The aim of this research study is based on efficient gene selection and classification of microarray data analysis using hybrid machine learning algorithms. The beginning of microarray technology has enabled the resea...
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The aim of this research study is based on efficient gene selection and classification of microarray data analysis using hybrid machine learning algorithms. The beginning of microarray technology has enabled the researchers to quickly measure the position of thousands of genes expressed in an organic/biological tissue samples in a solitary experiment. One of the important applications of this microarray technology is to classify the tissue samples using their gene expression representation, identify numerous type of cancer. Cancer is a group of diseases in which a set of cells shows uncontrolled growth, instance that interrupts upon and destroys nearby tissues and spreading to other locations in the body via lymph or blood. Cancer has becomes a one of the major important disease in current scenario. DNA microarrays turn out to be an effectual tool utilized in molecular biology and cancer diagnosis. Microarrays can be measured to establish the relative quantity of mRNAs in two or additional organic/biological tissue samples for thousands/several thousands of genes at the same time. As the superiority of this technique become exactly analysis/identifying the suitable assessment of microarray data in various open issues. In the field of medical sciences multi-category cancer classification play a major important role to classify the cancer types according to the gene expression. The need of the cancer classification has been become indispensible, ecause the numbers of cancer victims are increasing steadily identified by recent years. To perform this proposed a combination of Integer-Coded Genetic algorithm (ICGA) and artificial bee colony algorithm (ABC), coupled with an Adaptive Extreme Learning Machine (AELM), is used for gene selection and cancer classification. ICGA is used with ABC based AELM classifier to chose an optimal set of genes which results in an efficient hybrid algorithm that can handle sparse data and sample imbalance. The performance of the proposed a
Wireless networks have gained considerable popularity during recent years. Optimum deployment of sensors in wireless networks has turned into one of the most significant topics of this area. Extensive research has bee...
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This paper proposes a modified artificial bee colony algorithm (MABC) for economic load dispatch (ELD) problem in the transmission system. Traditional economic dispatch focuses mainly on the minimization of the total ...
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ISBN:
(纸本)9781479917983
This paper proposes a modified artificial bee colony algorithm (MABC) for economic load dispatch (ELD) problem in the transmission system. Traditional economic dispatch focuses mainly on the minimization of the total operation cost of the power system. With the appearance of environmental pollution and energy crisis becoming a public issue, environmental effects of generator should be taken into consideration throughout the dispatch process. In this study economic dispatch problem which considers the integration of wind power is solved whose objective includes the minimization of generation cost. A modified chaotic artificial bee colony algorithm (MCABC) is proposed for finding optimal generation dispatch for minimum cost .The artificial bee colony algorithm (ABC) is selected for solving the problem due to its superiority over other recent evolutionary optimization techniques. The ABC is unique in its implementation of exploration and exploitation phases during the search of optimal solution. Search stagnation is also avoided in ABC by using a controlled scout bee phase. ABC also has least number of control parameters. A chaotic mutation is introduced in the MABC to simulate the chaotic behavior of bees in nature while searching for nectar. The transmission losses used in solution of problem have been calculated by B-loss matrix. The developed model is tested on an IEEE 30-bus system, with wind power generation embedded.
In this paper we present a modified version of an existing honey bee optimization algorithm: the modified fast marriage in honey bee optimization (MFMBO). Then we compare performances of this new algorithm and three e...
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ISBN:
(纸本)9781424481835
In this paper we present a modified version of an existing honey bee optimization algorithm: the modified fast marriage in honey bee optimization (MFMBO). Then we compare performances of this new algorithm and three existing beealgorithms, i.e. the artificialbeecolony (ABC), the queen bee (QB), and the fast marriage in honey bee optimization (FMBO) on four benchmark functions for various numbers of variables up to 100. The obtained results show that the modified algorithm is faster than the others in most cases. Especially for Griewank and Schwefel functions by increasing precision of answer and number of variables, the difference between the speeds of the FMBO and MFMBO becomes prominent. In general the MFMBO algorithm is quite competitive with the other mentioned algorithms.
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