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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.
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Articles 39 Documents
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Search results for , issue "vol 15, no 3: september 2026" : 39 Documents clear
A novel combined deep learning-based technique for electricity theft detection in a smart grid Etienne Mouckomey; Jacques Bikai; Camille Franklin Mbey; Felix Gislain Yem Souhe; Alexandre Teplaira Boum; Vinny Junior Foba Kakeu
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.pp1098-1111

Abstract

Energy theft represents a major challenge for electricity distribution companies worldwide and constitutes a key component of non-technical losses (NTLs), alongside faulty meters and billing inaccuracies. Despite the rise of artificial intelligence techniques for electricity theft detection, existing approaches exhibit limitations in classification and data analysis when applied to large-scale datasets. These shortcomings can reduce the effectiveness and precision of the theft detection systems. Therefore, this article proposes a bidirectional long short-term memory (Bi-LSTM) with the lookahead adaptive moment estimation (LADAM) optimizer and a support vector machine (SVM) with a bidirectional gated recurrent unit (Bi-GRU) for the detection of energy theft in a smart electricity grid to assess energy supplier companies. In this work, the proposed model is implemented on the State Grid Corporation of China (SGCC) dataset. In the proposed framework, the Bi-LSTM is used for feature extraction, while the LADAM optimizer is employed for feature selection and normalization. The refined feature set is subsequently fed into the SVM classifier to distinguish between theft and non-theft instances. The obtained findings highlight the superior efficiency of the proposed strategy compared with those in the literature, highlighting its potential for practical deployment in real-world smart grid systems.
COVID-19 infection detection using convolutional self-attention network with voting classifier Mustafa Hussein Zwayyer; Ammar A. Ali; Rusul Hussein Hasan
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.pp1072-1084

Abstract

Early and accurate detection of COVID-19 from chest computed tomography (CT) scans are becoming essential for effective clinical decision-making and disease control. This study is proposing a robust deep learning framework that integrates a convolutional self-attention network (CSAN), gamma correction for image enhancement, and a voting-based ensemble classifier to improving diagnostic performance. The model is being evaluated on a dataset of 2,271 CT images and is achieving an accuracy of 95.12%, sensitivity of 97.25%, specificity of 98.11%, F1-score of 96.46%, and area under the curve (AUC) of 0.977. Experimental results are demonstrating that the proposed method significantly surpasses baseline models, including standalone CSAN, residual networks 50 (ResNet-50), densely connected convolutional network 121 (DenseNet-121), and visual geometry group (VGG) 16, with improvements of up to 9.5% in accuracy. The integration of attention mechanisms, image enhancement, and ensemble learning are proving effective in capturing both local and global features, leading to more reliable classification. These findings are suggesting that the proposed framework is supporting automated COVID-19 diagnosis in real-world clinical applications.
Recent advances in computer vision segmentation for urban tree detection using street view imagery: a literature review Muhammad Fareez Rashdan; Nurbaity Sabri; Shafaf Ibrahim; Nor Masri Sahri
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.pp975-989

Abstract

Segmentation of trees in urban settings is important for tracking vegetation and managing green infrastructure in urban areas. However, there are numerous factors that make it difficult to obtain precise results, such as differences in tree species, season, and environmental variables. Street-view imagery can provide a valuable database to be used for the detection and identification of trees. Unfortunately, the process of labelling images manually is time-consuming and inconsistent. In recent years, deep learning models have provided better automation and precision. However, difficulties in obtaining results persist due to factors such as occlusion, background, and changes in lighting conditions. Therefore, this paper presents an overview of the current trends in the use of deep learning for the detection and segmentation of trees. Specifically, the following aspects will be discussed in this work: preprocessing of images, semantic and instance segmentation, models based on deep learning, and transformers. This literature review also analyzes current datasets, evaluation measures, and applications, offering knowledge about existing challenges and future directions for further studies. The article is designed to be a source of reference for scientists and practitioners working on problems related to urban vegetation classification and computer vision.
Drivers of salesperson performance: value transfer, personality congruence, and selling ambiance Evi Dian Kartikasari; Sulastri Sulastri; Isnurhadi Isnurhadi; Muchsin Saggaf Sihab
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.pp1155-1165

