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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 758 Documents
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Disturbance detection due to lightning at ionospheric D-region over Malaysia Suryadi Suryadi; Mariyam Jamilah Homam; Rohaida Mat Akir; Mardina Abdullah
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp825-833

Abstract

Previous research on the interference of very low frequency (VLF) signals in the equator region was inadequate and largely concentrated in the middle and high latitude regions. Therefore, this research aims to determine the disruption of VLF waves in the ionospheric D-region above Malaysia, which is in the equator area. This paper presents observations of early/fast, early/slow, and lightning-induced electron precipitation (LEP) events in January 2010. Broadband and narrowband data are monitored and investigated using Japan’s JJI Ebino transmitter (32°40' N, 130°81' E) to the receiver at the Universiti Kebangsaan Malaysia (2°55' N, 101°46' E). Broadband and narrowband data are analyzed with theoretical considerations and linked to events from interference in the ionospheric D-region. Many early/fast, early/slow, and LEP events are found to originate from the lightning release activity emitted and may alter the amplitude and VLF signal phase in the lower layer ionosphere over Malaysia.
Evaluating telemedicine diabetes mellitus: a mobile health app for type-2 diabetes Muhammad Zakwan Abdul Karim; Norashikin M. Thamrin; Ruhizan Liza Ahmad Shauri; Rozita Jailani; Mohd Haidzir Abd Manaf; Nurul Amirah Mustapa
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp791-799

Abstract

Telemedicine diabetes mellitus (Tele-DM) mobile health (mHealth) tool functionality, usefulness, and user feedback were examined in this study. Data from nine distinct users of type-2 diabetes (T2D) patients, healthcare professionals (HCPs), and administrators was analyzed to determine functionality. Data retrieval times increased with database user data amount, according to the study. A 3-month program with five T2D patients reduced weight (0.98 kg) and Hemoglobin A1c (HbA1c) (0.34%). This shows that Tele-DM helps manage diabetes, but more participants are needed to confirm. Nine Tele-DM customers were satisfied with the app's reception, according to 14 online questionnaires. Overall, Tele-DM simplifies diabetic self-management in a novel way. This study shows its potential to transform diabetes management and address major healthcare issues.
Evaluating metaverse platforms for educational purposes: a heuristic evaluation study Cadelina Cassandra; Mohamad Noorman Masrek; Fadhilah Aman
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp1023-1031

Abstract

This research's primary objective is to investigate the educational potential by investigating the metaverse website, emphasizing from users' perspectives. It is critical to examine the functionality to identify any potential faults in the learning experience because of the complexities of metaverse apps, which include 3D virtual environments and complex interactions. This study uses heuristic evaluation, which is a useful method for analyzing application success by utilizing well-established design principles and best practices in user interface and user experience design. To conduct this study, a panel of five experts, each with outstanding competence in the topic, was assembled to test the metaverse website in an educational setting. The evaluation was built on the ten heuristic evaluation standards, which served as a solid framework for measuring its usability and functionality. According to the findings of this examination, the average severity number of heuristic issues is one. This result indicates that the website is under "cosmetic problems only." While this implies that specific areas need to be addressed to improve overall quality, it also means that these difficulties do not take precedence over other vital ones. As a result, the website has significant promise as a metaverse instructional platform, and changes can be made to further improve its efficiency.
The production-inventory model with imperfect, rework, and scrap items under stochastic demand Dana Marsetiya Utama; Artha Bripka Putri
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp900-910

