检索结果-内蒙古大学图书馆

10th International Conference and Workshop on High Dimensional Data Analysis, ICW-HDDA 2020

作者： Sudrajat, R. Setiana, D. Rejito, J. Pradana, A. Suryana, I. Suryani, M. Zakaria, M.M. Permana, F.C. Ruchjana, B.N. Abdullah, A.S. Department of Computer Science Universitas Padjadjaran Jl. Raya Bandung Sumedang km 21 Jatinangor Sumedang45363 Indonesia Department of History Universitas Padjadjaran Jl. Raya Bandung Sumedang km 21 Jatinangor Sumedang45363 Indonesia Multimedia Education Study Program Universitas Pendidikan Indonesia Jl. Dr. Setiabudhi No. 229 Bandung40154 Indonesia Department of Mathematics Universitas Padjadjaran Jl. Raya Bandung Sumedang km 21 Jatinangor Sumedang45363 Indonesia

The toponymy of naming places in social culture in certain places can describe the characteristics of the society in the past, therefore it is interesting to study how the ancestors of the society culture named their place. This research describes and classifies the naming based on the word structure, including prefixes, syllables it contains, and the complete word in the naming of a "kampung" which is covered in village, describing and classifying the meaning of the naming of the "kampung" based on particular categories, mapping the naming of the "kampung" on the map of the location, as well as developing the study location of the composition of the intersection of sets by taking the sample of "kampung" naming database in districts in East Priangan areas of West Java. The system development method used in this research is the waterfall software development method. Waterfall is a systematic classical model employed in developing software. The stages of Waterfall consists of Communication (Project Initiation & Requirements Gathering), Planning (Estimating, Scheduling, tracking), Modelling (Analysis Design), Construction (Coding and System Test), Deployment (Delivery, support, feedback), and Maintenance. The result of the study showed that 8,75% of the "kampung" names mostly start with the prefix "Ci", "Pa", "Su" with 8,75%, 9,62%, and 5,12% respectively. The syllable mostly contains the meaning of nature, place, feeling, and communication. The meaning of "kampung" naming represents the character of the society, which becomes the guidance for them in living their daily life: loving the nature and the environment, religious spirit, artistic soul, being hard worker, respecting the ancestors, soft feeling, and loving communicating. The result of the intersection of sets of the "kampung" names can help the researchers to determine the destination of the study location under the study, while the set composition describes the taking of each set composition which has the dif

关键词： Location

来源：评论

学校读者我要写书评

暂无评论

An analysis of Fama and French three factor model in market reaction to Indonesia presidential election in 2019 3

An analysis of Fama and French three factor model in market ...

引用

3rd International Conference on science, ICOS 2019

作者： Diaraya Haryanto, L. Lawi, A. Amir, S. Navira, A. Raya, A.R. Computer Science Study Program Faculty of Mathematics and Natural Sciences Hasanuddin University Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences Hasanuddin University Indonesia Department of Accounting Faculty of Economy and Business Hasanuddin University Indonesia

This study reveals stock market reaction to Indonesia presidential election in 2019 using Fama and French Three Factor Model with accounting dependent variable being Average Abnormal Return (AAR) in addition to Earning Per Share (EPS), Debt to Equity ratio (DER), Return on Assets (ROA), and Return on Equity (ROE) as independent variables. The research population was all stocks of the companies registered in the Indonesia Stock Exchange in 2019 with their dividend announcement. The characteristic of the observed object was stock price measured by return, abnormal return and average abnormal return during 111-day-observation comprising of 11 days of windows period observation and 100 days of estimation period. The data from 101 companies as samples determined through the purposive sampling technique were analyzed using SPSS software version 23, which represented all industrial sectors in Indonesia Stock Exchange. The measure and analysis of companies stocks concluded that a gradual rise in AAR values was witnessed in all stock portfolios during the announcement date. © Published under licence by IOP Publishing Ltd.

关键词： Financial markets

来源：评论

学校读者我要写书评

暂无评论

Ethno-informatics for anthroponymy classification in Sumedang District using data mining 10

Ethno-informatics for anthroponymy classification in Sumedan...

引用

10th International Conference and Workshop on High Dimensional Data Analysis, ICW-HDDA 2020

作者： Abdullah, A.S. Rejito, J. Pradana, A. Zakaria, M.M. Permana, F.C. Ruchjana, B.N. Department of Computer Science Universitas Padjadjaran Jl. Raya Bandung Sumedang km 21 Jatinangor Sumedang45363 Indonesia Department of History Universitas Padjadjaran Jl. Raya Bandung Sumedang km 21 Jatinangor Sumedang45363 Indonesia Multimedia Education Study Program Universitas Pendidikan Indonesia Jl. Dr. Setiabudhi No. 229 Bandung40154 Indonesia Department of Mathematics Universitas Padjadjaran Jl.Raya Bandung Sumedang km 21 Jatinangor Sumedang45363 Indonesia

