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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 45 Documents
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Search results for , issue "Vol 14, No 4: December 2025" : 45 Documents clear
Optimizing VR-UX: analysis and adaptive recommendations for enhancing immersion and reducing motion sickness Aji Purnomo, Fendi; Arifin, Fatchul; Surjono, Herman Dwi
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.pp1181-1191

Abstract

This study presents an adaptive recommendation framework to enhance comfort and immersion in virtual reality (VR) by actively reducing motion sickness. Unlike prior research that views VR user experience (UX) as static, this approach integrates statistical analysis with dynamic system design. Using a Kaggle dataset of 1,000 entries, we applied descriptive statistics, Spearman correlation, Kruskal-Wallis tests, and regression to explore relationships among session duration, motion sickness, immersion, headset type, and user demographics. Findings show that session duration alone does not significantly predict motion sickness or immersion (R²=0.00, p>0.05), but certain user profiles, such as individuals over 30 using PlayStation VR, are more prone to discomfort. These insights inform a four-module framework: user profiling, real-time duration monitoring, rule-based adaptation logic (such as slowing navigation speed or adding a virtual nose for visual stability), and personalized in-VR recommendations. The system is compatible with Unity and Unreal Engine and integrates with commercial headset software development kits (SDKs). Future validation will use A/B testing, standardized questionnaires, simulator sickness questionnaire /immersion presence questionnaire (SSQ/IPQ), and physiological metrics. This work shifts VR design toward personalized, responsive systems that prioritize user well-being and immersive engagement.
The latest trends in internet of things usage in smart homes: a systematic literature review Widianto, Mochammad Haldi; Prabowo, Puji
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.pp1118-1128

Abstract

The Internet of Things (IoT) has recently developed very quickly. Equipped with increasingly mature information technology, especially for use in smart homes. This technology is integrated with IoT systems, which can now solve this problem. This paper helps discover the latest research trends and offers a broad perspective on what factors are used in intelligent housing by utilizing a systematic literature review (SLR). This article takes 2256 documents from Springer, IEEE, ACM, MDPI, ScienceDirect, Hindawi, IAES, and Google Scholar. 59 articles passed the specified exclusion and inclusion criteria. Furthermore, the new findings are that several research factors exist in smart homes, such as Artificial Intelligence (AI), Assistant Technology, Blockchain, IoT, energy-saving IoT, Network IoT, Robot IoT, and Security IoT. It is hoped that future research will provide insights to examine smart homes based on their factor.
Design and analysis of a portal frame test rig for vertical load testing of goalpost pipeline support Ghazali, Tsaqif Al Farrel; Yob, Mohd Shukri; Abd Latif, Mohd Juzaila; Kurdi, Ojo; Munir, Fudhail Abdul
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.pp1402-1410

Abstract

Pipe support is a crucial infrastructure in the oil and gas industry, requiring robust designs to withstand various loads and maintain operational stability. While numerical analysis is commonly used to assess the interaction between pipelines and supports, experimental testing remains essential for validation. However, field testing is often costly and difficult due to safety constraints. To overcome this, a reliable test rig with minimal deflection is needed to ensure accurate experimental results. This study uses finite element analysis (FEA) to evaluate both a goalpost pipeline support and a newly developed portal frame test rig. The test rig was analyzed under two conditions: the failure load of the goalpost support and an amplified load with a factor of 2.5 to simulate unexpected scenarios. Results show the test rig can safely withstand loads up to 40 kN, meeting the EN 1990 safety factor requirement of 1.5. Furthermore, critical components remained within the deflection limit specified by the British Constructional Steelwork Association (BCSA), which is under L/1,000 of the beam length. These results confirm the structural integrity and suitability of the portal frame test rig for accurate testing of the goalpost pipeline support structure.
A convolutional neural network with attention mechanism-based malaria detection from blood smear images Ghosh, Kingkar Prosad; Jibon, Ferdaus Anam; Haque, Shahina; Ali, Md. Suhag; Islam, Md. Monirul; Uddin, Jia
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.pp1010-1017

