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INDONESIA
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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Extension of Hermite-Hadamard type inequalities to Katugampola fractional integrals Das, Dipak Kr; Mishra, Shashi Kant; Kumar, Pankaj; Hamdi, Abdelouahed
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.pp1-18

Abstract

In this study, we introduce several new Hermite-Hadamard type general integral inequalities for exponentially (s,m)-convex functions via Katugampola fractional integral. The Katugampola fractional integral is a broader form of the Riemann–Liouville and Hadamard fractional integrals. We utilized the power mean integral inequality, the H¨older inequality and a few additional generalizations to derive these inequalities. Numerous limiting results are derived from the main results presented in the remarks. Furthermore, we provide an example illustrating our theoretical findings, supported by a graphical representation.
Corporate social responsibility by listed commercial banks in Vietnam: practice and financial performance Ha Nguyen, Viet; Nguyet Dang, Thi Minh
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.pp261-271

Abstract

This study examines the impacts of financial performance (FP) on corporate social responsibility (CSR). The article investigates whether FP, as measured by return on assets (ROA) and net interest margin (NIM), influences the likelihood of CSR disclosure, drawing on stakeholder theory and legitimacy theory. The analysis employs binary logistic regression models and an unbalanced panel dataset comprising 26 listed banks between 2014 and 2024. If the bank discloses its CSR practices in its annual or sustainability report, the code for CSR disclosure is 1; otherwise, it is 0. The results show that, while NIM shows a negative relationship, ROA significantly improves CSR. Furthermore, there is a positive correlation between bank size (TA), equity to asset (EA), and CSR; a negative relationship of loan to deposit ratio (LDR) with CSR, and no significant statistical correlation was observed between debt to equity (DTE) and CSR. The study adds to the body of knowledge on CSR in developing nations and offers recommendations for sustainability and bank governance.
Effectiveness of iBreast examination for screening breast lesions among women in India Ani Grace Kalaimathi, Samuel; Hemavathy, Venkatesan; Kanchana, Sambavadas; Sudha, Radhakrishnan; Tamilarasi, Perumal
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.pp178-186

Abstract

The breast has long been a representation of women's identity and an essential component of fertility. The breast lesions refer to an area of abnormal breast tissue. One frequent medical ailment that might worry women is breast lesions. It is estimated that at least 20% of females may develop breast lesions. It may vary in size, shape, and texture can be either benign or malignant. Mammography, clinical breast examination (CBE), and self-breast inspection are the accepted early breast cancer detection techniques. Mammography application in low and middle-income countries is limited because most of the women in these countries cannot afford it. Hence, iBreastExam was identified and validated as an alternative source for screening at the village level to identify breast lesions at an early stage. For the study, a cross-sectional research design using a quantitative research methodology was used. Adopted areas of the selected colleges were the setting for the study: MA Chidambaram College of Nursing, Adyar, Chennai; Sri Balaji College of Nursing, Chrompet, Chennai; Madha College of Nursing, Kundrathur, Chennai; Omayal Achi College of Nursing, Puzhal, Chennai. The sample size consisted of 14,000 women across all the 4 settings. A convenient sampling technique was used to select the samples for the study. A total of 13,988 women were screened, 55 women had positive breast lesions, and out of this 5 were confirmed to have breast cancer through mammogram diagnosis.
Experimental study on annealing S45C steel: effect of temperature and time on hardness, impact strength Sirman, Mahadir; Syahrisal, Syahrisal; Pasandang, Henny; Nur, Rusdi; Faraby, Muhira Dzar; Mukhlisin, Mukhlisin
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.pp343-354

Abstract

Steel generally exhibits poor wear and friction resistance, making it necessary to improve its surface mechanical properties, particularly hardness and microstructure, to enhance performance. Heat treatment is one of the most effective methods for achieving these improvements. This study aimed to optimize the heat treatment parameters of S45C medium-carbon steel to improve hardness and impact strength using response surface methodology (RSM). Experimental trials were conducted at annealing temperatures of 800 °C, 850 °C, and 900 °C with holding times of 30, 60, and 90 minutes, followed by cooling in water, oil, or air. Hardness (HRC) and impact strength (Nm/mm²) were measured, and the data were analyzed using RSM with a central composite design (CCD). Quadratic models were found to be statistically significant for both hardness (Prob > F = 0.0222) and impact strength (Prob > F = 0.0338), confirming their validity. The optimization results indicated that a holding time of 60 minutes within the 850-900 °C range provides the best balance between high hardness (>55 HRC) and adequate impact strength (>0.68 Nm/mm²). These findings not only validate the predictive capability of RSM in heat treatment optimization but also provide practical guidelines for industrial applications of S45C steel in automotive, tooling, and structural components.
Hybrid deep learning and ensemble learning approach for high accuracy thyroid disease classification Balusamy, Shuriya; Vivekanadhan, Balajishanmugam; Mabel John, Prathima; Bhosle, Sushma Sunil
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.pp303-312

