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arXiv 2024年

作者： Muluneh, Mequanent Argaw Peng, Yan-Tsung Degife, Worku Abebe Tadesse, Nigussie Abate Demeku, Aknachew Mebreku Su, Li Institute of Information Science Academia Sinica Taipei Taiwan Department of Information System University of Gondar Gondar Ethiopia Department of Computer Science National Chengchi University Taipei Taiwan Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Taiwan Department of Information Management National Taiwan University of Science and Technology Taipei Taiwan Department of Material Science and Engineering National Taiwan University of Science and Technology Taipei Taiwan

Computational music research plays a critical role in advancing music production, distribution, and understanding across various musical styles in the world. Despite the immense cultural and religious significance, the Ethiopian Orthodox Tewahedo Church (EOTC) chants are relatively underrepresented in computational music research. This paper contributes to this field by introducing a new dataset specifically tailored for the analysis of EOTC chants, also known as Yaredawi Zema. This work provides a comprehensive overview of a 10-hour dataset, 369 instances, creation, and curation process, including rigorous quality assurance measures. Our dataset has a detailed word-level temporal boundary and reading tone annotation along with the corresponding chanting mode label of audios. Moreover, we have also identified the chanting options associated with multiple chanting notations in the manuscript by annotating them accordingly. Our goal in making this dataset available to the public1 is to encourage more research and study of EOTC chants, including lyrics transcription, lyric-to-audio alignment, and music generation tasks. Such research work will advance knowledge and efforts to preserve this distinctive liturgical music, a priceless cultural artifact for the Ethiopian people. © 2024, CC BY.

关键词： Music

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AI-Based Feature Extraction and Cosine Similarity for Automation of Student Learning Assessment 24

AI-Based Feature Extraction and Cosine Similarity for Automa...

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24th International Arab Conference on information technology, ACIT 2023

作者： Wijanarko, Bambang Dwi Bachtiar Hassan, Rosilah Binti Murad, Dina Fitria Ihsan, Ridho Bramulya Heryadi, Yaya Kuncoro, Engkos Achmad Julianti, Eka Binus Online Learning Bina Nusantara University Computer Science Department Jakarta Indonesia Graduate School Universitas Terbuka Master of English Education Jakarta Indonesia Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Selangor Bangi43600 Malaysia Binus Online Learning Bina Nusantara University Information Systems Department Jakarta Indonesia Binus Online Learning Bina Nusantara University Manajemen Department Jakarta Indonesia Binus Graduate Program-Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Binus Business School Undergraduate Program Bina Nusantara University Management Department Jakarta Indonesia Universitas Terbuka Information System Department Jakarta Indonesia

ISBN: (纸本)9798350384307

This research is ongoing research into the student learning process which aims to develop artificial intelligence-based technology to calculate essay exam scores automatically, based on the textual proximity of students' answers to the answer key. This research uses an approach based on natural language processing (NLP) and text analysis. Feature extraction was carried out using the BERT model and similarity comparison using the Cosine distance method. Model evaluation was carried out using sensitivity and specificity tests. The model evaluation results show a sensitivity of 98.21%, which means this assessment model is able to detect positive cases well. © 2023 IEEE.

关键词： artificial intelligence BERT essay exam score natural language processing transformer

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Factors that Influence E-Satisfaction and Their Impact on Online Purchase Intention of Telemedicine Applications

Factors that Influence E-Satisfaction and Their Impact on On...

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Anderes Gui Idris Gautama So Dea Novantia Elfindah Princes Nesti Fronika Sianipar Hasnah Haron Information System Program School of Information Systems Bina Nusantara University Jakarta Indonesia Management Department Binus Business School Bina Nusantara University Jakarta Indonesia Information Systems Management Department Binus Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Food Technology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia Faculty of Economics and Muamalat Universiti Sains Islam Malaysia Nilai Malaysia

