检索结果-内蒙古大学图书馆

arXiv 2018年

作者： Siddiqui, Jalal K. Baskin, Elizabeth Liu, Mingrui Cantemir-Stone, Carmen Z. Zhang, Bofei Bonneville, Russell McElroy, Joseph P. Coombes, Kevin R. Mathé, Ewy A. Department of Biomedical Informatics College of Medicine Ohio State University ColumbusOH United States Biomedical Sciences Graduate Program Ohio State University ColumbusOH United States Comprehensive Cancer Center Department of Internal Medicine Ohio State University ColumbusOH United States Center for Biostatistics Ohio State University ColumbusOH United States

Background: Integration of transcriptomic and metabolomic data improves functional interpretation of disease-related metabolomic phenotypes, and facilitates discovery of putative metabolite biomarkers and gene targets. For this reason, these data are increasingly collected in large (> 100 participants) cohorts, thereby driving a need for the development of user-friendly and open-source methods/tools for their integration. Of note, clinical/translational studies typically provide snapshot (e.g. one time point) gene and metabolite profiles and, oftentimes, most metabolites measured are not identified. Thus, in these types of studies, pathway/network approaches that take into account the complexity of transcript-metabolite relationships may neither be applicable nor readily uncover novel relationships. With this in mind, we propose a simple linear modeling approach to capture disease-(or other phenotype) specific gene-metabolite associations, with the assumption that co-regulation patterns reflect functionally related genes and metabolites. Results: The proposed linear model, metabolite ~ gene + phenotype + gene:phenotype, specifically evaluates whether gene-metabolite relationships differ by phenotype, by testing whether the relationship in one phenotype is significantly different from the relationship in another phenotype (via a statistical interaction gene:phenotype p-value). Statistical interaction p-values for all possible gene-metabolite pairs are computed and significant pairs are then clustered by the directionality of associations (e.g. strong positive association in one phenotype, strong negative association in another phenotype). We implemented our approach as an R package, IntLIM, which includes a user-friendly R Shiny web interface, thereby making the integrative analyses accessible to non-computational experts. We applied IntLIM to two previously published datasets, collected in the NCI-60 cancer cell lines and in human breast tumor and non-tumor tissue,

关键词： Integration

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Data-augmented modeling of intracranial pressure

arXiv

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arXiv 2018年

作者： Wang, Jian-Xun Hu, Xiao Shadden, Shawn C. Mechanical Engineering University of California BerkeleyCA United States Aerospace and Mechanical Engineering Center of Informatics and Computational Science University of Notre Dame Notre DameIN United States Department of Physiological Nursing Department of Neurological surgery Institute of Computational Health Sciences UCSF Joint Bio-Engineering Graduate Program University of California San FranciscoCA United States

Precise management of patients with cerebral diseases often requires intracranial pressure (ICP) monitoring, which is highly invasive and requires a specialized ICU setting. The ability to noninvasively estimate ICP is highly compelling as an alternative to, or screening for, invasive ICP measurement. Most existing approaches for noninvasive ICP estimation aim to build a regression function that maps noninvasive measurements to an ICP estimate using statistical learning techniques. These data-based approaches have met limited success, likely because the amount of training data needed is onerous for this complex applications. In this work, we discuss an alternative strategy that aims to better utilize noninvasive measurement data by leveraging mechanistic understanding of physiology. Specifically, we developed a Bayesian framework that combines a multiscale model of intracranial physiology with noninvasive measurements of cerebral blood flow using transcranial Doppler. Virtual experiments with synthetic data are conducted to verify and analyze the proposed framework. A preliminary clinical application study on two patients is also performed in which we demonstrate the ability of this method to improve ICP prediction. Copyright © 2018, The Authors. All rights reserved.