Abstract

Thyroid disease is a common endocrine disorder affecting the thyroid gland, a small butterfly-shaped organ at the base of the neck. According to the World Health Organization (WHO), nearly one billion people worldwide are affected by thyroid-related conditions. Conventional diagnostic methods, such as thyroid scans and function tests, are often costly, time-consuming, and complex for clinicians to interpret. To overcome these limitations, this study introduces a novel temporal conditional-Markov random field (TC MRF) framework for early detection and classification of thyroid disease. The multi-modality images computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US) are collected from the ImageNet database and preprocessed using contrast stretching adaptive Gaussian star (CSAGS) filter to improve image clarity. The enhanced images are then processed over a convolutional neural network (CNN) for feature extraction. These features are classified using a random forest (RF) model to determine whether the thyroid condition is normal or abnormal. The proposed TC MRF achieves a classification accuracy of 98.27% and F1-score of 96.05%. The TC-MRF enhances the total accuracy range of 6.30%, 4.11%, and 5.36% better than naive Bayes, multilayer perceptron (MLP), and decision tree, respectively.
Robust multi-faces recognition and tracking via fuzzy genetic algorithms and deep coupled features Abushana, Adil Abdulhur; Mudhafar, Yousif Samer
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.pp209-218

Abstract

In real-world surveillance environments, face recognition and tracking remain challenging due to partial occlusion, pose variation, illumination changes, and background clutter. This paper presents a robust hybrid framework that integrates fuzzy genetic algorithms (FGA) with deep coupled feature learning for multi-face recognition and tracking. The proposed system comprises three main modules: i) face detection and pre processing using the multi-task cascaded convolutional network (MTCNN), ii) deep coupled ResNet embeddings that jointly learn identity and appearance-invariant representations, and iii) a fuzzy rule-based genetic optimizer that adaptively refines tracking decisions based on uncertainty in motion, appearance similarity, and occlusion levels. The novelty of this work lies in the fusion of fuzzy inference with evolutionary search to guide the genetic optimization process—allowing dynamic adaptation to noisy and uncertain visual conditions. Moreover, probabilistic data association filters (PDAF) and conditional joint likelihood filters (CJLF) are employed to further enhance temporal consistency under occlusion and appearance variation. The results confirm that fuzzy evolutionary optimization, when coupled with deep feature learning, significantly improves robustness and stability for real-time face tracking in complex, dynamic scenes.
An ensemble-based approach for breast cancer identification using mammography Joseph Annaiah, Naveen Ananda Kumar; Thirupathi Rao, Nakka; Reddy Parvathala, Balakesava; Lakshmi Jagan, Banana Omkar; Venkata Rajanna, Bodapati
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.pp133-141

Abstract

Breast cancer is among the most common cancers in women worldwide; timely detection is vitally important for improving chances of survival. The present study examines an innovative machine learning technique for the diagnosis of breast cancer using the breast cancer Wisconsin (diagnostic) dataset from Kaggle. The dataset includes 569 instances, and each instance has 30 attributes derived from digitized fine needle aspiration (FNA) images of masses found in the breast. We will present an ensemble deep learning (DL) model fusing a convolutional neural network (CNN) and LRAlexNet architectures to increase the accuracy and robustness of this type of cancer diagnosis. CNN models are well-known for their power to capture spatial hierarchies in image data, and LRAlexNet is a specialized deep CNN that excels at image classification due to its depth and parameter optimization. In this work, we use the ability to extract features of CNNs along with the superior classification performance of LRAlexNet to distinguish between benign and malignant cancers. The model will be trained and validated on the curated breast imaging subset of the digital database for screening mammography (CBIS-DDSM) dataset, and performance will be evaluated using sensitivity, accuracy, specificity, and the area under the curve (AUC) for the receiver operating characteristic. The results show that the ensemble CNN-LRAlexNet model achieved superior accuracy for breast cancer prediction when compared to traditional machine learning methods.
SAIDI and SAIFI indicators for the control of feeder A4502 of the Concepción transformer electrical substation Murillo Manrique, Margarita F.; Sánchez Ayte, Jorge Augusto; Joel Baygorrea Vega, William; Flores-Caceres, Richard
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.pp396-404

