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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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A hybrid features based malevolent domain detection in cyberspace using machine learning Samad, Saleem Raja Abdul; Ganesan, Pradeepa; Al-Kaabi, Amna Salim Rashid; Rajasekaran, Justin; Singaravelan, Murugan; Basha, Peerbasha Shebbeer
International Journal of Advances in Applied Sciences Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i3.pp916-927

Abstract

The rise of social media has changed modern communication, placing information at our fingertips. While these developments have made our lives easier, they have also increased cybercrime. Cyberspace has become a refuge for modern cybercriminals to conduct destructive actions. Most cyberattacks are carried out through malicious links shared on social media platforms, emails, or messaging services. These attacks can have serious consequences for individuals and organizations, including financial losses, sensitive data breaches, and damage to reputation. Early identification and blocking of such links are crucial to protecting internet users and securing cyberspace. Current research uses machine learning (ML) algorithms to detect malicious hyperlinks based on observed patterns in uniform resource locators (URLs) or web content. However, cyberattack tactics are constantly changing. To address this challenge, this paper introduces a robust method that performs a fine-grained analysis of URLs for classification. Lexical and n-gram features are examined separately, with URL n-grams represented using Word2Vec embeddings. The results from hybrid feature sets are combined using a logistic regression (LR) model to increase overall classification accuracy. This robust method allows the system to use both the structural components of the URL and the fine-grained patterns obtained by the n-grams.
Deep learning for image classification of submersible pump impeller Phuc, Phan Nguyen Ky; Chanh, Doan Huu; Luu, Trong Hieu
International Journal of Advances in Applied Sciences Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i3.pp838-848

Abstract

This study presented a deep learning-based model in the submersible pump impellers quality inspection process. The proposed method aimed to relieve worker workload, automate the system, as well as increase the accuracy in defect detection and classification. The proposed approach aims to be implemented on systems with low investment cost and limited resources, i.e., small single-board computers, enabling flexible deployment in industrial environments. The model consisted of three convolutional neural network (CNN) models, i.e., visual geometry group 16 (VGG16), ResNet50, and a custom model. The outputs of three networks were either synthesized later through an ensemble stage or used separately. A graphical user interface (GUI) was also developed for real-time inspection and user-friendly interaction. The approach achieved up to 99.8% accuracy in identifying defects, including surface scratches, corrosion, and geometric irregularities. The proposed method improved the quality assurance process by reducing manual inspection efforts. Future research could explore advanced techniques like anomaly detection to further enhance system performance and versatility.
Structural behavior of reinforced soil walls under seismic loads Roque, Reynaldo Melquiades Reyes; Menacho, Lincoln Jimmy Fernández; Huerta, Brayanm Reynaldo Reyes; Delgado, Fabrizio del Carpio
International Journal of Advances in Applied Sciences Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i3.pp711-723

Abstract

One of the main engineering challenges has been to design an economical soil retaining structure with high seismic resistance. From this perspective, reinforced soil walls have been developed with a focus on flexibility, in order to efficiently resist the effects of similar historical events in the event of a significant earthquake. The overall objective of this study was to compare the structural behavior of a geogrid-reinforced soil wall (Terramesh® system) under static and pseudo-static loads, and in a seismic environment simulated using the finite element method, in a shopping center in Trujillo, Peru. A case study was conducted using a mixed methodology, both applied and analytical-comparative in scope. Furthermore, the finite element methodology, material constitutive modeling, and dynamic time-history analysis of modal structures were chosen. It was determined that seismic loading can produce a 53.33% increase in deformations compared to the static state; Likewise, the overall safety factor under dynamic conditions tends to decrease by 27.85% compared to the static case. This study demonstrated the scope of geogrid reinforcement (Terramesh® system) through a practical case of a reinforced soil wall, using Plaxis 2D software to compare, estimate, and compare structural behavior in static, dynamic, and simulated environments.
When studying applied physics: what problems are there, and do pre-service physics teachers need? Afrizon, Renol; Mohtar, Lilia Ellany; Azmi, Mohd Syahriman Mohd; Hidayati, Hidayati
International Journal of Advances in Applied Sciences Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i3.pp650-661

