cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
,
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 903 Documents
 87   88   89   90   91 
Unified Numerical FFR with Adaptive Bandwidth for 5G and Beyond Multilayer Multisector Networks Muhammad Yaser; Iskandar; M. Sigit Arifianto; Khoirul Anwar
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-09

Abstract

The present research introduces a unified numerical formulation for Fractional Frequency Reuse (FFR) and a user composition-based adaptive bandwidth allocation strategy for multi-layer/multi-sector cell architectures. The proposed FFR metric explicitly accumulates sub-band usage across the inner–outer and inter-sector layers, thereby normalizing diverse reuse patterns into a single, consistent number. This formulation remains consistent with the classical definition (reducing to 1/N reuse under certain conditions), approaches full reuse when multi-layer/sector coordination is applied, and provides a simple yet powerful link between reuse configurations and capacity predictions in 5G and beyond networks. Comprehensive simulations based on a realistic urban macrocell environment show that increasing the architectural complexity from a single-layer to a 2-layer 6-sector network results in a remarkable 184% increase in average cell capacity. Furthermore, in the dynamic bandwidth allocation, the inner user-dominated scenario achieves the highest cell capacity, which is 41% higher than that in static bandwidth allocation. At the same time, dynamic allocation also improves fairness in the outer user-dominated scenario, increasing the Jain fairness index by up to 0.444. These results confirm that the combination of the new FFR formulation and adaptive resource allocation significantly improves spectrum efficiency, cell capacity, and fairness, and provides practical guidance for optimizing the implementation of 5G and beyond cellular network deployments.
Pattern Recognition Tasks with Personalized Federated Learning Md. Arifur Rahman; Isha Das; Mushfiqur Rahman Abir; B. M. Taslimul Haque; Abdullah Al Noman; Abir Ahmed; Md. Jakir Hossen
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-020

Abstract

Personalized Federated Learning (PFL) constitutes a novel paradigm that tailors Machine Learning (ML) models to individual clients, thereby furnishing personalized model updates whilst upholding stringent data privacy principles. Diverging from conventional standard Federated Learning (FL) approaches, PFL adapts models to distinct client data distributions, engendering heightened levels of accuracy, customization, and data security, all while minimizing communication overhead. This methodology proves particularly salient in contexts marked by pattern recognition tasks reliant upon heterogeneous data sources and underpinned by paramount privacy apprehensions. In the present research endeavor, this article undertake a comprehensive comparative analysis of seven distinct PFL algorithms deployed across three diverse datasets, namely MNIST, SignMNIST, and Digit5. The overarching objective entails ascertaining the preeminent PFL algorithm, within the framework of pattern recognition tasks, through a rigorous evaluation anchored in metrics encompassing Accuracy, Precision, Recall, and F1 Score. Concurrently, an in-depth scrutiny of these PFL algorithms is conducted, elucidating their operative workflows, advantages, and limitations. Through empirical investigation, the findings evince that APPLE, FedGC, and FedProto emerge as stalwart contenders, consistently furnishing superior performance across the spectrum of assessed datasets, while acknowledging the contextual specificity of alternative algorithms and the potential for iterative refinement to realize optimal outcomes.
Perceived Risk and Trust as Moderators Between Online Shopping Intention and Purchase Decisions Tu Ngoc Tran; Le Dinh Nghi; Dinh Thi Kieu Chinh
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-026

Abstract

This study aims to identify the factors influencing online shopping intention and purchase decisions among consumers in Ho Chi Minh City, while examining the moderating roles of perceived risk and trust in e-commerce. Data were collected through a structured survey of online shoppers and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The empirical results indicate that product quality, price, promotion programs, and website quality have significant positive effects on online shopping intention, which in turn exerts a strong influence on purchase decisions. Moreover, perceived risk negatively moderates, whereas trust in e-commerce positively moderates the relationship between online shopping intention and purchase decisions. These findings underscore the pivotal roles of perceived value, trust formation, and risk reduction in converting online shopping intention into actual purchasing behavior. The novelty of this study lies in simultaneously examining perceived risk and trust as moderating mechanisms within a PLS-SEM framework in an emerging market context. The results provide practical insights for e-commerce firms by highlighting the importance of improving website quality, enhancing security systems, adopting flexible pricing strategies, and designing targeted promotions to increase online shopping conversion rates.
Business Process Remaining Time Prediction Based on Bidirectional QRNN with Attention Mechanism Na Guo; Ting Lu; Cong Liu; Xingrong Xu; Qingtian Zeng
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-01

