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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 720 Documents
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Innovative climate information services: a scoping review and bibliometric analysis for climate change decision-making Husna, Jazimatul; Ibrahim, Imilia; Widiarti, Ika Wahyuning
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp65-76

Abstract

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.
Application of fuzzy logic for the evaluation of student academic performance in biomedical subjects Maraj, Elda; Peposhi, Anila; Bendo, Aida
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp236-244

Abstract

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.
Artificial intelligence-powered image recognition retail checkout systems Alias, Malyssa; Saidi, Dhaifina; Jia Huey, Lim; Qing Fang, Lee; S. Ragunathan, Durghaashini; Poh Soon, JosephNg; Koo Yuen, Phan; Jit Theam, Lim; See Wan, Wong
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp187-197

Abstract

The integration of artificial intelligence (AI) with big data analytics leads to substantial transformations in the retail sector. This research explores the impact of AI-powered image recognition checkout systems on the retail industry, focusing on operational efficiency, customer experience, and resource waste. Employing a mixed-methods approach, this study combines usability testing and data analytics to assess the viability of this technology in attaining automation and accuracy in retail operations. The study focuses on the creation of robust, resource-efficient systems that foster long-term industrial growth. The findings show that AI-powered solutions not only speed the checkout process but also contribute to sustainable infrastructure by reducing resource consumption and increasing energy efficiency. This report offers significant information, like the impact of AI-powered image recognition checkout systems on operational efficiency, customer experience, and the role of AI in promoting sustainable infrastructure for retailers and governments looking to advance the digitalization of the retail industry.
Enhancing service reliability in heavy-duty commercial vehicles industry Jonny, Jonny; Nasution, Januar
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp99-106

Abstract

Reducing breakdown lead time is a critical factor in ensuring customer productivity and sustaining competitiveness in the heavy-duty commercial vehicle (HDCV) industry. This was tackled by applying a methodology called define, measure, analyze, improve, and control (DMAIC), which stands for DMAIC. By deploying it, the breakdown lead time of an Indonesian HDCV company can be minimized. Before the initiative, the lead time was 4 days with 81.54% or 815,400 defects per million opportunities (DPMO) or less than 1 sigma with only 303 parts within target. The reduction target was 2 days as required by its customers, with 40% or 400,000 DPMO or less than 2 sigmas, with 658 parts within target. After following the methodology, the lead time was less than 2 days, meeting customer requirements with 31.2% or 312,000 DPMO, or about 2 sigmas. It shows an improved lead time, which is less than 2 days from 4 days, and a sigma level which is less than 2 sigmas from less than 1 sigma, with 908 parts within target. The study demonstrates how integrating digital applications, remanufactured spare parts, and a centralized command center significantly shortens breakdown handling.
Financial distress prediction for batik small and medium enterprises credit financing based on deep learning algorithm Taryadi, Taryadi; Sudiyatno, Bambang; Basiya, Robertus; Yunianto, Era
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp245-252

Abstract

One of the biggest obstacles that any finance provider has when evaluating a borrower's creditworthiness is the prediction of financial trouble. The credit decision-making process is made more difficult for small and medium enterprises (SMEs) due to their inherent ambiguity, which raises financing costs and lowers the chance of approval. In order to estimate a binomial classifier for predicting financial hardship using logistic regression (LR), extreme gradient boosting (XGBoost), and artificial neural network (ANN) techniques, this study examines data from batik SMEs in Pekalongan city. Financial ratios predict the first period and grow in a multi-period model based on temporal factors, credit history, and age. Financial distress is defined as a substantial obstacle to a business's capacity to pay its debts as opposed to the potential for bankruptcy. The long short-term memory (LSTM) algorithm with more variables yields the best prediction accuracy. The study's conclusion indicates that in order to guarantee the accuracy of financial distress prediction, the time at risk must be adjusted.
Analysis of railway accidents in Nigeria: a decade of insights Umar, Aliyu Mani; Mohd Lazi, Mohd Khairul Afzan; Hassan, Sitti Asmah; Che Hashim, Hanini Ilyana; Zhang, Yinggui; Yaro, Nura Shehu Aliyu; Sabari, Adam Ado; Wada, Surajo Abubakar
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp19-28

Abstract

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.
Temperature and pH effects on bioethanol production from wild cassava (Manihot glaziovii Muell. Arg) using simultaneous co-fermentation Tantri, Ida Ayu Pridari; Gunam, Ida Bagus Wayan; Made Dewi Anggreni, Anak Agung; Sujana, I Gede Arya
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp227-235