Abstract

With 249 million cases and 608,000 fatalities recorded in 2022, malaria is one of the major worldwide health concerns, particularly in areas with low resources. In this paper, we propose a custom convolutional neural network (CNN) with an integrated attention mechanism to inspect malaria from blood smear images. To improve model robustness, we combined three publicly available datasets from the NIH and Kaggle. The proposed model achieved 98.20% accuracy, 97.85% precision, 98.55% recall, and 98.20% F1-score, outperforming conventional di agnostic methods. In addition, we conduct comparative analyses using two transfer learning models, ResNet50 and DenseNet. ResNet50 attained 95.06% precision, 95.44% recall, with 95.05% F1-score, while DenseNet achieved a pre cision of 87.96%, recall of 88.33%, and F1-score of 87.90%. For interpretability, Grad-CAMandsaliency map visualizations highlighted key image regions, with saliency maps offering finer pixel-level localization. These results highlight the potential of our attention-based CNN as a feasible, interpretable diagnostic tool for malaria, particularly in low-resource settings.
Production of hydrogen gas from water via electrolysis for community power generation Hermharn, Wichien; Kradang-nga, Sittichot; Kachapongkun, Pongsakorn; Jirawongnuson, Sirichai
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.pp1444-1454

Abstract

Rural and remote communities often rely on diesel generators, which are costly, inefficient, and emit greenhouse gas and particulate pollutants. This study combines real-time hydrogen production via electrolytic water separation with a conventional 5,871-cc diesel backup generator to enhance combustion performance and reduce environmental impacts. A self-built electrolyzer was powered by a direct current (DC) battery and precisely controlled by an electronic control unit (ECU) to provide hydrogen output based on engine load conditions. The results of testing co-fueling improved fuel efficiency by 20-25%, with a peak 24.9% reduction in fuel consumption at 50% load. Emission measurements revealed significant reductions in black smoke, PM₂.₅, PM₁₀, and CO₂, with the maximum CO₂ reduction of 23.4 kg CO₂-e/hr. The system operates without the need for a hydrogen storage tank, thus improving safety and reliability. These findings demonstrate that this low-cost and low-emission approach represents a practical alternative for backup power in remote areas. Future work will focus on long-term stability and monitoring hydrogen flow rates for varying load conditions.
Financing model for demand response information services with bundled incentives Yuliza, Evi; Puspita, Fitri Maya; Rahayu, Fridha Aprisa
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.pp1229-1240

Abstract

This study attempts to build a new model for information service financing schemes by considering utility functions to measure heterogeneous consumer satisfaction. This model was developed by involving a combination of reverse charging, demand response, and heterogeneous incentive models, and considering the quality of user service measured by a quasi-linear utility function against the information service financing scheme. The incentive financing scheme is applied to a local data server, including traffic during peak hours and off-peak hours. This internet incentive financing model is solved using the LINGO 13.0 application. Furthermore, the development model for incentive financing for information services based on demand response and bundling in the information service financing scheme is subjected to sensitivity analysis with the aim of identifying parameters that affect model performance. Based on the analysis that has been done, the results of this study indicate that the new model in the incentive financing scheme for information services with a quasi-linear utility function involving a combination of reverse charging, demand response, and heterogeneous incentive models produces an optimal solution in a fixed cost financing scheme for data traffic usage during peak hours and off-peak hours.
Analysis of feature reduction for identifying stress levels electroencephalogram signal based Setyorini, Setyorini; Zaeni, Ilham Ari Elbaith; Elmusyah, Hakkun
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.pp1137-1145

