检索结果-内蒙古大学图书馆

arXiv 2017年

作者： Murdock, Jaimie Allen, Colin Borner, Katy Light, Robert McAlister, Simon Ravenscroft, Andrew Rose1, Robert Rose, Doori Otsuka, Jun Bourget, David Lawrence, John Reed, Chris Program in Cognitive Science Indiana University BloomingtonIN United States School of Informatics and Computing Indiana University BloomingtonIN United States Department of History and Philosophy of Science and Medicine Indiana University BloomingtonIN United States Indiana University Network Science Institute IUNI BloomingtonIN United States User-Centered Social Media Department of Computer Science and Applied Cognitive Science University of Duisburg-Essen Duisburg Germany International Centre for Public Pedagogy ICPuP Cass School of Education and Communities University of East London London Department of Mathematics Indiana University BloomingtonIN United States Department of Philosophy Kyoto University Kyoto Japan Department of Philosophy University of Western Ontario LondonON Canada Centre for G Technology University of Dundee Dundee United Kingdom

We show how faceted search using a combination of traditional classification sys-tems and mixed-membership topic models can go beyond keyword search to inform re-source discovery, hypothesis formulation, and argument extraction for interdisciplinary research. Our test domain is the history and philosophy of scientific work on animal mind and cognition. The methods can be generalized to other research areas and ul-timately support a system for semi-automatic identification of argument structures. We provide a case study for the application of the methods to the problem of identi-fying and extracting arguments about anthropomorphism during a critical period in the development of comparative psychology. We show how a combination of classifi-cation systems and mixed-membership models trained over large digital libraries can inform resource discovery in this domain. Through a novel approach of \drill-down" topic modeling|simultaneously reducing both the size of the corpus and the unit of analysis|we are able to reduce a large collection of fulltext volumes to a much smaller set of pages within six focal volumes containing arguments of interest to historiansand philosophers of comparative psychology. The volumes identified in this way did not appear among the first ten results of the keyword search in the HathiTrust digital library and the pages bear the kind of \close reading" needed to generate original in-terpretations that is the heart of scholarly work in the humanities. Zooming back out, we provide a way to place the books onto a map of science originally constructed from very different data and for different purposes. The multilevel approach advances un-derstanding of the intellectual and societal contexts in which writings are interpreted. Copyright © 2017, The Authors. All rights reserved.

关键词： Digital libraries

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Time-domain methods for quantifying dynamic cerebral blood flow autoregulation: Review and recommendations. A white paper from the Cerebrovascular Research Network (CARNet)

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Journal of Cerebral Blood Flow and Metabolism 2024年第9期44卷 1480-1514页

作者： Kostoglou, Kyriaki Bello-Robles, Felipe Brassard, Patrice Chacon, Max Claassen, Jurgen AHR Czosnyka, Marek Elting, Jan-Willem Hu, Kun Labrecque, Lawrence Liu, Jia Marmarelis, Vasilis Z Payne, Stephen J Shin, Dae Cheol Simpson, David Smirl, Jonathan Panerai, Ronney B Mitsis, Georgios D Department of Electrical and Computer Engineering McGill University Montreal QC Canada Institute of Neural Engineering Graz University of Technology Graz Austria Departamento de Ingeniería Informática Universidad de Santiago de Chile Santiago Chile Department of Kinesiology Faculty of Medicine Université Laval QC Canada Research Center of the Institut universitaire de cardiologie et de pneumologie de Québec Quebec QC Canada Department of Geriatrics Radboud University Medical Center Research Institute for Medical Innovation and Donders Institute Nijmegen Netherlands Cerebral Haemodynamics in Ageing and Stroke Medicine (CHiASM) Department of Cardiovascular Sciences University of Leicester Leicester United Kingdom Department of Clinical Neurosciences Neurosurgery Department University of Cambridge Cambridge United Kingdom Department of Neurology and Clinical Neurophysiology University Medical Center Groningen Groningen Netherlands Medical Biodynamics Program Division of Sleep and Circadian Disorders Brigham and Women's Hospital Harvard Medical School Boston MA United States Division of Sleep Medicine Harvard Medical School Boston MA United States Laboratory for Engineering and Scientific Computing Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Department Biomedical Engineering Viterbi School of Engineering University of Southern California Los Angeles CA United States Institute of Applied Mechanics National Taiwan University Taipei Taiwan Institute of Sound and Vibration Research University of Southampton Southampton United Kingdom Cerebrovascular Concussion Laboratory Faculty of Kinesiology University of Calgary Calgary AB Canada Sport Injury Prevention Research Centre Faculty of Kinesiology University of Calgary Calgary AB Canada Hotchkiss Brain Institute University of Calgary Calgary AB Canada NIHR Leicester Biomedical Research Centre Bri