Abstract

This study examines preference-based transfer of value (PTV) as a mediating mechanism linking adaptive selling (AS), salesperson-brand personality congruence (SPC), and positive selling ambiance (PSA) to salesperson performance (SP) in a retail banking context. Using a quantitative cross-sectional survey, questionnaires were collected from 262 frontline salespeople of PT Bank BNI (Persero) Tbk in Indonesia. The proposed model was tested using structural equation modeling (SEM). The findings show that AS, SPC, and PSA significantly enhance PTV, and PTV positively predicts SP. Mediation analysis indicates that PTV transmits the effects of these antecedents on performance, highlighting the importance of converting selling behaviors and interaction climates into customer-internalized value. The study contributes by introducing and empirically validating PTV as a novel psychological channel in sales performance research and offers managerial implications for recruitment, training, and performance assessment in business-to-consumer financial services. Future research may extend this model across industries and examine boundary conditions using multi-group designs.
Students' perspectives on the effectiveness of ChatGPT in virtual learning Isaac Asampana; Henry Akwetey Matey; Ben T. Ocra; Jones Yeboah Nyame
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.pp921-931

Abstract

This research investigates how students perceive chat generative pre-trained transformer (ChatGPT) as a learning aid, examining its effects on engagement, usability, and educational achievement. Using the technology acceptance model (TAM) as the conceptual framework, a 24-item survey was designed and administered through simple random sampling. Results reveal that students largely view ChatGPT positively, describing it as an accessible and beneficial tool that promotes flexible and independent learning. The findings confirm TAM predictions, showing that perceived ease of use and favorable attitudes significantly influence adoption. By combining insights from both distance and on-campus learners, the study enriches understanding of how generative artificial intelligence enhances cognitive development and learning adaptability. Overall, the research reflects cautious optimism about ChatGPT’s pedagogical value, acknowledging both its educational benefits and challenges while underscoring the essential role of usability and learner attitudes in fostering sustained artificial intelligence utilization in higher education.
Flight supervision and pilot training standards: a study of international aviation regulations Heni Puspita; Ade Gafar Abdullah; Agus Setiawan; Isma Widiaty; Ike Yuni Wulandari; Johanes Adi Prihantono; Erlian Supriyanto
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.pp1203-1213

Abstract

This paper analyzes variations in pilot training and licensing policies across four major organizations: the International Civil Aviation Organization (ICAO), the Federal Aviation Administration (FAA), the European Union Aviation Safety Agency (EASA), and the Directorate General of Civil Aviation (DGCA) of Indonesia. The main issue here is the impact of contradictory training pathway requirements, flight hours, simulator requirements, language proficiency requirements, and instructor qualification requirements on aviation safety, license transferability, and the construction of the pilot training system in Indonesia. This study employs a qualitative research design, combining a systematic literature review with regulatory comparative analysis. The findings demonstrate that the prescriptive and competency-based model is most prevalent in EASA, where simulation and multi-crew requirements are widely applied; whereas in the FAA, the emphasis is more on total flight experience and rules related to 1,500 hours; and in the Civil Aviation Safety Regulations (CASR), there is minimal implementation but overall consistent with ICAO minimum. The paper finds that the country must progressively harmonize with modern ICAO, EASA, and FAA standards of good practice, prioritizing competency-based training (CBT), fatigue risk management, simulator capacity, and instructor professionalization to enhance safety performance and international recognition.
Improving ride comfort of semi-active suspension with magnetorheological damper by fuzzy-PID control Zhang Kunlun; Azizan As’arry; Liucun Zhu; Abdul Aziz Hairuddin; Mohd Khair Hassan; Mohd Zarhamdy Md Zain
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.pp1038-1049

