 176 negative reviews. The analysis shows that 59.82% of users gave positive reviews regarding satisfaction with using the application, especially regarding transaction security and ease of use. As many as 17.35% of positive reviews emphasized the benefits of the application in transforming Islamic financial services. The main challenges identified included update system problems (35.23%), technical and server problems (30.11%), and registration complexity (5.68%). There was also a discrepancy between numerical ratings and review content. Important concerns include service problems (9.66%) and limited choice of Islamic banks (5.11%). This research provides important insights for the development of digital Islamic finance applications in the future, especially in the aspects of improving technological infrastructure, simplifying processes, and improving the quality of customer service. The results of this study contribute to a better understanding of user needs in the context of the digitization of Islamic financial services in Indonesia.
Volume: 15
Issue: 1
Page: 86-98
Publish at: 2026-03-01

Analysis of railway accidents in Nigeria: a decade of insights

10.11591/ijaas.v15.i1.pp19-28
Aliyu Mani Umar , Mohd Khairul Afzan Mohd Lazi , Sitti Asmah Hassan , Hanini Ilyana Che Hashim , Yinggui Zhang , Nura Shehu Aliyu Yaro , Adam Ado Sabari , Surajo Abubakar Wada
This study provides insights into the patterns and dynamics of railway accidents in Nigeria over the past decade. Findings indicate that Nigeria's rail network experiences fewer but more severe accidents than the United States of America (USA) and United Kingdom (UK), with significantly higher fatalities and injuries per million train kilometers 92% and up to 95% more, respectively, in 2023. A top-down approach was employed to establish a risk tree, revealing six railway accident categories recorded over the last decade. The established risk tree could provide a framework for conducting the rail network's comprehensive safety risk assessment. Finally, a root cause analysis of railway intrusion accidents, the most occurring railway accident category in the Nigerian rail network, was conducted. Six immediate and eleven underlying causes (UC) of railway intrusion accidents were identified. About 62% of all intrusion accidents were caused by negligence of road users. Several actionable preventive measures (PM) have been proposed for each identified UC based on best practices in developed rail networks. Infrastructure upgrades and safety awareness campaigns have been identified as the potentially most effective PM for railway intrusion accidents in Nigeria.
Volume: 15
Issue: 1
Page: 19-28
Publish at: 2026-03-01

Ensemble machine learning based model to estimate irrigation water requirement for wheat crop

10.11591/ijaas.v15.i1.pp142-154
Satendra Kumar Jain , Anil Kumar Gupta
India faces a serious water shortage issue, as its population grows faster than the percentage of fresh water available, with only 4% of the world's fresh water available to 18% of the world's population. Agriculture sector is more water-consuming sector in India. India's irrigation system still faces two significant problems: low irrigation efficiency and a lack of optimization during irrigation. To address these problems, agriculturists ought to be aware of the water requirements for crops beforehand. Innovative fields like machine learning, a branch of artificial intelligence, have a big potential to improve irrigation. Verifying the suitability of the gradient boosting regressor machine learning algorithm-based model for estimating irrigation water requirements (IWR) is the aim of this research. The experiment is conducted in Ludhiana, a city in Central Punjab, India, with a hot, semi-arid climate that features scorching summers and chilly winters. The results demonstrate the remarkably high accuracy rate with coefficient of determination (R2) =0.98 for predicting IWR. The suggested model, which is based on a gradient boosting regression, allows the stakeholders to accurately estimate the amount of water needed for irrigation, the number of irrigation applications for the growing season of wheat crops, and the interval between irrigations.
Volume: 15
Issue: 1
Page: 142-154
Publish at: 2026-03-01

State evolution approach for the axion conversion probability in magnetosphere of a neutron star

10.11591/ijaas.v15.i1.pp355-371
Bilal Ahmad , Shehreyar Ali
Neutron stars (NS), with their extreme gravitational and magnetic fields, provide an exceptional astrophysical laboratory for studying axion dark matter (DM). Through the Primakoff effect, axions can convert into photons within the magnetospheres of NS, a process that may produce observable radio and X-ray signals. In this work, we investigate axion-photon conversion using a novel, time-dependent state evolution formalism, moving beyond the commonly used stationary-path approximations. We derive a generic analytical expression for the conversion probability and calculate the associated radiated power. Our analysis demonstrates that this approach allows NS to strongly constrain the axion-photon coupling constant, reaching sensitivities of gaγγ ≃ 10−14 −10−15 GeV−1 for axion masses of ma ≃ 10−3 −10−10 eV. These results establish a new pathway to constrain gaγ via NS observations. Future campaigns using powerful observatories like the James Webb Space Telescope (JWST), Green Bank Telescope (GBT), and More Karoo Array Telescope (MeerKAT) array will be ideally suited to probe the distinct spectral signatures predicted by our model across multiple frequency domains.
Volume: 15
Issue: 1
Page: 355-371
Publish at: 2026-03-01

