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8th International Conference on Informatics and Computing, ICIC 2023

作者： Isnanto, R. Rizal Widodo, Catur Edi Windasari, Ike Pertiwi Saputri, Misik Puspajati Diponegoro University Information Systems Doctoral Program Semarang Indonesia Diponegoro University Computer Engineering Department Semarang Indonesia

ISBN: (纸本)9798350342604

Gastroenteritis is a common gastrointestinal disorder with varying degrees of severity, including cases without dehydration, mild dehydration, moderate dehydration, and severe dehydration. This research focuses on the development of a mobile-based expert system for detecting gastroenteritis, capable of distinguishing between these different dehydration levels. The methodology employed integrates the Extreme Structure Development Life Cycle (ESDLC) and the Best First Search algorithm. The expert system is designed to provide swift and accurate responses to users inputting symptoms, categorizing gastroenteritis cases based on the severity of dehydration. The application of ESDLC ensures a structured and organized development process, while the Best First Search method enhances the efficiency of the diagnostic process, facilitating timely and precise recommendations. Accessible through mobile platforms, the system allows users to input symptoms conveniently and receive rapid recommendations. Rigorous testing and evaluation affirm the system's high accuracy in discerning dehydration levels in gastroenteritis patients. In conclusion, the developed mobile-based expert system exhibits promising potential for advancing diagnostic services, particularly in the categorization of dehydration levels in gastroenteritis cases. The successful implementation of ESDLC and Best First Search paves the way for similar solutions in other healthcare domains, contributing positively to early and accurate disease management efforts. This innovation marks a significant stride towards improving healthcare technology for enhanced decision support in gastroenteritis diagnosis. © 2023 IEEE.

关键词： Life cycle

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Comparison of Feature Selection Methods on Medical Record Data

Comparison of Feature Selection Methods on Medical Record Da...

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2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Astuti, Lastri Widya Ermatita Rini, Dian Palupi Informatic Engineering Doctoral Program Sriwijaya University Palembang Indonesia Sriwijaya University Information System Palembang Indonesia Sriwijaya University Informatic Engineering Palembang Indonesia

ISBN: (纸本)9798350369359

Medical record data are an essential part of medicine, but the effective use of data has become difficult due to the rapid increase in data. Determining the selected features allows researchers to extract the information from medical data. Feature selection in classification provides the advantage of reduced time and storage. In addition, feature selection methods can improve performance by removing redundant or irrelevant data based on their weights. The benefits of feature selection are that it speeds up the training time, simplifies the model, makes interpretation easier, and improves generalization by reducing overfitting. Feature selection in this research uses two methods: Binary Whale Optimization (BWOA), Particle Swarm Optimization, and Hybrid with Gray Wolf Optimization (PSOGWO). Two feature selection methods were compared to determine the most optimal method for the classification process. A comparison of the two feature selection methods shows that the classification process with many attributes produces an optimal classification process, where the number of representative variables in the breast cancer dataset in the BWOA method has five features, and the PSOGWO method has 11 attributes. Meanwhile, the highest accuracy results in breast cancer classification using the BWOA average of 95.90% and PSOGWO Method average of 97.64% © 2023 IEEE.

关键词： Feature Selection

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Morphological Evolution of Andesite Railway Ballast Particle Under Rotating Drum Abrasion Test 6th

Morphological Evolution of Andesite Railway Ballast Particle...