Ethnoscience is used to look at culture from a scientific perspective, helping to understand how to develop different forms of knowledge and beliefs. One of the fields studied in Ethnoscience is Ethno-informatics, which is the application of informatics in culture. The methodology used is the process of knowledge discovery in databases in data mining, the process of automatically extracting knowledge from large databases, to get interesting patterns so that knowledge can be obtained. The application of anthroponymy by exploring the population database in Sumedang district, the results of the 2019 census from the Sumedang District Population Office. Then the anthroponymy application was developed in Sumedang district. The results of data processing show that the top ten names of people who are most used or most favorite in Sumedang district are: Muhammad, Muhamad, Asep, Siti, Agus, Dede, Ade, Ai, Tati, Dadang. Muhammad's name has been a favorite since 2000 until now 2020. Meanwhile, the top ten favorite names that have just appeared in the last ten years (2010-2020) are sorted based on ranking including;Naura, Arsyla, Keyla, Raffa, Rafka, Aqila, Zahra, Keusha and Alesha. In general, the Sundanese name in Sumedang district is still widely used, but generally it is relatively down-to-earth, replaced by new names as an uptake of other cultural names. Changes in self-naming in Sumedang regency in particular, generally self-names in Sundanese culture in West Java, were caused by the rapid development of information and communication technology. This research is expected to provide input for the community, especially for the younger generation as a guide in their daily life. It takes a significant effort from the government to maintain it, so that local culture and wisdom, especially the naming of people in Sumedang district, generally Sundanese culture can be well preserved. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

computer vision in image detection case study of tree damage type using convolutional neural network (CNN) algorithm

引用

AIP Conference Proceedings 2024年第1期2970卷

作者： Z. Nopriyanto R. Andrian R. Safe’i Department of Computer Science Faculty of Mathematics and Natural Science Lampung University Jl. Sumantri Brojonegoro No. 1 Bandar Lampung Lampung 35141 Indonesia Forestry Master Study Program Faculty of Agriculture Lampung University Jl. Sumantri Brojonegoro No. 1 Bandar Lampung Lampung 35141 Indonesia

So far, the identification of 16 types of tree damage is still following the guidelines stated in the Forest Health Monitoring method. The types of tree damage can be recognized by human vision, as well as by computers. computer vision allows computers to identify things that humans can remember. In this case, the study can be realized with computer vision to make work easier. This study aimed to identify 16 types of damage in Forest Health Monitoring using image data or photos with computer vision. The stages of this research are image acquisition (image acquisition), image processing (image preprocessing), and feature extraction (feature extraction). The results showed that computer vision could identify images in JPG/JPEG (Joint Photographic Experts) format, assisted by the Convolutional Neural Network (CNN) algorithm. The percentage value of success using the CNN algorithm reaches 99.06%, with an error detection of 0.94%. However, several classes were incorrectly identified, indicating that the dataset needs improvement. Thus, it can be concluded that identifying 16 types of damage using image data or photos with computer vision has been successfully carried out.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Digital Audio Watermarking by Amplitude Embedding System 15th

Digital Audio Watermarking by Amplitude Embedding System

引用

15th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2019

作者： Lin, Meng-Ju Zhao, Ming Chen, Shuo-Tsung Chen, Ching-Ju Department of Applied Mathematics Tunghai University No. 1727 Sec.4 Taiwan Boulevard Taichung Taiwan40704 Taiwan School of Computer Science Yangtze University Jingzhou CityHubei Province China College of Future Bachelor Program in Interdisciplinary Studies National Yunlin University of Science and Technology TaiwanYunlin64002 Taiwan National Yunlin University of Science and Technology TaiwanYunlin64002 Taiwan

ISBN: (纸本)9789811397134

This paper proposed an amplitude embedding system for digital audio watermarking in the wavelet domain. In this paper, the relationship between the watermarked coefficients and the original coefficients is first presented and then amplitude-based embedding rules are combined into an amplitude-based embedding system that incorporates a system state. For the embedding in this system, the information includes synchronization codes and the watermarks are embedded into the DWT lowest frequency sub-band coefficients which can be described by a matrix form. Moreover, the SNR is rewritten as a performance index which is a quadratic form. Then, a nonlinear function connecting the performance index and the embedding system is derived. Accordingly, an optimization-based embedding system is proposed. Finally, the Karush–Kuhn–Tucker (KKT) Theorem is used to obtain the optimal solution, which is an important formula for watermarking. In addition, the hidden information can be extracted without knowledge of the original audio. In the experiments, the performance of the proposed system is tested and the results verify the high SNR and strong robustness against multimedia signal processing or malicious attacks. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Biomedical data and AI

引用

science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Electronic-offering of fresh fruit bunches oil palm by independent farmer with a blockchain approach

引用

AIP Conference Proceedings 2025年第1期3223卷

作者： Harison Marimin Sukardi Faqih Udin Yani Nurhadriyani Engineer Professional Education Study Program Universitas Andalas Padang Indonesia - 25000 Department of Agro-industrial Technology Faculty of Agricultural Engineering and Technology IPB University Bogor Indonesia - 16680 Department of Computer Science Faculty of Mathematics and Natural Sciences IPB University Bogor Indonesia – 16680

