检索结果-内蒙古大学图书馆

2nd International Conference on Energy, Environmental and Information System, ICENIS 2017

作者： Prambudi, Dwi Arief Widodo, Catur Edi Widodo, Aris Puji Magister Program of Information System School of Postgraduate Studies Diponegoro University Semarang50242 Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang50275 Indonesia Department of Computer Science Faculty of Science and Mathematics Diponegoro University Semarang50275 Indonesia

The choice of contraceptive tools is not an easy thing because the risks or effects will give impact on the body that never using it previously. in the other side, there is no contraception always suit for everybody because the circumstances of each body is different, so the extensive knowledge must be needed to know the advantages and disadvantages of each contraceptive tools then adjusted to the user's *** expert system for contraceptive tools uses Forward Chaining search method combined with Certainty Factors Method. These method value the patient's indication. The Expert system gives the output data which define the kind of tool uses of the patient. the results obtained will be able to help people to find indications that lead to appropriate contraceptive tools and advice or suggestions about these tools. The success rate of the contraceptive tools decision experienced by experienced by the user by using forward chaining combined with the CF computation method is also influenced by the number of indication criteria selected by the user. Based on testing that has been done, expert system contraception tools has accuracy level equal to 75%. © 2018 The Authors, published by EDP sciences.

关键词： Expert systems

来源：评论

学校读者我要写书评

暂无评论

Technology of Holobox Augmented Reality Grebeg Pancasila rite for Mathematics learning in Elementary School

引用

Journal of Physics: Conference Series 2021年第1期1869卷

作者： S Septinaningrum K A Hakam W Setiawan M Agustin W Sopandi A S Surur Study Program of Elementary Education Postgraduate Program Universitas Pendidikan Indonesia Bandung Indonesia Department of Islamic Elementary Teacher Education State Institute of Tulungagung for Islamic Studies Tulungagung Indonesia Department of Character and General Education Faculty of Social Science Education Universitas Pendidikan Indonesia Bandung Indonesia Department of Computer Science Education Universitas Pendidikan Indonesia Bandung Indonesia Department of Early Childhood Education Universitas Pendidikan Indonesia Bandung Indonesia

The use of technology in learning needs to be encouraged starting from the elementary school level. Another thing that needs to be encouraged is local wisdom-based media to support character learning. This study aims to develop holobox augmented reality technology media of local wisdom of the Grebeg Pancasila rite for mathematics learning in elementary schools. This research produces multimedia based on technology Holobox Augmented Reality (AR) with the content of material Grebeg Pancasila for learning mathematics and building spaces in elementary schools. The method in media development using the Development Life Cycle developed by Luther consists of six stages, namely: concept, design, material collection, assembly, testing, and distribution. The media produced meets the standard of feasibility and can provide information and knowledge about understanding the concept of flat and space structures through the flag and gunungan symbols in the Rite Grebeg Pancasila. The implication of this study is that cultural diversity in local wisdom of the Rite Grebeg Pancasila can be used as a medium for learning mathematics in elementary schools (MI/SD) through Technology Holobox. Augmented Reality.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Novel Ensemble XGBoost and Deep Q-Network for Pregnancy Risk Prediction on Multi-Class Imbalanced Datasets

引用

ICT Express 2025年

作者： Kurnianingsih Sou Nobukawa Melyana Nurul Widyawati Cipta Pramana Nurseno Bayu Aji Afandi Nur Aziz Thohari Dwiana Hendrawati Eri Sato-Shimokawara Naoyuki Kubota Department of Electrical Engineering Politeknik Negeri Semarang H. Soedarto Tembalang Semarang 50275 Central Java Indonesia Department of Computer Science Chiba Institute of Technology 2-17-1 Tsudanuma Narashino 275-0016 Chiba Japan Department of Postgraduate Program Poltekkes Kemenkes Semarang Jl. Tirto Agung Pedalangan Kecamatan Banyumanik Semarang 50268 Central Java Indonesia Department of Obstetrics and Gynecology KRMT Wongsonegoro Hospital Jl. Tembalang Semarang 50272 Central Java Indonesia Department of Mechanical Engineering Politeknik Negeri Semarang H. Soedarto Tembalang Semarang 50275 Central Java Indonesia Faculty of Systems Design Tokyo Metropolitan University Tokyo 1910065 Japan

