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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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IoT for Water Quality Categorization 5

IoT for Water Quality Categorization

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5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Sastrohartono, Hermantoro Krisdiarto, Andreas Wahyu Uktoro, Arief Ika Rahutomo, Reza Suparyanto, Teddy Pardamean, Bens Institute of Agriculture Stiper Faculty of Agricultural Technology Agricultural Engineering Department Yogyakarta Indonesia Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics & Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350369489

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results. © 2023 IEEE.

关键词： Internet of things

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Reentrant hybrid flow shop scheduling with stockers in automated material handling systems using deep reinforcement learning

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Computers and Industrial Engineering 2024年 189卷

作者： Lin, Chun-Cheng Peng, Yi-Chun Chang, Yung-Sheng Chang, Chun-Hsiang Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu300 Taiwan International Master Program of Smart Manufacturing and Applied Information Science National Chin-Yi University of Technology Taichung411 Taiwan

In smart factories, automated material handling system (AMHSs) replace manual material handling to increase production efficiency, in which stockers serve as the temporary storage for work-in-process inventories. Furthermore, costly machines and complex processes in advanced manufacturing lead to the need for multiple reentrant processes of the same workstation. However, previous works on scheduling problems rarely considered the function of stockers and reentrant processes, and their approaches were mostly based on metaheuristic algorithms. Recent advances in artificial intelligence enable the possibility of solving scheduling problems using deep reinforcement learning (DRL). Therefore, this work investigates the reentrant hybrid flow shop scheduling problem with stockers (RHFS2) inspired by AMHSs, and solves it by DRL. Firstly, the states, actions, and rewards of the Markov decision process for this problem are designed;and then, two deep Q network (DQN) approaches based on the actions for determining machines and jobs, respectively, are proposed. Simulation results demonstrate that our proposed DQN approaches outperform for finding better solutions of different-scale problems than classical metaheuristic algorithms. © 2024 Elsevier Ltd

关键词： Reinforcement learning

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Analysis of the Musculoskeletal Load Generated when a Small Dog Walks on a Slippery Floor

Analysis of the Musculoskeletal Load Generated when a Small ...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Li, Shijian Chugo, Daisuke Muramatsu, Satoshi Yokota, Sho She, Jin-Hua Hashimoto, Hiroshi Takashi, Uemura Kamishina, Hiroaki Hata, Yoshiharu Yamada, Takayuki Uchida, Takahiro Kwansei Gakuin University Graduate School of Science and Technology Hyogo Sanda6691330 Japan Tokai University School of Information Science and Technology Kanagawa Hiratsuka2591292 Japan Toyo University Faculty of Science and Engineering Saitama Kawagoe3508585 Japan Tokyo University of Technology School of Engineering Hachioji Tokyo1920982 Japan Advanced Institute of Industrial Technology Master Program of Innovation for Design and Engineering Shinagawa Tokyo1400011 Japan KyotoAR Animal Advanced Medical Center Kuse Kyoto6130034 Japan Rinrei Co. Ltd. Chuo Tokyo1040061 Japan

ISBN: (纸本)9798331531614

The objective of this paper is to identify the stresses placed on the musculoskeletal system when small dogs walk on floors with low friction coefficients. In many households, and small dogs in particular are often kept indoors. According to veterinary experience, it is known that small dogs kept indoors that walk with their lower limbs slipping cause a great deal of stress on the musculoskeletal system, and that the incidence of joint disease in the lower limbs of such small dogs is high. In this study, we measured the body movements of small dogs walking on slippery floors using a three-dimensional motion capture system and force plate system. Furthermore, by inputting these measurements into the musculoskeletal simulation model we have developed to simulate the musculoskeletal system of dogs, we derived the load on the musculoskeletal system of small dogs on slippery and non-slippery floors. As a result, it was identified that walking on a slippery floor causes a strong load on the lower limb muscles of small dogs. © 2025 IEEE.

关键词： Friction

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Finding the Best Techniques for Predicting Term Deposit Subscriptions (Case Study UCI Machine Learning Dataset)

Finding the Best Techniques for Predicting Term Deposit Subs...