Abstract

Inventory is crucial in maintaining a smooth production process and meeting consumer demand for manufacturing companies. This research focuses on production problems involving defects, rework, and scrap items in stochastic demand. This research aims to develop a production-availability model by minimizing the expectation of total cost (ETC). The model includes four main decision variables, namely production quantity (Q), safety factor (k), production rate (P), and rework rate (P1). This research uses the Aquila optimizer algorithm to optimize the objective function. It compares with the heuristic procedure and Harris Hawk optimization algorithm. The results showed that the Aquila optimizer algorithm successfully optimized the production-availability problem. A comparison between algorithms indicates that the Aquila optimizer algorithm performs equivalently to the Harris Hawk optimization algorithm and outperforms the heuristic procedure. Sensitivity analysis shows that increasing demand uncertainty increases ETC and k. At the same time, it can decrease Q.
Enhanced U-Net models with encoder and augmentation for phytoplankton segmentation Ovide Decroly Wisnu Ardhi; Tri Retnaningsih Soeprobowati; Kusworo Adi; Esa Prakasa; Arief Rachman
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp1013-1022

Abstract

This study comprehensively analyzes U-Net models for semantic segmentation in phytoplankton image recognition, leveraging encoders such as EfficientNet-B5, MobileNetV2, ResNet50, and ResNeXt50 and employing the Adam optimizer. The research highlights the U-Net MobileNetV2 model with optical distortion, which achieves notable test scores with 93.69% Dice, 88.14% intersection over union (IoU), 99.89% Precision, and 100% Recall, underscoring the efficacy of the applied augmentation strategies, including geometric and distortion transforms, and color and blur techniques. The U-Net ResNet50 model with mix transform consistently demonstrates high accuracy in critical metrics, outperforming others, while EfficientNet-B5 with blur suggests increased model sensitivity with improved recall. These results underscore the crucial role of encoder-augmentation synergy in model performance. Training and testing times across models have remained under 250 seconds, reflecting methodological efficiency. Overall, these results demonstrate the model's excellent performance for the semantic segmentation task.
Factors influencing the intention to use m-commerce in Malaysian: an extended IS success model Moussa Barry; Ahasanul Haque; Muhammad Tahir Jan
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp961-973

Abstract

The progress of mobile technology has undergone substantial development in recent years, leading to the emergence of new and creative ideas. This paper investigates the factors influencing consumers' intentions to use mobile commerce in Malaysia. The DeLone and McLean updated information system success model served as the basis for this study's proposed model. A convenience sampling method was employed to collect 310 surveys from smartphone owners who conduct mobile commerce activities in Malaysia. A “two-stage structural equation modeling” (SEM) technique assessed the research model and the study's assumptions. The findings revealed that “information quality”, “service quality”, “system quality”, and “trust” significantly influence consumers' “intention to use mobile commerce in Malaysia”. The findings further reveal that “system quality” is the strongest factor influencing the “intention to use mobile commerce in Malaysia”. Therefore, the research outcomes will benefit academicians, researchers, policymakers, and practitioners in the mobile commerce industry in Malaysia. To the best of the authors’ knowledge, this is the first empirical study that expanded the “information system success model” by including “trust” in the context of Malaysian mobile commerce users’ “intentions”. However, further research is recommended to explore the factors influencing consumers' “intention to use mobile commerce in Malaysia”.
User perceptions of artificial intelligence powered phishing attacks on Facebook's resilient infrastructure JosephNg Poh Soon; Rou Qian Chan; Qian Hui Lee; Dick En Loke; Stevenson Ling Heng Chun; Phan Koo Yuen
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp882-890

Abstract

This study focuses on examining the user perceptions of a cybersecurity certificate transparency (CT) monitoring tool in the context of artificial intelligence (AI) powered phishing attacks on the Facebook platform. Implementing CT monitoring tools is one strategy for preventing these attacks. It reveals a significant level of concern among respondents regarding the potential risks associated with phishing attacks, indicating a growing awareness of the severity of such threats for future resilient infrastructure development. Users' knowledge and understanding of AI-driven phishing threats were found to vary, emphasizing the need for awareness campaigns towards sustainable development education. The study also highlights varying levels of confidence among users in effectively identifying and thwarting phishing efforts, suggesting the importance of user empowerment through improved training, tools, and technologies as responsive institutions. These findings underscore the significance of addressing user concerns, enhancing security awareness, and providing users with the necessary resources to protect themselves against sophisticated phishing attacks. The research contributes to the understanding of user perceptions and lays the groundwork for further improvements in security measures and user education in the fight against phishing threats on Facebook's inclusive growth.
Real-time smoke and fire detection using you only look once v8-based advanced computer vision and deep learning Shakila Rahman; Syed muhammad Hasnat Jamee; Jakaria Khan Rafi; Jafrin Sultana Juthi; Abdul Aziz Sajib; Jia Uddin
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp991-1003