Abstract

Business process prediction is essential for monitoring workflows and ensuring service quality. A key task in this area, remaining time prediction, focuses on estimating process duration and has been extensively studied. While Long Short-Term Memory (LSTM) networks are widely adopted, their limited parallelization and sequential modeling capabilities constrain performance. To address these limitations, we propose a remaining time prediction approach based on a bidirectional Quasi-Recurrent Neural Network (QRNN) with an attention mechanism. Specifically, the bidirectional QRNN is employed to construct the prediction model, while the attention mechanism enhances its ability to extract feature information. Next, a transfer training iteration strategy based on different trace prefix lengths is designed to address the imbalance in trace lengths. Then, a Word2Vec-based event representation learning approach is introduced to generate similarity vector of adjacent events, further improving prediction accuracy. Finally, using five publicly real-life event logs, the proposed approach is evaluated against state-of-the-art approaches. Experimental results demonstrate that it improves average prediction accuracy by nearly 15% while reducing average model training time by approximately 26%.
Evaluating Differential Privacy Mechanisms in Machine Learning with Emphasis on Utility and Robustness Rashmi Dwivedi; Basant Kumar; Vivek Mishra; Hothefa Jassim; Ozlem Kilickaya
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-07

Abstract

Federated learning enables collaborative model training across distributed clients without sharing raw data, yet it remains susceptible to inference threats such as membership inference attacks. This study aims to enhance the privacy of federated learning by integrating differential privacy and systematically evaluating its effects on model utility and adversarial robustness. A synthetic multimodal dataset was developed by combining demographic attributes from the UCI Adult dataset, mobility indicators from Google COVID-19 Mobility Reports, and semantic descriptors from LAION-400M, creating a high-dimensional and bias-reduced benchmark for privacy-preserving experimentation. Differentially private stochastic gradient descent (DP-SGD) was applied under multiple privacy budgets and ablation settings to isolate the individual contributions of gradient clipping and noise injection. Experimental results reveal that model accuracy increases with larger privacy budgets, while membership inference attack accuracy remains close to random guessing, confirming strong defense capability. Gradient clipping proved essential for training stability, whereas excessive noise caused measurable degradation in learning utility. The proposed framework establishes reproducible benchmarks for tuning differential privacy parameters in federated environments and demonstrates that robust privacy guarantees can be achieved without substantial loss of performance, providing practical guidance for deploying trustworthy, privacy-preserving machine learning systems across domains such as healthcare, finance, and mobility.
Supply Chain Digitalization and Performance: The Serial Mediation of Visibility and Resilience Thi Thanh Nhan Tran; Anh Duc Do; Minh Ngoc Nguyen
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-08

Abstract

This study aims to examine how supply chain digitalization enhances supply chain performance through the serial mediation of supply chain visibility and supply chain resilience. Grounded in the Resource-Based View and Dynamic Capabilities Theory, the research model was empirically tested using survey data collected from 379 managers of manufacturing firms operating in Vietnamese industrial parks. The data was analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that supply chain digitalization positively influences both visibility and performance. However, while visibility improves performance when examined independently, its direct effect becomes insignificant once resilience is incorporated, suggesting that visibility alone is insufficient to achieve superior performance without strong resilience capabilities. Moreover, the serial mediation path digitalization → visibility → resilience → performance was statistically significant, confirming the sequential mechanism through which digitalization improves performance outcomes. The study contributes to the literature by clarifying the complementary roles of visibility and resilience and highlighting resilience as the key capability linking digitalization to performance. From a managerial perspective, the results emphasize that investing in digital technologies should be accompanied by initiatives to strengthen resilience capabilities to maximize the benefits of digital transformation in supply chains.
Metaverse Marketing: Avatars as the Future of Brand Engagement Abdullah Sarwar; Aysa Siddika; Mohammad Ali Tareq; Hayford Asare Obeng; Shaikh Fazlur Rahman; Dzuljastri Abdul Razak
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

