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The wind turbine power curve (WTPC) shows the relationship between the wind speed and power output of the turbine. Power curves, which are provided by the manufacturers, are mainly used in planning, forecasting, performance monitoring and control of the wind turbines. Hence an accurate WTPC model is very important in predictive control and monitoring. This paper presents comparative analysis of various...
Intrusion detection is used to protect the system from inside and outside attacks. Evolutionary algorithm has an important role in intrusion detection. Evolutionary algorithms are highly responsive for feature space reduction. The minimal number of features can improve the performance of an intrusion detection system. Thus we propose an intrusion detection system with various feature selection methods...
Based on principal component analysis (PCA) and support vector machine (SVM), a new method for the fault diagnosis of TE Process is proposed. The fault recognition based on kernel principal component analysis (KPCA) is analyzed and SVM is employed as a classifier for fault classification. To establish a more efficient SVM model, genetic algorithm (GA) is used to determine the optimal kernel parameter...
Aiming at the problem of low accuracy in intrusion detection system, this paper established a genetic support vector machine (SVM) model according to the features of genetic algorithm and support vector machine algorithm. The model firstly optimizes the support vector parameters according to genetic algorithm, then we build the intrusion detection model with support vector machine optimized and use...
Over past few decades, statistical and soft-computing techniques have become an emerging research area for machine learning problems. Fuzzy logic with better generalization capability and rapport with reality is being used in classification problems immensely. In this paper a fuzzy rule based classification system is modeled as a combinatorial optimization problem. Thus the optimization power of Genetic...
In Inertia Confinement Fusion (ICF) physical experiments, the accuracy of target positioning affects the successful rate of target hitting directly. A 3-CCD camera system is often used for tiny target measurement in ICF target positioning. Most of the current pose measurement methods utilize the well-known digital image processing technology to extract the target features in each image, then calculates...
The wide application of Internet technology and media technology produces more and more data which also leads the arrival of the era of big data. However, it is difficult to extract the needed information from the original data directly except some special conditions. In recent years, the development of machine learning which provide a effective way to solve this problem for us. You can obtain lower...
Data mining concepts have been extensively used for disease prediction in the medical field. Many Hybrid Prediction Models (HPM) have been proposed and implemented in this area, however, there is always a need for increasing accuracy and efficiency. The existing methods take into account all the features to build the classifier model thus reducing the accuracy and increasing the overall processing...
Condition monitoring is a vital task in the maintenance of industry machines. This paper proposes a reliable condition monitoring method using a genetic algorithm (GA) which selects the most discriminate features by taking a transformation matrix. Experimental results show that the features selected by the GA outperforms original and randomly selected features using the same k-nearest neighbor (k-NN)...
We present an evolutionary multi-objective optimization method for sensor deployment applied to an indoor positioning system with range-difference measurements. Stationary sensors at known locations are used to obtain the position of a moving emitter. Coverage and accuracy of the positioning system depend on the number and location of the sensors for a given indoor space (floor plan) and on the properties...
This paper presents the development of a Neuro-genetic model for the prediction of coronary heart diseases. The novelty of this work is feature subset selection using multi-objective genetic algorithm without sacrificing the accuracy of ANN based heart disease predictor. Subsequently, the selected feature subset is used to predict the level of angiographic coronary heart disease using neural networks...
In biology, identifying the tertiary structure of a protein helps determine its functions. A step towards tertiary structure identification is predicting a protein's fold. Computational methods have been applied to determine a protein's fold by assembling information from its structural, physicochemical and/or evolutionary properties. It has been shown that evolutionary data helps improve prediction...
Random Forest RF is an ensemble learning approach that utilises a number of classifiers to contribute though voting to predicting the class label of any unlabelled instances. Parameters such as the size of the forest N and the number of features used at each split M, has significant impact on the performance of the RF especially on instances with very large number of attributes. In a previous work...
Optimally solving large scale Ready Mixed Concrete Dispatching Problems (RMCDPs) in polynomial time is a crucial issue and, in the absence of automated solutions, experts are hired to handle resource allocation tasks in concrete dispatching centres. Therefore, in the Ready Mixed Concrete (RMC) industry, the performance of experts is accepted as the only practical solution, although there is no benchmark...
In our previous study, a grouping-geneticalgorithm- based (GGA-based) attribute clustering process has been proposed for grouping features. In this paper, we further improve its performance and propose a center-based GGA for attribute clustering (CGGA). A new encoding scheme with corresponding crossover and mutation operators are designed, and an improved fitness function is proposed to achieve better...
Intermediate results of two state-of-the-art wrapper feature selection approaches (GA and SFFS) applied to hyperspectral data sets were used to derive information about band importance for specific land cover classification problems. Several feature selection performance scores (classification accuracies, Bhattacharyya separability) were tested. The impact of the number of selected bands on classification...
The needs for location-aware applications are increasing with the popularity of ubiquitous mobile computing. However, as an important factor affecting the positioning performance, the deployment of anchor nodes has received little attention during the past years. In this paper we investigate the anchor placement in the received signal strength (RSS) based wireless localization systems. An optimization...
Feature selection is one of the important preprocessing steps in analyzing high dimensional datasets. In this paper, first the ensemble of three different filter ranking methods including: Information Gain (IG), ReliefF and F-score are used to reduce the dimension of datasets. Afterward, reduced data are utilized as inputs of the meta-heuristic algorithm, Improved Binary Gravitational Search Algorithm...
One of the most interesting and important issues in the machine learning and data mining research areas is high accuracy classification. Imbalance data is a great challenge. The imbalance data is a kind of situation when the number of one data member class is significantly smaller than the other class. In the recent years this issue has got more attention among many researchers all over the world...
Due to the steep price of a microarray experiment, a microarray dataset usually contains a few experimental samples. While the number of experimental samples is small, the number of genes in an experimental sample is quite large. The fact that only a few of the large amount of genes are relevant to a diagnosis poses a challenge to the application of the microarray technology. This paper presents a...
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