Cerebral Autoregulation (CA) is an important physiological mechanism stabilizing cerebral blood flow (CBF) in response to changes in cerebral perfusion pressure (CPP). By maintaining an adequate, relatively constant supply of blood flow, CA plays a critical role in brain function. Quantifying CA under different physiological and pathological states is crucial for understanding its implications. This knowledge may serve as a foundation for informed clinical decision-making, particularly in cases where CA may become impaired. The quantification of CA functionality typically involves constructing models that capture the relationship between CPP (or arterial blood pressure) and experimental measures of CBF. Besides describing normal CA function, these models provide a means to detect possible deviations from the latter. In this context, a recent white paper from the Cerebrovascular Research Network focused on Transfer Function Analysis (TFA), which obtains frequency domain estimates of dynamic CA. In the present paper, we consider the use of time-domain techniques as an alternative approach. Due to their increased flexibility, time-domain methods enable the mitigation of measurement/physiological noise and the incorporation of nonlinearities and time variations in CA dynamics. Here, we provide practical recommendations and guidelines to support researchers and clinicians in effectively utilizing these techniques to study CA. © The Author(s) 2024.

关键词： CARNet cerebral autoregulation cerebral blood flow time-domain methods white paper

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Widely Tunable Berry Curvature in the Magnetic Semimetal Cr1+δTe2

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Advanced Materials 2023年第12期35卷

作者： Fujisawa, Yuita Pardo-Almanza, Markel Hsu, Chia-Hsiu Mohamed, Atwa Yamagami, Kohei Krishnadas, Anjana Chang, Guoqing Chuang, Feng-Chuan Khoo, Khoong Hong Zang, Jiadong Soumyanarayanan, Anjan Okada, Yoshinori Okinawa904-0495 Japan Department of Physics National Sun Yat-sen University Kaohsiung80424 Taiwan Physics Division National Center for Theoretical Sciences Taipei10617 Taiwan Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore Center for Theoretical and Computational Physics National Sun Yat-sen University Kaohsiung80424 Taiwan Institute of High Performance Computing Agency for Science Technology and Research Singapore138632 Singapore Department of Physics and Astronomy University of New Hampshire DurhamNH 03824 United States Materials Science Program University of New Hampshire DurhamNH 03824 United States Department of Physics National University of Singapore Singapore117551 Singapore Institute of Materials Research and Engineering Agency for Science Technology and Research Singapore138634 Singapore

Magnetic semimetals have increasingly emerged as lucrative platforms hosting spin-based topological phenomena in real and momentum spaces. Cr1+δTe2 is a self-intercalated magnetic transition metal dichalcogenide (TMD), which exhibits topological magnetism and tunable electron filling. While recent studies have explored real-space Berry curvature effects, similar considerations of momentum-space Berry curvature are lacking. Here, the electronic structure and transport properties of epitaxial Cr1+δTe2 thin films are systematically investigated over a range of doping, δ (0.33 – 0.71). Spectroscopic experiments reveal the presence of a characteristic semi-metallic band region, which shows a rigid like energy shift with δ. Transport experiments show that the intrinsic component of the anomalous Hall effect (AHE) is sizable and undergoes a sign flip across δ. Finally, density functional theory calculations establish a link between the doping evolution of the band structure and AHE: the AHE sign flip is shown to emerge from the sign change of the Berry curvature, as the semi-metallic band region crosses the Fermi energy. These findings underscore the increasing relevance of momentum-space Berry curvature in magnetic TMDs and provide a unique platform for intertwining topological physics in real and momentum spaces. © 2023 The Authors. Advanced Materials published by Wiley-VCH GmbH.

关键词： Density functional theory

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Location embedding based pairwise distance learning for fine-grained diagnosis of urinary stones

arXiv

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arXiv 2024年

作者： Jin, Qiangguo Huang, Jiapeng Sun, Changming Cui, Hui Xuan, Ping Su, Ran Wei, Leyi Wu, Yu-Jie Wu, Chia-An Duh, Henry B.L. Lu, Yueh-Hsun School of Software Northwestern Polytechnical University Shaanxi China Yangtze River Delta Research Institute of NPU Suzhou China CSIRO Data61 Sydney Australia Department of Computer Science and Information Technology La Trobe University Melbourne Australia School of Engineering Shantou University Guangdong China College of Intelligence and Computing Tianjin University Tianjin China School of Information Xiamen University Xiamen China AIDD Faculty of Applied Science Macao Polytechnic University China Department of Radiology Shuang-Ho Hospital Taipei Medical University Taipei Taiwan Master of Public Health Program National Yang Ming Chiao Tung University Taipei Taiwan School of Design The Hong Kong Polytechnic University Hong Kong Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan

