Titlebook: Intelligent Decision Technologies 2017; Proceedings of the 9 Ireneusz Czarnowski,Robert J. Howlett,Lakhmi C. Ja Conference proceedings 2018

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Silent-Ischemia

Conference proceedings 2018), held in Vilamoura, Algarve, Portugal on 21–23 June 2017. The conference addressed critical areas of computer science, as well as promoting knowledge transfer and the generation of new ideas in the field of intelligent decision making, project management and data analysis. The range of topics addr

Pruritus

Analysis of Multiple Classifiers Performance for Discretized Data in Authorship Attributioniple classifiers basing on the majority voting rule. Ensembles were composed from eight single well known classifiers. Influence of different discretization methods on the quality of the analyzed systems was also investigated.

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sultry

inflame

Digital Enterprise Architecture Management in Tourism – State of the Art and Future Directionsce to enterprises in Tourism. To cope with the resulting challenges, methods for the management of enterprise architectures are necessary. Therefore, we scrutinize approaches for managing enterprise architectures based on a literature research. We found many areas for future research on the use of Enterprise Architecture in Tourism.

红润

Pepsin

Fuzzy ARTMAP with Binary Relevance for Multi-label Classificationmark multi-label data classification problems. Its results are compared with those other methods in the literature. The performance of BR-FAM is encouraging, which indicate the potential of FAM-based models for handling multi-label data classification tasks.

通知

Rough Set-Based Text Mining from a Large Data Repository of Experts’ Diagnoses for Power Systemspany. The algorithms of rough set and statistical t-test are used to distinguish whether a subset can be classified in the object set or not. The expected-value-approach is also applied to calculate the fuzzy value with probability into a scalar value.

的阐明

A Development of Classification Model for Smartphone Addiction Recognition System Based on Smartphonng likelihood of having smartphone addiction. We trained the classification model based on data retrieved from subjects’ smartphone. The result showed that the best model can correctly classify the instance up to 78%.