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Proceedings of the 2022 10th International Conference on Information technology: IoT and Smart City

作者： Haohui Ma Xuemei Sun Canvard College Beijing Technology and Business University China and School of Management Wuhan University of Technology China Graduate Program and External Studies Technological University of the Philippines China and The school of Accounting and Finance Canvard College Beijing Technology and Business University China

ISBN: (纸本)9781450397438

With the continuous update of information technology in China, information technology has been widely used in many industries, effectively improving the efficiency of enterprise management work. Business Intelligence (BI) manages the relevant data, constructs the information processing system according to the established management decisions, according to the relevant data, and promotes the improvement of enterprise management level. In the material management enterprise, the supply of materials is directly related to the normal production process of enterprises, so the technical personnel need to adopt an advanced management mode to realize the accurate management of materials. This research deeply analyzes the specific application of Business Intelligence (BI) in enterprise material management.

关键词： precision management material management Business Intelligence (BI) enterprise data analysis

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Applying Smart Glasses in Situated Exploration for Learning English in a National Science Museum

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IEEE Transactions on Learning Technologies 2023年第5期16卷 820-830页

作者： Chen, Hong-Ren Lin, Wen-Shan Hsu, Tien-Yu Lin, Tzu-Chun Chen, Nian-Shing National Taichung University of Education Department of Digital Content and Technology Taichung City40306 Taiwan National Cheng Kung University Institute of International Management Tainan City701 Taiwan National Museum of Nature Science Department of Science Education Taichung City404023 Taiwan Graduate Institute of Library and Information Science National Chung Hsing University Taichung City40306 Taiwan National Taiwan Normal University Program of Learning Sciences Taipei City106 Taiwan

Research on the use of augmented reality technology in museums is mostly limited to scientific knowledge. The use of wearable device technology learning materials to benefit students in the process of English learning has been somewhat explored, along with differences in students' learning styles and their influence on students' learning motivation, but there remains much to be done in this field. This study explored the learning effect of smart glasses integration into the English language dinosaur exhibition at the Museum of Natural Science and investigated their impact on motivation. The experimental group used smart glasses learning strategy to facilitate their learning experience, whereas the control group used a tablet learning strategy. The research results show that: 1) Learning effectiveness and motivation were significantly superior when using smart glasses learning strategy, as compared to using a tablet learning strategy when learning through the English language situated exploration game. 2) The interaction between different learning strategies and learning styles had a significant impact on learning effectiveness. In the smart glasses learning strategy group and the tablet learning strategy group, the learning effectiveness of the kinesthetic students was significantly higher than those of the auditory students. The learning effectiveness of the visual students using the tablet learning strategy was higher than auditory students. 3) The interaction between different learning strategies and learning styles had no significant effect on learning motivation. In terms of learning motivation, visual students were more affected than auditory and kinesthetic students. © 2008-2011 IEEE.

关键词： Glass

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Gc-Red: Graph Convolution-Based Recurrent Encoder-Decoder for Anomaly Diagnoses in Multivariate Time-Series Data

SSRN

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SSRN 2023年

作者： Kim, Yejin Jeon, Daeseong Park, HyeRim Lee, Changyong Lee, Junghye Ulsan44919 Korea Republic of Ulsan44919 Korea Republic of Technology Management Economics and Policy Program Seoul National University Seoul08826 Korea Republic of Department of Public Administration Korea University Seoul02841 Korea Republic of Technology Management Economics and Policy Program Graduate School of Engineering Practice Seoul National University Seoul08826 Korea Republic of

Detecting anomalies in multivariate time-series data is an important task in various real-world industries, but doing so is non-trivial because both the temporal dependency within the time series and correlations between multiple time series must be considered. Previous studies have only addressed one issue, resulting in limited performance. To address this, we propose a graph convolution-based recurrent encoder-decoder, called GC-RED. The model incorporates intra-dependencies and inter-correlations using a graph-construction process and an encoding-decoding process that encodes the graph with an attention-enhanced graph convolutional LSTM and then restores it. Anomaly detection and root cause identification are performed based on the reconstruction error. Considering that anomalies with various durations can occur in actual situations, the model includes a vanilla model reflecting single scales and a model reflecting multiple scales. The feasibility and usefulness of GC-RED are demonstrated by intensive experiments on several benchmark and real-world datasets with comparisons to other competitive methods. © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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Disease Data Modeling by Using Machine Learning Approach

Disease Data Modeling by Using Machine Learning Approach

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International Conference on Information technology, Computer and Electrical Engineering (ICITACEE)