This study embarked on a rigorous examination of the factors driving user satisfaction and usage behavior in the context of telehealth applications. Utilizing a well- structured Google Form survey, distributed through an array of social media platforms, including WhatsApp, Discord, Instagram, and Line, the research gathered 251 responses. The purposive sampling technique was employed to ensure that the data captured the experiences of individuals who had actively engaged with telehealth services. One hypothesis, relating to the influence of Online Reviews, was found to be unsupported. This surprising result suggests that, contrary to expectations, the opinions and reviews shared online do not significantly affect users’ satisfaction or their behavioral decisions in the context of telehealth applications. This finding highlights the need for a more nuanced understanding of the factors influencing user preferences and choices in this rapidly evolving sector. Conversely, other hypotheses examining factors such as Social Influence, Facilitating Conditions, Perceived Reliability, Price Value, and Purchase Intention were substantiated. The positive influence of these factors on customer usage behavior underscores their significance in shaping user experiences and satisfaction within the telehealth ecosystem. Furthermore, the study posits that facilitating conditions should be closely aligned with technological advancements, as this alignment is conducive to the development of enhanced application features. This insight suggests that telehealth providers should continuously innovate and integrate advanced technologies to ensure that the facilitating conditions meet the ever-evolving needs and expectations of users.

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The Smart Analysis of Performing Scalable Inference for Big Data Analytics

The Smart Analysis of Performing Scalable Inference for Big ...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Vishakha D Bhandarkar Aruna Sri Rongali Mohamed Dawood Shamout Prof. Sachin Pund Gigih Forda Nama Shrishailappa Patil Applied Mathematics and Humanities Yeshwantrao Chavan College of Engineering India Graduate Doctoral Information Technology/Graduate (Information Technology Emphasis/Information Technology Ph.D.) University of The Cumberlands US Department of Management College of Business Administration University of Sharjah Sharjah United Arab Emirates Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India Department of Informatics Doctoral Program of Environtmental Science University of Lampung Bandar Lampung Computer engineering Vishwakarma Institute of Technology Pune

This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the “performing Scalable Inference” technique to cope with scalability troubles and to exploit current big statistics platforms for efficient computing and statistics garage of the statistics. In particular, the paper describes how to perform leak-free, parallelizable visible analytics over massive datasets using present extensive records analytics frameworks such as Apache Flink. This method presents an automated manner to execute analytics that preserves reproducibility and the ability to make adjustments without re-running the entire technique. The paper also demonstrates how these analytics may help several real-world use instances, explore affected person cohorts for studies, and develop stratified patient cohorts for hospital therapy. In the end, the paper observes how the proposed method may be exercised within the real world. Actively scalable inference for massive information analytics is pivotal in optimizing decision-making and allocation of assets. Typically, such inferences are made based on information accumulated from numerous sources, databases, unstructured data, and different digital sources. So one can ensure scalability, a complete cloud-primarily based platform has to be hired. This solution will permit the ***, deploying the essential records series and evaluation algorithms are prime here. It could permit the platform to recognize the styles inside the statistics and discover any ability correlations or traits. Additionally, predictive analytics and system mastering strategies may be incorporated to provide insights into the results of the information. In the long run, by leveraging those techniques, the platform can draw efficient inferences and appropriately compare situations in an agile and green way..

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Preliminary Plan for Data Collection on Stakeholder information Interdependence in Area-Business Continuity management: A Case Study of Flood management in Thai Industrial Zones

Preliminary Plan for Data Collection on Stakeholder Informat...

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International Symposium on Communications and information Technologies (ISCIT)

作者： Penpitcha Arayachookiat Kumpol Saengtabtim Akira Kodaka Natt Leelawat Jing Tang Kenji Watanabe Risk and Disaster Management Program Graduate School Chulalongkorn University Bangkok Thailand Department of Industrial Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Graduate School of System Design and Management Keio University Kanagawa Japan Disaster and Risk Management Information Systems Research Unit Chulalongkorn University Bangkok Thailand Faculty of Engineering International School of Engineering Chulalongkorn University Bangkok Thailand Graduate School of Social Engineering Nagoya Institute of Technology Nagoya Japan

ISBN: (数字)9798350353952

ISBN: (纸本)9798350353969

The 2011 flood in Thailand exposed significant vulnerabilities in industrial areas, highlighting the necessity for enhanced disaster risk management through Area-Business Continuity management (Area-BCM). This preliminary study focuses on identifying how stakeholders rely on information from each other to improve disaster preparedness and response. The research involves systematically identifying Area-BCM stakeholders and designing interview questions, which are evaluated by experts using the Index of Item-Objective Congruence (IOC) to ensure relevance and clarity. All interview questions surpassed the IOC threshold of 0.5, confirming their effectiveness in capturing information interdependencies. Expert feedback led to refinements in the questions, underscoring the importance of tailored data collection. This initial plan provides a critical foundation for understanding information interdependence and highlights the importance of well-designed data collection methods. The findings have significant implications for developing more resilient disaster management strategies in industrial areas, emphasizing the need for precise and relevant stakeholder communication to enhance Area-BCM effectiveness.

