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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Classification of Land Cover from Sentinel-2 Imagery Using Supervised Classification Technique (Preliminary study)

Classification of Land Cover from Sentinel-2 Imagery Using S...

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International Conference on information Management and Technology (ICIMTech)

作者： Eka Miranda Achmad Benny Mutiara Ernastuti Wahvu Catur Wibowo Information Systems Department Doctoral Program in Information Technology Gunadarma University Jakarta Indonesia Faculty of Computer Science and Information Technology Gunadarma University Jakarta Indonesia Computer Science Faculty Universitas Jakarta Indonesia

This paper intended to classify land cover of high-resolution satellite image using supervised classification method. The object of this research was the land cover image of the central java area in Indonesia which chosen as an area observed with the cloud cover consideration. The Satellite image was obtained from Sentinel-2 through https://***. Supervised Classification and ArcMap 10.5 was used to classified image object. This research classified the land cover into four classifications class namely Water, Forest, Urban and Bare Land due to show the main classes in the Land Cover Classification of RSNI-l National Standardization Body of Indonesia, RSNI-l was used as reference for classification process. The confusion matrix used to calculate classification accuracy value. Subsequently, confusion matrix results compared to the truth information. The truth information was derived from the actual value of RASTERVALUE obtained from Google Earth. This research shown supervised classification maximum-likelihood classification has been classified land cover into four class (forest, water body, urban and bare land) with overall accuracy = 1 and kappa value = 0.4896. This result showed this research got moderate kappa accuracy value but high overall accuracy value. High accuracy value reached due to fully supervised experiment during classification process.

关键词： Satellites Forestry Training Java Probability Standardization Vegetation mapping

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The Application of AGNES Algorithm to Optimize Knowledge Base for Tourism Chatbot

The Application of AGNES Algorithm to Optimize Knowledge Bas...

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International Conference on information Management and Technology (ICIMTech)

作者： Albert Verasius Dian Sano Tanto Daud Imanuel Mega Intanadias Calista Hendro Nindito Andreas Raharto Condrobimo Computer Science Study Program Bina Nusantara Institute of Creative Technology Malang Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia

Most tourists have limited time and schedule when they are visiting a particular tourism area. One of the main issues faced by those tourists is that they can not make simple queries to search engines about, for example, which sites or places to visit optimally if we only have one day or two-day visit? Search engines are better and better in replying users' queries with relevant answers. However, search engines are not always able to respond queries suited to users' characteristics such as their styles, personalities, knowledge backgrounds, etc. Also, search engines are not as flexible as chatbots in enabling dialogical-communication-queries. This case study aims to respond aformentioned issue by applying hierarchical cluster analysis on a set of tourism sites around Malang city, Malang regency, and Batu city based on AGNES (Agglomerative Nesting) algorithm. The result, then, is fed into chatbot's knowledge base. This chatbot will assist tourists to get information on which possible sites they have to visit optimally if they are under limited vacancy-time constraints.

关键词： Urban areas Knowledge based systems Clustering algorithms Search engines Merging Data mining

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Development of a Web Based Corruption Case Mapping Using Machine Learning with Artificial Neural Network 3

Development of a Web Based Corruption Case Mapping Using Mac...

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3rd International Conference on information Management and Technology, ICIMTech 2018

作者： Noerlina Dewanti, Retno Mursitama, Tirta Nugraha Fairianti, Sheila Putri Kristin, Desi Maya Sasmoko Muqsith, Andi Muhammad Krishti, Nathasya Shesilia Makalew, Brillv Andro Information Systems Department Bina Nusantara University School of Information Systems Jakarta11480 Indonesia Management Department Bina Nusantara University BINUS Business School Undergraduate Program Jakarta11480 Indonesia International Relations Department Faculty of Humanities Bina Nusantara University Jakarta11480 Indonesia Psychology Department Faculty of Humanities Bina Nusantara University Jakarta11480 Indonesia Primary Teacher Education Department Faculty of Humanities Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Bina Nusantara University School of Computer Science Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Mobile Application Technology Program Jakarta11480 Indonesia