Abstract

This study evaluated the reliability of feeder A4502 of the Concepción substation (Huancayo, Peru) through the analysis of system average interruption duration index (SAIDI) and system average interruption frequency index (SAIFI) indicators. The 46-year-old infrastructure presented 805 structural deficiencies (59%), with a predominance of corrosion in iron poles. Automatic recloser devices were implemented at strategic points, based on the fact that 67% of the 73 interruptions in 2021 were transient faults. Post-intervention results (2024) showed significant improvements: SAIDI was reduced from 9.87 to 7.39 hours (25%), nearing the regulatory limit of 7 hours; SAIFI decreased from 4.29 to 2.71 events (37%), falling within the limit of 4. Pearson correlation analysis confirmed a statistically significant relationship between structural deficiencies and the indicators (r =0.62 SAIDI, r =0.58 SAIFI, p <0.05). The integrated approach—diagnosis of deficiencies + automation with reclosers—proved to be technically viable and economically justifiable, also allowing for the meeting of new energy demands (240 kVA available). The results constitute a replicable model for other aging Latin American networks, validating the viability of regulatory compliance without prohibitive investments.
Performance comparison of feature extraction methods for electroencephalogram-based recognition of Balinese script Wirawan, I Made Agus; Pascima, Ida Bagus Nyoman; Mahendra, Gede Surya; Candiasa, I Made; Sukajaya, I Nyoman
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.pp55-64

Abstract

Recognizing Balinese script from electroencephalogram (EEG) signals remains a challenging problem due to low signal amplitude, non-stationary dynamics, and significant inter-subject variability. Despite previous attempts, no single feature extraction method has been universally effective in addressing these limitations. To fill this gap, this study systematically evaluates five feature extraction techniques—differential entropy (DE), power spectral density (PSD), discrete wavelet transforms (DWT), Hjorth parameters, and statistical features—on the Balinese imagined spelling using electroencephalography (BISE) dataset, which contains EEG recordings specifically designed for Balinese script recognition. For classification, both artificial neural networks (ANN) and support vector machines (SVM) are applied, and their performance is validated across multiple experimental settings. Results demonstrate that DE consistently provides more stable and discriminative features than the other methods, achieving the highest classification accuracy when combined with ANN. These findings highlight the potential of DE-based approaches to advance electroencephalogram driven Balinese script recognition, offering a culturally significant contribution to brain-computer interface (BCI) research and supporting future applications in inclusive artificial intelligence, digital heritage preservation, and assistive technologies.
A bibliometric review of critical chain project management in construction Bachwani, Dhiraj S.; Malek, MohammedShakil S.; Shaileshkumar Upadhyaya, Deep; Yadav, Neetu
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.pp272-280

Abstract

This study offers an extensive bibliometric analysis of critical chain project management (CCPM) research over the past twenty years, seeking to elucidate the discipline’s developmental trajectory and pinpoint emerging research frontiers. A comprehensive review of the literature revealed fundamental principles of CCPM, highlighting essential components such as buffer management strategies and resource-constrained scheduling methodologies. This initial analysis established the theoretical framework for the quantitative study and facilitated the identification of suitable metrics to integrate both foundational theories and contemporary advancements in CCPM scholarship. The study examined approximately 1,800 academic publications, including journal articles, conference proceedings, review papers, and book chapters published from 2000-2022, obtained from the Scopus database. The analytical framework encompassed various bibliometric dimensions, including performance metrics, relationship indicators, conceptual frameworks, publication characteristics, and VOSviewer network analysis, as essential elements of the data examination process. The developed framework has two main goals: it helps researchers find important publications, potential collaborators, and new areas of research, and it gives practitioners a structured place to store information about how to use CCPM methods in complicated projects with few resources.

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