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International Journal of Advances in Applied Sciences
Published by Institute of Advanced Engineering and Science
ISSN : 22528814     EISSN : 27222594     DOI : http://doi.org/10.11591/ijaas
Core Subject : Science, Education, Social,
Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics
International Journal of Advances in Applied Sciences (IJAAS) is a peer-reviewed and open access journal dedicated to publish significant research findings in the field of applied and theoretical sciences. The journal is designed to serve researchers, developers, professionals, graduate students and others interested in state-of-the art research activities in applied science areas, which cover topics including: chemistry, physics, materials, nanoscience and nanotechnology, mathematics, statistics, geology and earth sciences.
Arjuna Subject : -
Articles 797 Documents
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Hybrid machine learning for predictive spare parts replacement in medical equipment Fabian Halley Pata Alban Dattu; Syed Tarmizi Syed Shazali; Shirley Jonathan Tanjong; Abdul Rani Achmed Abdullah; Nurlaila Rosli
International Journal of Advances in Applied Sciences Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i3.pp990-997

Abstract

Stability of medical equipment is critical for uninterrupted healthcare services, yet hospital computerized maintenance management systems (CMMS) records are often incomplete, heterogeneous, and partially labeled for spare parts usage. In the analyzed CMMS dataset, spare-part-related work orders accounted for approximately 65.1% of maintenance cases, indicating that spare-part readiness is a major contributor to maintenance response time and potential equipment downtime. This study proposes a semi‑supervised hybrid random forest-neural network (RF-NN) model to predict whether a work order will require spare‑parts replacement and to support proactive inventory planning. Retrospective CMMS work‑order records from six hospitals (N = 295) were pre‑processed using leakage‑safe pipelines (categorical one‑hot encoding, text feature extraction from fault descriptions, and standardization of numeric fields). Semi‑supervised self‑training was used to exploit unlabeled or weakly labeled records through iterative pseudo‑labelling. Models were evaluated using stratified 10‑fold cross‑validation and reported as mean ± SD. The hybrid RF-NN achieved Accuracy = 0.986 ± 0.018, precision = 0.980 ± 0.026, recall = 1.000 ± 0.000, and F1‑score = 0.990 ± 0.013, outperforming RF, NN, and Markov baselines. These results demonstrate the practical advantage of combining semi‑supervised learning with hybrid modelling to reduce missed spare‑parts cases, minimize downtime risk, and strengthen CMMS‑ready procurement decision support.
Modeling Indonesia’s regional economic growth for sustainable development planning Julia Loisa; Suryo Widiantoro; Caecelia Cynthia Limanan; Kezia Kezia; Kenvil Lesmana
International Journal of Advances in Applied Sciences Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i3.pp1286-1296

Abstract

This study aims to evaluate Indonesia’s regional economic performance using gross regional domestic product (GRDP) per capita at the provincial level from 2015 to 2021. Utilizing business intelligence technology through Tableau, the study integrates descriptive, predictive, and prescriptive analytics to examine interregional GRDP dynamics. The k-means clustering method is employed to classify provinces into three clusters based on economic similarity. The results reveal a persistent economic disparity between the western and eastern regions of Indonesia, with provinces such as DKI Jakarta and East Kalimantan grouped in the high GRDP cluster, while East Nusa Tenggara and Papua fall into the lowest cluster. Trendline and forecasting analyses provide projections of economic growth through 2030, indicating that some provinces demonstrate stable upward trends, whereas others show stagnation or declining patterns. Based on these findings, this research offers data-driven policy recommendations aimed at promoting more equitable and sustainable economic development across Indonesia.
Intrusion detection in evolving internet of things environments using decentralized data systems Zobayer Alam; Arnab Bishakh Sarker; Jariatun Islam; Sifat Rahman Ahona
International Journal of Advances in Applied Sciences Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i3.pp1252-1261

Abstract

Growing internet of things (IoT) deployments have widened the attack surface for cyber threats that static, signature-dependent intrusion detection systems (IDSs) struggle to counter, particularly against previously unseen attack variants. This research introduces a hybrid IDS framework built on a stacking ensemble of four heterogeneous base classifiers, namely random forest (RF), extreme gradient boosting (XGBoost), light gradient-boosting machine (LightGBM), and a shallow multi-layer perceptron (MLP), coupled with a PyTorch-based neural network meta-classifier. Recursive feature elimination (RFE) guided by a RF estimator selected the 15 most informative behavioral flow features, while a hybrid random sampling approach corrected severe class imbalance in the training data. Detection outputs are stored immutably through the interplanetary file system (IPFS) via the Pinata gateway, enabling decentralized, content-addressed logging of IDS alerts. Evaluated on the CIC-BCCC-NRC-TabularIoT-2024 benchmark, the model achieved a classification accuracy of 99.51%, precision of 99.71%, recall of 99.30%, and an F1-score of 99.51%, establishing that pairing meta-learning with decentralized log storage yields a robust, auditable IDS suited for dynamic IoT environments.
Multilevel local region sparse shape composition model for liver cancer classification Balasubramanian Sakthisaravanan; Ramakrishnan Meenakshi; Thirugnanasambandam Akila; Saravanan Durga Devi; Subbiah Murugan
International Journal of Advances in Applied Sciences Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i3.pp932-943