ISBN: (纸本)9781538658215

Cumulative state loss over the years caused by corruption in Indonesia has reached a fantastic number of 15 Billion usn up until 2016 [1]. To imagine the severances of it, 10.000 KM of highway can be built with that much amount of fund. Several responses has been done by Indonesian government to fight corruption from both prevention and persecution. This work focuses on the development of a web application aimed to provide insight to corruption case per province in Indonesia. The web application was developed using Machine Learning, specifically Backpropagation Artificial Neural Network (ANN). Web crawling and web scraping techniques are used to gather news content from 7 major news portal in Indonesia. Accuracy is measured by comparing correct prediction by ANN to its true value. Upon finding corruption news, data is saved and further analysis is done to establish the number of corruption news per region. Finally, the output is visualized using Google Map API. The purpose of this work is to provide regional depiction of corruption case to give further insight to be used by decision makers. Upon using this web application, objectivity of program development is expected to increase. The final output of the web application is a map of corruption case in Indonesia per region. The accuracy of ANN used in this work to classify corruption and non-corruption news is 96.91 % using Sigmoid Activation Function. © 2018 IEEE.

关键词： Neural networks

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E-Translator Kawi to Bahasa 2

E-Translator Kawi to Bahasa

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2nd International Joint Conference on Advanced Engineering and Technology, IJCAET 2017 and International Symposium on Advanced Mechanical and Power Engineering, ISAMPE 2017

作者： Sudana, Oka Putra, Darma Sudarma, M. Hartati, Rukmi S. Pradnya Prastika, Ryan Wirdiani, Ayu Study Program of Doctoral Engineering Science Faculty of Engineering Udayana University Kabupaten Badung-Bali80361 Indonesia Information Technology Departement Faculty of Engineering Udayana University Kabupaten Badung-Bali80361 Indonesia Electrical and Computer EngineeringDepartement Faculty of Engineering Udayana University Jalan Raya Bukit Jimbaran Kabupaten Badung-Bali80361 Indonesia

Kawi Language is a kind of language which has developed in Java during the Hindu-Buddhist era in the ancient Indonesian archipelago. As the time passed, Kawi began to come to extinction. To overcome this, preservation of Kawi language can be done by enhancing the understanding of culture through the use of technology. Technology that can be used for cultural preservation is one of NLP based applications such as Kawi Language to Bahasa Translator Application which is developed in this research. The translator application is an application that translates from one language to another. The result of translation of the translator application is a language that is easy to understand by the people or commonly used language. This translator application is developed to processes Kawi Language text into bahasa based on search method which each word inserted. The stemming method used is Bobby Nazief & Mima Adriani Algorithm using Kawi Language rule. The translated Kawi Language text can be a word, sentence or paragraph with the result of the Text in Bahasa as well as words, sentences or paragraphs. © The Authors, published by EDP sciences, 2018.

关键词： Translation (languages)

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Development Conceptual Model and Validation Instrument for E-Learning Succes Model at Universities in Indonesia: Perspectives influence of Instructor's Activities and Motivation

Development Conceptual Model and Validation Instrument for E...

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International Congress on Applied information Technology

作者： Titan Erick Fernando Dina Fitria Murad Harco Leslie Hendric Spits Warnars Kristianus Oktriono BINUS Online Learning Bina Nusantara University Jakarta Indonesia School of Information Systems Bina Nusantara University Jakarta Indonesia BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Language Center Faculty of Humanities Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728158624

ISBN: (纸本)9781728158631

The purpose of this study is to develop conceptual models and validation instruments for the success of e-learning systems in Indonesian universities from factors that influence instructor activity and user motivation. Model development is based on the IS success model of DeLone and Mclean by adding instructor activity and motivational variables based on related literature studies. Evaluation is done by testing the reliability and validity of the instrument. Data collection by distributing instruments online with the snowball method, respondents obtained were 234 used as data analysis with SmartPLS 2.0 software. The results of this study produced a conceptual model consisting of 9 variables and 83 instruments which were validated according to the provisions. From these results, it can be implemented in future research by increasing larger respondents to get results that represent the success of e-learning systems at universities in Indonesia.

关键词： Electronic learning information systems Instruments Reliability Learning systems Organizations Computational modeling

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Systematic literature review on corruption prevention efforts towards sustainable economy in Indonesia: ICT perspective 2

Systematic literature review on corruption prevention effort...

