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According to the noise and overlapping characteristics of agricultural irrigation water quality monitoring data for the comprehensive evaluation may bring about the boundary fuzzy problem. This paper proposes an improved Genetic Algorithm (GA) to avoid premature convergence, the global optimal solution of the function of the Projection Pursuit (PP) function is used as the comprehensive evaluation...
Unemployment, poverty and similar problems that have come to the fore with the increase in population in our country have caused the municipalities to take charge in the field of social assistance and social services. For this purpose, it is very important that the municipalities that undertake social assistance and social service tasks are able to use the present data quickly during distribution...
Diabetes Mellitus is a dreadful disease characterized by increased levels of glucose in the blood, termed as the condition of hyperglycemia. As this disease is prominent among the tropical countries like India, an intense research is being carried out to deliver a machine learning model that could learn from previous patient records in order to deliver smart diagnosis. This research work aims to improve...
Advances in highly multi-parametric measurements by mass cytometry have made possible the accurate detection of acute myeloid leukemia (AML) cells in complex cell populations. However, current informatics methods bottlenecks data processing by being labor-intensive, time-consuming, and prone to user bias. To address these problems, major efforts have been made to automate the detection of AML cells...
In view of textual remote sensing image classification, a classification approach based on Extreme Learning Machine (ELM) in introduced. As the performance of ELM is mainly affected by the value of input weights and hidden biases genetic algorithm (GA) and particle swarm optimization algorithm (PSO) have been used to learn these parameters for ELM in order to improve the stability of extreme learning...
Nowadays with the rapid development of network-based services and users of the internet in everyday life, intrusion detection becomes a promising area of research in the domain of security. Intrusion detection system (IDS) can detect the intrusions of someone who is not authorized to the present computer system automatically, so intrusion detection system has emerged as an essential component and...
We take inspirations from nature very often in solving many complex scientific and day to day problems. Nature inspired computing is a branch of computer engineering deals with the development of algorithms simulating behaviors of natural species for solving complex problems not easily solvable by available computational models. Based on biological systems, various algorithms have been presented in...
Fruit fly optimization algorithm (FOA) is a new method for finding global optimization based on food finding behavior of the fruit fly. The original FOA can only solve problems that have optimal solutions in zero vicinity. To make FOA more universal for the continuous optimization problems, especially for those problems with optimal solution that are not zero. This paper proposes a hybrid fruit fly...
In real world, the datasets are having varying dimensions which incorporates noisy, irrelevant and redundant data which is hard to analyze. Feature selection is a preprocessing step used for selecting the significant information. The selection of optimal feature subset is an optimization problem which has been solved by several versions of metaheuristic algorithms. The metaheuristic optimization algorithm...
Feature selection is a key step in data analysis. However, most of the existing feature selection techniques are serial and inefficient to be applied to massive data sets. We propose a feature selection method based on a multi-population weighted intelligent genetic algorithm to enhance the reliability of diagnoses in e-Health applications. The proposed approach, called PIGAS, utilizes a weighted...
Feature selection is an important preprocessing in data mining, it aims to reduce the computational complexity of learning algorithm, and to improve the performance of data mining algorithms by removing irrelevant and redundant features. In the framework of discrete-valued feature selection, this paper experimentally compares two feature selection methods which are based on generic algorithm. The...
Support Vector Machine (SVM) is one of the most popular machine learning algorithm to perform classification tasks and help organizations in different ways to improve their efficiency. A lot of studies have been made to improve SVM including speed, accuracy, and/or scalability. The algorithm possesses parameters that need precision tuning to perform well. This work proposes a novel parallelized parameter...
A novel differential evolution algorithm is proposed for constrained optimization problems (COPs). The proposed algorithm combines the ideas between the self-adaptive differential evolution algorithm (JDE) and simple penalty function method (SPFM). Simulation results on the bump problem show that the solutions of the new algorithm is better than those of the algorithms in the almost exiting literature...
Fruit fly Optimization Algorithm (FOA) is a kind of relatively new swarm intelligence optimization algorithm with strong performance. In this paper a kind of FOA based on dynamic population and direction correct (DPDC-FOA) is proposed on the question of premature convergence. By changing the fruit fly group's search range, number of individuals and group position selection strategy during the movement...
This paper presents a new hybrid HPSO-DE classification algorithm that combines the advantages of particle swarm optimization algorithm and differential evolution algorithm. Major improvements achieved by this combination are 1) flight improvement — flight behaviors are more and better diversified because each of the top 3 particles gets put into 3 different groups of the rest and then each group...
This paper presents a method for identification of fuzzy classifiers by means of data analysis. The method is based on the assumption that the data of the same classes form compact regions (clusters) in the input space. The algorithms for structure generation and parameter optimization of the fuzzy classifiers are proposed.
The analysis of the human migration process has been studied in various fields of science. This work, focuses on migration indicators proposed by the International Migration Policy, with the aim of identifying the most important indicator from the point of view of data mining. This study identifies migrant stock as the most important factor related to the values obtained by the F1Score and the ROC...
Traffic congestion has been an important problem all over the world. Advanced transportation management system (ATMS) that provides information for traffic control and management addresses this problem. McMaster Algorithm, one of the most classical congestion detection algorithms, has been widely used in practice. However, it still has some limitations such as difficulty of determining its parameters...
In order to improve the accuracy of support vector machine (SVM) classification of wetland remote sensing images, the selection of kernel function parameters in support vector machines becomes an effective approach. In this paper, Particle Swarm Optimization and Genetic Algorithms (PSO-GA) co-evolutionary algorithm are used to optimize the SVM parameters. Because of the complementarity of evolutionary...
Nature inspired algorithms are gaining popularity for optimizing complex problems. These algorithms have been classified into 2 general categories, namely Evolutionary and Swarm Intelligence, which have further been divided into a couple of algorithms. This paper presents a comparative study between Bat Algorithm, Genetic algorithm, Artificial Bee Colony Algorithm and Ant Colony Optimization Algorithm...
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