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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 30 Documents
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Search results for , issue "Vol. 10 No. 1 (2026): February" : 30 Documents clear
Probability Density Function Adjustment for Estimating Quantile Regression Coefficients Amphanthong, Pimpan; Riansut, Warangkhana
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-023

Abstract

This study aims to improve the estimation of quantile regression coefficients by adjusting probability density functions using a selected τ-function that exhibits symmetric properties. The research focuses on five quantile levels Q(20)th, Q(25)th, Q(50)th, Q(75)th, and Q(80)th and compares the proposed method with conventional multiple regression through simulation experiments under varying sample sizes and distributional conditions. Performance is evaluated using the mean absolute error (MAE) as the primary metric. The findings indicate that for small sample sizes (n=8, n=15), both multiple and quantile regression methods perform well, especially at lower quantiles (Q(20)th to Q(50)th). However, as sample sizes increase (n=50, n=100), quantile regression at higher quantiles (Q(50)th, Q(75)th, Q(80)th) demonstrates superior estimation accuracy. In relation to kurtosis and skewness, the Q(50)th and Q(80)th quantiles are sensitive to distributional changes, effectively capturing transitions from high to normal kurtosis and central shifts in skewed distributions. The novelty of this research lies in the integration of the τ-function into the quantile regression framework, enhancing robustness and accuracy in coefficient estimation under non-normal conditions. This approach contributes to methodological advancements in regression analysis, particularly in applications involving non-standard data distributions.
IoT-Driven Emotional Data Analytics for Medical Applications: Insights and Innovations Akila, D.; Pal, Souvik; Vijayarani, M.; Sarkar, Bikramjit; Anbananthen, Kalaiarasi Sonai Muthu; Muthaiyah, Saravanan
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-05

Abstract

This study introduces the Internet of Things-based Emotional State Detection Model (IoT-ESDM), a comprehensive and intelligent emotional computing framework aimed at detecting and managing anxiety-related behavior in healthcare environments. The model leverages a multi-modal approach that combines facial expression analysis, physiological signal monitoring, and AI-driven classification to accurately identify emotional states in real time. Core components of the system include fuzzy color filtering, histogram analysis, and virtual face modeling, which work together to extract relevant emotional features from input data. These features are then analyzed to provide adaptive, personalized feedback to patients or caregivers, enhancing emotional well-being support. Experimental results demonstrate the superior performance of IoT-ESDM over existing emotion detection systems. The model achieved a feedback ratio of 97.54%, accessibility ratio of 95.3%, detection accuracy of 92.7%, and a classification accuracy of 98.13%. Additionally, it showed a quality assurance rate of 94.13%, contributed to a 29.1% reduction in anxiety levels, and yielded a health outcome ratio of 94.5%. These metrics validate the system's effectiveness in clinical and real-world applications. The success of IoT-ESDM highlights its potential as a powerful tool for emotion-aware AI interventions, paving the way for future advancements in mental health monitoring and personalized healthcare solutions.
Multi-Country GHG Emissions Forecasting by Sector Using a GCN-LSTM Model Tonny, Babey Dimla; Jaroensutasinee, Krisanadej; Jaroensutasinee, Mullica; Sparrow, Elena B.
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-03

Abstract

This study developed a novel hybrid Graph Convolutional Network–Long Short-Term Memory (GCN–LSTM) model to forecast greenhouse gas (GHG) emissions across multiple country sectors, aiming to enhance climate policy. We analyzed 52 years (1970–2022) of GHG emissions data (CO₂, CH₄, N₂O, F-Gases) from 163 countries and eight sectors (Agriculture, Buildings, Fuel Exploitation, Industrial Combustion, Power Industry, Processes, Transport, Waste), sourced from the EDGAR v8 database. The GCN adjacency matrix captures spatial relationships on a weighted sum of Haversine distance and cosine similarity, while the LSTM models temporal dynamics. Data preprocessing includes min-max scaling and outlier handling with Interquartile Range capping. The model was trained on 70% of the data, validated on 15%, and tested on 15%, using Mean Squared Error (MSE) loss and the Adam optimizer. The performance was evaluated with Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²). The GCN–LSTM model outperformed baseline models (ARIMA, Simple LSTM, Stacked LSTM), achieving the lowest MAE (0.0207 in Waste) and highest R² (0.9756 in Waste). Model interpretability highlighted strong regional connections, such as Thailand–Cambodia in the Waste sector, suggesting that spatial and temporal dependencies offer superior forecasting accuracy, informing targeted climate action.
Performance Evaluation of Inclined-Step and Wall Roughness on Battery Thermal Management System Oyewola, Olanrewaju M.; Idowu, Emmanuel T.; Labiran, Morakinyo J.; Hatfield, Michael C.; Drabo, Mebougna L.
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

