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INDONESIA
Emerging Science Journal
Published by Ital Publication
ISSN : 26109182     EISSN : -     DOI : -
Core Subject : Social,
Environmental Science
Emerging Science Journal is not limited to a specific aspect of science and engineering but is instead devoted to a wide range of subfields in the engineering and sciences. While it encourages a broad spectrum of contribution in the engineering and sciences. Articles of interdisciplinary nature are particularly welcome.
Arjuna Subject : -
Articles 1,093 Documents
 106   107   108   109   110 
Stroke Detection Using EEG and Deep Learning: A Comparative Study of Feature Engineering Techniques May Issa Aldossary; Fatemah H. Alghamedy; Dina A. Alabbad; Reem A. H. Alshami; Haya A. Alzahim; Renad A. Alnuaim; Maimonah S. Altaweel; Shahad F. Alotaibi; Sumayh S. Aljameel; Areej A. Almalki; Sunday O. Olatunji
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-023

Abstract

Strokes remain one of the leading causes of disability and mortality worldwide, underscoring the need for effective early detection and intervention methods. Recently, researchers have shown a growing interest in harnessing bio-signals, natural indicators produced by the human body, as potential markers for stroke detection. Multiple types of bio-signals, such as electroencephalography (EEG), are currently being explored in stroke diagnostic studies. This approach is promising because it offers a non-invasive, cost-effective, accurate, and portable means of detecting strokes. The objectives of this research are to investigate the effectiveness of deep learning (DL) techniques, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), Recurrent Neural Networks (RNN), CNN-LSTM hybrid models, and CNN-Gated Recurrent Unit (CNN-GRU) models, for detecting early-stage strokes based on EEG data. In addition, the impact of diverse feature extraction techniques, including utilizing all features, selecting features with a Decision Tree (DT) based on different thresholds, Principal Component Analysis (PCA), and Independent Component Analysis (ICA), is analyzed to evaluate their influence on model performance. A comparative discussion is conducted across multiple experimental setups to identify the most effective DL and feature engineering combinations for stroke detection. Across 35 different experiments, the CNN-LSTM model with seven selected features using the DT method yields the best results, achieving 86% accuracy, 99% precision, 81% recall, and an F1-score of 89%.
A Hierarchical Hybrid Closest Access Point–Medoids Algorithm for Improved Clustering-Based Fingerprint Localization Abdulmalik Shehu Yaro; Filip Maly; Kateřina Frončková
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-010

Abstract

Traditional clustering algorithms in fingerprint-based localization often struggle with outliers, overlapping clusters, and irregular RSS variations in fingerprint databases, which reduces clustering accuracy. To address these issues, this study proposes a hierarchical hybrid approach, the closest access point–medoids (CAP-medoids) algorithm, which combines the closest access point (CAP) method with k-medoids clustering. The CAP algorithm generates initial clusters based on the strongest received signal strength (RSS) from nearby wireless access points (APs), while k-medoids refines clusters by selecting actual fingerprint vectors as cluster centers, improving robustness against noise and irregular RSS variations. The algorithm was evaluated on four publicly available fingerprint databases of varying size and density. Performance was assessed using Euclidean, Manhattan, and cosine similarity distances as similarity metrics, with silhouette scores and Davies Bouldin (DB) indices as clustering performance metrics. Results show that the CAP-medoids algorithm consistently produces more compact and well-separated clusters than standard k-medoids in small databases, with silhouette scores increasing up to 75% and DB indices decreasing up to 63%. For larger, high-density databases, performance declines, indicating sensitivity to database size. Comparisons with other hybrid algorithms, including CAP+k-means++ and k-density-based spatial clustering of applications with noise (k-DBSCAN) algorithms, confirm its overall robustness and adaptability.
Fractional White Smell Agent Optimization for CNN-Based Transfer Learning in Melanoma Classification Vijaya P; Basant Kumar; Joseph Mani; Satish Chander; Roshan Fernandes; Mohamed Sirajudeen Yoosuf
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-02