The precise diagnosis of urinary stones is crucial for devising effective treatment strategies. The diagnostic process, however, is often complicated by the low contrast between stones and surrounding tissues, as well as the variability in stone locations across different patients. To address this issue, we propose a novel location embedding based pairwise distance learning network (LEPD-Net) that leverages low-dose abdominal X-ray imaging combined with location information for the fine-grained diagnosis of urinary stones. LEPD-Net enhances the representation of stone-related features through context-aware region enhancement, incorporates critical location knowledge via stone location embedding, and achieves recognition of fine-grained objects with our innovative fine-grained pairwise distance learning. Additionally, we have established an in-house dataset on urinary tract stones to demonstrate the effectiveness of our proposed approach. Comprehensive experiments conducted on this dataset reveal that our framework significantly surpasses existing state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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Journal of Allergy and Clinical Immunology: Global 2024年第4期3卷 100326-100326页

作者： Herrmann, Susan Kulkarni, Rishabh Trevenen, Michelle Karuppasamy, Hana Willis, Catherine Berry, Renee Von Ungern-Sternberg, Britta Warrier, Niklesh Li, Ian Murray, Kevin Lucas, Michaela School of Medicine The University of Western Australia Perth Australia Department of Immunology Sir Charles Gairdner Hospital Perth Australia Centre for Applied Statistics School of Physics Mathematics and Computing The University of Western Australia Perth Australia Department of Immunology Fiona Stanley Hospital Perth Australia Department of Anaesthesia and Pain Medicine Perth Children's Hospital Perth Australia Division of Emergency Medicine Anaesthesia and Pain Medicine Medical School The University of Western Australia Perth Australia Institute for Paediatric Perioperative Excellence The University of Western Australia Perth Australia Perioperative Medicine Team Perioperative Care Program Telethon Kids Institute Perth Australia National Centre for Student Equity in Higher Education Curtin University Perth Australia School of Population Health The University of Western Australia Perth Australia

Background: The clinical consequences of an antibiotic allergy label are detrimental, impacting health care delivery and patient outcomes. We assessed hospital inpatients with intent to offer free antibiotic allergy labeling (AAL) assessment within a randomized controlled trial. Objective: We sought to determine the feasibility of establishing an adult antibiotic allergy delabeling service in a Western Australian tertiary public hospital. Methods: Inpatients (N = 1503) with AAL were identified through medical records and screened for eligibility to participate in a randomized controlled trial. Those recruited were randomized to undergo assessment by skin testing ± oral challenge, or direct oral challenge. A control group received usual care. Results: Of the 1503 inpatients with an AAL, 429 (28.5%) were eligible for AAL assessment. The primary excluding factor (1074 [71.5%]) was contraindicated medication use (387 [36.0%]), followed by cognitive impairment (298 [27.9%]). Thirty-nine patients were randomized, of which 20 received allergy testing and 19 usual care;all patients were followed up for 5 years. Older patients were less likely to be eligible (10-year increase: odds ratio, 0.82;95% CI, 0.77-0.88;P < .0001), whereas surgical patients were more likely to be eligible than medical patients (odds ratio, 2.49;95% CI, 1.97-3.16;P < .0001). Conclusions: Antibiotic allergy delabeling in the acute care context is not straightforward. Competing clinical concerns and patient acceptance are some barriers to an inpatient service. Nor is it apparent that inpatient versus outpatient testing is cost saving although select patient groups may benefit. Testing younger people and those with predicted high antibiotic usage will derive maximal individual and health system benefits. © 2024 The Authors

关键词： adult inpatients Antibiotic allergy delabeling drug allergy

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Middle East and North African Health Informatics Association (MENAHIA): A New Opportunity for the Region

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Yearbook of Medical Informatics 2019年第1期28卷 292-296页