Abstract

Machine learning and computer-assisted disease detection are two technological innovations that have significantly improved medical advancement, and recent research has demonstrated their effectiveness. Liver cancer is one of the important causes of cancer-related deaths internationally. Tumor detection in the liver and its classification is challenging due to poor accuracy and high processing requirements available in the existing techniques. In these, there is a loss of edge information and the quality of the images considered. To address these issues in the segmentation and classification of liver cancer, a novel feature extraction method and algorithm called the novel multilevel local region (MLR)-based sparse shape composition model (NMLR-SSC) were developed. This innovative technique identifies the existence of tumors on abdominal computed tomography (CT) images. The input images were obtained from the 3D-IRCADb-01 dataset. The algorithm showed an improvement of 0.22%. When compared to other classifiers. The proposed algorithm showed 100% specificity, sensitivity, and a 98% accuracy rate in detecting the liver tumor.
Smart internet of things driven vehicle safety and battery management system Batchalakuri Jyothi; Bhavana Pabbuleti; Ravi Ponnala; Patil Mounika; Edara Shiva Prasad; Enukonda Soumya; Rakesh Teerdala; Srungaram Ravi Teja; Malligunta Kiran Kumar; Kambhampati Venkata Govardhan Rao; Chilakala Rami Reddy
International Journal of Advances in Applied Sciences Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i3.pp1085-1097

Abstract

The proposed system design, the essential results obtained, and its relevance to modern automotive applications. The updated version now begins by identifying the dual challenge of increasing road accidents and the performance degradation of electric vehicle (EV) batteries, emphasizing the lack of integrated solutions that address both safety and battery reliability in a unified manner. Then, this study concisely describes the system design, highlighting the use of an internet of things (IoT)-enabled architecture incorporating an ADXL345 accelerometer for collision detection, global positioning system (GPS)/global system for mobile communications (GSM) modules for emergency alerting, and voltage, current, and temperature sensors for real-time battery monitoring, all interconnected through Arduino and ESP8266-based cloud communication. The abstract now summarizes key experimental results, including the system’s demonstrated accuracy in impact recognition, reduced alert latency, and reliable battery-parameter tracking supported by effective cloud visualization. Finally, this study strengthened the automotive relevance by emphasizing how this integrated platform enhances emergency responsiveness, supports predictive maintenance, and offers practical value for EVs, commercial fleet operations, and broader intelligent transportation applications. The revised abstract now provides a balanced, complete, and technically informative overview aligned with academic and automotive industry expectations.
Comparative analysis of decision tree-based machine learning algorithms: a trade-off between accuracy and interpretability Alwatben Batoul Rashed; Aeshah Almutairi; Mansir Abubakar; Mardiyya Lawal Bagiwa
International Journal of Advances in Applied Sciences Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i3.pp1029-1037

Abstract

This paper presents a comparative analysis of decision tree (DT)-based machine learning (ML) algorithms using 11 publicly available datasets from the knowledge extraction based on evolutionary learning (KEEL) repository. The study focuses on two critical aspects of DT-based algorithms: interpretability and accuracy. These measures often conflict, making it challenging to optimize both simultaneously. The research examines the trade-offs between accuracy and interpretability in three algorithms: Java implementation of the repeated incremental pruning to produce error reduction algorithm (JRip), projective and recurring trees (PART), and decision table. Experimental results demonstrate that JRip achieves the highest accuracy among the three algorithms across the datasets. However, decision table exhibits the highest interpretability, followed by PART, while JRip scores the lowest due to its pruning mechanism. These findings provide valuable insights into the balance between accuracy and interpretability, assisting researchers and practitioners in selecting the most suitable algorithm for classification tasks. The findings show a definite trade-off between accuracy and interpretability. Although JRip generates more complicated rule sets, it consistently delivers better accuracy across the majority of datasets. Decision tables offer simpler and easier-to-understand models, despite their lower accuracy.
A study of onshore wind turbine spatial arrangement in Thailand Suparatchai Vorarat; Watcharapong Tantawat
International Journal of Advances in Applied Sciences Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i3.pp1297-1315