作者： Gunawan Wang Rakha Gusti Wardhana Farida Sibuea Anderes Gui Information Systems Management Department BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Head of Data Analytics department Data and Information Technology Centre (Pusdatin) Ministry of Health Jakarta Indonesia School of Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350389302

ISBN: (纸本)9798350389319

Since Covid outbreak in 2020, Indonesian Ministry of Health (MoH) has intensively embraced digital transformation initiatives in healthcare management and decision-making process. The latest healthcare digital transformation roadmap 2024, has emphasized the use of AI technology in data analytics and support policy making. The article examines the use of common Machine Learning (AI) algorithms, such as: Logistic Regression, Random Forest, Support Vector Machine (SVM), Decision Tree, and XGBoost. The article applies these algorithms to analyse the common diseases faced by Indonesian such as Measles, Malaria, Leprosy, and Diphtheria. Those diseases are classical diseases, however, still infecting many people in large areas. The article takes a case study of national health profile within period 2017–2022, to study the data model of those diseases. The outcome of the article is expected to be used for further policy making.

关键词： Support vector machines Logistic regression Machine learning algorithms Malaria Measles Medical services Predictive models Prediction algorithms Decision trees Random forests

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University Students’ Attitudes toward Adoption of Mobile Health Application for Serving Older People

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Sensors and Materials 2023年第7期35卷 2581-2601页

作者： Chao, Pei-Ju Cheng, Yu-Huei Liu, Yi-Hui Hsu, Tsu-Hsuan Travel & Tourism Program International College Ming Chuan University 5 De Ming Rd. Gui Shan District Taoyuan333 Taiwan Department of Information and Communication Engineering Chaoyang University of Technology 168 Jifeng E. Rd. Wufeng District Taichung413 Taiwan Department of Senior Citizen Service Management National Taichung University of Science and Technology No. 129 Sec. 3 Sanmin Rd. North District Taichung404 Taiwan Graduate Institute of Rehabilitation Counseling National Changhua University of Education 1 Jin-De Rd. Changhua City500 Taiwan

In this quantitative study, we examined the awareness of mobile health (mHealth) applications among Taiwan’s university students, particularly the government-operated My Health Bank application. The My Health Bank application allows users to access their personal health information simply through their smartphone’s sensor-assisted facial recognition camera and fingerprint sensor. Whether these university students might help older adults to use this application if they perceive it to be helpful was also explored. Two instruments were used in this study, namely, the Perceptions toward My Health Bank (PMHB) and Geriatric Attitudes Scale (GAS). Two hundred twenty-five (225) university students from the Departments of Senior Citizen Service management (n = 136) and Nursing (n = 89) at two universities participated in this study. The findings indicated low adoption of My Health Bank and related mHealth applications among students even though they had high adoption rates of smartphones. However, they perceived that the functions of this application are useful and should be promoted to older adults. Thus, building a basic foundation of mHealth and related technology courses is recommended to assist healthcare students in being well-prepared for working with older adults in the digital society. © MYU K.K.

关键词： mHealth

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Experimentation of Integrating Roadmapping and Sci-Fi Prototyping Methods

Experimentation of Integrating Roadmapping and Sci-Fi Protot...

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

作者： Miwa Nishinaka Kunio Shirahada Yusuke Kishita Dohjin Miyamoto Hirotaka Osawa Sachiko Kiyokawa Hideaki Takeda The Graduate School of Management Kagawa University Takamatsu Kagawa Japan School of Knowledge Science Transformative Knowledge Management Research Area Japan Advanced Institute of Science and Technology (JAIST) Nomi Ishikawa Japan Department of Precision Engineering The University of Tokyo Tokyo Japan Communication in Science and Technology Education and Research Program Hokkaido University Sapporo Japan Faculty of Science and Technology Keio University Yokohama Kanagawa Japan Graduate School of Education The University of Tokyo Tokyo Japan National Institute of Informatics Tokyo Japan