Abstract

Semi-active suspension using magnetorheological (MR) damper has become one of the popular choices for improving vehicle ride comfort due to its adjustable damping, affordable cost, and quick response. MR damper has highly nonlinear and obvious hysteresis behavior, so its control becomes complicated. Fuzzy control combined with proportional-integral-derivative (PID) controller is used to study the vibration reduction control strategy of semi-active suspension with MR damper. PID is highly robust, but the control parameters cannot be changed during the control process. Therefore, it has good adjustment ability for specific situations, but cannot effectively adjust nonlinear systems. Fuzzy control performs fuzzy inference based on input variables and fuzzy rules constructed from expert knowledge, and continuously outputs effective control parameters to adjust nonlinear systems. The advantages of both are used to construct fuzzy-PID controller to control MR damper. A quarter-car semi-active suspension with MR damper model is established using MATLAB/Simulink for simulation analysis. Compared with passive and PID, fuzzy-PID reduces the root mean square (RMS) values of body acceleration, suspension travel, and tire deflection, reduces body vibration, and improves ride comfort.
Ethical issues in digital mental health delivery: a comprehensive scoping review of global challenges Nurul Ain Ain Mohd Mohd Daud; Hapsah Md Yusof; Norazani Ahmad; Fauziah Mohd Sa’ad; Nurul Hasyimah Mat Rani; Pau Kee; Nur Shuhana Mohd Sansuddin
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.pp911-920

Abstract

Digital technologies are becoming increasingly integrated into mental health counselling. While expanding the boundaries of service provision, these technologies have also raised numerous ethical challenges that urgently need clarification. This study conducts a scoping review following the standardized methodology developed by Arksey and O’Malley. It identifies five core ethical challenges facing practitioners: privacy and data security; informed consent and autonomy; service accessibility and equality; algorithmic fairness and bias mitigation; and inclusiveness and equity. Existing comparative analyses show that Western countries, including the United States (US) and the United Kingdom (UK), have mature digital mental health regulatory frameworks. In contrast, non-Western countries generally face gaps in legal protections, which have created a global ethical divide. Artificial intelligence (AI)-related risks, such as algorithmic bias and lack of transparency, further exacerbate these problems. The study calls on policymakers to improve relevant regulatory frameworks and requires practitioners to receive specialized ethical training.
Flying ad hoc network applications, mobility models, routing protocols, and challenges: a review Taher M. Ghazal; Nabil El Kadhi; Munir Ahmad
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.pp1316-1333

Abstract

Applications of unmanned aerial vehicles (UAVs) are rapidly increasing in surveillance, disaster management, precision agriculture, logistics, environmental monitoring, and intelligent transportation systems. UAVs/drones have made their way towards the application of ad hoc networks. Multiple interconnected UAVs flying together have presented us with a new type of ad hoc network known as flying ad hoc networks (FANETs). While multiple small UAVs acting as a FANET offer a lot of benefits, there are also several challenges concerning this type of network. This study presents a systematic literature review (SLR) of FANET technologies by reviewing recent literature published between 2017 and 2025 from IEEE Xplore, Scopus, ScienceDirect, and Google Scholar databases. A structured review methodology based on inclusion and exclusion criteria was adopted to analyze more than 150 research articles. The study presents a comprehensive taxonomy of FANET mobility models and routing protocols, along with a comparative analysis of communication architectures, trajectory optimization methods, and implementation challenges. The findings reveal that routing efficiency, adaptive mobility models, secure communication, energy optimization, and scalable architectures are the major determinants of FANET performance. This article concludes that good connectivity, improved security, effective routing, carefully selected mobility models, and the architecture of FANET nodes are key parameters for obtaining optimal results.
Optimization of tensile strength and modulus elasticity from recycled mask polymer blends Indah Widiastuti; Slamet Jatmiko; Budi Harjanto; Arif Bagus Panuntun
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.pp894-901

Abstract

This research aims to analyze the effect of differences in the proportion of recycled polypropylene (r-PP), compatibilizer, and type of mask on the tensile strength and modulus of elasticity of recycled mask product materials. The research design used in this research uses the Taguchi orthogonal L4 experimental method, which uses a quantitative approach, namely by manipulating certain conditions and providing control over the test samples. This research focused on the effect of varying the proportion of r-PP composition, maleic anhydride polypropylene (MAPP) compatibilizer, as well as varying the type of mask waste used. In this research, the composition of r-PP used was 10% and 30%. Meanwhile, the MAPP composition used is 0% and 6%. In this study, tensile testing was carried out in accordance with the American Society for Testing and Materials (ASTM) D 638 standard type V in the form of a dogbone. The results of the research show that differences in the proportion of r-PP in the composition of the polymer mixture have an effect on the tensile strength and modulus of elasticity of the recycled mask product material.

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