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

Abstract

In this study, the effects of inclined steps and wall roughness on the step-like plenum of the Z-type battery thermal management system (BTMS) are examined, extending the literature on its design. Due to the performance of the design in achieving a reduction in maximum temperature (Tmax), additional modifications are required to provide more insight into further enhancing the thermal performance and overcoming the design’s drawback, such as higher pressure drop (ΔP). The performance of the system was evaluated in terms of the  and maximum temperature difference (ΔTmax) of batteries in the systems and ΔP across the system. The temperature values were selected after comparing the maximum temperatures recorded on each battery.  Investigations were carried out using a Computational Fluid Dynamics (CFD) method, which was validated by comparing with experimental data from the literature. Findings revealed that the step designs with inclined angles of 5°, 45° and 85° reduced the Tmax by 3.18 K, 3.9 K, and 4.34 K, respectively, when compared to the Z-type design. However, the Z-type design has the lowest ΔP value (16.50 Pa), while the original step-like design system produced the highest value (20.96 Pa). When considering the roughness, by increasing the roughness height from 5 μm to 10 μm, an increase in Tmax was observed, while wall roughness generally decreases the ΔP. From 0 to 10 μm, Tmax increased by 0.03 K (0.01%) and ΔP increased by 0.07 Pa (0.29 %), indicating negligible effects. The study, therefore, concludes that adequate selection of step design with different angles, air inlet velocity, temperature, and wall roughness will be highly beneficial for designing cost-effective and efficient BTMSs.
Hybrid Neural Networks vs. Econometric Models for Fresh Durian Export Value Forecasting: A Comparative Analysis Damrongsakmethee, Thitimanan; Chanthawong, Anuman; Nupueng, Somjai; Ade Kesuma, Sambas
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-018

Abstract

This study compares machine learning and econometric approaches for forecasting agricultural export values in volatile global markets, examining predictive accuracy and economic interpretability trade-offs. Monthly data from January 2014 to December 2023 were analyzed using five models: Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Hybrid ANN-LSTM, Ordinary Least Squares (OLS), and Autoregressive Distributed Lag (ARDL). Key predictors included durian, mangosteen, and longan export values/volumes, plus China's GDP. Performance evaluation used MAE, RMSE, MAPE, and R² metrics with systematic hyperparameter optimization through grid search and 5-fold cross-validation. ANN achieved the highest absolute accuracy (MAE: 1,684,667,401.55; RMSE: 2,602,671,952.28), while Hybrid ANN-LSTM delivered superior relative accuracy (MAPE: 1.58%). ARDL demonstrated exceptional explanatory power (R²=0.83) for structural economic relationships. China's GDP emerged as the strongest determinant across all models. Longan export value showed contrasting effects between approaches, positive in machine learning models versus negative in econometric models, reflecting different paradigmatic interpretations of market substitution dynamics. This research introduces the first comprehensive comparative framework integrating advanced hybrid neural networks with traditional econometric methods for multi-commodity agricultural forecasting, addressing cross-commodity substitution effects previously unexplored while offering complementary perspectives for both predictive accuracy and economic policy interpretation.
Comparative Assessment of Machine Learning Approaches for Early Lung Cancer Diagnosis Maheshwari , Garvit; Tiwari, Babita; Tinka, Domonkos; Singh, Satyanand
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

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

Abstract

Lung cancer, a leading cause of cancer-related mortality worldwide, often escapes early detection due to the absence of distinct symptoms in its initial stages. This work investigates how Machine Learning (ML) might improve early diagnosis by analyzing Electronic Health Records (EHR) data. Multiple ML models were developed and evaluated on a synthetic dataset created to replicate real-world patient characteristics, allowing controlled experimentation while safeguarding privacy. Model performance was tuned using both conventional optimization methods and nature-inspired approaches, with the aim of balancing predictive accuracy and computational efficiency. In our synthetic dataset experiments, ensemble learners optimized with metaheuristic techniques reached accuracy levels approaching 99 percent while maintaining computational efficiency and generally outperformed simpler baselines. The contribution of this work lies in exploring the integration of GFO and WOA for feature selection and hyperparameter tuning of XGBoost, together with a soft-voting ensemble. This approach provides an experimental pathway for enhancing predictive performance under computational constraints. However, as the dataset is synthetic, the conclusion remains experimental; validation against clinical records will be essential before translation into practice.
Artificial Intelligence and Business Process Management: A Responsible Framework for Sustainable Transformation Sarkambayeva , Shynara; Singh, Satyanand; Mukhanova , Gulmira; Amralinova, Bakytzhan; Turegeldinova , Aliya
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-022