A bibliometric review of critical chain project management in construction

10.11591/ijaas.v15.i1.pp272-280
Dhiraj S. Bachwani , MohammedShakil S. Malek , Deep Shaileshkumar Upadhyaya , Neetu Yadav
This study offers an extensive bibliometric analysis of critical chain project management (CCPM) research over the past twenty years, seeking to elucidate the discipline’s developmental trajectory and pinpoint emerging research frontiers. A comprehensive review of the literature revealed fundamental principles of CCPM, highlighting essential components such as buffer management strategies and resource-constrained scheduling methodologies. This initial analysis established the theoretical framework for the quantitative study and facilitated the identification of suitable metrics to integrate both foundational theories and contemporary advancements in CCPM scholarship. The study examined approximately 1,800 academic publications, including journal articles, conference proceedings, review papers, and book chapters published from 2000-2022, obtained from the Scopus database. The analytical framework encompassed various bibliometric dimensions, including performance metrics, relationship indicators, conceptual frameworks, publication characteristics, and VOSviewer network analysis, as essential elements of the data examination process. The developed framework has two main goals: it helps researchers find important publications, potential collaborators, and new areas of research, and it gives practitioners a structured place to store information about how to use CCPM methods in complicated projects with few resources.
Volume: 15
Issue: 1
Page: 272-280
Publish at: 2026-03-01

An ensemble-based approach for breast cancer identification using mammography

10.11591/ijaas.v15.i1.pp133-141
Naveen Ananda Kumar Joseph Annaiah , Nakka Thirupathi Rao , Balakesava Reddy Parvathala , Banana Omkar Lakshmi Jagan , Bodapati Venkata Rajanna
Breast cancer is among the most common cancers in women worldwide; timely detection is vitally important for improving chances of survival. The present study examines an innovative machine learning technique for the diagnosis of breast cancer using the breast cancer Wisconsin (diagnostic) dataset from Kaggle. The dataset includes 569 instances, and each instance has 30 attributes derived from digitized fine needle aspiration (FNA) images of masses found in the breast. We will present an ensemble deep learning (DL) model fusing a convolutional neural network (CNN) and LRAlexNet architectures to increase the accuracy and robustness of this type of cancer diagnosis. CNN models are well-known for their power to capture spatial hierarchies in image data, and LRAlexNet is a specialized deep CNN that excels at image classification due to its depth and parameter optimization. In this work, we use the ability to extract features of CNNs along with the superior classification performance of LRAlexNet to distinguish between benign and malignant cancers. The model will be trained and validated on the curated breast imaging subset of the digital database for screening mammography (CBIS-DDSM) dataset, and performance will be evaluated using sensitivity, accuracy, specificity, and the area under the curve (AUC) for the receiver operating characteristic. The results show that the ensemble CNN-LRAlexNet model achieved superior accuracy for breast cancer prediction when compared to traditional machine learning methods.
Volume: 15
Issue: 1
Page: 133-141
Publish at: 2026-03-01

Performance comparison of feature extraction methods for electroencephalogram-based recognition of Balinese script

10.11591/ijaas.v15.i1.pp55-64
I Made Agus Wirawan , Ida Bagus Nyoman Pascima , Gede Surya Mahendra , I Made Candiasa , I Nyoman Sukajaya
Recognizing Balinese script from electroencephalogram (EEG) signals remains a challenging problem due to low signal amplitude, non-stationary dynamics, and significant inter-subject variability. Despite previous attempts, no single feature extraction method has been universally effective in addressing these limitations. To fill this gap, this study systematically evaluates five feature extraction techniques—differential entropy (DE), power spectral density (PSD), discrete wavelet transforms (DWT), Hjorth parameters, and statistical features—on the Balinese imagined spelling using electroencephalography (BISE) dataset, which contains EEG recordings specifically designed for Balinese script recognition. For classification, both artificial neural networks (ANN) and support vector machines (SVM) are applied, and their performance is validated across multiple experimental settings. Results demonstrate that DE consistently provides more stable and discriminative features than the other methods, achieving the highest classification accuracy when combined with ANN. These findings highlight the potential of DE-based approaches to advance electroencephalogram driven Balinese script recognition, offering a culturally significant contribution to brain-computer interface (BCI) research and supporting future applications in inclusive artificial intelligence, digital heritage preservation, and assistive technologies.
Volume: 15
Issue: 1
Page: 55-64
Publish at: 2026-03-01

Robust multi-faces recognition and tracking via fuzzy genetic algorithms and deep coupled features

10.11591/ijaas.v15.i1.pp209-218
Adil Abdulhur Abushana , Yousif Samer Mudhafar
In real-world surveillance environments, face recognition and tracking remain challenging due to partial occlusion, pose variation, illumination changes, and background clutter. This paper presents a robust hybrid framework that integrates fuzzy genetic algorithms (FGA) with deep coupled feature learning for multi-face recognition and tracking. The proposed system comprises three main modules: i) face detection and pre processing using the multi-task cascaded convolutional network (MTCNN), ii) deep coupled ResNet embeddings that jointly learn identity and appearance-invariant representations, and iii) a fuzzy rule-based genetic optimizer that adaptively refines tracking decisions based on uncertainty in motion, appearance similarity, and occlusion levels. The novelty of this work lies in the fusion of fuzzy inference with evolutionary search to guide the genetic optimization process—allowing dynamic adaptation to noisy and uncertain visual conditions. Moreover, probabilistic data association filters (PDAF) and conditional joint likelihood filters (CJLF) are employed to further enhance temporal consistency under occlusion and appearance variation. The results confirm that fuzzy evolutionary optimization, when coupled with deep feature learning, significantly improves robustness and stability for real-time face tracking in complex, dynamic scenes.
Volume: 15
Issue: 1
Page: 209-218
Publish at: 2026-03-01

