bluuebunny/crossref_metadata_2025_split huggingface dataset:
Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis
Authors: Almeida Fernando Pedro Silva , Castelli Mauro , Côrte-Real Nadine 
Published: December 2024

Accurate cooling consumption forecasts are crucial for optimizing energy management, storage, and overall efficiency in interconnected HVAC systems. Weather conditions, building characteristics, and operational parameters significantly impact prediction accuracy. Since meteorological conditions highly influence cooling demand, leveraging external air data and user metrics offers a promising approach to estimate a building's hourly cooling energy usage. This study addresses the gap in existing research by comprehensively analyzing the performance of various machine learning algorithms, including ensemble learning and deep learning models, to improve prediction accuracy. By leveraging weather conditions, building characteristics, and operational parameters, we aim to predict cooling consumption across multiple systems (Cooling Ceiling, Ventilation, Free Cooling, and Total Cooling). Data from four weather stations, encompassing diverse features relevant to the European Central Bank (ECB) building's cooling consumption in Frankfurt, were employed. Our methodology includes the use of K-Nearest Neighbor, Decision Tree, Support Vector Regression, Linear Regression, Random Forest, Gradient Boosting, XGBoost, Adaboost, Long-Short-Term Memory, and Gated Recurrent Unit. Models. The results consistently demonstrate the superiority of the Random Forest model across different weather stations and feature sets. This model achieved a Mean Squared Error of approximately 0.002-0.003, Mean Absolute Error of around 0.031-0.034, and Root Mean Squared Error of about 0.052-0.069. These findings contribute to improved building cooling load management, promoting insights into optimal energy utilization and sustainable building practices. Doi: 10.28991/ESJ-2024-08-06-01 Full Text: PDF
BSHPC: Improve Big Data Privacy Based on Blockchain and High-Performance Computing (HPC)
Authors: Alsumayt Albandari 
Published: December 2024

The vast expansion and sharp rise in data across many facets of society have made it increasingly difficult to manage big data effectively. Using traditional methods to ensure the security and privacy of users’ data is no longer sufficient. In keeping with this worry, massive data storage is still crucial. High-Performance Computing (HPC) is examined to determine the need for handling blockchain issues and protecting large data in a decentralized manner that strives for resilience. This study proposes the Big Data Storage High-Performance Computing (BSHPC) approach, which addresses big data considerations in storage management to maintain accuracy and enables the usage of blockchain. The best storage management is the primary benefit of BSHPC, as only critical data is kept on the blockchain, and other data may be kept in an off-chain database using the interplanetary file system (IPFS). Furthermore, the network's node authentication in this strategy depends on trustworthy nodes. On HPC computers, data authenticity and provenance tracking would be guaranteed, and managing large data across blockchains would be more secure. The proposed method is simulated using the Python-MPI version, and the results confirm the effectiveness of the proposed method based on performance and transactions. Moreover, the proposed method is evaluated with another study in the literature on MEC-based sharing, and it proves its effectiveness. Doi: 10.28991/ESJ-2024-08-06-011 Full Text: PDF
Enterprise Innovation Decision-Making Towards Green and Sustainability from the Perspective of Cognitive Innovation
Authors: Tran Thi Anh Phuong , Hou Xinshuo 
Published: December 2024

Although numerous current studies on green consumption and sustainable enterprise development have been carried out, the majority of them examined the issues from customers' viewpoints. This study aims to explore the mechanism that shapes firm innovation decision-making in the context of green and sustainable development from the perspective of business awareness under the impact of customer expectations. The study conducted an online survey (via Google Forms) with the participation of 301 employees from different enterprises in the Mekong Delta, Vietnam. To restrict the common method biases, Cronbach’s alpha was checked by using SPSS to ensure the reliability of the initial scales. Based on a deductive approach and testing hypotheses through evaluating the measurement model and structural model using SmartPLS software, the research results determined the mechanism of forming firm innovation decisions in this study via the impact of customer expectations as a stimulating factor leading to awareness of innovation. Customer expectations were positively associated with perceived marketing innovation. Perceived marketing innovation was not only positively associated with perceived process innovation but also related to firm innovation. Similarly, perceived process innovation was significantly positively associated with firm innovation. In alignment with research findings, significant practical and academic contributions were also proposed. Doi: 10.28991/ESJ-2024-08-06-013 Full Text: PDF
Breast Cancer Prediction Using Transfer Learning-Based Classification Model
Authors: Armoogum Sheeba , Motean Kezhilen , Dewi Deshinta Arrova , Kurniawan Tri Basuki , Kijsomporn Jureerat 
Published: December 2024