Abstract

Melanoma is the deadliest form of skin cancer, and early diagnosis and treatment can significantly reduce mortality rates. However, existing strategies for classifying melanoma from dermoscopic skin images still face significant challenges. Therefore, this study aims to develop an accurate method for melanoma classification using dermoscopic skin images. A novel melanoma classification framework, termed Fractional White Smell Agent Optimization-enabled Convolutional Neural Network-based Transfer Learning (FWSAO_CNN-based TL), is proposed. First, the input skin image is preprocessed using an Adaptive Kalman Filter. Subsequently, skin lesion segmentation is performed using LinkNet, where the network is trained using White Smell Agent Optimization (WSAO). Following segmentation, image augmentation is applied, and feature extraction is conducted. Finally, melanoma classification is performed using a CNN-based transfer learning model trained with the proposed Fractional White Smell Agent Optimization (FWSAO), which integrates the Fractional concept, Smell Agent Optimization (SAO), and White Shark Optimizer (WSO). The CNN utilizes hyperparameters derived from a pretrained GoogLeNet model. The performance of the proposed FWSAO_CNN-based TL framework was evaluated using accuracy, True Positive Rate (TPR), and True Negative Rate (TNR). The proposed method achieved values of 91.565%, 90.090%, and 91.269%, respectively. Furthermore, the proposed model demonstrated performance improvements of 18.4%, 8.1%, 17.5%, 12.55%, 8.2%, and 6.23% compared with conventional approaches.
Exploring AI-Enabled Cloud Transformation Towards Digitalization Value in Accounting Firms Sudawadee Intho; Daranee Uachanachit
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-030

Abstract

This study examines how AI-enabled, cloud-based accounting platforms generate digitalization value within Thai accounting firms by identifying the internal mechanisms through which cloud capability and cloud user capability translate into economic outcomes. Using survey data from 360 cloud-using firms, we employed covariance-based structural equation modeling with maximum likelihood estimation and bias-corrected bootstrapping (5,000 resamples) to test direct and mediated pathways. The results show that the qualitative of accounting information produced by cloud systems strongly enhances digitalization value added (β = 0.49, p < 0.001) and promotes proactive work behavior (β = 0.59, p < 0.001). Proactive work behavior further contributes to digitalization value added (β = 0.30, p < 0.01). Cloud user capability improves information quality (β = 0.19, p < 0.05) and proactive behavior (β = 0.35, p < 0.01), while cloud attributes improve information quality (β = 0.10, p < 0.05). Mediation analysis confirms that value arises indirectly via the sequential chain from cloud capability to information quality and then to proactive behavior, indicating that technical readiness alone is insufficient without informational uplift and behavioral execution. Theoretically, we extend information-systems and capability-based perspectives by positioning qualitative accounting information and proactive work behavior as the conversion channels that render cloud technology economically meaningful. Practically, firms and regulators should shift emphasis from infrastructure deployment to managing information quality, building user capability, and institutionalizing proactive work routines within AI-enabled cloud settings so that cloud adoption yields measurable value.
Construction, Validity, and Reliability of the Pedagogical Competence Assessment Instrument for Proficient Teachers Jusuf Blegur; Andreas J. F. Lumba; Berliana; Intan Christine Adriani Siki; Harmawati; Alfred Tobok Siahaan; Haira; Vena Yuliana
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-034