Addressing imbalanced data is essential for accurate prediction. We propose a novel ensemble method of XGBoost and deep Q-learning networks (DQN) for pregnancy risk prediction. First, we train the majority class utilizing XGBoost. We subsequently utilize DQN to train the minority class into binary classifications. Finally, we use the trained models from DQN and XGBoost in ensemble learning to generate the final classification results. The XGBoost-DQN model achieves high performance with 0.9819 in precision, recall, F1-score, and accuracy, outperforming several baseline classifiers on private data from 5,313 pregnant women in Indonesia and showing superior results on public datasets.

关键词： Imbalanced dataset ensemble learning XGBoost Tree Deep Q-Network pregnancy risk

来源：评论

学校读者我要写书评

暂无评论

Optimization Sentiments of Analysis from Tweets in myXLCare using Naïve Bayes Algorithm and Synthetic Minority Over Sampling Technique Method

引用

Journal of Physics: Conference Series 2020年第1期1471卷

作者： Dedi Dwi Saputra Windu Gata Nia Kusuma Wardhani Ketut Sakho Parthama Hendra Setiawan Sularso Budilaksono Dimas Yogatama Agus Hadiyatna Endah Putri Purnamasari Bryan Pratama Deny Novianti Master of Computer Science-Postgraduate Program Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri Jakarta Indonesia Graduate School Master's Degree Computer Science Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri Jakarta Indonesia Master Faculty of Computer Science-Postgraduate Program University of Mercubuana Jakarta Indonesia Graduate School Master's Degree Computer Science University Paramita Indonesia Jakarta Indonesia Graduate School Master's Degree Computer Science STMIK Bani Saleh Jakarta Indonesia Graduate School Master's Degree Computer Science University of Persada Indonesia YAI Jakarta Indonesia Graduate School Master's Degree Computer Science University of Gadjah Mada Jakarta Indonesia

Text mining can be used to classify opinions about complaints or not complaints experienced by XL customers. This study aims to find and compare classifications in the sentiments of analysis from the view of XL customers. This dataset was derived from tweets of XL customers written on myXLCare Twitter account. In text mining techniques, 'transform case', 'tokenize', 'token filters by length', 'n-gram', 'stemming' were used to build classification and sentiments of analysis. Gataframework tools were used to help during preprocessing and cleansing processes. RapidMiner is used to help create the sentiment of analysis to search and compare two different classifications methods between datasets using the Naïve Bayes algorithm only and Naïve Bayes algorithm with Synthetic Minority Over-sampling Technique (SMOTE). The results of the two methods in this study found that the highest results were using the Naïve Bayes algorithm with Synthetic Minority Over-sampling Technique (SMOTE) with an accuracy of 86.33%, precision 82.85%, and recall ratio 92.38%.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Global prevalence and gender inequalities in at least 60 min of self-reported moderate-to-vigorous physical activity 1 or more days per week:An analysis with 707,616 adolescents