Breast cancer is currently the most prevalent type of cancer in women, with a growing number of fatalities worldwide. Different imaging methods like mammography, computed tomography, Magnetic Resonance Imaging, ultrasound, and biopsies assist in detecting breast cancer. Recent developments in deep learning have revolutionized breast cancer pathology by facilitating accurate image categorization. This study introduces a novel approach to enhance detection and classification using the Convolutional Neural Network Deep Learning method and Transfer Learning to create a high-speed, accurate image classification model. The model is trained on pre-processed data subjected to thorough analysis and augmentation to ensure the quality of inputs. The experimental results from the Breast Ultrasound Image dataset indicate that our model, with a 0.1 test size ratio, outperforms its counterparts. It achieved an accuracy of 90.12%, with a loss of 0.2641, validation accuracy of 90.15%, and validation loss of 0.31, evidencing its superior classification capability. This research introduces an innovative approach to the automated diagnosis of breast cancer. By combining CNN, Transfer Learning, and data augmentation, we have developed a desktop application that expedites the classification process and significantly improves accuracy. This advancement represents a key development in machine learning applications for breast cancer prognostics and diagnostics. Doi: 10.28991/ESJ-2024-08-06-014 Full Text: PDF
SHAP-Instance Weighted and Anchor Explainable AI: Enhancing XGBoost for Financial Fraud Detection
Authors: Thanathamathee Putthiporn , Sawangarreerak Siriporn , Chantamunee Siripinyo , Mohd Nizam Dinna Nina 
Published: December 2024

This research aims to enhance financial fraud detection by integrating SHAP-Instance Weighting and Anchor Explainable AI with XGBoost, addressing challenges of class imbalance and model interpretability. The study extends SHAP values beyond feature importance to instance weighting, assigning higher weights to more influential instances. This focuses model learning on critical samples. It combines this with Anchor Explainable AI to generate interpretable if-then rules explaining model decisions. The approach is applied to a dataset of financial statements from the listed companies on the Stock Exchange of Thailand. The method significantly improves fraud detection performance, achieving perfect recall for fraudulent instances and substantial gains in accuracy while maintaining high precision. It effectively differentiates between non-fraudulent, fraudulent, and grey area cases. The generated rules provide transparent insights into model decisions, offering nuanced guidance for risk management and compliance. This research introduces instance weighting based on SHAP values as a novel concept in financial fraud detection. By simultaneously addressing class imbalance and interpretability, the integrated approach outperforms traditional methods and sets a new standard in the field. It provides a robust, explainable solution that reduces false positives and increases trust in fraud detection models. Doi: 10.28991/ESJ-2024-08-06-016 Full Text: PDF
Enhancing Efficiency: The Impact of Cloud Computing Adoption on Small and Medium Enterprises Performance
Authors: Abdalla Reem A. , Ramayah T. , Sankar Jayendira P. , Hidaytalla Lamya A. , John Jeena Ann 
Published: December 2024