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6th International Conference on Advances in Civil engineering, ICACE 2022

作者： Debanath, O.C. Matsushima, T. Ijichi, T. Miwa, M. Doctoral Program in Engineering Mechanics and Energy University of Tsukuba Tsukuba Japan Department of Civil Engineering CUET Chittagong Bangladesh Department of Engineering Mechanics and Energy University of Tsukuba Tsukuba Japan JR Central Nagoya Japan

ISBN: (纸本)9789819938254

Railway ballast is a natural aggregate widely used in railway infrastructure. The mechanical strength and geometric shape of ballast particles have an important role in the durability and maintenance economy of the ballasted track. This paper aimed to investigate the abrasive behavior of andesite ballast particles using a rotating drum abrasion test and 3D morphological shape analysis. The photogrammetric reconstruction method is adopted here and modified to make it more efficient and user-friendly. The evolution of mass, particle shape, and grain indices were compared for different shape classifications. The experimental results show that the evolution of elongation and flakiness that describe overall particle shape is inconsistent during abrasion, whereas the equivalent ellipsoid volume ratio and rotational resistant angle, which mainly describe particle surface angularity evolve gradually throughout this long-term abrasion process. So, the latter two shape indices describe particle abrasion behavior more accurately. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Abrasion

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Parallel Computing Approach for Preprocessing AIS Data in Transshipment Feature Extraction

Parallel Computing Approach for Preprocessing AIS Data in Tr...

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2023 International Conference on Smart-Green Technology in Electrical and Information Systems, ICSGTEIS 2023

作者： Setiawan, Widyadi Linawati, Linawati Made Oka Widyantara, I. Made Wiharta, Dewa Mertasana, Putu Arya Andriati Asri, Sri Udayana University Doctoral Program of Engineering Science Denpasar Indonesia Udayana University Electrical Engineering Departement Denpasar Indonesia Politeknik Negeri Bali Electrical Engineering Departement Denpasar Indonesia

ISBN: (纸本)9798350382822

AIS (Automatic Identification System) data is an important data source in the shipping and logistics industry to track and monitor ships in real-time. Extraction of transshipment features from AIS data is essential in understanding the pattern and characteristics of transshipment activity at a particular port or place. This study discusses an efficient AIS data preprocessing framework to prepare the transshipment feature extraction. The preprocessing steps include data collection, data cleansing, trajectory extraction, trajectory cleaning, and overlapping trajectory extraction. One of the crucial approaches used in this study is the application of parallel computing. Parallel computing allows us to execute independent data processing tasks at the same time simultaneously. In AIS data preprocessing, the parallel approach allows us to clean, populate, and process data from many vessels in less time. To test the applicability, we created a final comparison for different types of ships based on its AIS data for Indonesia from November 2022 to April 2023. Using parallel computing to process AIS data makes data processing more efficient, especially for large and complex AIS data. This efficiency accelerates data preparation before transshipment feature extraction. The results show that the framework can accelerate runtime and improve trajectory quality. © 2023 IEEE.

关键词： Feature extraction

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Identification of Green Construction Indicators and Project Performance in Green Construction Based Project Management Using the Delphi Method 8th

Identification of Green Construction Indicators and Project ...

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8th International Conference on Civil engineering, ICOCE 2024

作者： Istri Mas Pertiwi, I.G.A. Zaika, Yulvi Negara, Kartika Puspa Solimun Agung Wibowo, M. Brawijaya University Malang Indonesia Doctoral Program of Civil Engineering Brawijaya University Malang Indonesia Department of Civil Engineering Brawijaya University Malang Indonesia Department of Statistic Brawijaya University Malang Indonesia Diponegoro University Semarang Indonesia Departemen of Civil Engineering Diponegoro University Semarang Indonesia