关键词： Diagnosis

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Considering discrepancy when calibrating a mechanistic electrophysiology model

arXiv

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arXiv 2020年

作者： Lei, Chon Lok Ghosh, Sanmitra Whittaker, Dominic G. Aboelkassem, Yasser Beattie, Kylie A. Cantwell, Chris D. Delhaas, Tammo Houston, Charles Novaes, Gustavo Montes Panfilov, Alexander V. Pathmanathan, Pras Riabiz, Marina Weber dos Santos, Rodrigo Worden, Keith Mirams, Gary R. Wilkinson, Richard D. Computational Biology & Health Informatics Dept. of Computer Science University of Oxford United Kingdom MRC Biostatistics Unit University of Cambridge United Kingdom Centre for Mathematical Medicine & Biology School of Mathematical Sciences University of Nottingham United Kingdom Department of Bioengineering University of California San Diego United States Systems Modeling and Translational Biology GlaxoSmithKline R&D Stevenage United Kingdom ElectroCardioMaths Programme Centre for Cardiac Engineering Imperial College London United Kingdom CARIM School for Cardiovascular Diseases Maastricht University Netherlands Graduate Program in Computational Modeling Universidade Federal de Juiz de Fora Brazil Department of Physics and Astronomy Ghent University Belgium Laboratory of Computational Biology and Medicine Ural Federal University Ekaterinburg Russia U.S. Food and Drug Administration Center for Devices and Radiological Health Office of Science and Engineering Laboratories United States Department of Biomedical Engineering King’s College London Alan Turing Institute United Kingdom Dynamics Research Group Department of Mechanical Engineering University of Sheffield United Kingdom School of Mathematics and Statistics University of Sheffield United Kingdom

Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterise uncertainty in model inputs and how that propagates through to outputs or predictions. In this perspective piece we draw attention to an important and under-addressed source of uncertainty in our predictions — that of uncertainty in the model structure or the equations themselves. The difference between imperfect models and reality is termed model discrepancy, and we are often uncertain as to the size and consequences of this discrepancy. Here we provide two examples of the consequences of discrepancy when calibrating models at the ion channel and action potential scales. Furthermore, we attempt to account for this discrepancy when calibrating and validating an ion channel model using different methods, based on modelling the discrepancy using Gaussian processes (GPs) and autoregressive-moving-average (ARMA) models, then highlight the advantages and shortcomings of each approach. Finally, suggestions and lines of enquiry for future work are provided. Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Correction to: binomialRF: interpretable combinatoric efficiency of random forests to identify biomarker interactions

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BMC bioinformatics 2020年第1期21卷 495页

作者： Samir Rachid Zaim Colleen Kenost Joanne Berghout Wesley Chiu Liam Wilson Hao Helen Zhang Yves A Lussier Center for Biomedical Informatics and Biostatistics University of Arizona Health Sciences 1230 N. Cherry Ave Tucson AZ 85721 USA. The Graduate Interdisciplinary Program in Statistics The University of Arizona 617 N. Santa Rita Ave Tucson AZ 85721 USA. College of Medicine Tucson 1501 N. Campbell Ave Tucson AZ 85721 USA. Center for Biomedical Informatics and Biostatistics University of Arizona Health Sciences 1230 N. Cherry Ave Tucson AZ 85721 USA. hzhang@math.arizona.edu. The Graduate Interdisciplinary Program in Statistics The University of Arizona 617 N. Santa Rita Ave Tucson AZ 85721 USA. hzhang@math.arizona.edu. Department of Mathematics College of Sciences The University of Arizona 617 N. Santa Rita Ave Tucson AZ 85721 USA. hzhang@math.arizona.edu. Center for Biomedical Informatics and Biostatistics University of Arizona Health Sciences 1230 N. Cherry Ave Tucson AZ 85721 USA. Lussier.Y@***. The Graduate Interdisciplinary Program in Statistics The University of Arizona 617 N. Santa Rita Ave Tucson AZ 85721 USA. Lussier.Y@***. College of Medicine Tucson 1501 N. Campbell Ave Tucson AZ 85721 USA. Lussier.Y@***. The Center for Applied Genetic and Genomic Medicine 1295 N. Martin Tucson AZ 85721 USA. Lussier.Y@***. The University of Arizona Cancer Center 3838 N. Campbell Ave Tucson AZ 85721 USA. Lussier.Y@***. The University of Arizona BIO5 Institute 1657 E. Helen Street Tucson AZ 85721 USA. Lussier.Y@***.

