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International Conference of Computer science and information technology

作者： Adli A Nababan Sutarman Muhammad Zarlis Erna B Nababan Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan Indonesia Department of Information Systems Management BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350399080

Air pollution makes it worse in populated regions. Indonesia's major cities are also impacted by air pollution. Due to rising traffic, material consumption by vehicles, industrial growth, land burning, and garbage collection, air quality has altered significantly. Accurate measurement and classification of air quality are required. Results of accurate classification aid in forming governmental regulations. To reach the criteria for livable air quality, we intend to manage controls. Some of the characteristics of air pollution include particulate (PM10) or PM25, sulfides (in the form of SO2), carbon monoxide (CO), ozone (O3), and nitrites (NO2). Using the XGBoost algorithm and synthetic minority oversampling (SMOTE) methods based on the Air Pollution Standard Index (ISPU) category, this study seeks to uncover factors that affect air quality. XGBoost, an ensemble machine learning approach built on a decision tree and applying a gradient reinforcement framework, is the classification algorithm in use. Air quality categorization has been tested and shown to work with the XGBoost algorithm. The dataset used in this study is air quality in 2021 which was collected by the Jakarta Environmental Ministry The proposed classification model will be evaluated using the Repeated k-fold cross-validation method for objective results. The results of the study prove that SMOTE and XGBoost have better performance than using only the XGBoost method in predicting air quality, where the overall accuracy value of SMOTE with XGBoost is 99.60%, The overall precision value is 99.60%, the overall recall value is 99.60% and the overall f1-score is 99.60% and the overall ROC AUC value is 99.96%. The overall SMOTE method with the proposed XGBoost has given better performance in air pollution prediction.

关键词： Machine learning algorithms Urban areas Air pollution Prediction algorithms Particle measurements Regulation Classification algorithms

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Using LSTM and Regression Analysis for Railway Passenger Traffic Volume Prediction

Using LSTM and Regression Analysis for Railway Passenger Tra...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Fu Jie Tey Chih-Chi Tuan Cheng-En Cai Ming-Xuan Wu Yun-Sheng Wu Tin-Yu Wu Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Master Program of E-Learning for Multimedia and Network Communications National Ilan University Yilan Taiwan Department of Management Information Systems National Pingtung University of Science and Technology Pingtung County Taiwan

ISBN: (纸本)9781665492331

The purpose of this paper is to investigate the factors affecting the passenger traffic volume on the high-capacity MRT, to analyze the traffic volume trends, and to make traffic volume predictions more accurately. This paper uses the LSTM (Long Short-term Memory) model and regression analysis for Taipei MRT passenger traffic volume prediction. The training data, including dates, temperatures and number of passengers, were collected, and a regression analysis was performed to find trends in the data.

关键词： Temperature distribution Solid modeling Government Transportation Training data Predictive models Market research

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Assessing the ecosystem service values of tea plantations using conventional and organic farming methods: Is organic farming always better?

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Ecological Indicators 2024年 158卷

作者： Liu, Wan-Yu Chiang, Che-Yu Yap, Jia-Ler Lin, Chun-Cheng Department of Forestry National Chung Hsing University Taichung402 Taiwan Innovation and Development Center of Sustainable Agriculture National Chung Hsing University Taichung402 Taiwan Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu300 Taiwan Department of International Master Program in Smart Manufacturing and Applied Information Science National Chin-Yi University of Technology Taichung411 Taiwan

Low-elevation mountainous (LEM) areas in some countries are reclaimed for agriculture (especially, for deep cultivation of tea trees) due to their accessibility, higher temperature, and more rainfall, contributing to biodiversity and ecological balance. However, extensive development in these areas causes lasting ecological damage;and hence it is crucial to evaluate the ecological value of these areas for formulating relevant ecological protection policies. Therefore, this study evaluated the ecosystem service value of tea plantations in LEM areas using the contingent valuation method based on the subjective consciousness of farmers, rather than an objective assessment. A questionnaire survey was conducted on tea farmers in Mingjian Township (which is one of the major tea-producing LEM areas in Mid-Taiwan);and ecosystem service values of tea plantations between conventional farming methods (CFMs) and organic farming methods (OFMs) were compared. Firstly, the differences in willingness to pay (WTP) for the samples including and excluding protest responses were investigated, and then a regression analysis to estimate the WTP price for maintaining the tea plantation ecology was conducted. Empirical results showed a significantly negative correlation between the WTP price and the education level of farmers in tea plantations using CFMs, but a significantly positive correlation in tea plantations using both CFMs and OFMs. Most respondents were male and used CFMs to operate tea plantations;and those with higher monthly income and using both CFMs and OFMs were willing to pay more. Regarding ecosystem services of tea plantations, the respondents preferred the "provisioning" ecosystem services, and were willing to pay an average of NT$ 718.92 and NT$ 794.32 per year for maintaining the ecology of tea plantations using CFMs and OFMs, respectively. The unit ecological value of tea plantations was not inferior to those of coniferous and broad-leaved forests, paddy fields, and a