引用

Journal of Sport and Health science 2024年第5期13卷 709-716页

作者： Raphael H.O.Araujo AndrúO.Werneck Clarice L.Martins Luciana L.Barboza Rafael M.Tassitano Nicolas Aguilar-Farias Gilmar M.Jesus Robinson Ramírez-Véelez Riki Tesler Adewale L.Oyeyemi Ellen C.M.Silva Robert G.Weaver Mark S.Tremblay Javier Brazo-Sayavera Grégore I.Mielke Danilo R.P.Silva Graduation Program in Health Sciences Londrina State University(UEL)Londrina 86057-970Brazil Center for Epidemiological Research in Nutrition and Health Department of NutritionSchool of Public HealthUniversidade de Sao Paulo(USP)Sao Paulo 01246-904Brazil Department of Physical Education Federal University of ParaıbaParaıba 58051-900Brazil Research Centre of Physical Activity Health and LeisureFaculty of SportsLaboratory for Integrative and Translational Research in Population Health(ITR)University of PortoPorto 4500Portugal Postgraduate Program in Physical Education University of BrasıliaBrasılia 70910-900Brazil Department of Kinesiology and Community Health University of Illinois at Urbana-ChampaignChampaignIL 61801-3841USA Department of Physical Education Sports and RecreationUniversidad de La FronteraTemuco 4680000Chile Department of Health State University of Feira de SantanaFeira de Santana 44036-900Brazil Navarrabiomed Hospital Universitario de Navarra(HUN)Navarra Institute for Health Research(IdiSNA)Universidad Publica de Navarra(UPNA)Pamplona 31006Spain Facultad de Ciencias de la Educacion Unidad Central del Valle del Cauca(UCEVA)TuluaValle del Cauca 763022Colombia Health Management Department Faculty of Heath sciencesAriel UniversityAriel 40700Israel College of Health Solutions Arizona State UniversityPhoenixAZ 85004USA Arnold School of Public Health University of South CarolinaColumbiaSC 29208USA Healthy Active Living and Obesity Research Group Children’s Hospital of Eastern Ontario Research InstituteOttawaON K1H 8L1Canada Department of Pediatrics University of OttawaOttawaON K1H 8L1Canada Department of Health Sciences Carleton UniversityOttawaON K1S 5B6Canada Department of Sports and Computer Science Universidad Pablo de Olavide(UPO)Seville 41013Spain Centro Universitario Regional Noreste Universidad de la RepublicaRivera 40000Uruguay School of Public Health The University of QueenslandBrisbaneQLD 4006Australia Department of Physical Education

Background:Guidelines recommend that adolescents should accumulate an average of 60 min per day of moderate-to-vigorous physical activity(MVPA).However,using only this cut-off could hide important *** instance,from a population-level point of view,increasing physical activity for those with no or low physical activity could provide more health benefits than increasing physical activity for those with intermediate ***,including a more sensitive cut-point of≥1 days per week could be an additional strategy for identifying those with low access/opportunities for physical activity ***,the current study aims to estimate the prevalence of≥60 min of MVPA≥1 days per week among adolescents globally,and to describe any relevant gender ***:We used representative datasets from 146 countries/territories collected between 2003 and *** was *** were grouped into younger(≤14 years old)and older(>14 years old)*** Poisson regression models were used to identify the relative differences in≥60 min of MVPA≥1 days per week between boys and girls,and random-effects meta-analysis models were used to identify the pooled *** were stratified by country and ***:Approximately 80%of both younger and older adolescents reported≥60 min of MVPA≥1 days per *** prevalence was≥94%in Europe and Central Asia and North America,while the estimates for the other regions were<77%.The prevalence of≥60 min of MVPA≥1 days per week was higher among boys than girls,with the largest differences occurring among the oldest adolescents(Prevalence ratio_(≤14y)=1.04(95%confidence interval(95%CI)):1.03-1.04)*** ratio_(>14y)=1.09(95%CI:1.08-1.10)).Conclusion:Approximately 8 out of 10 adolescents reported accumulating≥60 min of MVPA≥1 days per week,with notable differences between *** differences were observed in several countries,especially among the oldest *** fo

关键词： Global School-based Student Health Survey Health Behaviour Among School-aged Children survey Inequality Surveillance Youth

来源：评论

学校读者我要写书评

暂无评论

Specify of Estimation Using C4.5 Algorithm of Software Project Estimation at the Point of Sales Application with Cocomo II 4

Specify of Estimation Using C4.5 Algorithm of Software Proje...

引用

4th International Conference on science and Technology, ICST 2018

作者： Novel, Kadinar Sfenrianto, Sfenrianto Gata, Windu Nainggolan, Kaman Wahyudi, Mochamad Jakarta Indonesia Information Systems Management Department BINUS Graduate Program Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia Master of Computer Science Postgraduate Programs STMIK Nusa Mandiri Jakarta Indonesia

ISBN: (纸本)9781538658130

In software development, it is required an appropriate estimate. One of the most commonly used software project estimation models is Constructive Cost Model(COCOMO II). The model is often used to obtain accurate results in estimating important factors such as cost and human resources. However, to obtain the more accurate estimation results, this study proposes a C4.5 algorithm based on COCOMO II estimation results. In this study, software project estimates are used in the Point of Sales (POS) applications. Based on these data with COCOMO II method, it is estimated that the schedule, staff, and cost are also specifying estimation from the result of COCOMO II using a C4.5 algorithm. The accuracy of the estimation results is around 90% with Algorithm C4.5. The value can be used as a reference for the development of the next POS software project. © 2018 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