Abstract

This study aims to develop and evaluate the Pedagogical Competence Assessment for Proficient Teachers (PCAI-PT) as an instrument for assessing teachers’ pedagogical competence in Indonesia. The research employed a development method based on the ADDIE framework (Analyze, Design, Develop, Implement, and Evaluate). Thirteen pedagogical competence items representing core skills of novice expert teachers were formulated from document analysis. These items were validated by six expert raters and tested on 231 teachers across various educational levels. Validation results indicated that all PCAI-PT items were valid after revision, with Aiken’s V ranging from 0.78 to 1.00. Intraclass correlation analysis showed increased inter-rater consistency from the initial validation to revalidation, demonstrating strong inter-rater reliability. Construct testing using CFA revealed that all items met the criteria, with factor loadings >0.70, Cronbach’s alpha and composite reliability >0.70, and AVE >0.50. Discriminant validity was confirmed, and model fit indices indicated good to excellent fit (RMSEA = 0.088; SRMR = 0.034; CFI = 0.955). Concurrent validity testing using the Kruskal-Wallis test showed no significant differences across educational levels (p>0.05), indicating consistent application of the instrument. Overall, PCAI-PT is valid, reliable, and representative, providing a comprehensive and sustainable tool for assessing teachers’ pedagogical competence across diverse educational contexts.
Determinants of Customers' Purchasing Intention of Fresh Agricultural Products: SEM Analysis Dongrun Ma; Somjai Nupueng; Yan Chen; Zhenhua Xu
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-012

Abstract

With the ongoing advancement and widespread use of internet technologies, e-commerce has become a dominant retail channel, and the emergence of live-streaming platforms now offers companies a dynamic new way to market and sell products. Nevertheless, this business model also heightens the issue of information asymmetry, as consumers often face challenges in accurately assessing product quality and determining the credibility of sellers, making the enhancement of consumer trust a key concern for e-commerce enterprises. This study examines how information acquisition ability and online word of mouth regarding fresh agricultural products on Chinese live-streaming platforms influence consumers’ purchase intentions, with a particular focus on the mediating role of perceived value. Analysis of 372 valid survey responses reveals that both information acquisition ability and online word of mouth significantly increase purchase intentions, and that perceived value acts as a critical mechanism linking these factors. The findings also indicate the importance of real-time interaction in boosting sales and fostering deeper consumer engagement. Based on these insights, the study recommends strengthening consumers’ ability to access and evaluate product information, cultivating credible and positive online word of mouth, and leveraging interactive live-streaming features to enhance product displays and review systems. These contributions advance theoretical understanding of consumer behavior in digital marketing contexts and provide practical guidance for enterprises seeking to improve their live-streaming e-commerce strategies.
Corn Cob-Derived Activated Carbon for Chloramphenicol Removal: An Optimization and Mass Transfer Model Study Mohamad Razif Mohd Ramli; Abdul Wahab Mohammad; Mohd Sobri Takriff; Mohd Azmier Ahmad; Noor Fazliani Shoparwe; Ebenezer I. Oluwasola
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-08

Abstract

This study developed a sustainable activated carbon (AC) from corn cob agricultural waste for efficient chloramphenicol (CP) removal from aqueous solutions and to improve the predictive understanding of the adsorption process. Microwave-assisted physicochemical activation using potassium hydroxide (KOH) was optimized through response surface methodology (RSM), with activation time, microwave radiation power, and impregnation ratio (IR) identified as the key preparation variables. Under the optimal conditions (3.86 min, 616 W, and 2.5 g/g), the resulting AC achieved a yield of 16.6% and a CP adsorption capacity of 20.2 mg/g. The optimized AC exhibited a high BET surface area (832.68 m²/g), a mesopore-dominated pore structure (mesoporous surface area of 623.45 m²/g), a pore volume of 0.09067 cm³/g, and an average pore diameter of 1.93 nm, leading to a maximum experimental adsorption capacity of 20.68 mg/g at 30 °C. In addition, a mass transfer (MT) model was successfully applied to predict an equilibrium adsorption capacity of 21.48 mg/g with a low average error of 3.29% and R² ≥ 0.90. By integrating process optimization with mass transfer modeling, this study improves the understanding of CP adsorption and provides a practical framework for designing efficient, waste-derived adsorbents for antibiotic-contaminated water treatment.
An Empirical Analysis of the Relationship Between the Omega Ratio and Yield Skewness in European Government Bonds Attila Bányai; Tibor Tatay; Gergő Thalmeiner; László Pataki
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-031

