Data Mining - Concepts and Techniques.

Swarm intelligence (SI) is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by SI are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony (ABC) is the one which has been most widely studied on and applied to solve the real world problems, so far. Day by day the number of researchers being interested in ABC algorithm increases rapidly. This work presents a comprehensive survey of the advances with ABC and its applications. It is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.

A comprehensive survey: artificial bee colony (ABC) algorithm and applications

Artificial bee colony algorithm for solving multi-objective optimal power flow problem

This paper reviews current optimization technique developed to solve reservoir operation problems in water resources. The application of conventional, especially evolutionary computation, combination of simulation-optimization and multi objectives optimization in reservoir operation will be discussed and investigated. Furthermore, new optimization algorithm from other applications will be presented by focusing on Artificial Bee Colony (ABC) and Gravitational Search Algorithm (GSA) as alternative methods that can be explored by researchers in water resources field. Finally this paper looks into the challenges and issues of climate change in reservoir optimization.

Reservoir Optimization in Water Resources: a Review

Diagnosis of the three-phase induction motor using thermal imaging

A hybrid dermoscopy images segmentation approach based on neutrosophic clustering and histogram estimation

Reactive power optimization (RPO) is an important issue for providing the secure and economic run of the power systems. The importance of reactive power planning on economic profit and secure running has been increasing, because of the increasing fuel costs and investment funds. It is also quite important for an electric operator to provide voltage in a specified range for the customers. As such, RPO provides voltage control in power systems. Furthermore, it is used for decreasing active power loss and making better power coefficents. In this study, multi-objective RPO was used considering voltage deviations of buses, active power losses and reactive power generator costs. In this study, a new metaheuristic optimization method, which is an artificial bee colony (ABC), was used for the optimization. However, ABC was applied on ten bus system and the results were compared with the improving strength pareto evolutionary algorithm. It was observed that the system runs more effectively and economically with the results found with ABC.

	 

	Key words: Artificial bee colony, reactive power optimization, active power loss.

https://www.academicjournals.org/article/article1380537927_Ozturk et al.pdf

Reactive power optimization with artificial bee colony algorithm

The aim of this paper is to compare two important machine learning platform results for the same dataset. With this aim, we conducted an experiment to classify heart disease both in Matlab© environment and WEKA©, by using six different algorithms. Linear SVM, Quadratic SVM, Cubic SVM, Medium Gaussian SVM, Decision Tree and Ensemble Subspace Discriminant machine learning approaches are used for classifying the heart disease.

Comparative study of heart disease classification

A fuzzy image clustering method based on an improved backtracking search optimization algorithm with an inertia weight parameter

The segmentation process is defined by separating the objects as clustering in the images. The most used method in the segmentation is k-means clustering algorithm. k-means clustering algorithm needs the number of clusters, the initial central points of clusters as well as the image information. However, there is no preliminary information about the number of clusters in real-life problems. The parameters defined by the user in the segmentation algorithms affect the results of segmentation process. In this study, a general approach performing segmentation without requiring any parameters has been developed. The optimum cluster number has been obtained searching the histogram both vertically and horizontally and recording the local and global maximum values. The quite nearly values have been omitted, since the near local peaks are nearly the same objects. Segmentation processes have been performed with k-means clustering giving the possible centroids of the clusters and the optimum cluster number obtained from the histogram. Finally, thanks to histogram method, the number of clusters of k-means clustering has been automatically found for each image dataset. And also, the histogram-based finding of the number of clusters in datasets could be used prior to clustering algorithm for other signal or image-based datasets. These results have shown that the proposed hybrid method based on histogram and k-means clustering method has obtained very promising results in the image segmentation problems.

Pakize Erdogmus

Papers

A hybrid dermoscopy images segmentation approach based on neutrosophic clustering and histogram estimation

Reactive power optimization with artificial bee colony algorithm

Comparative study of heart disease classification

A fuzzy image clustering method based on an improved backtracking search optimization algorithm with an inertia weight parameter

Histogram-based automatic segmentation of images