Abstract

This study aims to develop a responsible and sustainable framework for implementing artificial intelligence (AI) in business process management (BPM), with a focus on aligning technological advancement with strategic economic transformation. It addresses the need for ethical, sector-sensitive AI adoption in emerging economies undergoing digital modernization and diversification. The research integrates enterprise information system considerations, privacy-preserving modular architectures, and national regulatory frameworks related to data localization and cybersecurity. A sectoral analysis is conducted to assess global AI adoption maturity and its implications for economic transformation, using Kazakhstan as a contextual reference point. The results reveal that consumer-facing sectors such as retail and financial services exhibit high near-term adoption potential, while healthcare requires gradual infrastructure and talent development. More significantly, mid-term opportunities in manufacturing, logistics, and transportation sectors present Kazakhstan with a comparative advantage. AI adoption in manufacturing is projected to grow by 83% within three to seven years, underscoring the importance of timely investments in automation, smart technologies, and workforce upskilling. This study contributes a context-aware framework for responsible AI-enabled BPM. It offers actionable insights for policymakers and business leaders in emerging economies, advocating for sectoral prioritization, strategic timing, and capacity-building to ensure sustainable digital transformation.
A New Scale for Evaluating Disclosure in Earnings Calls on Emerging Markets dos Reis Maia, Rodrigo; Bravo, Jorge Miguel
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-012

Abstract

Corporate disclosure via earnings calls is a vital mechanism for financial transparency and stakeholder communication, enabling investors to evaluate firms’ performance, governance practices, and strategic direction. Yet existing approaches to assessing disclosure quality are often outdated, fragmented, or narrowly focused on sustainability, thereby neglecting the governance dimension of transparency. This study develops and validates a comprehensive, reliable, and optimized scale to measure the quality of corporate disclosure in earnings calls, integrating both sustainability and governance perspectives. Using a systematic scale-development process grounded in a robust conceptual framework, we collected data from 74 investors and analysts across multiple stages, focusing on Brazilian listed companies. Exploratory factor analysis yielded a refined three-dimensional structure comprising Analyst Disclosure, ESG (environmental, social and governance), and Artificial Intelligence. Findings indicate that investors increasingly regard artificial intelligence as central to evaluating disclosure credibility and informing investment decisions. This research advances disclosure measurement by offering a novel, empirically validated instrument that captures evolving communication dynamics. The proposed scale provides theoretical and practical contributions by strengthening the links between governance, sustainability, and financial transparency, and it establishes a foundation for future cross-market validation in emerging economies.
Metaheuristic Hyperparameter Optimization and Explainable Deep Learning for Baggage Threat Detection Maseleno, Andino; Huda, Miftachul; Fudholi, Ahmad; Ratanamahatana, Chotirat Ann
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-06

Abstract

The American Statistical Association reports that Bangkok, the capital and largest city of Thailand, holds the top spot as the most visited city worldwide in 2023. X-ray imaging for security screening plays a crucial role in upholding transportation security by detecting a diverse range of threats or prohibited items carried by passengers. This study introduces an advanced deep learning model leveraging YOLOv8, renowned for its enhanced efficiency in automating baggage detection processes. To enhance the model's hyperparameters and adjust them finely during the training process using the baggage dataset, the system utilized a metaheuristic optimization algorithm known as Evolutionary Genetic Algorithm, which is based on evolutionary principles. Incorporating explainable artificial intelligence techniques such as Local Interpretable Model-Agnostic Explanations (LIME) and Gradient-weighted Class Activation Mapping (Grad-CAM) allows for visual interpretation of predictions, aiding operators in utilizing the model effectively. We trained and tested the baggage dataset, which included 8,312 images and five classes: gun, knife, pliers, scissors, and wrench. The YOLOv8 model achieved the following metrics for the detection of prohibited objects in baggage inspection: an overall precision of 90.5%, recall of 83.3%, mAP50 of 91.3%, and mAP50-95 of 67%. The proposed method can fully automate the recognition of prohibited objects during baggage inspection. This approach is beneficial for designing an integrated, automatic, and non-destructive X-ray image-based classification system.
Impact of Oil Price Shocks on GCC Stock Markets: Tail-Driven MTNARDL Evidence Al-Mohamad, Somar; Jreisat, Ammar; Chehade, Imad; El-Kanj, Nasser; Hoti, Altin
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-020

Abstract

This paper examines the asymmetric impact of recent oil price fluctuations on stock markets in the Gulf Cooperation Council (GCC) region from 2019 to 2024, a period marked by the COVID-19 pandemic and the prolonged Ukrainian crisis. Using the Nonlinear Autoregressive Distributed Lag (NARDL) and an enhanced Multiple Threshold Nonlinear Autoregressive Distributed Lag (MTNARDL) framework, the study examines whether extreme positive and negative shocks in oil prices, S&P 500, Bitcoin, and gold induce heterogeneous transmission effects on GCC equity indices. The empirical findings show that both extreme positive and negative oil price shocks exert a stronger and more persistent influence on GCC stock markets than fluctuations in global equities, cryptocurrencies, or precious metals. This confirms the dominant role of oil as a key driver of financial dynamics in oil-dependent economies, particularly during periods of heightened uncertainty. The main contribution of this study lies in the improvement of the MTNARDL specification, which allows for a clearer identification of tail-risk behavior and asymmetric volatility spillovers. The enhanced model captures multi-threshold nonlinearities more effectively than conventional approaches, offering a robust framework for policymakers and investors to better understand shock transmission mechanisms in hydrocarbon-based markets.

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