引用

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

来源：评论

学校读者我要写书评

暂无评论

Framework Design of Information Retrieval System for Official Letter Using Extraction of Geometry Feature Method

引用

Journal of Physics: Conference Series 2020年第3期1477卷

作者： L Fabrianto Hendra Surohman Sfenrianto NM Faizah Master of Computer Science-Postgraduate Program STMIK Nusa Mandiri Jakarta Indonesia Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 Information Systems Department Universitas Tama Jagakarsa Jakarta Indonesia

In a government institution many official letters are made daily, the letters have an official format in the Tanggal, No. Surat, Lampiran dan Hal. area, with an OCR (Optical Character Recognition) scanner an official letter can be digitized and processed to obtain characters that will be used as searches, but in fact in certain desired parts there are often handwriting, a method of pattern recognition is needed to recognize the handwriting. The process in this study including of normalization, segmentation and pattern recognition from labels in the Tanggal, No. Surat, Lampiran dan Hal. area,this system will generate output in text form, the output text is result that will use as a term in the process of the Information Retrieval System, so the search for an official letter will be easy to do. This study is only makes the framework design for the next research authors will explain the detail of process and results obtained.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Measurement of activeness lecturers in SmartLecturer based learning using fuzzy Mamdani and Sugeno

引用

IOP Conference Series: Materials science and Engineering 2020年第1期725卷

作者： Sfenrianto Sfenrianto Muhammad Yasin Kaman Nainggolan Mesra Betty Yel Information Systems Management Department BINUS Graduate Program – Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 Informatics Management Politeknik LP3I Jakarta Indonesia 10450 Master of Computer Science - Postgraduate Programs STMIK Nusa Mandiri Faculty of Computer Science STIKOM CKI Jakarta Indonesia

The success of learning in a classroom that uses supporting media demands lecturer activity. The level of activity of lecturers in class and using learning media such as Smart lecturer can be measured using a fuzzy logic approach. This study aims to measure the level of active lecturers using the approach, namely: Mamdani and Sugeno method. Stages of lecturer activity measurement by forming a fuzzy set, composition rules as many as 24 rules and the defuzzification process using the centroid method that produces the level of activity of each is low, medium and high. Based on 173 lecturer activity data, the results of the Mamdani method indicates low 66%, medium 31%, and high 3%. While the Sugeno method produces a level of activity low 75%, moderate 17%, and high 8%. Therefore, Mamdani method is more suitable for the calculation because the spread of results is relatively evenly distributed at each level.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Exploring Data Augmentation to Improve Music Genre Classification with ConvNets

Exploring Data Augmentation to Improve Music Genre Classific...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Rafael L. Aguiar Yandre M.G. Costa Carlos N. Silla Postgraduate Program in Informatics (PPGIa) Pontifical Catholic University of Paraná (PUCPR) Curitiba Brazil Graduate Program in Computer Science (PCC) State University of Maringá (UEM) Maringá Brazil

In this work we address the automatic music genre classification as a pattern recognition task. The content of the music pieces were handled in the visual domain, using spectrograms created from the audio signal. This kind of image has been successfully used in this task since 2011 by extracting handcrafted features based on texture, since it is the main visual attribute found in spectrograms. In this work, the patterns were described by representation learning obtained with the use of convolutional neural network (CNN). CNN is a deep learning architecture and it has been widely used in the pattern recognition literature. Overfitting is a recurrent problem when a classification task is addressed by using CNN, it may occur due to the lack of training samples and/or due to the high dimensionality of the space. To increase the generalization capability we propose to explore data augmentation techniques. In this work, we have carefully selected strategies of data augmentation that are suitable for this kind of application, which are: adding noise, pitch shifting, loudness variation and time stretching. Experiments were conducted on the Latin Music Database (LMD), and the best obtained accuracy overcame the state of the art considering approaches based only in CNN.

关键词： Spectrogram Music Machine learning Training Task analysis Visualization Multiple signal classification

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：