Abstract

Applied physics courses are essential for pre-service physics teachers (PsPTs), but they often encounter challenges in pursuing this educational pathway. This study aims to identify the problems and learning elements that PsPTs need in applied physics learning using the McKillip discrepancy model. The data were collected using questionnaires and bibliometric techniques. A total of 23 PsPTs participated in the study. Additionally, 1,000 articles were consulted as a data source. The data analysis uses descriptive statistics and the VOSviewer software. The first finding is primary issues identified in applied physics learning e.g., the difficulty of locating suitable learning resources, the dearth of in-depth physics comprehension, the absence of visualization like augmented reality (AR), the failure to undertake empirical activities in the laboratory, and global warming and climate change topic were pertinent at the high school level, entailed intricate issues, and were abstract. The second finding is a learning module that is integrated with science, technology, engineering, and mathematics (STEM), and AR is needed by PsPTs. Finally, this need has been paramount over the past decade to meet PsPTs' needs. Thus, the needs analysis results serve as an initial reference point for decision-makers to identify elements and develop integrated STEM and AR applied physics learning modules.
Effectiveness of dashboard as a work progress scheduling, monitoring, and decision-making in construction projects Luthan, Putri Lynna Adelina; Sitanggang, Nathanael
International Journal of Advances in Applied Sciences Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i3.pp878-885

Abstract

Scheduling, monitoring, and decision-making are important factors in determining the general achievement of sustainable construction. Therefore, this study was conducted to determine the effectiveness of a dashboard as a measuring tool for construction project scheduling, monitoring, and decision-making. A survey with a Likert scale (5 scale) on each viewpoint, including planning, oversight, and independent direction, of 15 respondents from project executors and 7 respondents from supervisors was used as instrumentation. The results showed that the dashboard was evaluated with a value of 92.25 among executors and supervisors linked to product characteristics. Executors also used the scheduling dashboard with a value of 91.73, and the feature of employing the concept for supervision was appropriate as a measuring instrument, scoring 92.15. Furthermore, the final step was the aspect of using the dashboard for decision-making, which was tested and used with a value of 88.14. The use dashboard model is an effective tool for work progress scheduling, monitoring, and decision-making in construction projects.
Design and implementation of an internet of things-based automatic waste sorting system Taufik, Akhmad; Paisal, Paisal; Djalal, Muhammad Ruswandi; Dillah, Zahran Atha; Ismail, Haryono
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.pp1155-1165

Abstract

This paper presents the design and development of an internet of things (IoT)-based automatic waste sorting system that classifies waste into four categories: organic, non-organic, metal, and others. The system integrates an Arduino Mega for control, multiple proximity sensors (inductive, capacitive, and infrared), and ultrasonic sensors for level detection, and a NodeMCU ESP8266 for real-time monitoring via the Blynk platform. A total of 100 tests (25 per bin) were conducted. Classification success rates were 92% (metal), 80% (inorganic), 84% (organic), and 100% (others), resulting in an overall accuracy of 89%. The main contribution is a combined automatic sorting and IoT monitoring framework suitable for campus-scale deployment.
Harvesting insights: exploring machine learning for crop selection and predictive farming Deshmukh, Tanvi; Rajawat, Anand Singh; Potgantwar, Amol
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.pp999-1009

Abstract

Modern agriculture has undergone a significant evolution, adopting advanced techniques to streamline crop management processes. One such advancement is the integration of machine learning (ML) technology, which shows great promise in optimizing crop selection and enhancing economic returns. Key determinants of crop productivity, including water availability, soil quality, weather conditions, and timely resource allocation, play pivotal roles in the farming ecosystem. Harnessing these factors, ML algorithms facilitate the identification of optimal crop choices and provide continuous monitoring of cultivation processes to anticipate evolving crop needs. This paper investigates various ML techniques employed for crop selection and evaluates their effectiveness in agricultural settings. Through a comparative analysis, we highlight the advantages of these techniques and provide insights into their potential impact on current farming management practices. By leveraging ML for predictive farming, stakeholders can make informed decisions to maximize yields, minimize resource wastage, and promote sustainable agricultural practices. This study contributes to the ongoing discourse on the integration of technology in agriculture and underscores the transformative potential of ML in shaping the future of crop management. We investigate recent papers from the years 2020 to 2024.
Computer simulation and software engineering in optical analysis of phosphor-converted white light-emitting diodes utilizing barium sulfate Trang, Le Thi; Loan, Nguyen Thi Phuong; Cong, Pham Hong; 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.pp1384-1392

