1 resultado para ANORECTAL ANOMALIES
em Bulgarian Digital Mathematics Library at IMI-BAS
Filtro por publicador
- Archimer: Archive de l'Institut francais de recherche pour l'exploitation de la mer (2)
- Aston University Research Archive (3)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (12)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (83)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (29)
- Brock University, Canada (3)
- Bulgarian Digital Mathematics Library at IMI-BAS (1)
- CamPuce - an association for the promotion of science and humanities in African Countries (1)
- CentAUR: Central Archive University of Reading - UK (76)
- Cochin University of Science & Technology (CUSAT), India (15)
- Consorci de Serveis Universitaris de Catalunya (CSUC), Spain (52)
- Corvinus Research Archive - The institutional repository for the Corvinus University of Budapest (1)
- Dalarna University College Electronic Archive (1)
- Digital Commons at Florida International University (3)
- Digital Howard @ Howard University | Howard University Research (1)
- DigitalCommons@The Texas Medical Center (4)
- Diposit Digital de la UB - Universidade de Barcelona (4)
- Doria (National Library of Finland DSpace Services) - National Library of Finland, Finland (24)
- Escola Superior de Educação de Paula Frassinetti (1)
- Gallica, Bibliotheque Numerique - Bibliothèque nationale de France (French National Library) (BnF), France (17)
- Georgian Library Association, Georgia (2)
- Illinois Digital Environment for Access to Learning and Scholarship Repository (1)
- Instituto Nacional de Saúde de Portugal (4)
- Instituto Politécnico do Porto, Portugal (24)
- Iowa Publications Online (IPO) - State Library, State of Iowa (Iowa), United States (3)
- National Center for Biotechnology Information - NCBI (2)
- Publishing Network for Geoscientific & Environmental Data (14)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (1)
- ReCiL - Repositório Científico Lusófona - Grupo Lusófona, Portugal (2)
- Repositório Científico do Instituto Politécnico de Lisboa - Portugal (26)
- Repositório da Produção Científica e Intelectual da Unicamp (4)
- Repositório da Universidade Federal do Espírito Santo (UFES), Brazil (1)
- REPOSITORIO DIGITAL IMARPE - INSTITUTO DEL MAR DEL PERÚ, Peru (3)
- Repositório do Centro Hospitalar de Lisboa Central, EPE - Centro Hospitalar de Lisboa Central, EPE, Portugal (10)
- Repositório Institucional da Universidade Estadual de São Paulo - UNESP (1)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (19)
- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (19)
- School of Medicine, Washington University, United States (1)
- Scielo Saúde Pública - SP (57)
- Scottish Institute for Research in Economics (SIRE) (SIRE), United Kingdom (5)
- Universidad de Alicante (1)
- Universidad del Rosario, Colombia (15)
- Universidad Politécnica de Madrid (1)
- Universidade Complutense de Madrid (1)
- Universidade do Minho (4)
- Universidade dos Açores - Portugal (4)
- Universidade Federal do Pará (1)
- Universidade Técnica de Lisboa (1)
- Universitat de Girona, Spain (6)
- Université de Lausanne, Switzerland (290)
- Université de Montréal (4)
- Université de Montréal, Canada (89)
- University of Michigan (12)
- University of Queensland eSpace - Australia (31)
- University of Washington (1)
Resumo:
In this paper an agent-based approach for anomalies monitoring in distributed systems such as computer networks, or Grid systems is proposed. This approach envisages on-line and off-line monitoring in order to analyze users’ activity. On-line monitoring is carried in real time, and is used to predict user actions. Off-line monitoring is done after the user has ended his work, and is based on the analysis of statistical information obtained during user’s work. In both cases neural networks are used in order to predict user actions and to distinguish normal and anomalous user behavior.