作者： Riyad Alshammari Najeeb Al-Shorbaji Zakiuddin Ahmed Zaid Al-Hamadan Abdulwahhab Alshammari Osama El Hassan Dari Alhuwail Salim Al Salmi Eiman Al-Jafar Hassan Ghazal College of Public Health and Health Informatics King Saud bin Abdulaziz University for Health Sciences Riyadh Saudi Arabia King Abdullah International Medical Research Center Riyadh Saudi Arabia Ministry of the National Guard- Health Affairs Riyadh Saudi Arabia President of the Saudi Health Informatics Association President of the eHealth Development Association of Jordan Program Director RAHAH King Saud University Project Director Riphah Institute of Health Improvement & Safety CEO Digital Care Pakistan Associate Professor Jordan University of Science and Technology College of Applied Medical Sciences King Saud bin Abdulaziz University for Health Sciences Riyadh Saudi Arabia Vice-Chair of Emirates Health Informatics Society Head of eHealth section at Dubai Health Authority Dubai United Arab Emirates Department of Information Science College of Computing Sciences and Engineering Kuwait University Kuwait Health Informatics Unit Dasman Diabetes Institute Kuwait Associate Deanship for Health Information Management Program Oman College of Health Sciences Muscat Sultanate of Oman Department of Health Informatics and Information Management Faculty of Allied Health Sciences Kuwait University Kuwait. National Center for Scientific and Technological Research Rabat and President of the Moroccan Society for Telemedicine and eHealth Rabat Morocco

During the 2018 IMIA General Assembly (GA) held on Oct 2018 in Sri Lanka, a new organization, the Middle East and North African Health Informatics Association (MENAHIA) was established as a regional non-governmental organization to promote Health Informatics in the region. Health Informatics activities in applications, education, and research have been growing in the Middle East and North African region for many years. Over the past year, health informatics initiatives within the region focused on the education sector and the workforce. The need to enhance the education delivery and methods is essential in promoting Health Informatics in the region as well as establishing master programs in many countries within the region. This paper provides an overview of the variety of initiatives that are occurring in the region and are endorsed by MENAHIA.

关键词： Saudi Arabia Pakistan Jordan United Arab Emirates Morocco Kuwait Oman Bahrain Health Informatics the Middle East

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Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI 41

Position: Bayesian Deep Learning is Needed in the Age of Lar...

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41st International Conference on Machine Learning, ICML 2024

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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The association for the advancement of artificial intelligence 2020 workshop program

The association for the advancement of artificial intelligen...

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作者： Bang, Grace Barash, Guy Bea, Ryan Cali, Jacques Castillo-Effen, Mauricio Chen, Xin Cynthia Chhaya, Niyati Dhoopar, Rohan Dumancic, Sebastijan Espinoza, Huáscar Farchi, Eitan Fioretto, Ferdinando Fuentetaja, Raquel Galley, Michel Geib, Christopher Hernández-Orallo, José Huang, Xiaowei Keren, Sarah Kim, Seokhwan Jaidka, Kokil Liu, Xiaomo Lu, Tyler Ma, Zhiqiang Mallah, Richard McDermid, John Michalowski, Martin Mirsky, Reuth Héigeartaigh, Seán Ó. Ramachandran, Deepak Segovia-Aguas, Javier Shaban-Nejad, Arash Shehory, Onn Shwartz, Vered Srivastava, Siddharth Talamadupula, Kartik Tang, Jian Zhang, Dell Zhang, Jian S&P Global. Western Digital University of Southampton United Kingdom Blue Prism Group PLC Lockheed Martin's Advanced Technology Laboratories ArlingtonVA United States University of Hong Kong Hong Kong Adobe Research Google Research Katholieke Universiteit Leuven Belgium Commissariat à ĺÉnergie Atomique France IBM Research Department of Electrical Engineering and Computer Science Syracuse University United States Universidad Carlos III de Madrid Spain Microsoft Research AI SIFT LLC Universitat Politècnica de València Spain Leverhulme Centre for the Future of Intelligence United Kingdom Department of Computer Science University of Liverpool United Kingdom National University of Singapore Singapore Harvard School of Engineering and Applied Sciences in the of Fields of Multiagent Environment Design and Goal Recognition Design Amazon Alexa AI Future of Life Institute United States University of York Assuring Autonomy International Program United Kingdom University of Minnesota School of Nursing MinneapolisMN United States University of Texas at Austin AustinTX United States Institut de Robòtica i Informàtica Industrial Spanish Council for Scientific Research Technical University of Catalonia Spain Oak Ridge National Laboratory Center for Biomedical Informatics Department of Pediatrics University of Tennessee Health Science Center United States Bar Ilan University Israel University of Washington SeattleWA United States Autonomous Agents and Intelligent Robots lab of the School of Computing Informatics Decision Systems Arizona State University United States DiDi AI Labs School of Industrial and System Engineering Georgia Institute of Technology United States University of London United Kingdom Microsoft Azure

The Association for the Advancement of Artificial Intelligence 2020 Workshop program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but not all, of the workshop chairs. Copyright © 2020, Association for the Advancement of Artificial Intelligence. All rights reserved.