Experimental study on annealing S45C steel: effect of temperature and time on hardness, impact strength

10.11591/ijaas.v15.i1.pp343-354
Mahadir Sirman , Syahrisal Syahrisal , Henny Pasandang , Rusdi Nur , Muhira Dzar Faraby , Mukhlisin Mukhlisin
Steel generally exhibits poor wear and friction resistance, making it necessary to improve its surface mechanical properties, particularly hardness and microstructure, to enhance performance. Heat treatment is one of the most effective methods for achieving these improvements. This study aimed to optimize the heat treatment parameters of S45C medium-carbon steel to improve hardness and impact strength using response surface methodology (RSM). Experimental trials were conducted at annealing temperatures of 800 °C, 850 °C, and 900 °C with holding times of 30, 60, and 90 minutes, followed by cooling in water, oil, or air. Hardness (HRC) and impact strength (Nm/mm²) were measured, and the data were analyzed using RSM with a central composite design (CCD). Quadratic models were found to be statistically significant for both hardness (Prob > F = 0.0222) and impact strength (Prob > F = 0.0338), confirming their validity. The optimization results indicated that a holding time of 60 minutes within the 850-900 °C range provides the best balance between high hardness (>55 HRC) and adequate impact strength (>0.68 Nm/mm²). These findings not only validate the predictive capability of RSM in heat treatment optimization but also provide practical guidelines for industrial applications of S45C steel in automotive, tooling, and structural components.
Volume: 15
Issue: 1
Page: 343-354
Publish at: 2026-03-01

Innovative climate information services: a scoping review and bibliometric analysis for climate change decision-making

10.11591/ijaas.v15.i1.pp65-76
Jazimatul Husna , Imilia Ibrahim , Ika Wahyuning Widiarti
This research aims to develop innovative information services to strengthen decision-making in climate change mitigation through a scoping review and bibliometric analysis (ScoRBA). A systematic search of the Scopus database identified 1,214 publications from 2009 to 2023, with 383 meeting inclusion criteria. Using the patterns, advances, gaps, evidence, and recommendations (PAGER) framework, this research provides a transparent synthesis of evidence on climate information services (CIS). The analysis reveals four major thematic clusters: i) emerging technologies and innovations, ii) climate and environmental studies, iii) information systems and decision making, and iv) context awareness and applications. Technologies such as service-oriented architecture (SOA), internet of things (IoT), and cloud computing are key enablers for improving CIS accuracy and efficiency. Evidence shows that these technologies have been successfully applied in agriculture and aquaculture across Vietnam, Bangladesh, and Australia. North African countries have adopted IoT-based water management systems to address water scarcity, while India employs similar technologies to optimize agricultural resources. Integrating local knowledge with scientific data—particularly in Africa, Southeast Asia, and South America—has proven essential for effective adaptation strategies. This research advances theoretical and practical understanding of CIS, offering evidence-based insights to guide the development of adaptive and equitable climate information frameworks.
Volume: 15
Issue: 1
Page: 65-76
Publish at: 2026-03-01

SAIDI and SAIFI indicators for the control of feeder A4502 of the Concepción transformer electrical substation

10.11591/ijaas.v15.i1.pp396-404
Margarita F. Murillo Manrique , Jorge Augusto Sánchez Ayte , William Joel Baygorrea Vega , Richard Flores-Caceres
This study evaluated the reliability of feeder A4502 of the Concepción substation (Huancayo, Peru) through the analysis of system average interruption duration index (SAIDI) and system average interruption frequency index (SAIFI) indicators. The 46-year-old infrastructure presented 805 structural deficiencies (59%), with a predominance of corrosion in iron poles. Automatic recloser devices were implemented at strategic points, based on the fact that 67% of the 73 interruptions in 2021 were transient faults. Post-intervention results (2024) showed significant improvements: SAIDI was reduced from 9.87 to 7.39 hours (25%), nearing the regulatory limit of 7 hours; SAIFI decreased from 4.29 to 2.71 events (37%), falling within the limit of 4. Pearson correlation analysis confirmed a statistically significant relationship between structural deficiencies and the indicators (r =0.62 SAIDI, r =0.58 SAIFI, p <0.05). The integrated approach—diagnosis of deficiencies + automation with reclosers—proved to be technically viable and economically justifiable, also allowing for the meeting of new energy demands (240 kVA available). The results constitute a replicable model for other aging Latin American networks, validating the viability of regulatory compliance without prohibitive investments.
Volume: 15
Issue: 1
Page: 396-404
Publish at: 2026-03-01
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