ISBN: (纸本)9789819759095

A green-oriented project management system will be different from projects in general. Differences occur in the planning process to the construction process. To support sustainable and environmentally friendly development, it is very necessary to implement green buildings. For this reason, the Indonesian government, through the Public Works and Public Housing (PUPR) Ministry, has standards regarding green buildings in the form of indicators of green construction variables as requirements for green building certification. Green construction practices also influence performance achievements, such as cost, schedule, quality, environmental, and occupational health and safety (OHS) performance, hereinafter referred to as project performance variables. The aim of the research is to identify green construction indicators that will be applied and project performance indicators that will be used to assess project performance in green construction based project management. There are 17 indicators from the results of previous research synthesis. Meanwhile, the project performance variables obtained were nine cost performance items, five schedule performance items, six quality performance items, and eight OHS performance items. Analysis using the Delphi method, the first round was carried out to identify indicators for each variable that were ranked in the high category. The second round was to obtain consensus from all respondents. The Delphi survey was conducted using Google Form on 20 respondents representing all stakeholders in the construction services industry and project owners with a five-point Likert scale. The analysis resulted in 17 indicators identified in the initial survey being highly rated for application to green projects. Meanwhile, for project performance, the results obtained were nine cost performance items, four schedule performance items, six quality performance items, and seven K3 performance items identified in the initial survey which received high rat

关键词： Green buildings

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A Real-time Affordance-based Object Pose Estimation Approach for Robotic Grasp Pose Estimation

A Real-time Affordance-based Object Pose Estimation Approach...

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2023 International Conference on System Science and engineering, ICSSE 2023

作者： Wong, Shang-Wen Chiu, Yu-Chen Tsai, Chi-Yi Tamkang University Doctoral Program in Robotics College of Engineering New Taipei City Taiwan Tamkang University Department of Electrical and Computer Engineering New Taipei City Taiwan

ISBN: (纸本)9798350322941

This paper proposes a pose estimation system for robot grasping based on a novel Object Affordance Detection and Segmentation (OADS) network. The proposed system consists of four modules: (1) OADS network;(2) point cloud extraction;(3) object pose estimation;(4) grasp pose estimation. Based on the OADS network, the proposed system achieves affordance-based object pose estimation results. The proposed grasp pose estimation system is evaluated on a laboratory-made dual-arm robot. Experimental results show that the proposed system achieves high detection rate and high accuracy in affordance detection and segmentation tasks, leading to a high success rate in object grasping tasks with lab-made dual-arm robot. © 2023 IEEE.

关键词： Object detection

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Numerical Simulation Co-Firing Of EFB And LRC Injected At Different Burner Levels In Tangential Combustion-Type Pulverized Boilers 10

Numerical Simulation Co-Firing Of EFB And LRC Injected At Di...

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10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Agus Adi Saputra, I Nyoman Prabowo Setiyawan, Atok Bagus Wijaya Kusuma, I Gusti Ihsan, Sobar Darmawan, Arif Institut Teknologi Sepuluh Nopember Doctoral Program of Mechanical Engineering Department Surabaya Indonesia Institut Teknologi Sepuluh Nopember Mechanical Engineering Department Surabaya Indonesia Udayana university Mechanical Engineering Department Indonesia Indonesia

ISBN: (纸本)9798350363104

The exploration of biomass fuels is crucial for enhancing the economic value of solid waste from palm oil and mitigating the environmental impact of greenhouse gases emitted by power plants. This study conducted a numerical analysis on a pulverized coal (PC) boiler with co-firing at various burner positions. The numerical simulation results indicate that co-firing with empty fruit bunch (EFB) can enhance the ignition characteristics of Low Rank Coal (LRC). shown in the FEGT area an average increase in temperature of 0.2-0.45%. However, it should be noted that this temperature value is still within safe limits because it does not cause overheating of the heater. Furthermore, the formation of unburned particles can be reduced through the distribution of CO2. Additionally, the co-firing case 2 exhibits low NOx emissions, with a decrease rate of 24.6% when burner B is set at a condition of 1,63E-07. This condition is considered the most ideal as it is also accompanied by a significant reduction in SO2 emissions, ranging from 21.82% to 24.7%. © 2024 IEEE.

