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Feature selection is an important task in data mining, which aims to reduce the dimensionality of the data sets while at least maintaining the classification performance. Chicken swarm optimization algorithm (CSO) has been widely applied to feature selection because of its efficiency and effectiveness. However, since feature selection is a challenging task with a complex search space, CSO quickly...
This paper proposed an intelligent approach to predict the biochar yield. The biochar is an important renewable energy that produced from biomass thermochemical processes with yields that depend on different operating conditions. There are some approaches that are used to predict the production of biochar such as least square support vector machine. However, this approach suffers from some drawbacks...
Epilepsy is one of the most common a chronic neurological disorders of the brain that affect millions of the world's populations. It is characterized by recurrent seizures, which are physical reactions to sudden, usually brief, excessive electrical discharges in a group of brain cells. Hence, seizure identification has great importance in clinical therapy of epileptic patients. Electroencephalogram...
Nowadays, a dataset includes a huge number of features with irrelevant and redundant ones. Feature selection is required for a better machine-learning algorithms' performance. A system for feature selection is proposed in this work using a sine cosine algorithm (SCA). SCA is a new stochastic search algorithm for optimization problems. SCA optimization adaptively balances the exploration and exploitation...
In this work, a system for feature selection based on hybrid Monkey Algorithm (MA) with Krill Herd Algorithm (KHA) is proposed. Data sets ordinarily includes a huge number of attributes, with irrelevant and redundant attribute. A system for feature selection is proposed in this work using a hybrid Monkey Algorithm and Krill Herd Algorithm (MAKHA). The MAKHA algorithm adaptively balance the exploration...
In this paper, an automated liver CT image clustering approach based on evolutionary metaheuristic algorithm called invasive weed optimization is presented without any prior information about the number of naturally occurring groups in the images. The fitness function used in the genetic algorithm is k-means objective function for searching of the smoothed compact cluster. The experimental results...
This paper presents an approach for detecting network traffic anomalies using detectors generated by a genetic algorithm with deterministic crowding Niching technique. Particularly, the suggested approach is inspired by the negative selection mechanism of the immune system that can detect foreign patterns in the complement (non-self) space. In our paper, we run a number of experiments on the relatively...
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