An amendment to this paper has been published and can be accessed via the original article.

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Correction: A COVID-19 Pandemic Artificial Intelligence-Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study

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Journal of medical Internet research 2021年第7期23卷 e31085页

作者： Cheng-Sheng Yu Shy-Shin Chang Tzu-Hao Chang Jenny L Wu Yu-Jiun Lin Hsiung-Fei Chien Ray-Jade Chen Department of Family Medicine School of Medicine College of Medicine Taipei Medical University Taipei Taiwan. Department of Family Medicine Taipei Medical University Hospital Taipei Taiwan. Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan. Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan. Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan. Division of Plastic Surgery Department of Surgery Taipei Medical University Hospital and School of Medicine College of Medicine Taipei Medical University Taipei Taiwan. Department of Surgery School of Medicine College of Medicine Taipei Medical University Taipei Taiwan. Division of General Surgery Department of Surgery Taipei Medical University Hospital Taipei Taiwan.

[This corrects the article DOI: 10.2196/27806.].

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Monitoring the pointing of the prototype LST-1 using star reconstruction in the Cherenkov camera

arXiv

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arXiv 2021年

作者： Abe, H. Aguasca, A. Agudo, I. Antonelli, L.A. Aramo, C. Armstrong, T. Artero, M. Asano, K. Ashkar, H. Aubert, P. Baktash, A. Bamba, A. Larriva, A. Baquero Baroncelli, L. de Almeida, U. Barres Barrio, J.A. Batkovic, I. González, J. Becerra Bernardos, M.I. Berti, A. Biederbeck, N. Bigongiari, C. Blanch, O. Bonnoli, G. Bordas, P. Bose, D. Bulgarelli, A. Burelli, I. Buscemi, M. Cardillo, M. Caroff, S. Carosi, A. Cassol, F. Cerruti, M. Chai, Y. Cheng, K. Chikawa, M. Chytka, L. Contreras, J.L. Cortina, J. Costantini, H. Dalchenko, M. de Angelis, A. de Bony de Lavergne, M. Deleglise, G. Delgado, C. Mengual, J. Delgado Volpe, D. della Depaoli, D. Di Pierro, F. Di Venere, L. Díaz, C. Dominik, R.M. Prester, D. Dominis Donini, A. Dorner, D. Doro, M. Elsässer, D. Emery, G. Escudero, J. Fiasson, A. Foffano, L. Fonseca, M.V. Coromina, L. Freixas Fukami, S. Fukazawa, Y. Garcia, E. López, R. Garcia Giglietto, N. Giordano, F. Gliwny, P. Godinovic, N. Green, D. Grespan, P. Gunji, S. Hackfeld, J. Hadasch, D. Hahn, A. Hassan, T. Hayashi, K. Heckmann, L. Heller, M. Llorente, J. Herrera Hirotani, K. Hoffmann, D. Horns, D. Houles, J. Hrabovsky, M. Hrupec, D. Hui, D. Hütten, M. Inada, T. Inome, Y. Iori, M. Ishio, K. Iwamura, Y. Jacquemont, M. Martinez, I. Jimenez Jouvin, L. Jurysek, J. Kagaya, M. Karas, V. Katagiri, H. Kataoka, J. Kerszberg, D. Kobayashi, Y. Kong, A. Kubo, H. Kushida, J. Lamanna, G. Lamastra, A. Le Flour, T. Longo, F. López-Coto, R. López-Moya, M. López-Oramas, A. Luque-Escamilla, P.L. Majumdar, P. Makariev, M. Mandat, D. Manganaro, M. Mannheim, K. Mariotti, M. Marquez, P. Marsella, G. Martí, J. Martinez, O. Martínez, G. Martínez, M. Marusevec, P. Mas, A. Maurin, G. Mazin, D. Guillen, E. Mestre Micanovic, S. Miceli, D. Miener, T. Miranda, J.M. Miranda, L.D.M. Mirzoyan, R. Mizuno, T. Molina, E. Montaruli, T. Monteiro, I. Moralejo, A. Morcuende, D. Moretti, E. Morselli, A. Mrakovcic, K. Murase, K. Nagai, A. Nakamori, T. Nickel, L. University of Geneva quai Ernest Ansermet 24 Geneva Switzerland Institute for Cosmic