关键词： Coal

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A Proposal of the Improved Method for Measuring Insulation Resistance between a Photovoltaic Module String and the Earth using the Self-biased Method

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IEEJ Transactions on Power and Energy 2025年第1期145卷 13-20页

作者： Kazama, Takuro Kato, Kazuhiko Doctoral Program in Risk and Resilience Engineering University of Tsukuba 1-1-1 Tennodai Tsukuba305-8573 Japan Renewable Energy Research Center National Institute of Advanced Industrial Science and Technology 2-2-9 Machiikedai Koriyama963-0298 Japan

For safe operation of a photovoltaic generating system, an insulation monitoring function, which can immediately detect deterioration of insulation between photovoltaic module strings and the earth, is indispensable. This function requires accurate measurement, low leakage current and suppression of rising voltage against the earth. In this paper, a new insulation monitoring method of photovoltaic module strings is proposed by the improved existing self-biased method that has an inevitable voltage rise under measurement. In this new method, both terminals of photovoltaic module strings are always connected to the ground with appropriate resistors. Theoretical discussion and experiments for this new method showed precise measurement, and acceptable leakage current under various outdoor conditions. In addition, it was confirmed that this method could sufficiently decrease in the voltage rise compared with the existing self-biased method. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Photovoltaics

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Bibliometric Analysis of Trends and Directions in Machine Learning Applied to FinTech 4

Bibliometric Analysis of Trends and Directions in Machine Le...

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4th International Conference on Electronic and Electrical engineering and Intelligent System, ICE3IS 2024

作者： Arif, Erman Suherman, Suherman Widodo, Aris Puji Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Diponegoro University Faculty of Engineering Department of Chemical Engineering Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Informatics Semarang Indonesia

ISBN: (纸本)9798350378368

Our article presents a comprehensive analysis of trends and directions in the application of machine learning in the FinTech sector. By utilizing data from academic databases such as Scopus, our objective is to showcase the advancements in this interdisciplinary research area. The study identifies key trends, influential authors, leading journals, and significant collaborative networks that shape the field. The methodology employed involves quantitative bibliometric analysis to evaluate publications from the past five years. The results indicate a substantial increase in the number of studies during this period, highlighting the importance of machine learning in addressing critical financial challenges, including fraud detection, algorithmic trading, credit scoring, and risk management. Additionally, we delve into the main themes, countries, and institutions that dominate the current research agenda. This analysis provides valuable insights into the present state and future direction of machine learning in FinTech, serving as a resource for researchers and practitioners seeking to comprehend and contribute to this dynamic and rapidly evolving field. We also discuss implications for future research, emphasizing the necessity for continuous innovation and interdisciplinary collaboration to maximize the potential of machine learning in transforming financial services. © 2024 IEEE.

关键词： Adversarial machine learning

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Application of Recurrence Plots and VGG Deep Learning Model to the Study of Condition Monitoring of Robotic Grinding

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International Journal of Precision engineering and Manufacturing 2023年第9期24卷 1675-1683页

作者： Chung, Kuan-Jung Dai, Cheng-Han Chiang, Tung-Chun Xie, June-Jia Lin, Ming-Tzer Department of Mechatronics Engineering National Changhua University of Education Changhua500 Taiwan Graduate Institute of Precision Engineering National Chung Hsing University Taichung402 Taiwan Doctoral Program in Industrial and Smart Technology Academy of Circular Economy National Chung Hsing University Nantou540 Taiwan

This study adopted AI to identify the normal and abnormal vibration signals generated during robotic grinding. This study classified four fundamental factors affecting grinding into three levels to obtain a widely used result and designed an L9(3 4) orthogonal array for the grinding experiment. During experimentation, part of the grinding wheels was added weight to produce abnormal vibration signals, which an accelerometer would measure. The study transformed the collected vibration signals into recurrence plots and conducted model training with VGG16 CNN architecture. Finally, this study tested a model with 89.6% training accuracy. The results showed the model could identify whether the recurrence plots stand for normal or abnormal vibrations, with an accuracy of 85%. This means it could predict normal and abnormal grinding conditions and help avoid problems caused by abnormal vibrations. © 2023, The Author(s), under exclusive licence to Korean Society for Precision engineering.

关键词： Grinding (machining)

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