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International Conference on Sustainable Engineering and Creative Computing (ICSECC)

作者： Lila Setiyani Ayu Indahsari Rosalina Tjong Wansen Master of Science Information Technology study program President University Bekasi Indonesia Informatics Study Program President University Bekasi Indonesia

Knowing a lot about attributes, such as bank client data, marketing campaigns, social and economic context, and so on, is necessary to predict which bank customers are likely to sign up for time deposits. The direct marketing campaigns dataset for the bank institution was made available by the UCI machine learning repository. This dataset had previously been used in studies to forecast customers who would sign up for time deposits, but the outcomes of the two studies were different. The purpose of this study is to determine which machine learning methods are most effective for predicting customers who will sign up for time deposits. In this study, researchers used machine learning methods like random forests, K-nearest neighbors, decision trees, naive bayes, logistic regression, SVM, and XGBoost to uncover the truth. Results showed that XGBoost had the highest accuracy (91.73%), followed by the DT classifier (88.79%), LR classifier (91.2%), NB classifier (84.78%), KNN classifier (90.6%), SVM (89.6%), and XGBoost (91.44%).

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Research on Traffic Sign Detection and Recognition System Using Deep Ensemble Learning

Research on Traffic Sign Detection and Recognition System Us...

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IEEE International Conference on Computer Communication and the Internet (ICCCI)

作者： Lung-Jen Wang Taweelap Suwattanapunkul Jarinya Thalauy Parinya Jansengrat Department of Computer Science and Information Engineering National Pingtung University Pingtung Taiwan International Master Program in Information Technology and Applications International College National Pingtung University Pingtung Taiwan Department of Big Data Management and Analytics Rajamangala University of Technology Thanyaburi Pathum Thani Thailand

ISBN: (数字)9798350351767

ISBN: (纸本)9798350351774

There is a rise in car accidents due to human errors on the road. A critical task of self-driving cars that can reduce accidents on the road is traffic sign detection and recognition (TSDR), which is vital in alerting drivers to the presence of traffic signs in advance. This research will separate the proposed deep ensemble learning algorithm into two methods. First, after the traffic scene process, the algorithm will detect the traffic sign as two categories with the YOLOv5s network. Then, process the traffic sign to recognize the traffic sign into seven classes with the MobileNet network. The detection model was trained with the Taiwan Traffic Sign Detection (TTSD) dataset collected from Taiwan roads. The recognition model was trained with the Taiwan Traffic Sign Recognition (TTSR) dataset. The result of the proposed algorithm showed high performance when experimenting with 95.83% accuracy, 87.34% true prediction, and 191.3 milliseconds (ms) of inference time.

关键词： YOLO Roads Prediction algorithms Inference algorithms Internet Autonomous automobiles Ensemble learning

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Lower bounds for minimizing two types of unnecessary costs in the film and television industry

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Computers and Operations Research 2024年 170卷

作者： Lin, Lie-Fen Wang, Jen-Ya Master's Program in Statistics and Actuarial Science Feng Chia University Taiwan Taiwan Department of Information Management National Taichung University of Science and Technology Taiwan Taiwan

To optimize film and television production costs, we aim to minimize unnecessary expenditures such as the idle time of top-paid actors and the rental periods of expensive equipment. By scheduling scenes that require these resources on consecutive days, significant cost reductions are achieved. In this study, a branch-and-bound algorithm with two lower bounds has been developed to serve as a benchmark for evaluating other metaheuristic algorithms. The novelty of these lower bounds stems from their set-based operations, which avoid time-consuming computations involving large sparse matrixes. The computational results confirm that the proposed lower bounds effectively reduce these costs. Clearly, this approach can be extended to other industries with similarly complex job compositions, such as scenarios involving multiple small tasks in a single job. © 2024 Elsevier Ltd

关键词： Branch-and-bound algorithm Lower bound Multi-agent scheduling Overlap effect Set operation Talent scheduling

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Improving Interoperability in Healthcare: A User-Friendly International Standard Data Conversion Framework 13th

Improving Interoperability in Healthcare: A User-Friendly In...

