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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 680 Documents
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Quantum-inspired magnetic resonance imaging sequence optimization for detecting neurological diseases Savan Kumar, Kotichintala Venkata Narasimha; Kumar, Nitin
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1208-1216

Abstract

According to a research study by the National Institutes of Health, India, a magnetic resonance imaging (MRI) holds 89% diagnostic accuracy for acute stroke, while a computed tomography (CT) holds only 54%. Means there is still 11% area of improvement for accuracy measures required and there is 84% specific in identifying nerve enlargement. The possible solution is to use quantum computing; this is new era of technology in advanced design and implementation for computing techniques as compared with that of classical computers. With the goal of improving patient care, this is the area-of research using quantum technology to solve the neurological disorders. MRI and Microsoft’s quantum-inspired algorithms to enhance approach to detecting neurological disorders. To improve accuracy of MRI results in less time, an approach called magnetic resonance fingerprinting (MRF) was explored. This paper mainly focused on optimizing the sequence using Microsoft azure simulator. By generating an optimized pulse sequence and map to the accurate predefined patterns, able to create a solution that improves the diagnostic capability of MRI. Conventional computers will take long time to predict, but accuracy may alter. The proposed quantum-inspired optimization improved MRI diagnostic accuracy up to 92%, with faster sequence optimization compared to classical methods. This simulation-based proof of concept demonstrates potential for enhanced neurological disease detection while acknowledging current limitations such as simulator dependency and limited datasets.
Implementation of XGBoost for diabetes mellitus risk prediction based on health history Riansyah, Andi; Ghufron, Ghufron; Fitriyah, Lailatul; Suyanto, Suyanto
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1028-1039

Abstract

Diabetes mellitus (DM) is a chronic disease with a growing global burden and specific challenges for early management, particularly in regions with limited access to healthcare. This study develops a web-based system to classify diabetes risk from medical history using extreme gradient boosting (XGBoost), an ensemble model of decision trees. The dataset comprised 520 respondents (320 DM, 200 non-DM) and underwent labeling, standardization, and an 80:20 train–test split, followed by hyperparameter selection via grid search and 5-fold cross-validation (CV). On the test set, the model achieved an accuracy of 0.9888, precision of 1.0000, recall of 0.9718, and an F1-score of 0.9857; discriminative performance was also strong with an area under the receiver operating characteristic curve (AUC ROC) of 0.839. These findings confirm that XGBoost effectively handles complex or imbalanced medical data while providing probabilistic outputs that are clinically meaningful. Deployed as a web application, the system can support early screening, triage, and clinical decision-making, thereby expediting referrals and personalizing interventions in primary care and hospital settings, especially in resource-constrained environments. This work lays the groundwork for further development, including the integration of explainable artificial intelligence (XAI) techniques to enhance clinical transparency.
Redesigning retail spaces based on customer habits and halal standards using market basket analysis Yanti, Roaida; Qurtubi, Qurtubi; Setiawan, Danang; Maradjabessy, Prita Nurkhalisa; Faisol, Nasruddin
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1089-1098

Abstract

The halal retailing standard was first introduced by the Department of Malaysian Standards in 2010, known as MS 2400-3:2010. This standard was developed to protect Muslim consumers with the assurance of halal products. However, this management system needs to be more prescriptive on how the retail layout should be organized. In addition, this management also overrides the consideration of customer purchase behaviors or preferences. This research aims to design the layout of retail outlets by considering customer buying behavior and halal retailing standards. This study used the association rule-market basket analysis (AR-MBA) to determine the pattern of customer shopping behavior. One Islamic retail in Indonesia was used as a case study, where one-month sales transaction data was analyzed using AR-MBA. In addition, the activity relationship chart (ARC) was used to qualitatively analyze the placement of a product department by considering halal retailing standards. The results of AR-MBA obtained 21 associations among product departments, which were then used as a basis for proposed layouts while still considering the product characteristics and halal retailing standards. This research output provided a proposed product layout for retail outlets by considering quantitative factors (AR-MBA output) and qualitative factors (MS standard).
Exploring the need for a kickstarter toolkit for special education educators to teach authoring and illustrating Arumugam, Shyielathy; Mustafa, Mazlina Che; Abdullah, Norazilawati; Shamsudin, lylia Dayana; Mohd Jamil, Mohd Ridhuan; Dzainudin, Masayu
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1375-1383