Ray Research University of Tokyo Departament de Física Quàntica i Astrofísica Institut de Ciències del Cosmos Universitat de Barcelona IEEC-UB Instituto de Astrofísica de Andalucía-CSIC INAF Osservatorio Astronomico di Roma INFN Sezione di Napoli Aix Marseille Univ CNRS IN2P3 CPPM The Barcelona Institute of Science and Technology IRFU CEA Université Paris-Saclay LAPP Univ. Grenoble Alpes Univ. Savoie Mont Blanc CNRS-IN2P3 Annecy France Universität Hamburg Institut für Experimentalphysik Graduate School of Science University of Tokyo EMFTEL department IPARCOS Universidad Complutense de Madrid INAF Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Centro Brasileiro de Pesquisas Físicas INFN Sezione di Padova Università degli Studi di Padova Instituto de Astrofísica de Canarias Departamento de Astrofísica Universidad de La Laguna Max-Planck-Institut für Physik Department of Physics TU Dortmund University Saha Institute of Nuclear Physics INFN Sezione di Trieste Università degli Studi di Udine INFN Sezione di Catania University of Geneva Département de Physique Nucléaire et Corpusculaire Palacky University Olomouc Faculty of Science CIEMAT Port d'Informació Científica INFN Sezione di Torino Dipartimento di Fisica Universitá degli Studi di Torino INFN Sezione di Bari Università di Bari University of Rijeka Department of Physics Institute for Theoretical Physics and Astrophysics Universität Würzburg Physics Program Graduate School of Advanced Science and Engineering Hiroshima University INFN Sezione di Bari Politecnico di Bari Faculty of Physics and Applied Informatics University of Lodz University of Split FESB Department of Physics Yamagata University Institut für Theoretische Physik Lehrstuhl IV: Plasma-Astroteilchenphysik Ruhr-Universität Bochum Tohoku University Astronomical Institute Josip Juraj Strossmayer University of Osijek Department of Physics INFN Sezione di Roma La

The first Large-Sized Telescope (LST-1) proposed for the forthcoming Cherenkov Telescope Array (CTA) has started to operate in 2019 in La Palma. The large structure of LST-1 - with a 23 m mirror dish diameter - imposes a strict control of its deformations that could affect the pointing accuracy and its overall performance. According to CTA specifications that are conceived to resolve e.g. the fine structure of galactic sources, the LST post-calibration pointing accuracy should be better than 14 arcseconds. To fulfill this requirement, the telescope pointing precision is monitored with two dedicated CCD cameras located at the dish center. The analysis of their images allows us to disentangle different systematic deformations of the structure. In this work, we investigate a complementary approach that offers the possibility to monitor the pointing of the telescope during the acquisition of sky data. After properly cleaning the events from the Cherenkov showers, the reconstructed positions of the stars imaged in the camera field of view are compared to their nominal expected positions in catalogues. This provides a direct measurement of the telescope pointing, that can be used to cross-check the other methods and as a real-time monitoring of the optical properties of the telescope and of the pointing corrections applied by the bending models. Additionally, this method benefits from not relying on specific hardware or dedicated observations. In this contribution we will illustrate this analysis and show results based on simulations of LST-1. Copyright © 2021, The Authors. All rights reserved.

