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The paper introduces a proposal for an automated magnetic resonance (MR) image segmentation called Case-Based Genetic Algorithm Location-Dependent Image Classification (CBGA-LDIC) and presents its evaluation results. This method finds an appropriate cell set towards efficient image segmentation. It uses location-dependent image classification (LDIC), which is integrated by genetic algorithm (GA) combined...
Latent Dirichlet Allocation(LDA) does not consider the input feature selection. The topic of each word is allocated by LDA in original feature space, which contains many insignificant words and affects quality of topics. In this paper, we proposed a feature selection method based on Genetic Algorithm(GA), which reduces the dimension of LDA input features and makes the generated topic more meaningful...
Support vector machine (SVM) now attracts increasing attention in gas classification due to its high performance towards small samples and nonlinearity problems of the dataset. Previously, the probable mismatch between the dataset and the training parameters determined by trial and error or grid search may hinder the exploration of the best result. In this paper, we propose a novel approach to estimate...
Persons are often asked to provide information about themselves. These data are very heterogeneous and result in as many “profiles” as contexts. Sorting a large amount of profiles from different contexts and assigning them back to a specific individual is quite a difficult problem. Semantic processing and machine learning are key tools to achieve this goal. This paper describes a framework to address...
Aiming at application to automated recognition of knee bone magnetic resonance (MR) images, an evolutional classification method called CBGA-LDIC is proposed. CBGA-LDIC finds an appropriate cell set towards efficient image segmentation. This method uses location-dependent image classification (LDIC), which is integrated by genetic algorithm (GA) combined with case based reasoning (CB). LDIC introduces...
Fault diagnosis on Multilevel Inverter (MLI) has been a subject of research for about a decade. This paper is an attempt to deliver a performance analysis of Genetic Algorithm (GA) and the Modified Genetic Algorithm (MGA) working to optimize Artificial Neural Network (ANN) that trains itself on the fault detection and reconfiguration of the Cascaded Multilevel Inverters (CMLI). The open circuit (OC)...
The traditional information retrieving method, which is based on keyword, cannot meet the needs of users of online recruitment. We proposed an efficient algorithm that is based on an automatically modeling of user demands. We use vector to present job and resume and the core part is the Genetic Algorithm. The GA algorithm is used to learn the recruitment records of a job and then establish the user's...
Information about public transport travel time is a key indicator of service performance, and is valued by passengers and operators. Among many different approaches, Support Vector Machines (SVM) has recently gained attention in predicting bus travel times. The training process of SVMs involves solving a quadratic programming problem which is slow when dealing with large training data. This paper...
Application of genetic algorithm to the formation of the optimal structure of fuzzy neural networks used in the short-term forecasting of power consumption is presented.
It is very important to maintain a high level security to ensure safe and trusted communication of information between various organizations. As the growth of computer networks is increasing day by day in modern society, therefore network security is one of the hottest issue to be solved. This paper proposes application of Genetic Algorithm (GA) for network intrusion detection system. Any action that...
Class imbalance problem refers to unequal distribution of data instances between classes. Due to this, popular classifiers misclassify data instances of minority class into majority class. Initially, Extreme learning machine was proposed with the prime objective of handling real valued datasets. Though, it a fast learning technique, it suffers from the drawback of misclassification of imbalanced dataset...
The present study elaborates a probabilistic framework of ECOC technique, via replacement of predesigned ECOC matrix by sufficiently large random codes. Further mathematical grounds of deploying random codes through probability formulations are part of novelty of this study. Random variants of ECOC techniques were applied in previous literatures, however, often failing to deliver sufficient theoretical...
The query results returned from the meta-search engine come from the independent search engine and the results will be evaluated the professional correlation; additional information must be dogged beyond its location information. This paper proposes a web page ranking algorithm based on genetic algorithms, and discusses the principle of the algorithm and implementation process, researches and realizes...
Software developers and maintainers often need to locate code units responsible for a particular bug. A number of Information Retrieval (IR) techniques have been proposed to map natural language bug descriptions to the associated code units. The vector space model (VSM) with the standard tf-idf weighting scheme (VSM natural), has been shown to outperform nine other state-of-the-art IR techniques....
In this paper, we present a Genetic Algorithm (GA) approach with an improved initial population and selection operator, to efficiently detect various types of network intrusions. GA is used to optimize the search of attack scenarios in audit files, thanks to its good balance exploration / exploitation; it provides the subset of potential attacks which are present in the audit file in a reasonable...
This paper presents a new algorithm to improve the speed of threshold searching process in C4.5 by using the technique of genetic algorithms. In the threshold searching process in C4.5, the values in a numerical attribute are sorted first and then the mid-point between every two consecutive values is calculated and designated as a candidate threshold. This process can be time consuming and it is not...
Support vector machine (SVM) can ensure the promotion capability of machine model, so it is widely used in various fields. The selection of SVM's parameters has a great effect on its performance, if genetic algorithm (GA) is introduced to optimize support vector machine's parameters, the effect will be better. Traditional GA-SVM algorithm can optimize SVM parameters including penalty factor C and...
Artificial neural network (ANN) has been successfully applied into the engineering quality evaluation. With high robustness and fault-tolerant ability, this method works much better than multiple discriminant analysis (MDA) and logistic regression. In order to settle the traditional BP neural network's problem of slow convergence speed and running into the local least value easily while estimating...
This paper studies the structure and algorithm of fuzzy pattern recognition, and proposes some improvements with its inherent limitations. In order to improve the accuracy of recognition better, this paper designs the genetic algorithm fuzzy classifier (GA-FPC) which is an optimization method of fuzzy pattern recognition using genetic algorithm. We have done some experiments on UCI datasets using...
Anomaly-based network intrusion detection techniques are a valuable technology to shield our systems and networks against the malicious activities. Anomaly detection is done by soft margin Support Vector Machine(SVM), which classify the input into any one of the label (normal and anomalous) category with respect to its anomalous behavior. SVM gives much better classification, out of wide variety of...
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