关键词： Biodiversity

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A Genetic Algorithm to Optimize Cargo Stability in Container Transportation

A Genetic Algorithm to Optimize Cargo Stability in Container...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Chi-Jung Hsieh Kuang-Sung Wu Tin-Yu Wu Fu Jie Tey Yi-Kai Chen Jye Yi Ho Department of Computer Science and Information Engineering National Ilan University Ilan County Taiwan Master Program of E-Learning for Multimedia and Network Communications National Ilan University Yilan Taiwan Department of Management Information Systems National Pingtung University of Science and Technology Pingtung County Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (纸本)9781665492331

In the era of the global village, frequent cross-border trade in goods has made container transportation a significant part in delivery of cargo. However, rollover accidents of container trucks often occur because of tall truck height, improper cargo stowage or crooked roads. Therefore, how to find the optimal center of gravity (CG) of the packed cargo remains an issue that the transportation industry should not neglect. To optimize cargo stability and reduce the incidence of rollovers, this paper proposes a genetic algorithm that scores the cargo stacking process to find the best CG of the cargo based on the CG of the vehicle. According to the deviation angle and the point closest to the best CG, we can Figure out the optimal cargo stacking method.

关键词： Rollover Transportation industry Roads Stacking Transportation Containers Safety

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Reconstructing suspended sediment concentrations in the Mekong River Basin via semi-supervised-based deep neural networks

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science of the Total Environment 2024年 955卷 176758页

作者： Nguyen, Thi Thu Ha Vu, Duc Quang Doan, Ngoc Phu Chi, Huynh Thi Khanh Li, Peixin Binh, Doan Van An, Yimeng Dung, Pham Tuan Hoang, Tuan A. Son, Mai Thai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat City Viet Nam

The Mekong River Basin (MRB) is crucial for the livelihoods of over 60 million people across six Southeast Asian countries. Understanding long-term sediment changes is crucial for management and contingency plans, but the sediment concentration data in the MRB are extremely sporadic, making analysis challenging. This study focuses on reconstructing long-term suspended sediment concentration (SSC) data using a novel semi-supervised machine learning (ML) model. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on daily hydroclimate and SSC data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the superior performance of the proposed method compared to the state-of-the-art supervised techniques (i.e., Random Forest, XGBoost, CatBoost, MLP, CNN, and LSTM), and surpasses existing semi-supervised methods (i.e., CoReg, ⊓ Model, ICT, and Mean Teacher). This approach is the first semi-supervised method to reconstruct sediment data in the field and has the potential for broader application in other river systems. © 2024 The Author(s)

关键词： Self-supervised learning

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Construction and Evaluation of Situational System for Introducing Interactive technology into Ecotourism of Active Aging Group 22nd

Construction and Evaluation of Situational System for Introd...

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22nd International Conference on Human Computer Interaction, HCII 2020

作者： Lu, Li-Shu Wu, PeiFen Huan, Guan-Yuan Department and Graduate School of Digital Media Design National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management and Master Program in Digital Content Technology and Management National Changhua University of Education Changhua Taiwan

ISBN: (纸本)9783030601485

Due to the issue of population aging faced by Taiwan, the government is implementing policies on encouraging the elderly to engage in social activities in their life after retirement, so as to enable them to enjoy a contented and happy retirement life and further forget their ages. In the population aging society, the elderly have an increasingly greater demand for ecotourism. Therefore, in this study, by taking ecotourism as an example, a research on the engagement of the elderly in technology enhanced interactive scenarios of ecotourism was conducted. Purpose of this study was mainly to propose the design system of technology enhanced interactive scenarios suitable for experience of the elderly. In this study, experience demands of the elderly were understood through relevant literature review, preliminary survey and field survey. Subsequently, design concepts of interactive scenarios were proposed through Scenario Approach and a prototype of interactive scenario was designed through Rapid Prototyping Method. Research results: a total of 8 concepts of interactive scenarios were proposed in this study through demand investigation, including provision of experience information, voice guide experience, enhancement of experience fun, trigger of experience scenario, enhancement of intellectual fun, basic knowledge query, enhancement of sensory experience, and sharing and memory. At last, the prototype of "i-Travel" interactive experience scenario was proposed through Prototyping Method. The interactive system consists of 8 interactive scenario concepts, experience contents and navigation design, design of scenario navigation APP, etc. Results of this study will be applied for subsequent assessment and validation of scenario experience awareness of this system. © 2020, Springer Nature Switzerland AG.

关键词： Surveys

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Improving Team Winning Rate through Experiential and Observational Learning in E-Sports Games: A Multilevel Analysis 42

Improving Team Winning Rate through Experiential and Observa...