Abstract

This study examines the empirical relationship between the Omega ratio and yield skewness in European sovereign bond markets, addressing whether distributional asymmetry is systematically reflected in Omega-based performance evaluation. The analysis is guided by two research questions: whether a statistically significant association exists between the Omega ratio and yield skewness across different time horizons, and whether this relationship exhibits cross-country heterogeneity consistent with a core–periphery structure. Using daily data for 10-year government bonds from 27 European countries over the period 2015–2025, we construct constant-maturity total returns and apply a robust Omega ratio formulation with inflation-adjusted thresholds. Yield skewness is measured using time-adjusted daily yield changes. The empirical strategy combines rolling-window correlation analysis, hierarchical clustering based on Kendall’s τ, and Independent Component Analysis to capture both short-term dynamics and latent structural patterns. The results provide strong and consistent evidence of a significant relationship between the Omega ratio and yield skewness across short-, medium-, and long-term horizons, confirming that the Omega ratio captures meaningful aspects of return asymmetry in fixed-income markets. Importantly, the findings reveal pronounced regional heterogeneity: core and Northern European markets exhibit stable positive associations, while several peripheral and emerging markets display weaker or negative relationships. These results imply that Omega-based performance measures reflect not only statistical asymmetry but also underlying differences in market liquidity, risk premia, and institutional structure. Overall, the study highlights the relevance of distribution-sensitive performance measures for sovereign bond evaluation and contributes novel evidence from the European fixed-income context.
Supply Chain Integration and Sustainable Organizational Performance: Moderating Role of Blockchain Adoption Tipon Tanchangya; Kazi Omar Siddiqi; Kamron Naher; Miguel Angel Esquivias; Naimul Islam; Shishir Das; Shajib Chowdaury; Sheikh Fariha
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-027

Abstract

This research aims to investigate the impact of supply chain integration (SCI) on sustainable organizational performance (SOP). SCI includes supply chain agility (SCA), supply chain agility visibility (SCV), and supply chain agility flexibility (SCF), which examined the effect on SOP and green supply chain management (GSCM). This query also examines the moderating role of blockchain adoption (BA) on the relationship between SCI and GSCM, and the mediating role of GSCM in the relationship between SCI and SOP. A purposive sampling approach is used to collect data from 364 blockchain adopters who are also directly connected to SCI within the Bangladeshi manufacturing sector. The dataset is analyzed by the Structural Equation Model (SEM). The results revealed a substantial impact of SCA, SCV, and SCF on SOP. Additionally, GSCM plays a significant role in SCA, SCF, and SOP, whereas there is no significant effect of GSCM on SCV and SOP. In the moderation analysis, BA significantly and positively moderates the relationships among SCA, SCF, and GSCM. However, no significant effect of BA was found between SCV and GSCM. This research adds value to the existing literature on the Bangladeshi manufacturing sector by integrating SCA, SCV, and SCF, which were examined separately in previous studies.
Net Working Capital and Firm Performance: The Moderating Role of Firm Size in Emerging Markets Thuy Thi Cam Nguyen
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-017

Abstract

This study examines the relationship between net working capital and business performance and analyzes the moderating role of firm size in this relationship in emerging markets. Drawing on trade-off theory, the resource-based perspective, and agency theory, the study suggests that the impact of net working capital on performance varies with firm size. Using panel data on 370 non-financial publicly traded companies in Vietnam from 2013 to 2024, the study employs an interaction regression model with firm size as the moderating variable. The analysis proceeds in multiple steps, including OLS, fixed-effects, random-effects, and FGLS models, and, in particular, a GMM-robust model to address endogeneity, heteroskedasticity, and autocorrelation. Empirical results show that net working capital significantly affects business performance, as measured by ROA and ROE. Firm size not only directly affects operational efficiency but also moderates the relationship between net working capital and operational efficiency. Specifically, the impact of net working capital differs significantly across firm sizes, reflecting differences in resource access and management efficiency. This research contributes to the existing literature by clarifying the moderating mechanism of firm size in working capital management and by providing new empirical evidence from emerging markets, offering important implications for managers and policymakers.

Page 108 of 110 | Total Record : 1093

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