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Landscape of global urban environmental resistome and its association with local socioeconomic and medical status

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Science China(Life Sciences) 2024年第6期67卷 1292-1301页

作者： Jun Wu Yige Hu Michael H Perlin David Danko Jun Lu Manuela Oliveira Johannes Werner Maria M Zambrano Maria A Sierra Olayinka O Osuolale Paweł Łabaj Nicolás Rascovan Nur Hazlin Hazrin-Chong Soojin Jang Haruo Suzuki Marina Nieto-Caballero Bharath Prithiviraj Patrick K H Lee Agnieszka Chmielarczyk Anna Różańska Yongxiang Zhao Lan Wang Christopher E Mason Tieliu Shi Center for Bioinformatics and Computational Biology and the Institute of Biomedical SciencesSchool of Life SciencesEast China Normal UniversityShanghai 200241China Department of Biology Program on Disease EvolutionUniversity of LouisvilleLouisville 40292USA Weill Cornell Medicine New York 10065USA The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine New York 10065USA Department of Pulmonary Medicine Shanghai Chest HospitalShanghai Jiao Tong UniversityShanghai 200025China i3S-Instituto de Investiga玢o e Inova玢o em Saúde Universidade do PortoPorto 4050-290Portugal Ipatimup-Instituto de Patologia e Imunologia Molecular da Universidade do Porto Porto 4200-465Portugal Departamento de Biologia Faculdade de CiênciasUniversidade do PortoPorto 4050-290Portugal High Performance and Cloud Computing Group Zentrum für Datenverarbeitung(ZDV)Eberhard Karls University of TübingenW鋍hterstra遝7672074TübingenGermany Corporación Corpogen Research Center Bogotá110311Colombia Applied Environmental Metagenomics and Infectious Diseases Research(AEMIDR) Department of Biological SciencesElizade UniversityIlara-Mokin 340271Nigeria Maopolska Centre of Biotechnology Jagiellonian UniversityKraków 30-005Poland Aix-Marseille Université Mediterranean Institute of OceanologyUniversitéde ToulonCNRSIRDUM 110Marseille 83041France Department of Biological Sciences and Biotechnology Faculty of Science and TechnologyUniversiti Kebangsaan Malaysia43600 UKM BangiSelangor Institut Pasteur Korea Seoul 13488Republic of Korea Faculty of Environment and Information Studies Keio UniversityFujisawaKanagawa 252-0882Japan University of Colorado at Boulder CivilEnvironmental and Architectural DepartmentBoulder 80303USA Brooklyn College of the City University of New York Brooklyn 11210USA School of Energy and Environment City University of Hong KongHong Kong 999077China Department of Microbiology Faculty of MedicineJagiellonian UniversityKraków 30-005Poland Biological Targeting Diagnosis and The

Antimicrobial resistance(AMR)poses a critical threat to global health and development,with environmental factors—particularly in urban areas—contributing significantly to the spread of antibiotic resistance genes(ARGs).However,most research to date has been conducted at a local level,leaving significant gaps in our understanding of the global status of antibiotic resistance in urban *** address this issue,we thoroughly analyzed a total of 86,213 ARGs detected within 4,728 metagenome samples,which were collected by the Meta SUB International Consortium involving diverse urban environments in 60 cities of 27 countries,utilizing a deep-learning based *** findings demonstrated the strong geographical specificity of urban environmental resistome,and their correlation with various local socioeconomic and medical *** also identified distinctive evolutionary patterns of ARG-related biosynthetic gene clusters(BGCs)across different countries,and discovered that the urban environment represents a rich source of novel *** study provides a comprehensive overview of the global urban environmental resistome,and fills a significant gap in our knowledge of large-scale urban antibiotic resistome analysis.

关键词： urban environment metagenome antibiotic resistance resistance diversity biosynthetic gene clusters

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Correction: Extremely boosted neural network for more accurate multi-stage Cyber attack prediction in cloud computing environment

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Journal of Cloud computing 2023年第1期12卷 1-1页

作者： Dalal, Surjeet Manoharan, Poongodi Kumar, Lilhore Umesh Seth, Bijeta alsekait, Deema Mohammed Simaiya, Sarita Hamdi, Mounir Raahemifar, Kaamran Department of Computer Science and Engineering Amity University Haryana Gurugram India Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar School of Computing University of Louisiana Lafayette USA Department of Computer Science and Engineering Chandigarh University Mohali India Department of Computer Science and Engineering B. M. Institute of Engineering & Technology Sonipat India Department of Computer Science and Information Technology Applied College Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia College of Information Sciences and Technology Data Science and Artificial Intelligence Program State College USA School of Optometry and Vision Science Faculty of Science University of Waterloo Waterloo Canada Faculty of Engineering University of Waterloo Waterloo Canada

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