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International Conference on Informatics, Multimedia, Cyber and information System (ICIMCIS)

作者： Yulistia Ermatita Reza Firsandaya Malik Informatic Doctoral Program Sriwijaya University STMIK MDP Palembang Indonesia Information System Sriwijaya University Palembang Indonesia Computer Science Faculty Sriwijaya University Palembang Indonesia

ISBN: (纸本)9781728129310

Knowledge management System (KMS) as one of the management system to reach effective and competitive advantage, has been applied in many company or institution. One of KMS phase is knowledge discovery systems with specific activity called knowledge transfer. SECI model is one of knowledge transfer which used by many researcher in KMS journal. SECI model developed into inukshuk model which complete the component that is not available in SECI. The problem is, both SECI and inukshuk models are generally used for common institution or company, and not specialized for educational institution such as higher education. This paper will discused and formulate model for knowledge transfer activity based on SECI and Inukshuk model, based on research about higher education knowledge transfer and knowledge management concept in private higher education, which already made by some researchers. The method in this paper using review journal method with observation and analysis approach. Result of this paper is an additional factor for transfer knowledge suggested for Private Higher education, it is People and Policy, added in the knowledge transfer phase in order to emphasis the Socialization and Externalization in SECI Model.

关键词： data mining educational computing educational institutions further education knowledge management Transfer (Psychology) Educational institutions computer uses in education knowledge management Continuing Education data mining higher education KMS gene Externalization

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Anomaly Detection in Climate Data Using Stacked and Densely Connected Long Short-Term Memory Model

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Journal of computers (Taiwan) 2020年第4期31卷 42-53页

作者： Saleh Abbas, Bahtiar Wahyono, Teguh Heryadi, Yaya Soeparno, Haryono Doctor of Computer Science Binus Graduate Program Bina Nusantara University Jakarta Indonesia Faculty of Information Technology Satya Wacana Christian University Salatiga Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Industrial Engineering Bina Nusantara University Jakarta Indonesia

Climate anomalies are considered as an important factor closely related to many disasters causing many human losses, such as airline crash, wildfires, drought and flooding in many areas. Many researchers have projected that the rising global temperature will increase the draught, especially in the mid-latitude areas. Taking those problems into account, studies on anomaly detection in climate are crucial. While climate prediction aims to analyze and model regular pattern of climate, climate anomaly studies aim to model climate deviation from its previous general patterns. Long Short Term Memory (LSTM) is a method employed in this research because it has been proven to work effectively in several anomaly detection studies, especially for data with similar characteristics. This paper presents empirical results of using Basic LSTM, Densely Connected (DC) LSTM, and Stacked-DC LSTM models to detect temperature anomaly as a manifestation of climate change in Semarang City. The results show that Stacked DC LSTM produced higher accuracy in detecting anomaly than the other two methods. © 2020 computer Society of the Republic of China. All rights reserved.

关键词： Long short-term memory

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A PARALLEL VARIATIONAL MESH QUALITY IMPROVEMENT METHOD FOR TETRAHEDRAL MESHES 28

A PARALLEL VARIATIONAL MESH QUALITY IMPROVEMENT METHOD FOR T...

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28th International Meshing Roundtable, IMR 2019

作者： Shontz, Suzanne M. Varilla, M.A.L. Huang, Weizhang Department of Electrical Engineering and Computer Science Bioengineering Program Information and Telecommunication Technology Center University of Kansas LawrenceKS United States Husky Injection Molding Systems Ltd. Luxembourg Luxembourg Department of Mathematics University of Kansas LawrenceKS United States

ISBN: (纸本)9781733489003

There are numerous large-scale applications requiring mesh adaptivity, e.g., computational uid dynamics and weather prediction. Parallel processing is needed for simulations involving large-scale adaptive meshes. In this paper, we propose a parallel variational mesh quality improvement algorithm for use with distributed memory machines. Our method parallelizes the serial variational mesh quality improvement method by Huang and Kamenski. Their approach is based on the use of the Moving Mesh PDE method to adapt the mesh based on the minimization of an energy functional for mesh equidistribution and alignment. This leads to a system of ordinary differential equations (ODEs) to be solved which determine where to move the interior mesh nodes. An efficient solution is obtained by solving the ODEs on subregions of the mesh with overlapped communication and computation. Strong and weak scaling experiments on up to 128 cores for meshes with up to 160M elements demonstrate excellent results. © 2019 Proceedings of the 28th International Meshing Roundtable, IMR 2019. All rights reserved.