关键词： Deformation

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Applying the MVC EA-IRPM to reporting-guidelines in medicine: A strategy that is a web system 4

Applying the MVC EA-IRPM to reporting-guidelines in medicine...

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4th IEEE EMBS International Conference on Biomedical and Health informatics, BHI 2017

作者： Letouze, Patrick De Souza, J.I.M. Silva, Valeria Martins Climaco, Glaubos Chagas, F.B. Ishihara, Joao Y. Graduate Program of Computational Modeling of Systems Universidade Federal Do Tocantins Quadra 109 Norte Avenida NS-15 Palmas77001-090 Brazil Graduate Program of Systems' Engineering and Computation - PESC Universidade Federal Do Rio de Janeiro Rio de Janeiro Brazil Department of Informatics at the Universidade Federal Do Maranh/ Ao - UFMA Brazil Faculty of Computer Science Faculdade Pitagoras Imperatriz Imperatriz Brazil Faculty of Electrical Engineering Universidade de Brasília Brasília Brazil

ISBN: (纸本)9781509041794

Reporting-Guidelines in Medicine play an important role in promoting the quality of reports in health-related research. For instance, a poorly reported research may induce misinterpretation and inappropriate clinical settings. Basically, three major issues regarding Reporting-Guidelines in Medicine has to be addressed: (1) the existence of lots of reporting guidelines covering a broad spectrum of medical research increases the difficulties when reporting a medical research because it may not be clear which guidelines are applicable, or if all related guidelines are considered;(2) most guideline developers have not described how their guidance was developed, consequently tracking guidelines and features for evaluation, or even for correcting, may be not possible;(3) few guideline developers have evaluated the effectiveness of their guidance, then the real value that the guideline brings to reporting the research is not assured. Hence, we propose a solution to these three issues based on the Model-View-Controller Evolutionary Acquisition Interdisciplinary Research Project Management (MVC EA-IRPM) that is simultaneously a strategy and a web system for the development and management of reporting guidelines. © 2017 IEEE.

关键词： Clinical research

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MAGIC observations of HESS J1809-193 using the Very Large Zenith Angle technique at energies above TeV 37

MAGIC observations of HESS J1809-193 using the Very Large Ze...

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37th International Cosmic Ray Conference, ICRC 2021

