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In this paper, we propose a new effective algorithm for the categorical syllogisms by using a calculus system Syllogistic Logic with Carroll Diagrams, which determines a formal approach to logical reasoning with diagrams, for representations of the fundamental Aristotelian categorical syllogisms. We show that this logical reasoning is closed under the syllogistic criterion of inference. Therefore, the calculus system is implemented to let the formalism which comprises synchronically bilateral and trilateral diagrammatical appearance and naive algorithmic nature. And also, there is no need specific knowledge or exclusive ability to understand this decision procedure as well as to use it in an algorithmic system. Consequently, the empirical contributions of this paper are to design a polynomial-time algorithm at the first time in the literature to conduce to researchers getting into the act in different areas of science used categorical syllogisms such as artificial intelligence, engineering, computer science and etc. [ABSTRACT FROM AUTHOR]

Inspired by the self/nonself discrimination theory of the natural immune system, the negative selection algorithm (NSA) is an emerging computational intelligence method. Generally, detectors in the original NSA are first generated in a random manner. However, those detectors matching the self samples are eliminated thereafter. The remaining detectors can therefore be employed to detect any anomaly. Unfortunately, conventional NSA detectors are not adaptive for dealing with time-varying circumstances. In the present paper, a novel neural networks-based NSA is proposed. The principle and structure of this NSA are discussed, and its training algorithm is derived. Taking advantage of efficient neural networks training, it has the distinguishing capability of adaptation, which is well suited for handling dynamical problems. A fault diagnosis scheme using the new NSA is also introduced. Two illustrative simulation examples of anomaly detection in chaotic time series and inner raceway fault diagnosis of motor bearings demonstrate the efficiency of the proposed neural networks-based NSA. [ABSTRACT FROM AUTHOR]

This paper proposes an information system that classifies Web pages according a taxonomy, which is mainly used from seven search engines/directories. The proposed classifier is a four-layer generalised regression neural network (GRNN) that aims to perform the information segmentation according to information filtering techniques using content descriptor vectors. Eight categories of Web pages were used in order to evaluate the robustness of the method, while no restrictions were imposed except for the language of the content, which is English. The system can be used as an assistant and consultative tool for classification purposes as well as for estimating the population of Web pages at any given point in time. [ABSTRACT FROM AUTHOR]