关键词： Ordinary differential equations

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Radiotherapy Communication Skills Training using Chatbot-Based Prototype (SCIMORT)

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Journal of Medical Imaging and Radiation sciences 2022年第4期53卷 S1-S1页

作者： Nor, A Nur, S Muhamad, K Rozilawati, A Noorazrul, Y Muhammad, F Nani, S Noraini, W Saiful, H Liyana, S Diagnostic Imaging and Radiotherapy Program School of Diagnostic and Applied Health Sciences Faculty of Health Sciences Universiti Kebangsaan Malaysia Jalan Raja Muda Abdul Aziz 50300 Kuala Lumpur Malaysia Department of Mathematical Sciences Faculty of Science and Technology Universiti Kebangsaan Malaysia UKM Bangi 43600 Selangor Darul Ehsan Malaysia Department of Information Systems Faculty of Computer Science and Information Technology University of Malaya 50603 Kuala Lumpur Malaysia

IntroductionEffective communication skills are integral to providing adequate patient care and are fundamental to its effectiveness. This is particularly important for radiotherapy students while interacting with patients. Students tend to feel disempowered and inadequate to communicate effectively due to repeated miscommunication between students and patients. For students seeking to develop their abilities, confidence, knowledge, and critical thinking, blended learning-based communication skills training may have been an alternative approach. In this study, a chatbot-based prototype model (SCIMORT module) was designed to effectively improve the communication skills of undergraduates in Diagnostic Imaging and Radiotherapy at Universiti Kebangsaan Malaysia. MethodsSCIMORT was developed in compliance with the Clinical Practice for Radiotherapy course's academic syllabus and learning objectives. SCIMORT was constructed using Blender with final exportation to Bot Libre. The research study is divided into two phases. The first phase focuses on developing the SCIMORT module, while the second phase includes properly evaluating the prototype created in the first phase via a survey questionnaire. ResultsThe virtual patient created in the first phase was displayed a visual cue (e.g. blinking, eye movement) and auditory cues (e.g. verbal response, intonation of voice). In the second phase, the expert validation of the questionnaire resulted in a high Content Validity Ratio of more than 0.83 percent and a Cronbach Alpha Coefficient of more than 70%. Both the control group's (users' acceptance = 3.919 ± 1.245; users' engagement = 3.526 ± 1.270) and Trial group's (users' acceptance = 3953 ± 1.114; users' engagement = 3.568 ± 1.142) users' acceptance and engagement with SCIMORT were neutral. ConclusionThe SCIMORT has proven to be an effective and acceptable tool for pre-clinical learning, but with limited levels of engagement. The SCIMORT module should be continually improved by

关键词： SCIMORT module communication skill virtual patients virtual clinical experience breast cancer simulation

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EBITDA time series forecasting case study: Provincial waterworks authority 4

EBITDA time series forecasting case study: Provincial waterw...

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4th Joint International Conference on Digital Arts, Media and technology and 2nd ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT-NCON 2019

作者： Khusuwan, Kraisak Waiyamai, Kitsana Science Program in Information Technology Computer Engineering Kasetsart University 343/347 Plum Condo Chaengwattana Chaengwattana Rd. Talat Bang Khen Lak Si Bangkok10210 Thailand Computer Engineering Kasetsart University 50 Ngam Wong Wan Rd. Lad Yao Chatuchak Bangkok10900 Thailand

ISBN: (纸本)9781538680728

EBITDA is a measure of a company's financial performance. It can determine the direction of business decisions for the organization, can be used to analyze and compare profitability among companies. By its linear and non-linear combination, appropriate forecasting technique must be determined. The aim of this paper is to propose a methodology for forecasting EBITDA. We propose a three-steps methodology which is composed of data preparation, forecasting model comparison and evaluation steps. The results from using 10 years monthly data of the Provincial Waterworks Authority (PWA) reveal that the best model is ARIMA-ANN hybrid model. Thus, this model can be used to forecast the other time series data such as Selling Expenses, Operating Expense, General and Administrative Expenses in the future. © 2019 IEEE.