作者： Acciari, V.A. Ansoldi, S. Antonelli, L.A. Arbet Engels, A. Artero, M. Asano, K. Baack, D. Babić, A. Baquero, A. Barres de Almeida, U. Barrio, J.A. Batković, I. Becerra González, J. Bednarek, W. Bellizzi, L. Bernardini, E. Bernardos, M. Berti, A. Besenrieder, J. Bhattacharyya, W. Bigongiari, C. Biland, A. Blanch, O. Bökenkamp, H. Bonnoli, G. Bošnjak, Ž. Busetto, G. Carosi, R. Ceribella, G. Cerruti, M. Chai, Y. Chilingarian, A. Cikota, S. Colak, S.M. Colombo, E. Contreras, J.L. Cortina, J. Covino, S. D'Amico, G. D'Elia, V. Da Vela, P. Dazzi, F. De Angelis, A. De Lotto, B. Delfino, M. Delgado, J. Delgado Mendez, C. Depaoli, D. Di Pierro, F. Di Venere, L. Do Souto Espiñeira, E. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Elsaesser, D. Fallah Ramazani, V. Fattorini, A. Fonseca, M.V. Font, L. Fruck, C. Fukami, S. Fukazawa, Y. García López, R.J. Garczarczyk, M. Gasparyan, S. Gaug, M. Giglietto, N. Giordano, F. Gliwny, P. Godinović, N. Green, J.G. Green, D. Hadasch, D. Hahn, A. Heckmann, L. Herrera, J. Hoang, J. Hrupec, D. Hütten, M. Inada, T. Ishio, K. Iwamura, Y. Jiménez Martínez, I. Jormanainen, J. Jouvin, L. Karjalainen, M. Kerszberg, D. Kobayashi, Y. Kubo, H. Kushida, J. Lamastra, A. Lelas, D. Leone, F. Lindfors, E. Linhoff, L. Lombardi, S. Longo, F. López-Coto, R. López-Moya, M. López-Oramas, A. Loporchio, S. Machado de Oliveira Fraga, B. Maggio, C. Majumdar, P. Makariev, M. Mallamaci, M. Maneva, G. Manganaro, M. Mannheim, K. Maraschi, L. Mariotti, M. Martínez, M. Mazin, D. Menchiari, S. Mender, S. Mićanović, S. Miceli, D. Miener, T. Miranda, J.M. Mirzoyan, R. Molina, E. Moralejo, A. Morcuende, D. Moreno, V. Moretti, E. Nakamori, T. Nava, L. Neustroev, V. Nigro, C. Nilsson, K. Nishijima, K. Noda, K. Nozaki, S. Ohtani, Y. Oka, T. Otero-Santos, J. Paiano, S. Palatiello, M. Paneque, D. Paoletti, R. Paredes, J.M. Pavletić, L. Peñil, P. Persic, M. Pihet, M. Prada Moroni, P.G. Prandini, E. Priyadarshi, C. Puljak, I. Rhode, W. Ribó, M. Instituto de Astrofísica de Canarias Dpto. de Astrofísica Universidad de La Laguna La Laguna TenerifeE-38200 Spain Università di Udine INFN Trieste UdineI-33100 Italy RomeI-00136 Italy ETH Zürich ZürichCH-8093 Switzerland Bellaterra BarcelonaE-08193 Spain The University of Tokyo Chiba Kashiwa277-8582 Japan Technische Universität Dortmund DortmundD-44221 Germany Zagreb10000 Croatia IPARCOS Institute EMFTEL Department Universidad Complutense de Madrid MadridE-28040 Spain URCA RJ Rio de Janeiro22290-180 Brazil Università di Padova INFN PadovaI-35131 Italy University of Lodz Faculty of Physics and Applied Informatics Department of Astrophysics Lodz90-236 Poland Università di Siena INFN Pisa SienaI-53100 Italy ZeuthenD-15738 Germany Max-Planck-Institut für Physik MünchenD-80805 Germany Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Università di Pisa INFN Pisa PisaI-56126 Italy Universitat de Barcelona ICCUB IEEC-UB BarcelonaE-08028 Spain A. Alikhanyan National Science Laboratory Yerevan0036 Armenia Centro de Investigaciones Energéticas Medioambientales y Tecnológicas MadridE-28040 Spain INFN Sezione di Torino Università degli Studi di Torino TorinoI-10125 Italy INFN Sezione di Bari Dipartimento Interateneo di Fisica Università e del Politecnico di Bari BariI-70125 Italy University of Rijeka Department of Physics Rijeka51000 Croatia Universität Würzburg WürzburgD-97074 Germany Finnish Centre for Astronomy with ESO University of Turku TurkuFI-20014 Finland Departament de Física CERES-IEEC Universitat Autònoma de Barcelona BellaterraE-08193 Spain Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima739-8526 Japan ICRANet-Armenia NAS RA Yerevan0019 Armenia Split21000 Croatia Josip Juraj Strossmayer University of Osijek Department of Physics Osijek31000 Croatia Department of Physics Kyoto University Kyoto