关键词： Disasters Disaster management Data collection information and communication technology Stakeholders Floods Risk management Iterative methods Interviews Resilience

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Energy consumption of high-rise double skin façade buildings, a machine learning analysis

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Journal of Building Engineering 2024年 89卷

作者： Rezaie, Maedeh Kariminia, Shahab Band, Shahab S. Ameri, Rasoul Farokhi, Maryam Pai, Hao-Ting Gocer, Ozgur Rismanchi, Behzad Shooshtarian, Salman Department of Architecture Advancement in Architecture and Urban Planning Research Center Najafabad Branch Islamic Azad University Najafabad Iran Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Taiwan Department of Information Management National Yunlin University of Science and Technology Taiwan Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan School of Architecture Design and Planning The University of Sydney SydneyNSW Australia Renewable Energy and Energy Efficiency Group Department of Infrastructure Engineering Faculty of Engineering and Information Technology The University of Melbourne Australia School of Property Construction and Project Management RMIT University Melbourne Australia

Most energy exchanges take place through the building skin. The skin characteristics play a decisive role in the extent of these exchanges, but they are somewhat more varied in the double skin façade (DSF). Among these characteristics, cavity segmentation has a noticeable effect on the implementation of the DSF in different directions during the hot and cold seasons. The aim of this study was to investigate the role of DSF segmentation in energy consumption and natural ventilation of high-rise buildings in hot and dry climates. This study used DesignBuilder software to study sixty-four segmentation component scenarios in an eight-story residential building in Isfahan, Iran. Moreover, a proposed hybrid model utilizing the hybridization of the Hunger Game Search and Gradient Boosting (HGS-GB) algorithm was employed to estimate energy consumption in various scenarios involving lighting, heating, cooling, and total scenarios. The available outcomes revealed that the HGS-GB model had a better performance in comparison with other individual models, such as Gradient Boosting (GB), Random Forest (RF), and K-nearest neighbors (KNN). The R2 values for lighting, heating, cooling, and total energy estimation were 0.9993, 0.9958, 0.9991, and 0.9922, respectively. The findings of this study suggest the significance of DSF segmentation in energy consumption and natural ventilation in high-rise buildings in hot and dry climates. © 2024 Elsevier Ltd

关键词： Forestry

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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis

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Procedia Computer Science 2025年 253卷 84-93页

作者： Anak Agung Ayu Putri Ardyanti Zhiqiang Xie Andrea Stevens Karnyoto Bens Pardamean Ni Made Estiyanti School of Computer Science and Technology Harbin University of Science and Technology Harbin 150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Doctoral School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, Computer Science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions.

关键词： integrated scheduling assembly job shop scheduling heuristic algorithm genetic algorithm bibliometric analysis

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Assessing the Performance of Machine Learning Algorithms for Water Level Prediction in the Chao Phraya River and its Tributaries: A Focus on Low and High Water Levels

SSRN

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SSRN 2024年

作者： Priyasiri, Wilmat D.S.M. Rittima, Areeya Kraisangka, Jidapa Sawangphol, Wudhichart Phankamolsil, Yutthana Talaluxmana, Yutthana Graduate Program in Environmental and Water Resources Engineering Department of Civil and Environmental Engineering Faculty of Engineering Mahidol University Thailand Faculty of Information and Communication Technology Mahidol University Thailand Environmental Engineering and Disaster Management Program Mahidol University Kanchanaburi Campus Thailand Department of Water Resources Engineering Faculty of Engineering Kasetsart University Thailand

High precision of hydrological prediction is crucial for real–time operation of flood and drought risk mitigation and strategic planning. This study assessed the predictive performances of three Machine Learning (ML) algorithms;XGBoost, Random Forest (RF), and Multi–Layer Neural Networks (MLNNs) for water level prediction with a focus on low and high water levels. The one–day and one–week water level prediction models for six key gauged stations along the Chao Phraya River and its major tributaries were accordingly developed. Selecting the combination of input features was carried out and structured in the prediction model at all gauged stations based on the physical river–reservoir system using past water level, rainfall, controlled reservoir outflow, and upstream discharges with different travel times. The statistical evaluation indicated that both XGBoost and RF, the tree–based ML models, with rainfall input robustly outperformed than MLNNs, as it strongly achieved higher R2 from 0.957 to 0.999 for model training and from 0.743 to 0.995 for model testing and lower MAE, MSE, and RMSE values for all prediction scenarios. Among these three ML algorithms applied for daily prediction, RF demonstrated the superior performance for low water level prediction exhibiting the smallest percentage error of overestimating lying between +0.0088% and +0.9380%. XGBoost, RF, and MLNNs, demonstrated acceptable performance level for high water level prediction as it shows small percentage error of both overestimating and underestimating lying between –2.2696% and +1.1587%. Additionally, it can capture the entire testing dataset with high precision than weekly model. © 2024, The Authors. All rights reserved.