关键词： Forecasting

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Analysis of Seismic Hazard Prediction Using Non Parametric Conic Multivariate Adaptive Regression Splines (C-Mars) Methods

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Journal of Physics: Conference Series 2020年第1期1641卷

作者： Dadang Priyanto Muhammad Zarlis Herman Mawengkang Syahril Efendi Graduate Program Of Computer Science Department Of Computer Science Faculty Of Computer Science And Information Technology Universitas Sumatera Utara Medan Indonesia. Department of Computer Science Faculty of computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia.

The data mining process requires a data set that can be used in determining a number of specific patterns to gain new knowledge. Large data sets (Big data) require special methods to get effective results. Included in this study are using big data related to the earthquake in Lombok. Earthquake research, especially in Lombok, is needed because Lombok is on three active plates in Indonesia, so that the danger of earthquake damage can be minimized. Earthquake data obtained from the Geophysical Station (BMKG) of Mataram has different characteristics and is complex, an appropriate method is needed, namely by detecting a non-parametric method with the Multivariate Adaptive Regression Spline (MARS). The use of the backward stepwise algorithm with the Conical Quadratic programming (CQP) framework of MARS, referred to as CMARS (Conic Multivariate Adaptive Regression Splines), is used for optimizing the results. The conclusions of this study are 1. A mathematical model with a total of 12 basis functions (BF) has contributed to the prediction analysis of the PGA dependent variable. 2. Contributions of the influence of independent variables on the PGA value are the epicenter distance (Repi) of 100% and the Magnitude (Mw) of 31.08608%, while the temperature of the incident location (SUHU) of 5.48525% and depth (Depth) of 3,52988%. 3. Acquired areas that have earthquake hazard levels in the order of the most vulnerable are Malaka, Genggelang, Tegal Maja, Senggigi and Mangsit.

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Utilization of GPS technology in The Maritime Sector on Motor Sailing Yachts

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IOP Conference Series: Earth and Environmental science 2021年第1期698卷

作者： Melani Quintania Danny Faturachman T. Husain Bayu Pasupati Ahmad Taufik Management Department Faculty of Economy Darma Persada University Indonesia Marine Engineering Department Faculty of Marine Technology Darma Persada University Indonesia Information System Department School of Management and Computer Science STMIK Widuri Jakarta – Indonesia and Student of Doctoral Management YAI Persada University Indonesia Student of Doctoral Management YAI Persada University Indonesia Information System Department School of Management and Computer Science STMIK Widuri Jakarta – Indonesia

The utilization of technology only aims to be fast, precise, and accurate in each implementation and find a better solution at that time in several sectors. This study aims to identify the use of Global Positioning technology (GPS) technology to track the position of the motor sailing yachts, specifically with the features used on a device or smart-phone during a trip or expedition. This research uses a type of applied research to solve problems that are faced clearly, and the results can be used as a basis for solving corrective steps. The results found that GPS-technology on motor sailing yachts in 'Pelayaran Rakyat' maritime can receive coordinates point and then send these coordinates to the vessel-tracking systems to determine the position of the motor sailing yachts between users.

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Implementation of Augmented Reality in Pretend Play Therapy for Children with Autism Spectrum Disorder 3

Implementation of Augmented Reality in Pretend Play Therapy ...

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3rd International Conference on Computing and Applied Informatics 2018, ICCAI 2018

作者： Syahputra, M.F. Angkasih, A.P. Purnamawati, S. Andayani, U. Abdullah, D. Sriadhi, S. Iskandar, A. Rosida, T.M. Fitriany, J. Sari, I.N. Utama, M.D. Nanuru, R.F. Department of Information Technology Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Utara Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Informatics Engineering STMIK AKBA Makassar Indonesia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Indonesia Department of Pediatric Universitas Malikussaleh Aceh Utara Indonesia Departement of Social Science Education Program Post Graduate Program Universitas Kanjuruhan Malang Malang Indonesia Faculty of Social and Political Science Universitas Kristen Indonesia Maluku Indonesia Universitas Kristen Indonesia Maluku Ambon Indonesia