The origin of Galactic Cosmic rays (GCRs), whose spectrum extends to PeV energies, is one of the longest-standing problems in astroparticle physics. One of the main sources of GCRs are regarded to be Supernova remnants (SNRs). While SNRs are known to accelerate protons, so far there is no evidence that SNRs can accelerate CRs to PeV energies. Providing that 10% of the parent Cosmic ray energy is converted to gamma rays, the gamma-ray spectrum extending up to 100 TeV would be a signature of a so-called Galactic PeVatron, an object responsible for the production of protons up to the knee of the Cosmic ray spectrum. The current multi-wavelength data indicate that HESS J1809-193 is one of the most promising Galactic PeVatron candidates. So far, no firm identification on the source nature has been established as there are several possible counterparts at lower energies;one of them being SNR G11.00.0. We report here the results of an observational campaign performed by the MAGIC telescopes on HESS J1809-193 since 2019 in the very-high-energy gamma-ray domain (E>100 GeV). The data were obtained with the Very Large Zenith Angle (VLZA) technique, which increased the collection area significantly to about one square kilometer. We used 60 hours of collected VLZA data to explore the spectrum and the morphology of the source at energies above several TeV. © 2022 Sissa Medialab Srl. All rights reserved.

关键词： Cosmology

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Comparison of color model for flower recognition

Comparison of color model for flower recognition

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International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： P. Rosyani M. Taufik A. A. Waskita D. H. Apriyanti Department of Informatics Universitas Pamulang Pamulang Tangerang Selatan Banten Indonesia Graduate Program Eresha School of Information Technology Tangerang Selatan Banten Indonesia Computing lab. Kawasan Puspiptek Serpong Tangerang Selatan 15310 Indonesia Purwodadi Botanic Garden - LIPI Jl. Raya Surabaya - Malang Km. 65 Purwodadi Pasuruan - Jawa Timur 67163 Indonesia

ISBN: (纸本)9781538670835;9781538670828

Comparison to RGB, HSV, LAB and YCbCr color model in a flower recognition has been conducted for 12 species from two families. Flower images were obtained from the ImageCLEF 2017 dataset. After segmenting the flower from its background using the k-means clustering method, the statistical parameters are extracted from each color model. These parameters including the minimum and maximum pixel value, and also mean and standard deviation. Then, the classifying result of SVM shows that the HSV color model performs the worst amongst the other investigated color model, which is around 30% using different kernel. While the other color model has the accuracy around 70% using linear and polynomial kernel. This result in line with the surface plot of their statistical characteristics.

关键词： Image color analysis Kernel Image segmentation Support vector machines Solid modeling Testing Data models

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Stream Learning and Anomaly-based Intrusion Detection in the Adversarial Settings

Stream Learning and Anomaly-based Intrusion Detection in the...

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IEEE Symposium on Computers and Communications

作者： Eduardo Viegas Altair Santin Vilmar Abreu Luiz S. Oliveira Graduate Program in Computer Science Pontifical Catholic University of Parana Curitiba Parana Brazil Informatics Department Federal University of Parana Curitiba Parana Brazil

Despite existing many anomaly-based intrusion detection studies in the literature, they are not frequently adopted by the industry in production environments (products). Such a usage gap occurs mainly due to the difficulty to maintain the detection rate in acceptable level, given the occurrence of false alarms. In general, the literature does not consider the adversarial settings, when an opponent attempt to evade the detection system, thus possibly rendering the system unreliable over time. In this paper, we propose and evaluate a new approach to reliably perform real time stream learning for anomaly-based intrusion detection. We employ a class-specific stream outlier detector to automatically update the intrusion detection engine over the time, and a rejection mechanism, which makes it possible to obtain indications that an evasion attempt might being happening. Furthermore, the proposal is resilient to causative attacks, providing a secure intrusion detection mechanism even when the attacker can inject misclassified instances in the training dataset. The evaluation tests show that the proposed approach is resilient to exploratory attacks, allowing the system administrator to know when an evasion attempt might be occurring.

关键词： Adversarial Settings Outlier Detection Stream Learning Intrusion Detection

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