Abstract

This study examines the need for developing a Kickstarter toolkit designed to support special education teachers in teaching entrepreneurship through the creation of children’s storybooks. Entrepreneurship education is increasingly recognized as a valuable addition to special education, promoting skills such as creativity, problem-solving, and independence. A survey conducted among 170 special education teachers from the Kuala Kangsar district, selected through judgmental sampling, revealed strong support for introducing entrepreneurship education into special education curricula (M=3.69, SD=0.71). Teachers also emphasized the importance of employing pedagogical approaches to effectively teach entrepreneurship concepts (M=3.89, SD=0.69). Moreover, the findings indicate an urgent need for a dedicated toolkit to facilitate such activities, with educators expressing high levels of agreement regarding its necessity (M=3.99, SD=0.73). These results underscore the positive perception of special education teachers toward entrepreneurship education and the crucial role a specialized toolkit could play in equipping educators with the resources needed to foster entrepreneurial competencies in students with special needs.
Convolutional neural network model for fingerprint-based gender classification using original and degraded images Pradini, Risqy Siwi; Kusuma, Wahyu Teja; Budi, Agung Setia
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1350-1358

Abstract

Fingerprint-based gender classification is a crucial component of soft biometrics, providing valuable additional information to narrow the search space in forensic investigations and large-scale identification systems. Although deep learning models, particularly convolutional neural networks (CNNs), have demonstrated significant potential, performance validation is typically performed on high-quality fingerprint images. This creates a gap between laboratory results and real-world applications, where fingerprint evidence is often found in a degraded state, such as smudged, distorted, or partially damaged. This study attempts to bridge this gap by proposing a more realistic training approach. We design a lightweight and computationally efficient CNN and train it on a comprehensive combined dataset. The main contribution of this study lies in the data training strategy, which explicitly combines real and synthetically modified fingerprint images from the Sokoto coventry fingerprint (SOCOFing) dataset into a single, unified training set. Experimental results show that the proposed model achieves very high classification accuracy (97.39%) on a test set that also includes a combination of original and degraded images. This finding not only confirms the effectiveness of diverse data-based training to produce more robust models but also establishes a new benchmark for fingerprint based gender classification research under conditions more representative of practical scenarios.
Forecasting internet traffic patterns for the campus Metro-E network using a hybrid machine learning model Arbain, Norakmar; Kassim, Murizah; Ali, Darmawaty Mohd; Saaidin, Shuria
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1433-1443

Abstract

Complex traffic patterns lead to crucial campus Metro-E network management and resource allocation. This paper presents an internet traffic forecasting by pre-processing data to offer better bandwidth quality of service (QoS). Eight (8) campuses' traffic data were analysed for modelling predictions using statistical analysis. A Metro-E campus network presents four (4) locations: A, E, F, and H have is a strong correlation between inbound and outbound traffic, with correlation values between 0.4547 and 0.5204. As the inbound traffic increases, outbound traffic tends to rise as well. Conversely, locations B, C, and G have weak correlations, indicating more independent traffic patterns. Data outliers were found for locations C and F, where unusual traffic spikes require further network exploration and show key trends in traffic data. Descriptive statistics reveal notable differences, with H has the highest average traffic at about 75 Mbps, while C has the lowest at around 30 Mbps. Location F shows the greatest traffic fluctuation with a standard deviation of 0.4076, whereas Location G has very little fluctuation with a standard deviation of 0.0240. Overall, this pre process data is use to combine machine learning (ML) to improve prediction abilities for better bandwidth management and real-time handling in digital campus environments.
AI-driven emotion recognition systems for sustainable mental health care: an engineering perspective Ahmad, Akram; Singh, Vaishali; Upreti, Kamal
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1111-1117

Abstract

Emotion recognition systems are transforming human-computer interaction (HCI) applications by enabling AI-driven, adaptive, and responsive mental health interventions. This study explores AI-based emotion recognition technologies using facial expressions, voice analysis, text-based sentiment processing, and physiological signals to develop scalable, real-time mental health support systems. Utilizing datasets such as FER2013, JAFFE, and CK+, our research examines deep learning models, including EfficientNet XGBoost, which achieved over 90% accuracy across key evaluation metrics. Unlike traditional mental health interventions, AI-driven systems provide cost-effective, accessible, and sustainable solutions through telemedicine, wearable biosensors, and virtual counselors. The study also highlights critical challenges such as algorithmic bias, ethical AI compliance, and the energy consumption of deep learning models. By integrating machine learning, cloud-based deployment, and edge computing, this research contributes to the development of sustainable, ethical, and user-centric AI solutions for mental health care. Future directions include AI model optimization for energy-efficient deployments and the creation of diverse, inclusive datasets to improve performance across global populations.
Methods used to enhance the physicochemical properties of natural ester insulating oils for transformers: a review Johal, Muhammad Syahrani; Ghani, Sharin Ab; Ahmad Khiar, Mohd Shahril; Sutan Chairul, Imran; Abu Bakar, Norazhar
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1393-1401