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42nd International Conference on information Systems: Building Sustainability and Resilience with IS: A Call for Action, ICIS 2021 TREOs

作者： Zhu, Jiantao Xiong, Bingqing Cai, Zhao Lim, Eric School of Management University of Science and Technology of China Jinzhai Road 96 Anhui Hefei China University of Science and Technology of China-City University of Hong Kong Joint Program Jinzhai Road 96 Anhui Hefei China Nottingham University Business School China University of Nottingham Ningbo China 199 Taikang East Road Ningbo China School of Information Systems and Technology Management UNSW Business School SydneyNSW2052 Australia

ISBN: (纸本)9781713893608

Despite participating in a growing number of tournaments, but can e-sports players continuously learn and improve? This question is deeply connected with experiential learning theory since players could acquire experience through repetitive participation but with increasingly smaller marginal returns. Observational learning is touted as a complementary learning mechanism by drawing on external sources of knowledge (or others) to accelerate learning progress. This study hence advances a dynamic and integrated research model that scrutinizes the moderating influence of peer- and rival-based observational learning on the relationship between experiential learning and team performance. Analyzing data gathered on 8,410 teams and 29,078 players in e-Sports games, we reveal that peer-based observational learning attenuates the negative effect of experiential learning on players’ incremental absorptive capacity, whereas rival-based observational learning reinforces the negative relationship between experiential learning and players’ incremental absorptive capacity. Players’ incremental absorptive capacity, in turn, enhances team performance. © 2021 42nd International Conference on information Systems, ICIS 2021 TREOs: "Building Sustainability and Resilience with IS: A Call for Action". All Rights Reserved.

关键词： Sports

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Developing the “Healthcare CEO App” for patients with type 1 diabetes transitioning from adolescence to young adulthood: A mixed-methods study

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Nursing Open 2023年第3期10卷 1755-1766页

作者： Chiang, Yueh-Tao Chang, Chi-Wen Yu, Hsing-Yi Tsay, Pei-Kwei Lo, Fu-Sung Chen, Chi-Wen Lin, Wen-Yen Hsu, Chien-Lung An, Chi Moons, Philip School of Nursing College of Medicine Chang-Gung University Taoyuan Taiwan Division of Pediatric Endocrinology & Genetics Department of Pediatrics Chang-Gung Memorial Hospital Taoyuan Taiwan Department of Nursing Chang-Gung Memorial Hospital Taoyuan Taiwan Department of Public Health and Center of Biostatistics College of Medicine Chang-Gung University Taoyuan Taiwan College of Medicine Chung-Gung University Taoyuan Taiwan College of Nursing National Yang Ming Chiao Tung University Taipei Taiwan Department of Information Management National Taichung University of Science and Technology Taichung Taiwan Department of Information Management Chang-Gung University Taoyuan Taiwan Graduate Institute of Management Chang Gung University Tao-Yuan Taiwan Master of Science Degree Program in Innovation for Smart Medicine Tao-Yuan Taiwan Department of Psychiatry Chang Gung Memorial Hospital at Linkou Tao-Yuan Taiwan Department of Public Health and Primary Care KU Leuven-University of Leuven Leuven Belgium Institute of Health and Care Sciences University of Gothenburg Gothenburg Sweden Department of Paediatrics and Child Health University of Cape Town Cape Town South Africa

Aim: To develop and test a mobile application that supports the disease self-management of adolescents with type 1 diabetes during their transition to early adulthood. Design: A sequential mixed-methods design was employed. Methods: The application content was designed according to previously identified care needs and expectations, followed by application development on the Android operating system. From the outpatient clinic of the Department of Paediatric Endocrinology and Metabolism at a medical centre in northern Taiwan, 35 individuals aged between 16–25 years participated in application testing. Results: The overall median score of the QUIS was 4–5, most of the 25% quartile was 4–5, and all of the 75% quartile was 5, indicating adequate user interaction satisfaction. © 2022 The Authors. Nursing Open published by John Wiley & Sons Ltd.

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Transformative Sport science Using Social Media as a Collaboration Tool 7

Transformative Sport Science Using Social Media as a Collabo...

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7th International Conference on ICT for Smart Society, ICISS 2020

作者： Oktavia, Tanty Gaol, Ford Lumban Hosoda, Takaaki Syahir, Arsyan Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan Bina Nusantara University BINUS Graduate Program - Master of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9781665404228

Sports science can have a significant and positive impact on coaches and athletes, as well as for the professional sports organization. According to this situation, more and more organizations are competing to implement sports science as the monitoring systems. Along with the development of technology, it becomes easier for humans to carry out their activities, including communication with each other. Advances in the field of communication technologies have led to the use of social networks to support all activity. With social media, all kinds of information that people want to search for can be found easily. Social networks have their uniqueness in their characteristics. Like LINE has free calling and video calling features, while Instagram has features like Instagram Story, where users can document their activities in the form of photos or recordings. By all the characteristics of social networks, it will support the activity of sports science. The methodology of this research focused on quantitative research based on the T-test approach. The result shows that social media involvement can significantly support collaboration in sports science, so it can increase people's motivation to interact with other participants in sport organizations. © 2020 IEEE.

关键词： Sports

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

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

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information technology (ACIT)

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

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.

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