This study investigated the factors influencing cloud computing adoption (CCA) and its impact on organizational performance (OP) among SMEs employees in Bahrain. The study used an online survey approach, which includes Likert scale questions to assess attitudes and views, multiple-choice questions for categorical data, and open-ended questions to obtain qualitative insights. The target audience comprises 300-350 small and medium-sized enterprises (SMEs) in Bahrain currently utilizing cloud computing technology, and 314 useful responses were received. A mixed two-step sampling technique was initiated by convenience sampling. Then, snowball sampling was used to guarantee the inclusion of various SME categories, thus ensuring representativeness. The measurements are derived from validated instruments used in academic research, with the questionnaire incorporating elements adapted from the studies conducted. Participants' responses to the Likert scale are analyzed using SmartPLS 4 to understand their perspectives. Full collinearity was used to assess common method bias, and VIF values below 3.3 indicated no bias. The measuring model's validity and reliability were evaluated by loadings, AVE, CR, and discriminant validity tests (HTMT), which ensured all constructs fulfilled thresholds. Path coefficients, standard errors, t-values, and p-values were used to evaluate the structural model using 10,000-sample bootstrapping. The research findings indicate that both Perceived Ease of Use (PEU) and Perceived Usefulness (PU) have a substantial impact on Cloud Computing Adoption (CCA), which in turn improves the performance of Bahraini SMEs. PEU and PU directly impact CCA while indirectly improving Organizational Performance (OP) by increasing cloud computing usage. These findings emphasize the importance of user-friendly and beneficial cloud solutions in increasing cloud computing adoption and enhancing business outcomes for SMEs. Doi: 10.28991/ESJ-2024-08-06-017 Full Text: PDF
Improving the Reliability of Biometric Authentication Processes Using a Model for Reducing Data Drift
Authors: Kuklin Vladimir Zh. , Ivanov Naur Z. , Muranov Alexander N. , Alexandrov Islam A. , Linskaya Elena Yu. 
Published: December 2024

Modern complexes providing biometric identification face several problems, such as information drift caused by the variability of facial patterns, voice timbres, and current states. Information drift can characteristically exhibit short-term (subjects' states have changed) or long-term changes. Simultaneously, the developed trusted systems should not have the properties of explainable AI to prevent the possibility of intruders, based on understanding the system behavior to perform actions to hack the system. This paper's objective is to improve the reliability of biometric authentication by increasing the informativity of the classified images by transforming the correlations between the information features using the Bayes-Minkowski measure. The paper puts forth the proposition of employing neuroimmune models that are founded upon the principles of both acquired and innate immunity, with an analogy to the natural immune system. In addition, the authors propose to analyze correlations between information features instead of the features themselves. To reduce the influence of data drift, the authors suggest using adaptive learning with a teacher and reinforcement, which helps to work even with small and unrepresentative data samples. The proposed algorithm demonstrates a high degree of accuracy, as evidenced by its equal error rate (EER), and is particularly well-suited to feature recognition tasks due to its adaptive model. The test results have shown that the proposed solutions increase the level of security of personal data and improve the reliability of biometric authentication against fraudulent actions of intruders, including approaches based on adversarial algorithms. The integration of the immune structure into the authentication system enables the algorithm to remain stable even when presented with a limited number of samples. The proposed algorithm mitigates the impact of data drift on the authentication outcome. Doi: 10.28991/ESJ-2024-08-06-018 Full Text: PDF
Fiber Optic Breakthrough: Terahertz Detection of Illegal Drugs
Authors: Noor Khalid Sifulla , Bani Most. Momtahina , Islam Md. Safiul , Ferdous A.H.M. Iftekharul , Hossen Md. Jakir , Al-Mamun Abdullah , Badhon Nasir Uddin 
Published: December 2024

The article presents an illegal drug detector that utilizes photonic crystal fiber (PCF). The fiber structure of the H-PCF comprises a dodecagonal core and circular air gaps in cladding areas. We have analyzed the designed terahertz (THz) frequency range utilizing the Finite Element Method (FEM) and the COMSOL Multiphysics application. The revised design has a high sensitivity in detecting amphetamine (n = 1.518) and cocaine (n = 1.5022) at a frequency of 3 THz, via detection rates of 99.43% and 99.20%, correspondingly. Furthermore, the suggested fiber, which operates at a frequency of 3 THz, has a relatively tiny confinement loss of 4.93×10-08 dB/m and 6.16×10-09 dB/m and a minimal effective material loss of construction of 0.0032 cm-1. In conclusion, it may be stated that drug misuse not only leads to immediate repercussions but also has severe and enduring impacts on human health, potentially resulting in fatality. Hence, it is imperative to accurately and effectively detect illicit substances. H-PCF architecture we offered is well-suited to detect illegal drugs. Doi: 10.28991/ESJ-2024-08-06-020 Full Text: PDF
Bio-Mechatronics Development of Robotic Exoskeleton System With Mobile-Prismatic Joint Mechanism for Passive Hand Wearable-Rehabilitation
Authors: Vargas Mariela , Mayorga J. , Oscco B. , Cuyotupac V. , Nacarino A. , Allcca D. , Gamarra-Vásquez L. , Tejada-Marroquin G. , Reategui M. , Maldonado-Gómez R. R. , Vasquez Y. , De la Barra Daira , Tapia-Yanayaco P. , Charapaqui Sandra , V. Rivera Milton , Palomares R. , Ramirez-Chipana M. , Cornejo Jorge , Cornejo José , De La Cruz-Vargas Jhony A. 
Published: December 2024

