Algorithms using Neural Network
Detecting Network Intrusion Using BPA & RBF Neural Network Algorithms
... to network of computer ...Propagation Neural Network (BPA) and Radial Basis Function Neural ...the network packet to look for known intrusive ...extraction algorithms makes the ...
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Prediction of Seismic zone in India using Neural Network Algorithms
... the neural network algorithms are used to find the seismic risky areas of ...propagation neural network has been constructed with the Levenberg-Marquardt and BFGS Quasi Newton ...
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CLASSIFICATION OF DISEASED PLANT LEAVES USING NEURAL NETWORK ALGORITHMS
... are using excessive pesticides for the plant disease ...types using various neural network ...leaves using Feed Forward Neural Network (FFNN), Learning Vector Quantization ...
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Neural Network Algorithms for using Radon Emanations as an Earthquake Precursor
... computed using Fast Fourier Transform has shown to improve reliability of prediction of earthquake The present paper deals with the use of neural network algorithms which can learn the ...
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Configuring spiking neural network training algorithms
... spiking neural networks comes at a ...second-generation neural network, with the backprop- agation algorithm being the gold standard, the question of training a spiking neural network ...
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New voting functions for neural network algorithms
... Neural Network and Convolutional Neural Network algorithms are among the best performing machine learning ...the algorithms may vary between multiple runs because of the ...
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A Comparative Study on Various Neural Network Algorithms
... Forward Neural Network is much slower than required and this has been a major drawback in past ...learning algorithms extensively used to train neural networks are very slow, and 2) those slow ...
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Nonlinear System Identification Using Hammerstein-Wiener Neural Network and subspace algorithms
... In this paper, parametric time domain method is utilized for identification. The first nonlinear static subsystem is simulated by a simple feed forward neural network. The second subsystem is a linear ...
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Evolution of Deep Neural Network Algorithms Using Dynamic Camera and Objects on CPU and FPGA
... a neural system concentrating on the speed and availability of techniques committed to the ...various algorithms and PYNQ Board are key point in future ...Binary Neural Network, Tensorflow, ...
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Spam detection in email body using hybrid of artificial neural network and evolutionary algorithms
... ABSTRACT Spam detection is a significant problem that is considered by many researchers through various developed strategies. Creating a particular model to categorize the wide range of spam categories is complex; with ...
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Medium term load demand forecast of Kano zone using neural network algorithms
... of neural network to obtain the Fuzzy parameters, nevertheless, when the number of input is large, the number of rules becomes large which increases computational burden, thus in turn affecting the ...
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Comparison of Neural Network Training Algorithms for Classification of Heart Diseases
... 2. RESEARCH METHOD This was a prospective cross-sectional study that measured and compared performance and functionality of artificial neural network training algorithms for classification of heart ...
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Comparative Study of Neural Network Algorithms for Servo Control Applications
... variations. Neural networks have emerged as a suitable tool for control applications especially under situations where the plant parameters are varying and a robust control is ...control using the ...
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Investigating the performance of neural network backpropagation algorithms for TEC estimations using South African GPS data
... estimations using the neural network (NN) technique have been done over many years with relative success ...parameters using a relevant training ...training algorithms employed in TEC ...
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An Enhanced Approach for Classification in Web Usage Mining using Neural Network Learning Algorithms for Supervised Learning
... of neural network concept, because only through neural network learning algorithms, such huge volume of web data can be handled and applied to any application for knowledge ...All ...
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Validation of Image Compression Algorithms using Neural Network
... 3. DCT & DCTBTC Block Truncation Coding is the simplest and fast lossy compression technique for gray scale images. The focus is on moment preservation quantization for block of pixels [2]. The input image is divided in ...
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NEURAL NETWORK FUNDAMENTALS WITH GRAPHS, ALGORITHMS, AND APPLICATIONS
... Networks 181 5.3.1 Algorithm for Design Based on VoD 183 5.3.2 Robustness and Size Issues 189 5.4 Unsupervised and Reinforcement Learning 192 5.4.1 Principal Component Analysis Networks [r] ...
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Swarm-based Algorithms for Neural Network Training
... 6.1 Regression Results First, the testing loss of the regression datasets must be examined. These results, shown in Table 6.1, are similar to the training loss results. Much like the training loss, PSO generated the best ...
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Noise Cancellation of ECG Signal Using Adaptive and Backpropagation Neural Network Algorithms
... Where n = 0, 1, 2....... B. NLMS Algorithm It is also a class of adaptive filter. The main disadvantages of LMS algorithm is that it is sensitive to the scaling of the inputs which hard to choose a stable learning rate ...
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Intrusion Detection Using Back Propagation Neural Network and Quick Reduct Algorithms
... Vinod Kumar Giri et al. [21] proposed A NN approach and Wavelet analysis for ECG classification. ECG is essentially the graphical representation of the electrical action of cardiac muscles amid constriction and discharge ...
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