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The deep convolutional neural network (CNN) is the state-of-the-art solution for large-scale visual recognition. Following some basic principles such as increasing network depth and constructing highway connections, researchers have manually designed a lot of fixed network architectures and verified their effectiveness.,,In this paper, we discuss the possibility of learning deep network structures...
Feature selection represents a key stage in electroencephalogram (EEG) classifications, because these applications involve numerous, high-dimensional samples. In recent literature, a multitude of supervised embedded feature selection procedures has been proposed. Regardless if they are configured as Single Objective (SOO) or Multi-Objective Optimizations (MOO), the embedded methods assess the quality...
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...
The main purpose of this work is to implement a new and accurate approach based on Time Domain Reflectometry (TDR) combined with Adaptive Neuro-Fuzzy Inference System (ANFIS) to solve the problem of soft faults detection and localization on complex wiring electric network. Firstly, the response of the transmission line is given by applying the Finite Difference Time Domain method (FDTD) on the transmission...
The influence of temperature, irradiance and shielding ratio on the output characteristic curve of photovoltaic cells was studied in this paper. In order to improve the photoelectric conversion efficiency of photovoltaic cells, combining three major factors that affect photovoltaic cells, a maximum power point tracking (MPPT) scheme based on large variation genetic algorithm was proposed. In this...
At present, most of the EEG emotion recognition studies have taken all electric shocks or filtered electrodes as a feature and they are integrated (combined) with simple features that are extracted from other signals as a single classifier Emotional classification, but there are problems such as low efficiency and low accuracy. Aiming at this problem, this paper proposes an EEG emotion classification...
At present, the detection of mixing uniformity in glass furnace batching system is mainly realized by artificial detection. However, this method is time-consuming and laborious, and there are some risks. For the problem of mixing uniformity detection, the nonlinear relation between the actual weight value and the mixing uniformity is established by the BP neural network, which can predict the mixing...
Deep neural networks enjoy high interest and have become the state-of-art methods in many fields of machine learning recently. Still, there is no easy way for a choice of network architecture. However, the choice of architecture can significantly influence the network performance. This work is the first step towards an automatic architecture design. We propose a genetic algorithm for an optimization...
This paper presents a deep analysis of literature on the problems of optimization of parameters and structure of the neural networks and the basic disadvantages that are present in the observed algorithms and methods. As a result, there is suggested a new algorithm for neural network structure optimization, which is free of the major shortcomings of other algorithms. The paper describes a detailed...
The paper presents a deep analysis of the literature on the problems of optimization of parameters and the structure of the neural networks and the basic disadvantages that are present in the observed algorithms and methods. As a result, there are suggested a new algorithm for neural network structure optimization, which is devoided of the major shortcomings of other algorithms. The paper includes...
Inductive learning has been employed successfully in various domains, however the inductive logic programming (ILP) systems focused on non-incremental learning tasks where independent sets of data are provided incoherently. In this paper, we propose a new genetic algorithm-based ILP system, called GAILP, for incremental learning. GAILP is a covering algorithm which extracts hypotheses/rules from a...
We discuss several ways to accelerate genetic algorithm-based instance selection, where the two objectives are a minimal number of training instances and maximal accuracy of the classifier (we use neural networks) on the test data. We discuss several ways to accelerate the process, but we especially focus on two parameters: fitness function and chromosome length reduction. We evaluate different fitness...
The paper proposes using a neuro-fuzzy controller in telecommunication networks for improving the routing process. An architecture of the neuro-fuzzy controller was developed. Linguistic variables, terms and membership functions for input and output values were defined. A rules base was developed. The operation of the neuro-fuzzy controller was simulated and trained.
In this paper, we present Genetic Algorithm based optimized feature selections for intrusion detection systems. We used one-point crossover for the Genetic Algorithm parameters instead of two-point crossover used by the previous research as it one-point crossover is faster. For evaluations, we used the NSL-KDD Cup 99 data set and we modified the data set by looking into to the recent attacks, hence...
The increasing amount of data to be processed coming from multiple sources, as in the case of sensor networks, and the need to cope with constraints of security and privacy, make necessary the use of computationally efficient techniques on simple and cheap hardware architectures often distributed in pervasive scenarios. Random Vector Functional-Link is a neural network model usually adopted for processing...
Ozonation is one of the most important processes during drinking water treatment. To improve the efficiency of ozonation and to stabilize the quality of the treated water, the ozone dosage should be a good trade-off between the requirement of disinfection and the restriction of bromate formation. However, because of the changes of raw water quality and the nonlinear behavior of ozonation process,...
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...
Affordable sensors lead to an increasing interest in acquiring and modeling data with multiple modalities. Learning from multiple modalities has shown to significantly improve performance in object recognition. However, in practice it is common that the sensing equipment experiences unforeseeable malfunction or configuration issues, leading to corrupted data with missing modalities. Most existing...
In this work, we propose two main contributions to hyperspectral image interpretation. Firstly, while the traditional Weighted Linear Combination optimized by Genetic Algorithms (WLC-GA) [1] intends to give more discriminant power to those classification approaches contributing the most, we extend it to make a fine tuning over the class probabilities within the combination process. Then, we compare...
We previously presented a crowd-powered digital contents evaluation system. This system shows a lot of pictures to the answerers and ask them to input the evaluations. It preferentially selects pictures which are predicted to be highly or poorly evaluated to the answerers, based on our assumption that high or poor evaluations are more informative results comparing with moderate evaluations. We have...
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