The World Health Organization (WHO) estimates that 15 million people are affected by stroke each year, causing deterioration of the upper limb, which is reflected in 70-80% of them, decreasing the performance of daily activities and quality of life, mainly affecting hand functions. Thus, the purpose of this study is to present a high-quality alternative to recover muscle tone and mobility, consisting of a hand-exoskeleton for passive rehabilitation. It covers a motion protocol for each finger and pressure sensors to give a safety pressure range during the gripping function. The bio-design method covers standards (ISO 13485 and VDI 2206) based on biomechanic and anthropometric fundamentals, where Fusion 360 was used for mechanical development and electrical-electronic circuit schematics. The prototyping process was based on 3D printing using polylactic acid (PLA); also, the actuators were servomotors DS3218, the pressure sensors were RP-C7.6-LT, and the microcontroller was Arduino Nano. The system has been validated by the Institute of Research in Biomedical Sciences (INICIB) at the Ricardo Palma University, where the novelty of this work lies in the introduction of a new mobile-prismatic joint mechanism. In conclusion, favorable results were achieved regarding the complete flexion and extension of the fingers (91.6% acceptance rate, tested in 100 subjects), so the next step proposes that the wearable device will be used in the Physical Medicine and Rehabilitation Departments of Medical Centers. Doi: 10.28991/ESJ-2024-08-06-02 Full Text: PDF
Integrated Learning Models for Micro-Teaching Course
Authors: Blegur Jusuf , Ma'mun Amung , Berliana . , Mahendra Agus , Alif Muhammad Nur , Juliantine Tite , Lumba Andreas J. F. 
Published: December 2024

The progressive world of education needs to be accelerated by fulfilling the competencies of prospective teachers who are also progressive through a series of performance tasks that are relevant to learning needs in the 21st century. This research used Analysis, Design, Development, Implementation, and Evaluation (ADDIE) to innovate an integrated learning model for a micro-teaching course. A needs analysis was conducted on 75 students, two lecturers, and 30 teachers to assess actual performance, confirm desired performance, and identify causes of performance gaps. Researchers then designed performance tasks and validated them by 10 raters, tested them on 337 students to test the outer and inner models, and tested them on 30 students, 28 lecturers, and 49 teachers to test differences. Test content validity using the Aiken-V formula and test inter-rater reliability using ICC. Meanwhile, testing the validity and reliability of the construct uses outer and inner model analysis (CB-SEM), and the difference test uses ANOVA. The content validity results prove that all task performance meets the Aiken parameters (0.75-1.00), the interrater reliability value is 0.573, and the Cronbach alpha value is 0.931. Testing the outer model proves that the loading factor task performance value ranges from 0.709-0.874, the Cronbach alpha value ranges from 0.768-0.880, the composite reliability value ranges from 0.768-0.879, the AVE value ranges from 0.580-0.649, and the discriminant validity value ranges from 0.761-0.806. The inner model test proves that the Chi-Square/df value = 2.254, RMSEA value = 0.061, SRMR value = 0.036, NFI value = 0.910, TLI value = 0.936, and CFI value = 0.948. Meanwhile, the results of the ANOVA test confirm that the Sig value = 0.098, so it can be concluded that there are no significant differences between the three sample groups regarding the model innovation results. Thus, the 25-task performance in the integrated learning model has a significant psychometric function relative to the actual situation, so it becomes one of the references that lecturers can use to improve the competency of prospective teachers in micro-teaching courses (not limited to teaching skills, analytical thinking skills, academic integrity, and transformational leadership). Doi: 10.28991/ESJ-2024-08-06-020 Full Text: PDF
Green Technology Innovation and Corporate Reputation: Key Drivers of ESG and Firm Performance
Authors: Chen Xiyi , Lakkanawanit Pankaewta , Suttipun Muttanachai , Swatdikun Trairong , Huang Shi-Zheng 
Published: December 2024

