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In this paper, a novel method to do feature selection to detect botnets at their phase of Command and Control (C&C) is presented. A major problem is that researchers have proposed features based on their expertise, but there is no a method to evaluate these features since some of these features could get a lower detection rate than other. To this aim, we find the feature set based on connections...
Artificial immune system (AIS) is considered as an adaptive computational intelligence method that could be used for detecting and preventing current computer network threats. AIS generates Antibodies (self) competent in recognizing Antigen (non-self), which is considered as an anomaly technique. This paper aims to develop artificial immune system (AIS) that consists of two levels. Level one is developed...
This paper proposes a GA-SVM classification method which is applied to the dynamic evaluation of taekwondo. For classifying a dynamic action, we converted a dynamic action signal to a frequency spectrum signal for analysis. However, the useful features were concentrated in a part of the frequency spectrum, and the useless features led to a decline in accuracy, operation speed, and efficiency of the...
Character recognition is one of the fields of research in pattern recognition. Recognition of hand-written characters can be done either On-line or Offline. Not much substantial work has been published in the past on the development of hand-written character recognition (HWCR) systems for Telugu text. However none of them give 100% accuracy in recognition of Telugu characters. Therefore, it is an...
Detecting cyber-attacks in cloud infrastructures is essential for protecting cloud infrastructures from cyber-attacks. It is difficult to detect cyber-attacks in cloud infrastructures due to the complex and distributed natures of cloud infrastructures. In addition, various computing and storage devices, both mobile and stationary, are connected to cloud infrastructures to facilitate users access,...
Efforts have been made in financial markets to deal with price movement predicting. Recent studies have shown that the market can be outperformed by methodologies with the aid of science. In other words, it has been shown that methods based on computational intelligence can be more profitable than a buy-and-hold strategy. This paper proposes a probabilistic and dynamic chart pattern recognition hybrid...
This paper describes two new algorithms for optimising the structure of trained Evolving Connectionist System (ECoS) artificial neural networks (ANN). It also presents the results of preliminary empirical evaluations of the algorithms. While ECoS are fast and efficient constructive ANN algorithms they can lose efficiency if they are allowed to grow too large. The algorithms presented in this paper...
To diagnose and classify the dysarthric speech, speech language pathologist (SLP) conducts a listening test. On the basis of the scores given by listeners the dysarthria is diagnosed and assessed. The above mentioned method is costly, time consuming and not very accurate. Unlike the traditional method, this research proposes an automatic diagnosis and assessment of dysarthria. The aim of this paper...
This paper presents a non-linear, data driven Adaptive Network based Fuzzy Inference System (ANFIS) modeling of a Two Tanks Hydraulic System (TTHS). The paper also addresses the design of a Type 1 Fuzzy Logic Controller optimized with Genetic Algorithms (GA). The controller was designed and tested in simulation with the obtained ANFIS model and validated in real-time with the actual TTHS. Obtained...
A Deep Neural Network (DNN) using the same activation function for all hidden neurons has an optimization limitation due to its single mathematical functionality. To solve it, a new DNN with different activation functions is designed to globally optimize both parameters (weights and biases) and function selections. In addition, a novel Genetic Deep Neural Network (GDNN) with different activation functions...
In this study, seismic attributes have been used to estimate well logs in one of the Iranian petroleum reservoirs. Three static methods have been evaluated: the linear model, the multilayer perceptron (MLP) and the radial basis function (RBF). For linear case, the selection of appropriate attributes was determined by forward selection and for nonlinear one, the selection was based on the genetic algorithm...
In this paper, Ant Lion Optimizer (ALO) was presented to train Multi-Layer Perceptron (MLP). ALO was used to find the weights and biases of the MLP to achieve a minimum error and a high classification rate. Four standard classification datasets were used to benchmark the performance of the proposed method. In addition, the performance of the proposed method were compared with three well-known optimization...
Financial time series forecasting is regarded as one of the most challenging applications of time series forecasting. Many researchers have been focusing on this topic due to the potential of yielding significant profits on the invested money in a short time frame. Believing in the predictability of stock markets, traders have been using Technical Analysis tools for a very long time to analyze and...
Dissolved Gas Analysis (DGA) is an established method for detecting and predicting faults contained in power transformers. Support Vector Machine (SVM) has been actively applied to classify faults using historic DGA data. However, redundant and irrelevant features can reduce SVM classification performance. Therefore, this study proposes the use of GA-SVM wrapper to eliminate these features and select...
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...
Polynomial Neural Network is a self-organizing network whose performance depends strongly on the number of input variables and the order of polynomial which are determined by trial and error. In this paper, a training algorithm for Polynomial Neural Network (PNN) based on Genetic Algorithm (GA) has been proposed for classification problems. A performance comparison of the proposed PNN-GA and Back...
The Content-Based Image Retrieval (CBIR) techniques comprise methodologies intended to retrieve self-content descriptors over the image data set being studied according to the type of the image. The main purpose of CBIR consists in classifying images avoiding the use of manual labels related to understanding of the image by the human being vision. In this work we provide a new CBIR procedure which...
The rapidly growing amount of data being produced in the world has become a challenging problem for decision support systems. These data are located in disparate sites while time, cost and privacy concerns makes it impossible to aggregate them into one location. Information fusion systems aim to make decisions by getting the outputs of the distributed sources. Since each source is making its local...
Comparing with the traditional method of credit evaluation, this paper presents a classification evaluation method based on the projection pursuit and the fuzzy rules. Firstly, we use projection pursuit technology to reducing the dimensionality of the training sample, and use genetic algorithm to optimize the projection direction to find the best projection value, classification in accordance with...
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