Abstract

Natural ester insulating oils, derived from vegetable-based feedstocks, are increasingly regarded as sustainable alternatives to conventional mineral oils due to their high fire point, biodegradability, and lower environmental impact. However, their widespread adoption in high-voltage equipment is constrained by their inherent limitations, such as lower oxidation stability, higher viscosity, and poor low-temperature performance. In this review, the three principal enhancement strategies developed to address these shortcomings are examined. The use of antioxidants is analysed for its role in improving oxidative resistance and flow characteristics. Transesterification is evaluated as a chemical modification method to alter the molecular structure, thereby enhancing viscosity and thermal stability. Refining and adsorbent treatments are discussed with respect to oil purification and regeneration, emphasising their adsorption efficiency and influence on dielectric performance. A comparative evaluation of these methods highlights their relative effectiveness, scalability, and practical challenges in implementation. This review underscores that no single approach is sufficient, and a combination of different methods is desirable to achieve optimal performance. These insights provide researchers with clear directions for further investigation while offering practitioners a knowledge base to guide the selection and application of enhanced natural ester insulating oils for reliable, long-term transformer operation.
Optimized transfer learning for detection susceptibility vessel sign in stroke using gorilla troops optimizer Albashah, Nur Lyana Shahfiqa Lyana; Faye, Ibrahima; Roslan, Nur Syahirah; Bakar, Rohani; Muslim, Norliana
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1040-1049

Abstract

The blockage of blood vessels causes ischemic stroke due to clots. The susceptibility vessel sign (SVS), observed through susceptibility-weighted imaging (SWI) via magnetic resonance imaging (MRI), is a key indicator that reveals clots within brain vessels. Early detection of these clots is crucial for timely and effective treatment. Image-based detection methods, particularly non-invasive techniques like MRI, offer a superior approach compared to other modalities. This study proposes an optimized method using transfer learning to classify SVS. The deep convolutional neural network (DCNN) residual network 50 version 2 (ResNet50V2) was applied for classification, with hyperparameters fine-tuned using the gorilla troops optimizer (GTO). The optimized proposed model achieved an accuracy of 94%, sensitivity of 100%, specificity of 88%, and an F1-score of 93%. This significantly outperforms the standard ResNet50V2 model using the default parameter, which achieved an accuracy of 91%, sensitivity of 82%, specificity of 100%, and an F1-score of 77%. These results demonstrate that the proposed method significantly enhances the detection of SVS, offering a promising tool for early ischemic stroke diagnosis.
Ambulance tracking system using GPS module and IoT based telegram messenger to find fastest route Akram, Rizalul; Novianda, Novianda; Atmaja, Teuku Hadi Wibowo; Anam, M. Khairul; Cut, Banta
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1322-1331

Abstract

Traffic congestion in urban areas affects ambulance trips to hospitals. This research aims to find the fastest route for ambulances to travel. The fastest route has criteria such as road shape, road width, shortest distance traveled, and fewer road users. This detection system applies internet of things (IoT) technology to each ambulance equipped with global positioning system (GPS), NodeMCU, and Wi-Fi modem that can send GPS coordinates to the cloud server, which will then mark the shortest distance from its current location to the hospital through the place where the emergency call is raised. The components used in this research are Neo6M GPS, NodeMCU ESP8266, cloud computing, and smartphone. This system can provide realtime information on all ambulance positions via android applications and Telegram messenger. The results obtained can determine the fastest path, distance, and travel time. In addition, the operation of this system takes 2-3 minutes to find the GPS signal at the beginning, then there is a 1-2 second delay from the GPS Tracking movement. Testing the route accuracy of this system and google maps by driving by motorcycle shows the results of this GPS system are more accurate in terms of distance and travel time.

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