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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 recent years, with increasing use of internet the computer systems are facing many number of security issues. Intrusion detection system (IDS) is one of the principal components of any information security system. Identification of anomalous activity in computer network is first step in identifying the threat to information system. Our focus is mainly on Genetic algorithm (GA) based anomaly detection...
This paper proposes a new type of filter called neural network filter for image denoising. The noisy images are fed as input to the network and the weights are updated with LMS algorithm. Further the weights are updated by using a recently developed novel optimization technique called Accelerated Particle Swarm Optimization (APSO) for gray level image denoising application. Implementation of APSO...
Military simulations, especially those for personnel training and equipment effectiveness analysis, require proper human behavior models (HBMs) to play blue or red. Traditionally, the HBMs are controlled through rule based scripts. However, the doctrine-driven behavior is rigid and predictable, and more often than not unable to adapt to new situations. In most cases, the subject matter experts (SMEs)...
Stock selection is an important issue when it comes to investing in the stock market. However, it is worth investigating the problem of selecting portfolios while considering not only low risk but also high return on investment. The calculation process of the traditional method is highly complex and is not comprehensive in terms of what it takes into consideration. Hence, this paper proposes a new...
In this paper, a comparison of two network traffic activities prediction models will be presented, namely a backpropagation neural network (BPNN) and a genetic algorithm based backpropagation neural network (GABPNN). A backpropagation neural network (BPNN) prediction model can be used to learn a time-series dataset. However, the performance of the BPNN can be improved by optimizing the BPNN using...
Today, Peer-to-peer (P2P) traffic is the most important network flow on the Internet; meanwhile it gives rise to many security problems for the network management. Therefore P2P traffic identification is the hottest topic of P2P traffic management. Support vector machine (SVM) has advantages with resolving small samples for P2P classification problems. However, the performance of SVM is primarily...
There area wider range of biometric authentication systems available; Face recognition system is one of them. It is an effective way for authentication and also considers many security aspects. Any face recognition system has to handle higher amount and dimension of image data. Whenever we consider this system at global level a large variety of problems frequently arrives. To overcome these problems...
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...
This paper extends the authors' previous research on a malware detection method, focusing on improving the accuracy of the perceptron based - One Side Class Perceptron algorithm via the use of Genetic Programming. We are concerned with finding a proper balance between the three basic requirements for malware detection algorithms: a) that their training time on large datasets falls below acceptable...
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...
Machine based systems can't keep up with the task of organizing the data in an up-to-date manner unless and until the data acquired is being planned or scheduled and managed in an appropriate manner. Today's datasets start as small chunk of information and grow exponentially over a period of time. Once the size is extremely large it becomes difficult to make decisions and to predict consistently and...
Expert systems for classification tasks in medical diagnosis systems require two properties. The true positives should be very high, as well as the true negatives, i.e. the system should correctly catch those who are ill, and correctly dismiss those who are healthy. The multi-modal evolutionary classifier uses a genetic algorithm to learn a reference vector for each class, and classification is done...
In many situations, such as medical records of rare diseases, early stages of flexible manufacturing system and continuous industrial process, only small training samples can be obtained to construct prediction model. When modelling with high dimensional spectral data, it is very much difficulty to construct efficient and effective prediction model with such a small sample. This research proposes...
The sequential covering strategy has been and still is a very common way to develop rule learning algorithms. This strategy follows a greedy procedure to learn rules, where, after each step one rule is obtained. Recently, we proposed a new sequential covering strategy that allowed the review of previously learned knowledge during the learning process itself. This review of knowledge allowed the algorithm...
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...
Analyzing inflation forecast problem, this paper proposes a SVM-based approach. Firstly, the paper reviews some former studies about inflation forecasting and predicting methodology, finding that SVM is a nonlinear adaptive data-driven model with strong approximation and generalization ability, which can be applied to complex forecasting tasks. Secondly, the paper establishes a SVM model and discusses...
The paper approaches the problem of modeling the microwave heating process using Neural Networks. The Neural Network was trained using Matlab and Comsol Multiphysics software. Numerical simulations were made in Comsol Multiphysics, obtaining the necessary input and output data to train the Neural Network. The training was made using Adaptive Neural Network tool from Matlab software.
In this paper we propose a novel optimization algorithm for grid-based RF fingerprinting to improve user equipment (UE) positioning accuracy. For this purpose we have used Multi-objective Genetic Algorithm (MOGA) which enables autonomous calibration of grid-cell layout (GCL) for better UE positioning as compared to that of the conventional fingerprinting approach. Performance evaluations were carried...
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...
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