Abstract

This study examines the impact of wind turbine spatial arrangement on electricity generation efficiency in onshore wind energy farms within Thailand's meteorological and geographical context. An empirical analysis was conducted on 20 operational wind energy farms representing approximately 96% of Thailand's installed onshore wind capacity. Geographic information system (GIS) techniques were applied to measure the spacing and layout of the turbines, and the National Renewable Energy Laboratory's (NREL's) system advisor model (SAM) was employed to simulate the amount of electricity generated each year, considering wake effects, terrain, and site-specific wind resources. Capacity factors were calculated and compared against operational data. Results indicate capacity factors range from 17.12% to 30.75%, with a mean of 23.34%. Research reveals that wind turbine spacing is not the primary determinant of wind energy farm performance in Thailand. When the wind resources and site characteristics are good, the wind energy farms can have good performance from many different spacing setups. On the other hand, layout optimization by itself can't make up for bad wind conditions. These findings demonstrate the need for a more comprehensive design for wind energy farms in areas with moderate wind resources.
Mapping significant clinical factors to relapse-free survival prognosis in breast cancer patients Boby Al Qurthuby; I Made Murwantara
International Journal of Advances in Applied Sciences Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i3.pp1193-1202

Abstract

This study explores the role of clinical factors in influencing relapse-free survival (RFS) in breast cancer patients through comprehensive survival analysis (SA). The data were derived from a large cohort of breast cancer patients, encompassing clinical variables such as age at diagnosis, tumor size, number of positive lymph nodes, tumor grade, histological subtype, and receipt of chemotherapy and radiotherapy. Analytical methods included Kaplan-Meier plots, log-rank tests, Cox proportional hazards modeling, and feature ranking based on univariate and multivariate log-rank and Cox tests. The results show that patients who received chemotherapy demonstrated a shorter median RFS compared to those who did not (102.7 vs. 145.0 months, p = 0.03), likely due to high-risk patients being selected for chemotherapy. Meanwhile, radiotherapy was associated with improved prognosis (hazard ratio (HR) < 1 in Cox model, p < 10−5), in line with meta-analyses showing reduced breast cancer mortality post-radiotherapy. The contribution maps the key clinical determinants of RFS in breast cancer and highlights the practicality of performing SA.
The role of online public relations and social influence on customer behavior at Vietnamese commercial banks Thao Phuong Nguyen; Tuan Minh Nguyen; Hang Thi Thanh Hoang; Huong Thi Thu Nguyen
International Journal of Advances in Applied Sciences Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i3.pp1015-1028

Abstract

This study examines the effects of personalization (Per), social media (SM), and timeliness (Tim) on online public relations (OPR) and customer behavior (CB), as well as the mediating role of OPR and the moderating role of social influence (Soc). Data from 337 customers of Vietnamese joint-stock commercial banks were analyzed using partial least squares structural equation modeling (PLS-SEM). The results show that Per and SM significantly enhance OPR, whereas Tim does not affect OPR but directly influences CB. The indirect effects of Per, SM, and Tim on CB through OPR are not significant, indicating that OPR does not mediate the relationships. Although OPR has a small positive effect on CB in the moderation model, Soc neither directly affects CB nor moderates the OPR–CB relationship. These findings suggest that OPR supports digital relationship management but does not automatically translate communication activities into behavioral responses. Banks should therefore prioritize personalized, credible, transparent, and secure communication.
Rhino optimizer for hyperparameter tuning in depression detection Simisani Ndaba; Rajalakshmi Selvaraj; Hlomani Hlomani
International Journal of Advances in Applied Sciences Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i3.pp1239-1251

Abstract

Depression detection using machine learning (ML) has gained increasing attention; however, model performance remains highly sensitive to hyperparameter configuration. This study introduces the Rhino optimizer, a novel bio-inspired metaheuristic algorithm for hyperparameter optimization (HPO) inspired by behavioral mechanisms observed in Rhinoceros movement and survival strategies. The optimizer integrates Lévy-flight exploration, elite-guided exploitation, wallowing-based escape behavior, and migration-based repositioning to balance exploration and exploitation while reducing premature convergence. The proposed approach was evaluated on a transformer-based depression-detection model using F1-score as the primary optimization objective due to the class imbalance commonly present in mental-health datasets. Performance was compared against traditional search methods, classical metaheuristics, and modern bio-inspired optimization algorithms under an identical evaluation budget. Rhino optimizer achieved a best observed F1-score of 0.8005, equivalent to the sine-cosine algorithm (SCA) and grey wolf optimization (GWO) with the same evaluation budget. Consistent convergence behavior and statistical analyses suggested that the proposed Rhino optimizer may serve as a competitive approach for HPO in depression detection and that behavior-inspired optimization strategies might be useful in machine-learning applications with limited computational resources.

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