Autism Spectrum Disorder is a pervasive developmental disorder that will affect children in terms of interpersonal communication, social interaction, and imaginative levels in play. Many therapies to help the motor neuron performance is one of them Pretend Play. Pretend Play is a therapy that invites children in playing to demonstrate something else and tell how to use objects that are considered in the child's imagination. However, in the era of highly developed technology, many fields have used the Augmented Reality method as a visualization of various aspects. With this method researchers will present the therapeutic visualization of the block to 3D transportation tool that is useful for strengthening motor nerves and visual strength of the child. The system can run well during marker detection, marker movement, and 3D object display with the accuracy of precision angle and distance between virtual world with real world reach 100% with angle 0 at distance 31 cm and the maximum distance from the marker is 46 cm and the maximum angle is 30. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Augmented reality

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Left ventricular hypertrophy detection using electrocardiographic signal

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Scientific reports 2023年第1期13卷 2556页

作者： Cheng-Wei Liu Fu-Hsing Wu Yu-Lun Hu Ren-Hao Pan Chuen-Horng Lin Yung-Fu Chen Guo-Shiang Tseng Yung-Kuan Chan Ching-Lin Wang Division of Cardiology Department of Internal Medicine Tri-Service General Hospital Songshan Branch National Defense Medical Center Taipei Taiwan. Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung Taiwan. Department of Management Information Systems National Chung-Hsing University Taichung Taiwan. La Vida Tec. Co. Ltd. Taichung Taiwan. Preventive Medicine Center National Yang Ming Chiao Tung University Taipei Taiwan. Department of Information Management Tunghai University Taichung Taiwan. Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan. Department of Dental Technology and Materials Science Central Taiwan University of Science and Technology Taichung Taiwan. Division of Cardiology Department of Internal Medicine Taoyuan Armed Force General Hospital Hsinchu Branch Hsinchu Taiwan. Department of Management Information Systems National Chung-Hsing University Taichung Taiwan. ykchan@nchu.edu.tw. Department of Information Management National Chin-Yi University of Technology Taichung Taiwan. clwang@ncut.edu.tw.

Left ventricular hypertrophy (LVH) indicates subclinical organ damage, associating with the incidence of cardiovascular diseases. From the medical perspective, electrocardiogram (ECG) is a low-cost, non-invasive, and easily reproducible tool that is often used as a preliminary diagnosis for the detection of heart disease. Nowadays, there are many criteria for assessing LVH by ECG. These criteria usually include that voltage combination of RS peaks in multi-lead ECG must be greater than one or more thresholds for diagnosis. We developed a system for detecting LVH using ECG signals by two steps: firstly, the R-peak and S-valley amplitudes of the 12-lead ECG were extracted to automatically obtain a total of 24 features and ECG beats of each case (LVH or non-LVH) were segmented; secondly, a back propagation neural network (BPN) was trained using a dataset with these features. Echocardiography (ECHO) was used as the gold standard for diagnosing LVH. The number of LVH cases (of a Taiwanese population) identified was 173. As each ECG sequence generally included 8 to 13 cycles (heartbeats) due to differences in heart rate, etc., we identified 1466 ECG cycles of LVH patients after beat segmentation. Results showed that our BPN model for detecting LVH reached the testing accuracy, precision, sensitivity, and specificity of 0.961, 0.958, 0.966 and 0.956, respectively. Detection performances of our BPN model, on the whole, outperform 7 methods using ECG criteria and many ECG-based artificial intelligence (AI) models reported previously for detecting LVH.

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Load balancing factor using greedy algorithm in the routing protocol for improving internet access 2

Load balancing factor using greedy algorithm in the routing ...

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2nd Nommensen International Conference on technology and Engineering, NICTE 2018

作者： Satria, Ady Sihombing, Poltak Sawaluddin Informatics Engineering Study Program Magister Faculty of Computer Science and Information Technology University of Sumatera Utara Indonesia

Load Balancing is very well applied to the distribution of internet access at any point of the Wi-Fi area so that the use of limited devices can be optimal and on target. The proper use of the Greedy Algorithms when applied to an access point device is very capable in resolving excessive loading in a single resource to take the best choice at every stage in an optimum process. Access point device is also a success factor in running the optimization of the distribution of Wi-Fi access on the Access Point is strongly influenced by the parameters set. In this research, user access factor in one very high Wi-Fi area also influences the possibility of incoming access failure. For the application of the results of this study can be utilized on all access point devices that have limits, but for speed internet access remains at the capacity provided bandwidth. © Published under licence by IOP Publishing Ltd.

关键词： Wireless local area networks (WLAN)

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