Abstract

Achieving uniform nanoparticle dispersion in electrospun polymer nanofibers remains a critical challenge, as conventional electrospinning often leads to particle agglomeration and nozzle clogging, reducing fiber uniformity and functional efficiency. This study explicitly addresses this problem by developing poly (vinyl alcohol) (PVA)/BaSO4 composite nanofibers through both conventional and ultrasonic-assisted electrospinning. Scanning electron microscopy (SEM) revealed that ultrasonication effectively disrupted nanoparticle agglomerates, yielding smoother and more uniform fiber morphologies. X-ray diffraction (XRD) analysis further confirmed that ultrasonic processing reduced the crystalline intensity of PVA and BaSO4, indicating enhanced polymer–filler interaction and finer BaSO4 distribution. Quantitatively, the agglomeration slope decreased from 0.039 (conventional) to 0.006, and the mean crystallite size was reduced from approximately 470 to 300 nm. These results are consistent with recent advances showing that ultrasonic electrospinning improves nanoparticle dispersion and stability in polymer matrices, thereby enhancing optical and mechanical properties. Ultimately, this work demonstrates that ultrasonic-assisted electrospinning provides a robust and scalable strategy to fabricate lightweight, flexible, and multifunctional PVA-based radiation shielding materials with superior nanoparticle dispersion and structural homogeneity.
Fit to-organization amplifies unethical pro-organizational behavior Umama, Hany Azza; Setyawati, Sri Murni; Wulandari, Siti Zulaikha
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.pp1050-1060

Abstract

The study of unethical pro-organizational behavior (UPB) has seen a sharp rise in recent years, with research results explaining that a potential cause of UPB is organizational identification (OI). However, there are inconsistencies in the findings regarding the effect of OI on UPB in the workplace. This study seeks to test the direct effect of OI on UPB, and to explore the mediating role of fit to-organization (Fit-O). To evaluate data collected from employees of micro and small enterprises, this study utilized structural equation modeling-partial least squares (SEM-PLS) analysis, along with the variance accounted for (VAF) method to test the mediation between OI and UPB. The findings confirm that the fit or compatibility of an individual with the organization can strengthen the effect of OI on UPB. The intervening role of Fit-O in the OI-UPB relationship is a crucial theoretical contribution. This research also implies that organizations must balance increasing OI with strong ethical standards to mitigate UPB.
Stacking architecture-endpoint detection: a hybrid multi layered architecture for endpoint threat detection Wahid, Abd Rahman; Anggreani, Desi; Hayat, Muhyiddin A. M.; Abd Rahman, Aedah; Faisal, Muhammad
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.pp1263-1280

Abstract

Modern endpoint threat detection systems face persistent challenges in balancing detection accuracy, resilience against zero-day attacks, and the interpretability of artificial intelligence (AI) models. Although deep learning (DL) approaches often achieve high accuracy on benchmark datasets, they remain vulnerable to adversarial perturbations and operate as opaque “black boxes,” thereby reducing trust and limiting practical adoption in critical infrastructures. This research introduces stacking architecture-endpoint detection (STACK-ED), a hybrid multi-layered architecture for endpoint threat detection. STACK-ED integrates three complementary paradigms: supervised learning for known attack patterns, self-supervised Fgraph-based learning for structural relationships, and unsupervised anomaly detection for emerging or unknown threats. The outputs are consolidated by a meta learner, followed by a post-hoc correction (PHC) mechanism to minimize false negatives. The framework was evaluated on a combined benchmark dataset (CSE-CIC-IDS2018 and UNSW-NB15, hereafter referred to as HIDS-Set). Experimental results demonstrate state-of-the-art performance, achieving an F2-score of 98.89% after hybrid integration and active learning, with the primary optimization objective being the reduction of undetected attacks. Furthermore, the Shapley additive explanations (SHAP) method enhances interpretability by revealing feature contributions, while the PHC successfully recovered 62.64% of missed zero-day candidates. The findings position STACK-ED not only as a highly accurate detection model but also as an adaptive, resilient, and transparent framework, offering practical implications for enterprise-grade endpoint defense and future zero-trust cybersecurity systems.

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