-Image segmentation is an important problem for image processing. The image processing applications are generally affectedfromthe segmentation success. There is noany image segmentation method which gives good results for all sorts of images. That's why there are many approaches and methods forimage segmentationin the literature. And one of the most used is the thresholding technique. Thresholding techniques can be categorized into two topics: bi-level and multi-level thresholding. Bi-level thresholding technique has one threshold value which separates the image into two groups. However, multi-level thresholding technique uses n threshold values where n greater than one. In this paper, two swarm optimization algorithms (Particle Swarm Optimization, PSO and Cat Swarm Optimization, CSO) are applied on finding the optimum threshold values for the multi-level thresholding. In literature, there are some minimization or maximization functions to find the best threshold values for thresholding problem. Some of these methods are: Tsalli's Entropy, Kapur's Entropy, Renyi's Entropy, Otsu's Method (within class variance/between class variance), the Minimum Cross Entropy Thresholding (MCET) etc.In this work, Otsu's (within class variance) method, which is one of these popular functions,is used as the fitness function of algorithms.In the experiments, five real images are segmented by usingParticle Swarm Algorithm and Cat Swarm Optimization Algorithms. The performances of the swarm algorithms on multi-level thresholding problem arecompared with Peak Signal-to-Noise Ratio (PSNR) and fitness function (FS) values. As a result, the PSO yields better performance than CSO.
Multilayer feed-forward artificial neural networks are one of the most frequently used data mining methods for classification, recognition, and prediction problems. The classification accuracy of a multilayer feed-forward artificial neural networks is proportional to training. A well-trained multilayer feed-forward artificial neural networks can predict the class value of an unseen sample correctly if provided with the optimum weights. Determining the optimum weights is a nonlinear continuous optimization problem that can be solved with metaheuristic algorithms. In this paper, we propose a novel multimean particle swarm optimization algorithm for multilayer feed-forward artificial neural networks training. The proposed multimean particle swarm optimization algorithm searches the solution space more efficiently with multiple swarms and finds better solutions than particle swarm optimization. To evaluate the performance of the proposed multimean particle swarm optimization algorithm, experiments are conducted on ten benchmark datasets from the UCI repository and the obtained results are compared to the results of particle swarm optimization and other previous research in the literature. The analysis of the results demonstrated that the proposed multimean particle swarm optimization algorithm performed well and it can be adopted as a novel algorithm for multilayer feedforward artificial neural networks training.
SUMMARYWith the increase of network components connected to the Internet, the need to ensure secure connectivity is becoming increasingly vital. Intrusion Detection Systems (IDSs) are one of the common security components that identify security violations. This paper proposes a novel multilevel hybrid classifier that uses different feature sets on each classifier. It presents the Discernibility Function based Feature Selection method and two classifiers involving multilayer perceptron (MLP) and decision tree (C4.5). Experiments are conducted on the KDD'99 Cup and ISCX datasets, and the proposal demonstrates better performance than individual classifiers and other proposed hybrid classifiers. The proposed method provides significant improvement in the detection rates of attack classes and Cost Per Example (CPE) which was the primary evaluation method in the KDD'99
Most of the machine learning and data mining algorithms use discrete data for the classification process. But, most data in practice include continuous features. Therefore, a discretization pre-processing step is applied on these datasets before the classification. Discretization process converts continuous values to discrete values. In the literature, there are many methods used for discretization process. These methods are grouped as supervised and unsupervised methods according to whether a class information is used or not. In this paper, we used two unsupervised methods: Equal Width Interval (EW), Equal Frequency (EF) and one supervised method: Entropy Based (EB) discretization. In the experiments, a well-known 10 dataset from UCI (Machine Learning Repository) is used in order to compare the effect of the discretization methods on the classification. The results show that, Naive Bayes (NB), C4.5 and ID3 classification algorithms obtain higher accuracy with EB discretization method.
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