Indonesia meets the needs of 50% of the world's palm oil needs. Sources of Indonesian palm oil, 34 % are produced by independent smallholders. The lack of governance of independent smallholders leads to low productivity of their crops. Global market demands for palm oil derivative sources become an obligation to compete. The development of blockchain technology that favors traceability and transparency is applied in the supply agriculture sector, and this is an opportunity for how blockchain technology can help the palm oil supply chain become transparent and can find its source. This research uses the system development life cycle (SDLC) method. And business process model and notation in business model design. The results of the design of the FFB sale and purchase transaction system with blockchain technology have succeeded in connecting farmer transactions as FFB providers with traders and PKS as FFB buyers. Every transaction sent by the farmer will be locked by a hash, as immunity makes the data sent immutable. The system can display the traceability of transactions while maintaining the integrity of member information.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence for modelling infectious disease epidemics

引用

Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Imaging 3D Chemistry at 1 nm Resolution with Fused Multi-Modal Electron Tomography

arXiv

引用

arXiv 2023年

作者： Schwartz, Jonathan Di, Zichao Wendy Jiang, Yi Manassa, Jason Pietryga, Jacob Qian, Yiwen Cho, Min Gee Rowell, Jonathan L. Zheng, Huihuo Robinson, Richard D. Gu, Junsi Kirilin, Alexey Rozeveld, Steve Ercius, Peter Fessler, Jeffrey A. Xu, Ting Scott, Mary Hovden, Robert Department of Materials Science and Engineering University of Michigan Ann ArborMI United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States Advanced Photon Source Facility Argonne National Laboratory LemontIL United States Department of Material Science and Engineering Northwestern University EvanstonIL United States Department of Materials Science and Engineering University of California at Berkeley BerkeleyCA United States Department of Chemistry and Chemical Biology Cornell University IthacaNY United States Argonne Leadership Computing Facility Argonne National Laboratory LemontIL United States Department of Material Science and Engineering Cornell University IthacaNY United States Kavli Institute at Cornell for Nanoscale Science Cornell University IthacaNY United States Dow Chemical Co. MidlandMI United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory BerkeleyCA United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States Materials Science Division Lawrence Berkeley National Laboratory BerkeleyCA United States Applied Physics Program University of Michigan Ann ArborMI United States

Measuring the three-dimensional (3D) distribution of chemistry in nanoscale matter is a longstanding challenge for metrological science. The inelastic scattering events required for 3D chemical imaging are too rare, requiring high beam exposure that destroys the specimen before an experiment completes. Even larger doses are required to achieve high resolution. Thus, chemical mapping in 3D has been unachievable except at lower resolution with the most radiation-hard materials. Here, high-resolution 3D chemical imaging is achieved near or below one nanometer resolution in a Au-Fe3O4 metamaterial, Co3O4 - Mn3O4 core-shell nanocrystals, and ZnS-Cu0.64S0.36 nanomaterial using fused multi-modal electron tomography. Multi-modal data fusion enables high-resolution chemical tomography often with 99% less dose by linking information encoded within both elastic (HAADF) and inelastic (EDX / EELS) signals. Now sub-nanometer 3D resolution of chemistry is measurable for a broad class of geometrically and compositionally complex materials. © 2023, CC BY.

关键词： Zinc sulfide

来源：评论

学校读者我要写书评

暂无评论

Enhancing healthcare AI stability with edge computing and machine learning for extubation prediction

引用

Scientific Reports 2025年第1期15卷 1-11页

作者： Huang, Kuo-Yang Hsu, Ying-Lin Chung, Che-Liang Chen, Huang-Chi Horng, Ming-Hwarng Lin, Ching-Hsiung Liu, Ching-Sen Xu, Jia-Lang Division of Chest Medicine Department of Internal Medicine Changhua Christian Hospital Changhua Taiwan Institute of Genomics and Bioinformatics National Chung Hsing University Taichung Taiwan Ph.D. Program in Medical Biotechnology National Chung Hsing University Taichung Taiwan Institute of Statistics National Chung Hsing University Taichung Taiwan Division of Chest Medicine Department of Internal Medicine Yuanlin Christian Hospital Changhua Taiwan Department of Applied Mathematics National Chung Hsing University Taichung Taiwan Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung Taiwan

The advancement of the Internet of Medical Things (IoMT) has revolutionized data acquisition and processing in critical care settings. Given the pivotal role of ventilators, accurately predicting extubation outcomes is essential to optimize patient care. This study presents an edge computing-based framework that incorporates machine learning algorithms to predict ventilator extubation success using real-time data collected directly from ventilators. The system was deployed on edge devices to enable on-site inference with minimal latency. Among the evaluated models, Random Forest and XGBoost, the latter demonstrated superior predictive performance under both holdout and tenfold cross-validation schemes. Notably, the edge-based architecture reduced server data transmissions by 83.33%, while improving system stability, resilience, and sustainability. This paper details the model evaluation and demonstrates the feasibility and efficiency of edge intelligence in ventilator weaning decision support. © The Author(s) 2025.

关键词： Clinical decision support system Edge-computing Extubation Internet of medical things Machine learning

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：