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Heart disease is a deadly disease that large population of people around the world suffers from. When considering death rates and large number of people who suffers from heart disease, it is revealed how important early diagnosis of heart disease. Traditional way of diagnosis is not sufficient for such an illness. Developing a medical diagnosis system based on machine learning for prediction of heart...
With the extensive application of machine learning algorithms in bioinformatics, more and more computer researchers are beginning to focus on this field. Polyadenylation of messenger RNA (mRNA) is one of the key steps of gene expression in eukaryotes, polyadenylation site marks the end of transcription, it is of great significance to explore prediction of the site of gene sequences encoding gene....
In this paper, we present a model for rainfall rate prediction 30 seconds ahead of time using an artificial neural network. The resultant predicted rainfall rate can then be used in determining an appropriate fade counter-measure, for instance, digital modulation scheme ahead of time, to keep the bit error rate (BER) on the link within acceptable levels to allow constant flow of data on the link during...
Scaling CMOS integrated circuit technology leads to decrease the chip price and increase processing performance in complex applications with re-configurability. Thus, VLSI architecture is a promising candidate in implementing neural network models nowadays. Backpropagation algorithm is used for training multilayer perceptron with high degree of parallel processing. Parallel computing implementation...
With the increase in the availability of information regarding energy use, there is an increase of forecasting software on the energy market, that can forecast on the short, medium and long term. In the paper, there is presented a software solution for load forecasting using Artificial Neural Networks (ANN) method. In the case study, we have written an application for a consumer which is engaged in...
Artificial neural networks (ANN) have become a powerful tool for machine learning. Resistive memory devices can be used for the realization of a non-von Neumann computational platform for ANN training in an area-efficient way. For instance, the conductance values of phase-change memory (PCM) devices can be used to represent synaptic weights and can be updated in-situ according to learning rules. However,...
The system security has turned into an extremely critical worry as system assaults have been extending with the ascent of hacking devices, inconvenience of systems and interruptions in number and brutality. This paper is centered around interruption identification by utilizing Multilayer Perceptron (MLP) with various calculation of backpropagation neural network. In this paper, performance of various...
An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. The gradient descent back-propagation rule is a powerful algorithm that is ubiquitous in deep learning, but it relies on the immediate availability of network-wide information stored with high-precision...
Carefully injected noise can speed the convergence and accuracy of video classification with recurrent backpropagation (RBP). This noise-boost uses the recent results that backpropagation is a special case of the generalized expectation maximization (EM) algorithm and that careful noise injection can always speed the average convergence of the EM algorithm to a local maximum of the log-likelihood...
The paper proposes a new method for improving the performance of Recurrent Neural Networks. The proposed method uses two parallel recurrent layers which execute independent of each other. The final output of recurrent layer at any time step is computed as the mean of the modulus of the output of these two layers. The proposed method attempts to overcome the limitations of the existing Recurrent Neural...
Next to stroke, the epilepsy is one of the most serious neurological disorders. Due to the hyperactive firing of neurons on a cellular level, epilepsy is caused. The activities of the cortical regions are recorded with the help of Electroencephalogram (EEG) which helps in the diagnosis of epilepsy. The normal patterns of the activities of neurons becomes severely disturbed in the case of epilepsy,...
The purpose of this research was to optimize the backpropagation algorithm process by adding the Nguyen-Widrow method in input layer of feed-forward process and adapting the learning rate parameter in backward process in the backpropagation. In the preprocessing usually the data have not been normalized so the significant to the target output need to be reduce in the input layer process [1]. By embedded...
This study aimed to evaluate the Artificial Neural Network (ANN) to establish a classification and analysis of degraded soils and its recovery in response to lime and gypsum application. The analyzed degraded soil was classified as Oxisol, and the physical attributes considered were: soil density, soil porosity (macroporosity and microporosity) and soil penetration resistance. The ANN used in this...
Modeling awareness is an important topic in the computer science as it is closely related to preparing systems that know what is needed (e.g. data accumulated or ignored, effector activated) to achieve a given goal. Preparing tools to build and compare dedicated or general aware computational systems can lead to step-by-step hierarchical construction of intelligent solutions. Within this text we show...
Training deep neural networks requires a large amount of memory, making very deep neural networks difficult to fit on accelerator memories. In order to overcome this limitation, we present a method to reduce the amount of memory for training a deep neural network. The method enables to suppress memory increase during the backward pass, by reusing the memory regions allocated for the forward pass....
Decision making tasks that involve processing of sequential stimuli with long delays pose a significant challenge to modeling using current methods in neural networks. However, decision making in animals involves storage of salient stimuli over long periods of time, robust maintenance of this information in the presence of noisy input, and subsequent recall and processing at the time of final decision...
In recent years, Neural Networks (NNs) have become widely popular for the execution of different machine learning algorithms. Training an NN is computationally intensive since it requires numerous multiplications of matrices that represent synaptic weights. It is therefore appealing to build a hardware-based NN accelerator to gain parallelism and efficient computation. Recently, we have proposed a...
This paper presents the use of artificial neural networks (ANN) to determine the solution one of the classic applications of differential equations, the mixing tank problem. An artificial neural network with feed-forward backpropagation is designed to predict the concentration of substance in the tank at any time t. The network has three layers of structure 5 - 10 - 2 and used the Levenberg-Marquadt...
In the paper a method of synthesis of a neural controller which goal is to reduce effects of coupling of the nonlinear multi-input multi-output (MIMO) plant inputs and outputs is presented. The designed neural controller contains a set of neural nets that determine values of parameters of linear decoupling controllers calculated for the adopted nonlinear plant model at its operating points. A known...
Neurocomputing have been adapted in time series forecasting arena, but the presence of outliers that usually occur in data time series may be harmful to the data network training. This is because the ability to automatically find out any patterns without prior assumptions and loss of generality. In theory, the most common training algorithm for Backpropagation algorithms leans on reducing ordinary...
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