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Text Summarization is a way of determining the key concepts that are covered in the given text under consideration. Various techniques have been presented in literature and many are used in commercially available systems. The primary aim of the present work is to find efficacy of using sentiments for text summarization. In this work we present a computationally efficient technique based on sentiments...
In the wide growth of information technology, security has one challenging phase for computer and networks. Attacks on the web are increasing day-by-day. Intrusion detection system is used to detect several types of malicious attacks that can compromise the security of a computer system. Data mining techniques are used to monitor and analyze large amount of network data & classify these network...
One of the key success factors of lending organizations in general and banks in particular is the assessment of borrower credit worthiness in advance during the credit evaluation process. Credit scoring models have been applied by many researchers to improve the process of assessing credit worthiness by differentiating between prospective loans on the basis of the likelihood of repayment. Thus, credit...
In the present study we investigate the evolutionary feature subset selection using wrapper based genetic algorithms on Multi-temporal datasets. Feature subset selection helps in reducing the original feature dimension and also yields high performance. The evolutionary strategy attains a global optimum by reducing the computations iteratively and by traversing intelligently in the entire feature space...
The thesis proposes a hybrid intrusion detection model based on the parallel genetic algorithm and the rough set theory. Due to the difficult for the status of intrusion detection rules. This model, taking the advantage of rough set's streamline the edge to data and genetic algorithm's high parallelism, succeeds in introducing the genetic-rough set theory to the instrusion detection. The application...
In this paper, we introduce an Intrusion Detection system (IDS) based Hybrid Evolutionary Neural Network (HENN). A brief overview of IDS, genetic algorithm, and related detection techniques are discussed. The system architecture is also introduced. Factors affecting the genetic algorithm are addressed in detail. Unlike other implementations of IDS, Input features, network structure and connection...
This article is based on Data mining technology how to apply in the personal credit. Using decision tree algorithm, supporting data processing methods and more potential information for firms in order to facilitate business-to-customer to take a different credit programs.
Fuzzy Rule-Based Classification Systems are a widely used tool in Data Mining because of the interpretability given by the concept of linguistic label. However, the use of this type of models implies a degree of uncertainty in the definition of the fuzzy partitions. In this work we will use the concept of Interval-Valued Fuzzy Set to deal with this problem. The aim of this contribution is to show...
Process mining aims at discovering process models from data logs in order to offer insight into the real use of information systems. Most of the existing process mining algorithms fail to discover complex constructs or have problems dealing with noise and infrequent behavior. The genetic process mining algorithm overcomes these issues by using genetic operators to search for the fittest solution in...
Two Computational Intelligence techniques, neural networks-based Multivariate Time Series Model Mining (MVTSMM) and Genetic Programming (GP), have been used to explore the possible relationship between solar activity and temperatures in Central England for the 1721 to 1967 period. Data driven analysis of multivariate, heterogeneous and incomplete time series are used in order to understand the extreme...
Bayesian linear classifier is the basic scheme to solve model classification basing on statistics. Face with the classification of three different nectar plant, the near infrared spectrum data was acquired. The character of the near infrared spectrums is known as litter sample and higher dimension. In this paper, the method has developed to acquire the feature wavelength based on genetic algorithm...
Nowadays, personal credit scoring models have played a vitally important part for many organizations in keeping away from credit risks. This paper establishes a personal credit scoring model via one of data mining approaches-genetic algorithm, which is a useful method applied to solve credit scoring problems. And the experiment of this model is proved to its high efficiency and evaluated its significant...
Analyzing the growth rhythm of a plant correctly is very important during modeling its whole growth process. Now the parametric L-system is a kind of popular plant modeling tool in which many parameters are introduced to build up the production rules. To depict growth details of a plant the growth function is combined with the L-system production rules in which the interaction among several plant...
Grid computing is an emerging computing paradigm that will have significant impact on the next generation information infrastructure. Due to the largeness and complexity of grid system, its quality of service, performance and reliability are difficult to model, analyze and evaluate. In real time evaluation, various noises will influence the model and which in turn accounts for increase in packet loss...
The modularization immunity neural network model is an intelligent solution to network security, but the function relationships among the neural network, immune algorithm and genetic algorithm in the model. By following biological mechanism, this paper builds up interaction functions and function systems among all the parts in the model, which leads to the organic combination among the neural network,...
According to the image matching demands of both the precision and instantaneity, a real-time two-level scene matching algorithm is proposed based on the least trimmed square Hausdorff distance (LTS-HD). In the first level, a coarse match point is obtained using pixel-jump searching through the reference image. In the second level, a point-by-point local searching is performed to get the accurate match...
BitTorrent has emerged as an effective peer-to-peer application for digital content distribution in the Internet. However, selecting peers in BitTorrent for efficient content distribution still poses a number of challenges due to high heterogeneities of peers with varied rates of uploading bandwidth and dynamic content. This paper presents GA-BT, a genetic algorithm based peer selection optimization...
This paper presents a study on the significance of length of remediation time, on the optimal groundwater remediation design by considering two possible groundwater remediation scenarios. The objective of this problem is to minimize the total remediation cost conditioned by some constraints. Flow and transport simulators MODFLOW and MT3DMS are coupled with a genetic algorithm (GA) based optimization...
Parametric studies have been carried out for the quartic-polynomial regression problem demonstrated in the Genetic Programming (GP) v3 toolbox of Matlab. Many classification schemes and modeling issues are polynomial based. Every possible combination originating from all available options between the two genetic parameters namely ?elitism? and ?sampling? has been analyzed while keeping all other parameters...
One of the ad-hoc networks challenges is the connectivity problem coming from changeable and dynamic topology of networks nodes. According to most of researches done on this problem, one of the solutions is adding static nodes in some points in network environment; and many attempts have been made to find these points by using different ways. However, in most of these studies no attention has been...
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