Recurrent neural network

in a plenty of literatures. However, the comprehensive comparison of their applicability in particular short-term (i.e. hourly) water level prediction under heavy rainfall events was rarely discussed. Therefore, in this study, the artificial neural networks (ANN), Intelligent multi agent approach, Markov Chain Monte Carlo (MCMC) were selected for

Machine Learning Techniques Since the plasmapause location is a continuous variable, techniques like regression support vector machine (SVM), tree bagger, and neural network were used to construct the model. In this study, the plasmapause location derived from 2013 March RBSP data is used, which is 89 observational data in total. For neural network approaches, 70% of the electron density data were used as the training data sets, the 15% were used as the validation data and the final 15% as the testing

Feed-forward neural network structure consist of three layers of nodes : input layer, hidden layer , and output layer . The input layer nodes denoted $x_i , i in mathbb{N}$ ,where each of one of them is connected to all nodes in the hidden layer $h_j , j in mathbb{N}$ via connection weight that. Also , each node in the hidden layer is connected to all nodes in the output layer $ y_k , k in mathbb{N}$ through different connection weights . } label{fig:ffn1} end{figure}

Extreme learning Machine (ELM) [1] is a single hidden layer feed forward network (SLFN) introduced by G. B. Huang in 2006. In ELM, the weights between input and hidden neurons and the bias for each hidden neuron are assigned randomly. The weight between output neurons and hidden neurons are generated using the Moore Penrose Generalized Inverse [18]. This makes ELM a fast learning classifier. It surmounts various traditional gradient based learning algorithms [1] such as Back Propagation (BP) and

analysis. For this purpose, this study uses the Neural Nets toolbox, SVM (Support Vector Machine) library, and Logistic Regression in Matlab, and Multivariable Regression in R to analyse and classify wine quality. This paper determines the most influential features for red wine quality ratings and answer the question of which machine learning algorithm performs the best in classifying wine quality. Experiment 4.1 Neural Nets We are using the Neural Nets Toolbox which is already implemented in

the main problem. So the author proposed an Artificial Neural Network (ANN). This Artificial Neural Network is capable of dealing and gives accurate result for non- linear systems. This research paper aims to give the proper solution to control the process input parameters to make better spot weld joints with specified strength. In the second stage of the research work, Genetic Algorithm with fitness function based on Artificial Neural Network (ANN) model was used for determining a set of optimized

hundreds millions of parameters to be tuned. For example, a deep learning structure for processing visual and other two-dimensional data, convolutional neural network (CNN) [1], which consists of three convolutional layers and three pooling layers, has more than 130 millions of parameters if the input has 28x28 pixels. While these large neural networks are powerful, we need high amount of training data. DL tasks need considerable data storage and memory bandwidth. Key-value stores provide users

Predictive Analytics: A Gold-Mine Yet To Be Exploited To Its Zenith Akanksha Pandey Information Technology Department, VESIT, Mumbai-74, India. Abstract 1. Introduction The proliferation, ubiquity and increasing power of computer technology has increased the volume of data oday`s mobile technologies and social media have collection and it`s storage manifold. This led to unleashed an exponential increase

accuracy of time series forecasting. In this paper, I have focused on one method i.e. Neural Networks. In the first section of the report, I will give brief introduction on time series forecasting and neural networks. In the next part, I will explain this neural method which is used for forecasting in the literature review. At last, I will conclude the paper. Moreover, the main aim of this paper is to define the neural network method among the different methods in the time series forecasting. Introduction

artificial neural network, seed key generator and chaotic map functions, for interest of the reader. Later in section 2, we present literature review of various encryption techniques from two perspectives: applications to highlight scope of research in this domain; and approaches to provide overall view of multimedia encryption. The section three provides a new two-layer encryption technique for JPEG2000 compatible images. The first step provides a single layer of encryption using a neural network to generate