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

Abstract

The Metaverse, a virtual space where users interact via avatars, presents a burgeoning platform for marketing endeavors. Unlike traditional social media marketing, which broadcasts to a passive audience, Metaverse marketing facilitates two-way engagement in immersive settings. This is especially relevant for brands aiming to connect with digitally savvy generations, such as Gen X, Millennials, and Gen Z, whose expectations for interactivity and personalization continue to grow. While previous studies have assessed the broader pros and cons of Metaverse marketing, this study examines the avatar as a catalyst for consumer interaction and brand loyalty. The central question was to investigate how avatars influence brand engagement, with a focus on emotional connection and self-expression within the virtual environment. This study explores consumer perceptions, attitudes, and behaviors towards avatars among 398 Malaysian adults. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to analyze the data. Findings reveal that avatars are effective in enhancing brand recall, trust, and emotional connection. The results also indicate that metaverse brand awareness, customer awareness, trust, and credibility, as well as emotional connection, serve as mediators in the relationship between avatar usage and Metaverse brand engagement. Implications for marketers and future study directions are discussed, highlighting the significance of avatar-based marketing in the evolving digital commerce landscape.
ZigBee Based Low Latency IoT and AI Integrated Framework for Real Time Telehealth Monitoring Basant Kumar; Mohammad Shahnawaz Shaikh
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-024

Abstract

The Internet of Things (IoT) and Artificial Intelligence (AI) have opened up new frontiers in remote health monitoring with the integration of technologies and transformative solutions in order to detect real-time health monitoring and disparities. This article shows an innovative and integrated wireless health surveillance system, which is aimed at auxiliary environments, especially for elderly and chronically ill patients. The system links IoT sensors to monitor heart rate, body temperature, and oxygen level with cloud-based AI-driven systems for continuous real-time health monitoring of data shared by IoT sensors. Taking advantage of the ZigBee protocol for low-power, reliable communication ensures spontaneous data transmission from a system wearer to a centralized processing unit. At the most basic level, the system uses advanced machine learning algorithms such as random forest, support vector machine (SVM), and logistic regression to identify health discrepancies with a high degree of accuracy. The random forest model in particular gets an impressive 95% accuracy and recalls 100%, ensuring reliable detection of minimum false negatives and important health issues. The modular structure of the system allows for the addition of more sensors, including blood pressure and glucose monitors, to ensure scalability and adaptability to suit the varying needs of different patients. In a real-world care facility, strict testing was carried out on the capability of monitoring the capacity system with just a 120 ms delay and a power consumption of 3.8 mW/h, which made it very suitable for long-term, energy-skilled deployment. By addressing some of the major issues such as high delays, false alarms, and lack of integration in current systems, this research provides a scalable, reliable, and user-friendly solution for telehealth. The proposed system not only adds more accuracy and freedom to the patient in the clinical setting but also lessens the burden of the healthcare providers, paving the way to a new generation of intelligent health solutions.
Multimodal Emotion Recognition Using Hybrid Large Language Models and Metaheuristic Algorithms Andino Maseleno; M. Teduh Uliniansyah; Agung Santosa; Lyla Ruslana Aini; Rini Wijayanti; Ahmad Fudholi; Chotirat Ann Ratanamahatana
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-015

Abstract

Emotion recognition is a vital component of human–computer interaction and intelligent systems, yet robust multimodal emotion recognition remains challenging due to high-dimensional input space, noisy features, and the complexity of integrating heterogeneous modalities. This study proposes a novel hybrid multimodal framework that enhances both accuracy and computational efficiency by combining the semantic representation capability of Large Language Models (LLMs) with the optimization strengths of metaheuristic algorithms. In the proposed approach, an LLM is utilized to extract high-level contextual features from text and audio streams, while the Binary Artificial Hummingbird Algorithm (BAHA) performs feature selection to remove redundant attributes. Subsequently, the Goose Algorithm (GA) optimizes classifier hyperparameters, and the Komodo Mlipir Algorithm (KMA) conducts late fusion of the final multimodal outputs. Experiments conducted on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) dataset, evaluated on six emotion categories, demonstrate that this hybrid approach successfully captures subtle affective cues and surpasses state-of-the-art baselines, achieving an accuracy of 87.5%. Integrating LLMs with multiple specialized metaheuristics therefore yields a substantially more robust emotion recognition pipeline and represents a promising direction toward the development of more emotionally intelligent systems.
How AI Literacy Drives Digital Entrepreneurial Intention: Evidence from an Emerging Economy Suchart Tripopsakul; Danupol Hoonsopon
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-05