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13th International Conference on Frontier Computing, FC 2023

作者： Yen, Lo-Hsien Huang, Tzu-Ting Hsu, Chien -Yeh Wu, Pin-Hua Liu, Chen-Yi Lee, Hsiu-An Department of Information Management National Taipei University of Nursing and Health Sciences Taipei112 Taiwan College of Public Health Master Program in Global Health and Development Taipei Medical University Taipei110 Taiwan College of Health Technology National Taipei University of Nursing and Health Sciences Taipei112 Taiwan National Health Research Institutes The National Institute of Cancer Research Tainan704 Taiwan

ISBN: (纸本)9789819994151

Standardization helps improve supplier compatibility, interoperability, repeatability, security, and quality. The SARS-CoV-2 pandemic has highlighted the need for global requirements regarding the interoperability of healthcare information. Currently, Taiwan still relies on the second edition of the Clinical Document Architecture (CDA) as the standard for exchanging medical data, which was formulated 17 years ago. There is currently no unified specification for medical data transformation or review mechanism in place. To address this issue, it is necessary to adopt the widely used international healthcare data exchange standard, Fast Healthcare Interoperability Resources (FHIR), established by Health Level Seven International (HL7). The aim of this research is to simplify the mapping of medical data and convert it into the international FHIR format, enabling rapid data transformation. Additionally, an interface will be designed to meet user habits and requirements. By utilizing common clinical data structures in Taiwan as an example, a mechanism for data conversion and verification will be developed to achieve fast health data exchange and enhance interoperability in the healthcare field. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： User interfaces

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Integration of Renewable Energy technology in Waste Recycling Utilization

Integration of Renewable Energy Technology in Waste Recyclin...

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Creative Communication and Innovative technology (ICCIT), IEEE

作者： Alwiyah Padeli Maulana Yusuf Hendra Kusumah Suwandi Accounting Study Program Wiraraja Madura University Sumenep Master of Information Technology University of Raharja Indonesia Department of Information System University of Raharja Indonesia Master of Information System University of Raharja Indonesia Department of Information Technology University of Catur Insan Cendikia Indonesia

The increase in the production of municipal solid waste around the world will be expected to continue to increase. Even though developed countries already have recycling technology to convert waste into products that can be reused, such as generating electricity, heat energy, composting fertilizers, and biofuels, there are still many challenges created by waste because in developing countries the countermeasures themselves are still so things are difficult starting from the inadequate collection, transportation, and disposal of waste. This research will solve the problem of evaluate various waste-to-energy technologies that have been developed since the invention that was able to convert recycled waste into renewable energy until now. This research can be divided into four groups. Namely starting from waste treatment thermally, biologically, biorefinery, and utilization of landfill gas. The novelty of this research for waste-to-energy technology with an integrated waste management system, it has been safely understood and tested feasible for energy renewal which can already show several examples from around the world. The method used to solve the unresolved waste problem in this scientific paper is to use WTE and ISWM-S technology which can certainly be a brilliant solution in converting urban solid waste into renewable energy for a clean world from waste to waste and also provides a future. Where people no longer need to worry about the limitations of limited natural resources to be processed into energy.

关键词： Waste management Waste materials Transportation technology transfer Developing countries Recycling Waste heat

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Joint green dynamic order batching and picker routing problem using PSO with global worst experience

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Applied Soft Computing 2024年 154卷

作者： Lin, Chun-Cheng Peng, Yi-Chun Kang, Jia-Rong Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu300 Taiwan International Master Program of Smart Manufacturing and Applied Information Science National Chin-Yi University of Technology Taichung411 Taiwan Department of Information Management Tatung University Taipei104 Taiwan

With the increasing importance of various countries’ initiatives on reducing carbon emissions (e.g., carbon border adjustment mechanism, CBAM), intelligent order picking systems have assisted electronic retailers in realizing a green supply chain. Previous studies on picker routing operations focused on adopting offline static customer order information to carry out operational decisions. However, in practice, the customer order information is updated dynamically;and high-efficiency warehouse layouts have been increasingly concerned. Therefore, this study creates a mixed-integer programming model for minimizing both the total carbon footprint and the total penalty cost for delayed orders in the joint order batching and picker routing problem with dynamic arriving orders and shipping time constraints in a high-efficiency fishbone warehouse layout. To solve this complex problem, a particle swarm optimization algorithm that integrates the swarm's previous global and local worst experiences and a migration mechanism is further proposed to increase solution quality and computing efficiency. In addition, a three-dimensional space for the warehouse floorplan and shipping time constraints is designed to divide orders into batches to address the constraints. Theoretical analysis of this algorithm is conducted, and experimental analysis shows that this algorithm finds superior solutions than current practical strategies, to offer a valuable reference for carbon emission reduction in greening warehousing. © 2024 Elsevier B.V.

关键词： Carbon footprint

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