ISBN: (数字)9781890843458

ISBN: (纸本)9798350369137

In 2023, we proposed a hypothetical knowledge management framework that incorporates science fiction prototyping (SFP) into the traditional roadmapping (RM) method to make RM more innovative and create a future beyond what we can currently imagine. Our main objective is to create a new organizational knowledge co-creation method for generating innovative ideas by incorporating humanistic factors utilizing SFP into RM. In the current article, we present the exploratory study we conducted to examine the hypothetical framework. Experimental workshops were held in November 2023 to compare the integrated method, in which SFP is applied to RM, with the original RM method. We followed a crossover design in the workshops and analyzed the results qualitatively and quantitatively following the perspectives proposed in the hypothetical framework. The qualitative analysis revealed successful integration regarding diverse and broader perspectives. The quantitative analysis utilizing Wilcoxon signed rank tests showed that the scores of the integrated method were significantly higher than those of RM for variables evaluating the effectiveness of the integration, while not for variables related to participants' feelings and satisfaction with the workshops, and sustainability. These findings indicate that the integration was successful in terms of its effectiveness as a method, while participants' subjective feelings toward the workshops were not different in both methods. As for sustainability, which requires a long-term perspective, no apparent differences were observed in these short-term workshops.

关键词： Statistical analysis Conferences Knowledge management Sustainable development Creativity

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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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Capnesterones A and B, new steroids isolated from the soft coral Capnella imbricata

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Phytochemistry Letters 2024年 61卷 115-119页

作者： Cheng, Ting-Yu Yang, Chia-Jung Chen, Po-Jen Chen, Yu-Li Peng, Bo-Rong Kung, Te-An Wen, Zhi-Hong Lai, Kuei-Hung Chung, Hsu-Ming Department of Applied Chemistry National Pingtung University Pingtung 900393 Taiwan Clinical Immunology Laboratory Department of Medical Research E-Da Hospital I-Shou University Kaohsiung 824005 Taiwan Research Center for Chinese Herbal Medicine Graduate Institute of Healthy Industry Technology College of Human Ecology Chang Gung University of Science and Technology Taoyuan 33305 Taiwan Graduate Institute of Biomedical Sciences Graduate Institute of Natural Products College of Medicine Chang Gung University Taoyuan 33305 Taiwan Graduate Institute of Pharmacognosy College of Pharmacy Taipei Medical University Taipei 11031 Taiwan Institute of Food Safety Management National Pingtung University of Science and Technology Pingtung 91201 Taiwan Department of Marine Biotechnology and Resources National Sun Yat-sen University Kaohsiung 804201 Taiwan Institute of BioPharmaceutical Sciences National Sun Yat-sen University Kaohsiung 804201 Taiwan PhD Program in Clinical Drug Development of Herbal Medicine College of Pharmacy Taipei Medical University Taipei 11031 Taiwan Traditional Herbal Medicine Research Center Taipei Medical University Hospital Taipei 11031 Taiwan

Two new non-withanolidal steroids with a cross-conjugated dienone structural unit in ring A, capnesterones A (1) and B (2), were isolated from the soft coral Capnella imbricata in the southeast waters of Taiwan. The compounds' structures were elucidated through a comprehensive analysis of spectroscopic data, including IR, ESIMS, ¹H NMR, and ¹³C NMR, and through comparison with previous literature. At a concentration of 10 μM, steroids 1 and 2 exhibited reductions in iNOS levels (60 % and 82 %) compared to LPS-stimulated control cells, along with increased release (134 % and 110 %) against COX-2 protein expressions. © 2024 Phytochemical Society of Europe

关键词： Capnella imbricata COX-2 INOS Steroid

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Integration of the Use of Conventional technology Towards Smart Home technology for People in the City of Jakarta

Integration of the Use of Conventional Technology Towards Sm...

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International Conference on ICT For Smart Society (ICISS)

作者： Tanty Oktavia Febian Wijaya Lintang Gerald Christian Ongko Griselda Valerie Michelle Benedicta Vieri Aska Juneio Sembiring Zaldy Raditya Ananta Takaaki Hosoda Ford Lumban Gaol Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Decision Science Department Advanced Institute of Industrial Technology Tokyo Japan Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Smart Home technology offers technological concepts that can be controlled through smart devices to make it easier for the community. Therefore, smart home technology is starting to seize the home electronics market in Indonesia, especially in the city of Jakarta. This study aims to determine the integration of conventional technology into smart home technology for people in Jakarta. The research model in this study uses UTAUT2 which analyzes nine variables, namely Perceived Usefulness, Perceived Cost, Perceived Ease of Use, Social Influence, Perceived Compatibility, Trust, Perceived Pleasure, and Effort Expectations, Intention to Use Smart Home technology. After the responses were received, data analysis was carried out using the PLS-SEM approach with the SmartPLS application. The results of this study show that there are many people who live in Jakarta who are helped by the presence of smart home technology. However, there are some people who don't really know how to use a smart home because of a lack of knowledge about this technology.

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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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