Abstract

Bioethanol is a clean alternative energy source, with wild cassava (Manihot glaziovii Muell. Arg) as a potential feedstock. Fermentation converts glucose from hydrolysis into ethanol. This study examines the effects of pH and fermentation temperature on bioethanol characteristics using a simultaneous saccharification and co-fermentation (SSCF) technique. A factorial randomized block design (RBD) was used with two factors: pH (4.5, 5.0, and 5.5) and fermentation temperature (30, 32.5, and 35 °C). Data were analyzed using variance and Duncan’s test. Results showed that pH and temperature significantly affected pH value, total soluble solids, reducing sugar, and ethanol content. The optimal conditions for bioethanol production were pH 4.5 and temperature 32.5 °C, yielding a pH of 3.55±0.07, total soluble solids of 9.3±0.57 °Brix, reducing sugar of 3.038±0.10 mg/mL, and ethanol content of 3.48±0.37 (%w/v). Based on the results of this study, wild cassava can be utilized as bioethanol by considering the effect of fermentation conditions.
Comparative analysis of YOLO variants and EfficientNet for detecting bone fractures in X-ray images Sarker, Shatabdi; Roy, Avizit; Sharmin, Shaila; Rahman, Shakila; Uddin, Jia
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp155-167

Abstract

A bone fracture is a serious medical problem, and accurate and prompt diagnosis is crucial for optimal treatment. This study highlights the progress of automatic bone fracture detection using deep learning (DL) models. A dataset containing 17 different fracture classes was used to train and evaluate the models. The dataset had class imbalance and minor fracture detection challenges. Extensive preprocessing, including data augmentation and resizing, has been applied to solve these problems, which has helped to increase the robustness of the model. Seven state-of-the-art models—you only look once (YOLO)v8, YOLOv9, YOLOv10, YOLOv11, EfficientNetB0, DenseNet169 and ResNet50—are trained and evaluated. Precision, recall, F1-score, and mean average precision (mAP) were used to evaluate the performance of the models. Among all models, YOLOv11 leads the others by achieving the highest precision, mAP, and precision-recall balance. YOLOv11 adds architectural improvements such as a deep backbone network and hybrid feature fusion, which make the model more reliable in different types of fracture detection. It is capable of reducing false detections and maintaining stable memory usage consistency even under different imaging conditions. Overall, YOLOv11 showed promising results and highlighted the potential of AI-powered diagnostic tools to improve clinical processes and patient care. As future work, the application field of the model can be extended to larger medical imaging tasks, and it can be further refined for effective use in resource-limited environments.
DCNNVA: a deep convolutional neural network for volcanic activity classification using satellite imagery Shakir, Yasir Hussein; Mutlag, Reem Ali; AL Mandhari, Eshaq Aziz Awadh; Abdulnabi, Mohamed Shabbir
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp281-292

Abstract

Monitoring and classifying volcanic activity are a critical task for disaster risk reduction and hazard management. Recent discoveries in machine learning and deep learning have proved excellent satellite image classification and volcanic anomaly identification capabilities, yet the majority of existing methods suffer from small datasets, particularly on solitary data modalities or particular cases, merely as examples. In this research work, we put forward develop deep convolutional neural network for volcanic activity (DCNNVA) classification specifically designed for satellite imagery on volcanic activity. We rigorously benchmarked DCNNVA model's strength against a total of eight state-of-the-art transfer learning models: ResNet50, NASNetLarge, DenseNet121, MobileNet, InceptionV3, Xception, VGG19, and VGG16. Comparative experimental results show that proposed DCNNVA framework's overall performance significantly surpasses its competitors with an accuracy of 99.33%, precision of 100%, recall of 98.67%, and F1-score of 99.33%, significantly beating existing state-of-the-art methods. Also, we create a deployable graphical user interface (GUI) system that is capable of real-time monitoring on volcanic activity and generates multi-modal alert processing that can make this research directly applicable for practical use on disaster management as well as in early warning systems. This research contributes a scalable, strong, as well as practical solution towards volcanic hazard identification as well as a baseline system toward developing future multi-modal as well as real-time geohazard tracking system frameworks.
Miniaturized circular fractal patch antenna with defected ground structure for high-selectivity dual-band X-band applications Thommandru, Raju; Saravanakumar, Rengaraj
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp372-383

Abstract

Microstrip patch antennas are easily fabricated and have a low profile, making them widely used in radar, satellite, and defence applications. Achieving high selectivity and miniaturization in X-band dual-band operation remains a challenge. Conventional designs using simple patch geometries and defected ground structures (DGS) often exhibit limited bandwidth, poor impedance matching, and reduced gain. To address these limitations, this work presents a miniaturized circular fractal patch antenna with an optimized DGS to enhance frequency selectivity, improve impedance matching, and maintain compact size. Circular fractal slots are introduced in the radiating patch to extend the effective current path while preserving the footprint. A centrally placed diamond-shaped slot provides capacitive loading that aids impedance tuning. Electromagnetic simulations were conducted in Ansys HFSS 2023 R2, and a prototype was fabricated on an FR-4 substrate with εr = 4.4, loss tangent = 0.02, and thickness 1.6mm. Measurements verify two passbands: 8.637–9.173GHz (center 8.8025GHz, return loss −22.0267dB, voltage standing wave ratio (VSWR) 1.1720, gain 4.82dB, efficiency 63.51%) and 10.121–10.956GHz (center 10.3700GHz, return loss −25.2864dB, VSWR 1.1199, gain 3.42dB, efficiency 72.58%). The antenna shows steady radiation and improved matching across both bands, supporting use in compact X-band front ends.

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