The purpose of this study is to investigate the impact of Environmental, Social, and Governance (ESG) performance and firm performance within China's manufacturing sector, with a novel focus on the mediating effect of green technology innovation and the moderating influence of corporate reputation. Using a 2011-2022 dataset from A-share listed manufacturing companies on the Shanghai and Shenzhen Stock Exchanges, the study employs multiple regression analysis with a two-way fixed-effects model to examine these relationships. Findings indicate that robust ESG practices significantly enhance company performance, mediated by green technological innovation. However, a negative moderating effect of corporate reputation suggests that higher corporate reputation weakens the ESG-financial performance relationship. Further analyses reveal that privately-owned firms, those in China's eastern region, and environmentally sustainable industries benefit most from strong ESG initiatives. This study addresses the challenge of disentangling key variables by analyzing their interconnected effects. The findings fill a gap in the existing literature by contributing to a deeper understanding of the relationship between ESG and corporate success, particularly through the mediating role of innovation and the moderating influence of reputation. Additionally, the study provides practical recommendations for managers and policymakers to enhance ESG strategies, promote growth, and support sustainable development. Doi: 10.28991/ESJ-2024-08-06-021 Full Text: PDF
Relationship between Emotional Intelligence, Social Skills, and Anxiety: A Quantitative Systematic Review
Authors: Ramos-Galarza Carlos , Rodríguez-Naranjo Brayan , Brito-Mora Deyaneira 
Published: December 2024

Introduction: Emotional intelligence allows us to manage, regulate, and recognize our emotions and those of others, also allowing us to face and solve problems by choosing to provide an appropriate response to the situation experienced by a subject. Social skills are the behaviors that an individual emits in the interpersonal context through their feelings, rights, and opinions, seeking to resolve conflict situations immediately, minimizing the likelihood of experiencing them in the future. Anxiety appears in the individual when he perceives certain situations as threatening or dangerous, hindering his ability to provide an adequate response, being excessive, uncontrollable, or lasting, and this is classified as a mental disorder. Objective: The objective of this study is to describe the relationship between emotional intelligence, social skills, and anxiety. Methods: A quantitative methodology has been employed, basing the study on a systematic review of previous research using the Scopus, Scielo, Redalyc, and Google Scholar repositories. Findings: An initial sample of 1722 articles was obtained, which passed through inclusion and exclusion criteria, resulting in 73 articles. Novelty:The contribution of this study lies in understanding that low anxiety levels lead to better performance of emotional intelligence and social skills. This situation allows people to resolve conflicts that arise in the daily lives of individuals. Doi: 10.28991/ESJ-2024-08-06-025 Full Text: PDF
Federated Risk-Based Access Control Model for P2P Lending Platforms: A Multi-Agent Systems (MAS) Approach
Authors: Muthaiyah Saravanan , Nguyen Lan Thi Phuong , Choong Yap Voon , Zaw Thein Oak Kyaw 
Published: December 2024

This study addresses the inherent risk management challenges in decentralized finance, particularly for peer-to-peer (P2P) lending platforms. We propose a novel framework that leverages a Multi-Agent System (MAS) to establish a collaborative network encompassing loan originators, investors, regulators, and service providers. This distributed approach facilitates federated risk management, where risk assessment and mitigation responsibilities are shared across these entities. The MAS employs a comprehensive nine-factor assessment (detailed in Table 5) to evaluate industry risk profiles, considering industry environment, competition, and internal capabilities. This data is further visualized using a color matrix (Tables 5 & 6) and utilized alongside state diagrams (Figure 2) to depict the workflow and manage tasks within the P2P lending process. Additionally, the MAS informs a novel Federated Risk-Based Access Control (FRkBAC) system that tailors access permissions (lending origination, disbursement, etc.) based on dynamic risk assessments of industry trends and individual borrower profiles. This data-driven approach fosters trust within the P2P ecosystem and represents a significant advancement in decentralized finance risk management compared to traditional methods. Doi: 10.28991/ESJ-2024-08-06-05 Full Text: PDF