关键词： Rain

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Preprocessing Techniques for Enhancing Vision-Based Detection System for Tire Sidewall Extraction

Preprocessing Techniques for Enhancing Vision-Based Detectio...

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International Seminar on Research of information technology and Intelligent Systems (ISRITI)

作者： Jia Hao Loo Veronica Lestari Jauw Chin Seong Lim Khairul-Rijal Sarmin Nunung Nurul Qomariyah Ahmad Nurul Fajar Department of Mechanical Materials and Manufacturing Engineering University of Nottingham Malaysia Malaysia Continental Tyre Technology Centre Malaysia Petaling Jaya Malaysia Computer Science Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia Information System Management Department BINUS Graduate Program-Master Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

This study aims to develop a system for extracting crucial information from tire sidewalls using Optical Character Recognition (OCR). Initially, images of tire were captured manually by smartphone cameras, including Redmi 9T, iPhone 11, and Galaxy S23 Ultra. The captured images are then transferred to a computer for storage. Subsequently, these images were cropped according to the boundaries identified by Hough Circle Transform (HCT). The cropped images were then further pre-processed. During the pre-processing phase, geometrical transformation and image sharpening techniques are applied to enhance the clarity and readability of the text images. The text is then extracted using Google Vision, with the extracted text categorized by size, DOT, brand and pattern. The results indicated that the effectiveness of image pre-processing was constrained by the accuracy of circle detection, which reached a maximum rate of 87.1%. This causes parts of the text to be cut out inaccurately, leading to a suboptimal extraction accuracy of 55.65%. It is also observed that the Redmi 9T camera produced inconsistent results compared to other devices. Specifically, the iPhone 11 and Samsung Galaxy S23 Ultra demonstrated superior extraction accuracies of 69.71% and 66.37%, respectively, whereas the Redmi 9T achieved a lower extraction accuracy of 37.76%.

关键词： Accuracy Optical character recognition Text categorization Lighting Transforms US Department of Transportation Cameras Tires Internet Data mining

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Comparative Study of Roadmapping and Sci-fi Prototyping Methods to Develop a knowledge management Framework

Comparative Study of Roadmapping and Sci-fi Prototyping Meth...

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Portland International Conference on management of Engineering and technology (PICMET)

作者： Miwa Nishinaka Kunio Shirahada Yusuke Kishita Hisashi Masuda Hideaki Takeda Dohjin Miyamoto Hirotaka Osawa The Graduate School of Management Kagawa University Takamatsu Kagawa Japan School of Knowledge Science Japan Advanced Institute of Science and Technology (JAIST) Nomi Ishikawa Japan Department of Precision Engineering The University of Tokyo Tokyo Japan aculty of Global Engagement Kyoto University of Foreign Studies Kyoto Japan National Institute of Informatics Tokyo Japan Graduate School of Information Science and Technology The University of Tokyo Tokyo Japan Faculty of Science and Technology Keio University Yokohama Kanagawa Japan

Roadmapping is a structured, logic-based method to support strategic planning for organizations and governments. However, it is not necessarily effective to produce destructive changes in the era of uncertainty. Specifically, roadmapping is useful for visualizing ideas for better communication among stakeholders but has limitations when creating unconventional knowledge through abduction. Additionally, it lacks the ability to create knowledge considering humanity and well-being to respond to current and future uncertain social situations. Therefore, this study aims to explore the key factors for incorporating the science-fiction (sci-fi) prototyping method into roadmapping, based on the literature review. Roadmapping is a logic-consistent, face-to-face workshop-based method, whereas sci-fi prototyping is a workshop-based method that utilizes fiction in a distributed virtual system. Both methods are used to conceptualize future innovative technologies and policy recommendations. Though they have similar objectives, the resulting future concepts are exactly the opposite. To integrate the two methods effectively, we break them down into modules and consider reconstructing them. After conducting comparative studies based on previous research, we propose a cooperative base of the two methods.

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