Abstract

The objective of this study is to examine how artificial intelligence literacy influences digital entrepreneurial intention through cognitive and motivational mechanisms. Drawing on the Theory of Planned Behavior and entrepreneurial cognition, the model incorporates perceived feasibility and perceived opportunity as mediating factors, while self-efficacy and risk-taking propensity are included as moderating variables. Data were collected from 312 adults in Thailand using a structured questionnaire, and the proposed relationships were analyzed using partial least squares structural equation modeling. The results indicate that artificial intelligence literacy has a direct and positive effect on digital entrepreneurial intention and also enhances individuals’ perceptions of feasibility and opportunity. Further analysis confirms that perceived feasibility and perceived opportunity partially mediate the relationship between artificial intelligence literacy and entrepreneurial intention. The moderating analysis shows that self-efficacy strengthens the effect of artificial intelligence literacy on perceived feasibility, while risk-taking propensity has no significant influence on the relationship between artificial intelligence literacy and perceived opportunity. These findings contribute to the understanding of digital entrepreneurship by identifying artificial intelligence literacy as a critical personal capability that stimulates entrepreneurial engagement through both cognitive and motivational pathways, particularly within the context of emerging economies.

Page 89 of 91 | Total Record : 903

 87   88   89   90   91 

Filter by Year

2017 2026


Reset
Filter By Issues
All Issue Vol. 10 No. 2 (2026): April Vol. 10 No. 1 (2026): February Vol. 9 No. 6 (2025): December Vol. 9 No. 5 (2025): October Vol. 9 No. 4 (2025): August Vol. 9 No. 3 (2025): June Vol 9, No 1 (2025): February Vol. 9 No. 1 (2025): February Vol. 9 (2025): Special Issue "Emerging Trends, Challenges, and Innovative Practices in Education" Vol 8, No 6 (2024): December Vol. 8 No. 5 (2024): October Vol 8, No 5 (2024): October Vol 8, No 4 (2024): August Vol 8, No 3 (2024): June Vol 8, No 2 (2024): April Vol 8, No 1 (2024): February Vol 8 (2024): Special Issue "Current Issues, Trends, and New Ideas in Education" Vol 7 (2023): Special Issue "COVID-19: Emerging Research" Vol 7, No 6 (2023): December Vol 7, No 5 (2023): October Vol 7, No 4 (2023): August Vol 7, No 3 (2023): June Vol 7, No 2 (2023): April Vol 7, No 1 (2023): February Vol 7 (2023): Special Issue "Current Issues, Trends, and New Ideas in Education" Vol 6 (2022): Special Issue "COVID-19: Emerging Research" Vol 6, No 6 (2022): December Vol 6, No 5 (2022): October Vol 6, No 4 (2022): August Vol 6, No 3 (2022): June Vol 6, No 2 (2022): April Vol 6, No 1 (2022): February Vol 6 (2022): Special Issue "Current Issues, Trends, and New Ideas in Education" Vol 5 (2021): Special Issue "COVID-19: Emerging Research" Vol 5, No 6 (2021): December Vol 5, No 5 (2021): October Vol 5, No 4 (2021): August Vol 5, No 3 (2021): June Vol 5, No 2 (2021): April Vol 5, No 1 (2021): February Vol 4 (2020): Special Issue "IoT, IoV, and Blockchain" (2020-2021) Vol 4, No 6 (2020): December Vol 4, No 5 (2020): October Vol 4, No 4 (2020): August Vol 4, No 3 (2020): June Vol 4, No 2 (2020): April Vol 4, No 1 (2020): February Vol 3, No 6 (2019): December Vol 3, No 5 (2019): October Vol 3, No 4 (2019): August Vol 3, No 3 (2019): June Vol 3, No 2 (2019): April Vol 3, No 1 (2019): February Vol 2, No 6 (2018): December Vol 2, No 5 (2018): October Vol 2, No 4 (2018): August Vol 2, No 3 (2018): June Vol 2, No 2 (2018): April Vol 2, No 1 (2018): February Vol 1, No 4 (2017): December Vol 1, No 3 (2017): October Vol 1, No 2 (2017): August Vol 1, No 1 (2017): June More Issue