Ahmet Arslan

In this paper, the idea of a new artificial intelligence based optimization algorithm, which is inspired from the nature of vortex, has been provided briefly. As also a bio-inspired computation algorithm, the idea is generally focused on a typical vortex flow / behavior in nature and inspires from some dynamics that are occurred in the sense of vortex nature. Briefly, the algorithm is also a swarm-oriented evolutional problem solution approach; because it includes many methods related to elimination of weak swarm members and trying to improve the solution process by supporting the solution space via new swarm members. In order have better idea about success of the algorithm; it has been tested via some benchmark functions. At this point, the obtained results show that the algorithm can be an alternative to the literature in terms of single-objective optimization solution ways. Vortex Optimization Algorithm (VOA) is the name suggestion by the authors; for this new idea of intelligent optimization approach.

Transcranial Doppler (TCD) is a non-invasive diagnosis method which is used in diagnosis of various brain diseases by measuring the blood flow velocities in brain arteries. In this study, chaos analysis of the TCD signals recorded from the middle arteries of the temporal region of brain of the 82 patients and 23 healthy people was investigated. Among 82 patients, 20 of them had cerebral aneurism, 10 had brain hemorrhage, 22 had cerebral oedema and the remaining 30 had brain tumor. Maximum Lyapunov exponent which is the strongest quantitative indicator of chaos was found to be positive for all TCD signals. The correlation dimension was found as greater than 2 and as fractional value for all TCD signals. These two features were used for training a NEFCLASS model. The NEFCLASS model had two input nodes for D2 and maximum Lyapunov exponent values and five output nodes representing the subject group to which the inputs belonged. In order to make k-fold cross-validation, the data set was randomly divided into 5 subsets of equal size. In an iterated manner, 4 of these subsets were used for training and the remaining 1 subset was used for testing. This operation was repeated for 3 times. The average accuracy for train and test set was found as %81 and %79, respectively. The performance of the NEFCLASS model was also assessed in the same manner with spectral parameters (i.e. resistivity index and pulsatility index) which were obtained from Doppler sonograms. The average accuracy was found as %67 and %63 for train and test set, respectively.

Chaos Theory is a kind of a scientific approach/research effort which is based on examining behaviors of nonlinear dynamical systems which are highly sensitive to their initial conditions. Currently, there are many different scientific studies based on the Chaos Theory and the related solution approaches, methods, or techniques for problems of this theory. Additionally, the theory is used for improving the introduced studies of different fields in order to get more effective, efficient, and accurate results. At this point, this chapter aims to provide a review-based study introducing recent implementations of the Chaos Theory on improving intelligent systems, which can be examined in the context of the Artificial Intelligence field. In this sense, the main research way is directed into the works performed or introduced mostly in years between 2008 and 2013. By providing a review-based study, the readers are enabled to have ideas on Chaos Theory, Artificial Intelligence, and the related works that can be examined within intersection of both fields. At this point, the chapter aims to discuss not only recent works, but also express ideas regarding future directions within the related implementations of chaotic systems to improve intelligent systems. The chapter is generally organized as a reference guide for academics, researchers, and scientists tracking the literature of the related fields: Artificial Intelligence and the Chaos Theory.