Artificial Intelligence and Soft Computing: 13th by Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Ryszard

By Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada

The two-volume set LNAI 8467 and LNAI 8468 constitutes the refereed court cases of the thirteenth foreign convention on man made Intelligence and tender Computing, ICAISC 2014, held in Zakopane, Poland in June 2014. The 139 revised complete papers awarded within the volumes, have been conscientiously reviewed and chosen from 331 submissions. The sixty nine papers incorporated within the first quantity are excited by the subsequent topical sections: Neural Networks and Their purposes, Fuzzy structures and Their functions, Evolutionary Algorithms and Their functions, type and Estimation, desktop imaginative and prescient, snapshot and Speech research and specified consultation three: clever tools in Databases. The seventy one papers within the moment quantity are geared up within the following matters: facts Mining, Bioinformatics, Biometrics and clinical functions, Agent platforms, Robotics and keep watch over, man made Intelligence in Modeling and Simulation, numerous difficulties of synthetic Intelligence, distinctive consultation 2: desktop studying for visible info research and defense, exact consultation 1: purposes and homes of Fuzzy Reasoning and Calculus and Clustering.

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Extra resources for Artificial Intelligence and Soft Computing: 13th International Conference, ICAISC 2014, Zakopane, Poland, June 1-5, 2014, Proceedings, Part II

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Can they be eliminated? – can the process be still improved? Simplified? In order to answer these questions, a deeper theoretical investigation of the whole procedure and its results has been performed. Moreover, a number of computer experiments have been carried out. The results are reported and discussed throughout the paper. L. Rutkowski et al. ): ICAISC 2014, Part II, LNAI 8468, pp. 27–39, 2014. c Springer International Publishing Switzerland 2014 28 M. Baran, K. Kułakowski, and A. Lig¸eza 2 Problem Statement Let us introduce a brief formal representation of the ranking or sorting problem.

When evaluating the models using AUC values, it has been shown that network features do offer improved predictions when compared to local features, though the differences are more pronounced on sooner predictions than later. This suggests that network information may be more dynamic, reflecting more current changes in customer behavior. According to these findings, valuable information is present in the social networks of customers. Taking advantage of this information can improve churn prediction models and the retention campaigns designed to reduce churn.

A social network can be represented graphically where the people are nodes and some relationship between them form links or edges. Networks are not limited to people, but have actually been used to represent many different types of relationship between entities. In this literature review, applications of network analysis have been found in many areas including nursing [15], behavior adoption [16], patent classification [17], fraud detection [18], and prison system communication [19]. [11] have applied network analysis to churn prediction, but their results were not compared to traditional prediction models.

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