Abstract

Fire and smoke pose severe threats, causing damage to property and the environment and endangering lives. Traditional fire detection methods struggle with accuracy and speed, hindering real-time detection. Thus, this study introduces an improved fire and smoke detection approach utilizing the you only look once (YOLO)v8-based deep learning model. This work aims to enhance accuracy and speed, which are crucial for early fire detection. The methodology involves preprocessing a large dataset containing 5,700 images depicting fire and smoke scenarios. YOLOv8 has been trained and validated, outperforming some baseline models- YOLOv7, YOLOv5, ResNet-32, and MobileNet-v2 in the precision, recall, and mean average precision (mAP) metrics. The proposed method achieves 68.3% precision, 54.6% recall, 60.7% F1 score, and 57.3% mAP. Integrating YOLOv8 in fire and smoke detection systems can significantly improve response times, enhance the ability to mitigate fire outbreaks, and potentially save lives and property. This research advances fire detection systems and establishes a precedent for applying deep learning techniques to critical safety applications, pushing the boundaries of innovation in public safety.
Early detection of coronary heart disease based on risk factors using interpretable machine learning Wiharto Wiharto; Farah Nada Mufidah
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp948-960

Abstract

Coronary heart disease (CHD) is the leading cause of death in the world. The risk of coronary heart disease can be reduced or even prevented by early detection. Early detection of CHD has been widely developed using machine learning, but the machine learning algorithms used sometimes have low interpretability. Low interpretability makes it difficult for users to understand the cause of the decision. Referring to this, this research aims to propose an early detection model using machine learning interpretability, which is implemented using the C5.0 algorithm and interpreted using Shapley additive explanations (SHAP). This research method is divided into 3 stages, namely preprocessing, interpretable machine learning, and performance evaluation. This study used 215 patient data from Dr. Moewardi Surakarta Hospital. Testing the resulting model using the k-folds cross-validation method. The test results show that the risk factors that make a high contribution to the output of the coronary heart disease detection model are systolic blood pressure, diastolic blood pressure, and employment level, with the resulting accuracy performance of 84.64%. The proposed model can be an alternative for early prediction of coronary heart disease which can explain the influence of each selected risk factor on the model output.
Assessment of heavy metals concentration of Mapanuepe Lake, Zambales, Philippines Ma. Shiela G. Mendoza; Danilo V. Rogayan Jr.
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp854-863

Abstract

Despite the absence of recent research, Mapanuepe Lake in the Philippines has been a significant environmental concern due to potential heavy metal contamination. Hence, the study assessed the heavy metal concentration of water and surface sediments and identified the other physicochemical properties of Mapanuepe Lake in San Marcelino, Zambales, Philippines. This descriptive research employed physical profiling and physicochemical characterization of water and surface sediments of the lake. Six sampling stations in the lake were selected based on their current land use and nearness to the point source of heavy metal pollution. The study found that the Mapanuepe Lake is a thriving place for algae and zooplankton. The heavy metal concentration of the lake water and sediment sample is within the standard limit. The water conductivity is considered to be within the standards. In terms of pH level, the sampling sites obtained a pH level within the acceptable limit. The concentration of heavy metals in the lake water and sediments is generally within the standard limit. Other physicochemical properties are also in the acceptable range. The community people and local government must collaborate to implement the crafted strategic environmental sustainability plan, which includes biodiversity conservation and ecotourism promotion. Likewise, the study provides updated and comprehensive data on the status of the lake's heavy metal concentration for policy formulation and further research.

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