Abstract

Stress identification based on electroencephalogram (EEG) signals has become a rapidly growing research topic, with the main approaches utilizing features from the frequency domain and time-frequency domain. This research aims to combine principal component analysis (PCA) and independent component analysis (ICA) for feature extraction to improve the accuracy of stress identification. Additionally, PCA+ICA features are reduced from 64 to 32 columns to optimize computational efficiency without losing important information from the EEG signal. The stress identification models used in this research include Ensemble, naive Bayes, and support vector machine (SVM). The data used are from the SAM-40 task Stroop color trials 1, 2, and 3. Experimental results indicate that the combination of PCA+ICA features improves accuracy only in the ensemble method. Reducing PCA+ICA features from 64 to 32 columns led to an improvement in accuracy only for Stroop trial 2 data with the naive Bayes method.
Development of a leakage detection and alert system for liquefied petroleum gas via a mobile application Salah, Wael A.; Abu Sneineh, Anees
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.pp1099-1110

Abstract

Nowadays, ensuring the comfort and safety of house users is a top priority, and this may be accomplished by implementing smart technology to lead a convenient and safe life. Leakage of liquefied petroleum gas (LPG), which is mostly utilized in the home kitchen for cooking, is one of the frequent risks. Using a gas sensing device, a gas control system, and wireless communication units, the goal of this study is to create an LPG gas leakage warning and management system to prevent the gas from exploding by detecting the leak. When LPG gas is brought near the sensor, it detects the leakage and the buzzer is activated by activating the audio-visual alarm and closing the gas cylinder valve. The system also generates alert messages and sends them to the fire station when the LPG gas leakage has reached a critical level. Testing results of the proposed LPG leakage system show a satisfactory performance of the developed device with a quick response to LPG gas leakage. In addition, powerful audio and visual alarms are activated. An immediate message was sent to homeowners and the fire station department regarding the leakage incident to prevent the risk of gas leakage.
Influence of potassium bromide phosphor on optical properties of white light-emitting diodes Cong, Pham Hong; Loan, Nguyen Thi Phuong; Anh, Nguyen Doan Quoc; Lee, Hsiao-Yi
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.pp1359-1366

Abstract

Conventional phosphor-converted light-emitting diodes (LEDs) using silicone binders often suffer from yellowing, moisture degradation, and limited spectral tunability, restricting their performance in high-power street lighting. To overcome these limitations, this study aims to develop an advanced LED illumination system integrating a KBr-doped sol-gel/silica phosphor with total internal reflection (TIR) lenses and a reflective housing, encapsulated by an atomic layer deposition (ALD)-coated minilens panel. The sol-gel matrix, synthesized from MTEOS, TEOS, and silica granules, was engineered to achieve uniform KBr particle dispersion, reduced thermal quenching, and improved chromatic stability. The ALD laminate provides an additional moisture and heat barrier, sealing micro-defects and minimizing stress-induced cracking. Optical performance was quantitatively assessed using Monte Carlo beam-tracking simulations under various street configurations, including focal, zigzag, and single-plane pole layouts. Results demonstrated enhanced luminous efficacy, precise glare control, and high uniformity in street illumination. Overall, this integrated sol-gel/ALD LED design effectively addresses the durability and color instability problems of traditional silicone systems, offering a scalable and energy efficient solution for next-generation street lighting.
AI-integrated pharmacy systems: bridging technology, ethics, and patient care El-Dalahmeh, Adi; Nedal, Nevien; Abu Maria, Khulood; Abu Tarboosh, Sara
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.pp1305-1321

Abstract

The operation of pharmacy systems undergoes transformation through artificial intelligence (AI), which advances from manual procedures to intelligent adaptive tools. These technologies enhance daily operations through prescription verification, drug interaction alerts, and inventory management while decreasing human mistakes. Through AI, patients gain access to customized medication recommendations, automatic appointment alerts, and virtual support services. The advancement of technology creates multiple new difficulties for healthcare systems. The increasing integration of AI in healthcare creates growing concerns about data privacy alongside algorithmic bias and the requirement for decision-making explanations. This paper evaluates AI systems against conventional pharmacy methods through an assessment of their precision and speed and their impact on patient safety and ethical preparedness. The adoption of AI systems requires strong ethical protections together with defined regulatory frameworks to maintain human clinical decision-making authority in patient care.

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