Computational Intelligence

Computational intelligence (CI) refers to recreating human-like intelligence in a computing machine. It consists of a set of computing systems with the ability to learn and deal with new situations such that the systems are perceived to have some attributes of intelligence. It is efficient in solving realworld problems which require reasoning and decision-making. It produces more robust, simpler, and tractable solutions than the traditional techniques. This paper provides a brief introduction to computational intelligence

Selected Algorithms of Computational Intelligence in Gastric Cancer Decision Making

Due to the latest research the subject of Computational Intelligence has been divided into five main regions, namely, neural networks, evolutionary algorithms, swarm intelligence, immunological systems and fuzzy systems. Our attention has been attracted by the possibilities of medical applications provided by immunological computation algorithms. Immunological computation systems are based on immune reactions of the living organisms in order to defend the bodies from pathological substances. Especially, the mechanisms of the T-cell reactions to detect strangers have been converted into artificial numerical algorithms. Immunological systems have been developed in scientific books and reports appearing during the two last decades. The basic negative selection algorithm NS was invented by Stefanie Forrest to give rise to some technical applications. We can note such applications of NS as computer virus detection, reduction of noise effect, communication of autonomous agents or identification of time varying systems. Even a trial of connection between a computer and biological systems has been proved by means of immunological computation. Hybrids made between different fields can provide researchers with richer results; therefore associations between immunological systems and neural networks have been developed as well. In the current chapter we propose another hybrid between the NS algorithm and chosen solutions coming from fuzzy systems. This hybrid constitutes the own model of adapting the NS algorithm to the operation decisions “operate” contra “do not operate” in gastric cancer surgery. The choice between two possibilities to treat patients is identified with the partition of a decision region in self and non-self, which is similar to the action of the NS algorithm. The partition is accomplished on the basis of patient data strings/vectors that contain codes of states concerning some essential biological markers. To be able to identify the strings that characterize the “operate” decision we add the own method of computing the patients’ characteristics as real values. The evaluation of the patients’ characteristics is supported by inserting importance weights assigned to powerful biological indices taking place in the operation decision process. To compute the weights of importance the Saaty algorithm is adopted

DScentTrail: A new way of viewing deception

The DScentTrail System has been created to support and demonstrate research theories in the joint disciplines of computational inference, forensic psychology and expert decision-making in the area of counter-terrorism. DScentTrail is a decision support system, incorporating artificial intelligence, and is intended to be used by investigators. The investigator is presented with a visual representation of a suspect‟s behaviour over time, allowing them to present multiple challenges from which they may prove the suspect guilty outright or receive cognitive or emotional clues of deception. There are links into a neural network, which attempts to identify deceptive behaviour of individuals; the results are fed back into DScentTrail hence giving further enrichment to the information available to the investigator