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2nd International Conference on Eco Engineering Development 2018, ICEED 2018

作者： Noerlina Kristin, D.M. Dewanti, R. Sasmoko Mursitama, T.N. Muqsith, A.M. Fajrianti, S.P. Krishti, N.S. Makalew, B.A. Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia Primary Teacher Education Department Faculty of Humanities Bina Nusantara University Jakarta11480 Indonesia Research Interest Group in Educational Technology Bina Nusantara University Jakarta11480 Indonesia International Relations Department Faculty of Humanities Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Psychology Department Faculty of Humanities Bina Nusantara University Jakarta11480 Indonesia Mobile Application and Technology Program School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Corruption is an extraordinary crime and must be eradicated not only as fast as possible, but also as efficient and as effective as possible as it hinders the process of achieving sustainable economy. In current modern society, information technology exists as a multidisciplinary tool to solve lingering problems that could not be solved by traditional efforts. This paper aims to provide knowledge regarding identified efforts to fight corruption specifically in Indonesia region. Systematic Literature Review (SLR) methodology is used as the methodology, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow is used as framework to identify relevant records. Scopus database is used as data source. A total of 10 records are labelled as relevant to this research, and the findings are synthesized. It was found from the records that the main areas of interest for ICT research for corruption prevention in Indonesia are (1) information transparency, (2) operational enhancement, (3) success evaluation, (4) security and fraud, and (4) decision support. The findings are expected to assist future research in term of providing valuable insights on corruption prevention using ICT in Indonesia. © 2018 Institute of Physics Publishing. All rights reserved.

关键词： Crime

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Analysis of Decision Tree and Smooth Support Vector Machine Methods on Data Mining

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Journal of Physics: Conference Series 2019年第1期1255卷

作者： Natalina Br Sitepu Sawaluddin M Zarlis Syahril Efendi Hanna Willa Dhany Informatic Engineering Study Program Magister Faculty of Computer Science and Information Technology Universitas Sumatra Utara Medan - Indonesia

This research is about smooth support vector machine (SSVM) and Decision Tree in data mining. Many researchers conduct and develop methods to improve the accuracy and classification of data on good results. This research was conducted by conducting an experiment on STMIK Neumann Medan student data. In this study, it was concluded that Decision Tree performance is better than SSVM, Decision Tree gets very good results that promise to help find the best students to get scholarships. This study is better than SSVM. The training process has a difference of 11.04% and the testing process is 10.08% with each Accuracy.

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Testing Real-Time Applications on Windows 10 IOT Using the Nyquist Theory

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Journal of Physics: Conference Series 2019年第1期1255卷

作者： Zulfahmi Syahputa M. Zarlis Beny B. Nasution Informatics Engineering Study Program Magister Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan-Indonesia

Raspberry Pi is a mini-computer that is provided to carry out activities quickly and precisely, but Raspberry Pi was created to not be able to do the real-time system with the support of Windows 10 IoT operating system, so the real-time system can be done on Raspberry Pi. The real-time applied in the application needs to be tested with the Nyquist theory. The purpose of this study was to get real-time system measurements available on Windows 10 IoT. This test is done using the Nyquist theory by calculating the results of measurements on mp3 streaming performed on Windows 10 IoT.

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Development and Validation of Instruments Adoption FinTech services in Indonesia (Perspective of Trust and Risk)

Development and Validation of Instruments Adoption FinTech s...

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International Conference on Sustainable information Engineering and Technology (SIET)

作者： Erick Fernando Surjandy Meyliana Derist Touriano Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Information Systems Audit Program Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Information Technology Department Faculty of computer science and engineering Adiwangsa Jambi University Jambi Indonesia

ISBN: (纸本)9781538674086;9781538674079

This study explores the influence of trust and risk in the use of FinTech in Indonesian society. This exploration is based on findings from previous studies that suggest that trust and risk are important aspects in considering the use of FinTech. The focus of this research is to develop and validate the research instruments that will be used next. The basic theory of model development uses the theory of Technology Acceptance Model (TAM) developed by Davis. The models and instruments developed will be through pilot studies involving 133 communities as respondents. This study is quantitative research and data obtained were analyzed using smart pls v3.0. This analysis is conducted to ensure the level of reliability and validity of the instrument. The results of this analysis find 31 instruments that are stated reliable and validity. So it can be used for collecting data from a survey in accordance with the research.

关键词： Instruments Reliability information systems Business Adaptation models Economics Transportation

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