Recurrent neural network

making problem, having wide practical application in various fields. Extreme Learning Machine (ELM) pro- posed by Huang et al.[1], is an effective machine learning technique for real valued classification. ELMis a single hidden layer feedfo5 rward network in which the weights between input and hidden layer are initialized randomly. ELM uses analytical approach to compute weights between hidden and output layer [2], which makes it faster compared to other gradient based classifiers ([3, 4]). Various

ABSTRACT- An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information [1]. Artificial Neural Networks (ANN) also called neuro-computing, or parallel distributed processing (PDP), provide an alternative approach to be applied to problems where the algorithmic and symbolic approaches are not well suited. The objective of the neural network is to transform the inputs into meaningful outputs. There

chapter, we discuss a brief introduction of neural network and biometrics . Traditionally, the term neural network had been used to refer to a network or circuit of biological neurons. Neural networks are inspired by our brains. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes. Thus, the term has two distinct usages: 2.1 Biological Neural Network generally, a biological neural network is consists of a set or sets of chemically

around these models. But rst, let 's take a look at the history of machine learning models in the eld of nancial predictions. At rst, White (1988) applied articial neural networks (ANN) to reveal nonlinear regularities in the IBM stock price movements. Subsequently, Kamijo and Tanigawa (1990) used a recurrent neural network for the recognition of price patterns in the Japanese market. Cheng, Wagner, and Lin (1996) used an ANN to predict the weekly price direction of the 30-year U.S. treasury

combination of each activation is also unique and hence the nodes fire differently.Prediction of neural net depends on weight and bias.As prediction should be high it’s desired that the prediction value should be as close to the actual output as

This paper studies various techniques used for the diagnosis of breast cancer. Different methods are explored for their merits and de-merits for the diagnosis of breast lesion. It was found that the recent use of the combination of Artificial Neural Networks in most of the instances give accurate results for the diagnosis of breast cancer and their use can also be extended to other diseases.

chapter, we discuss a brief introduction of neural network and biometrics . Traditionally, the term neural network had been used to refer to a network or circuit of biological neurons. Neural networks are inspired by our brains. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes. Thus, the term has two distinct usages: 2.1 Biological Neural Network generally, a biological neural network is consists of a set or sets of chemically

of heart disease. ECG consists of various waveforms of electric signals of heat .To process the ECG signal we will generate a multi-layered learning algorithm using a Learning vector Quantization (LVQ) neural network with generalized delta rules under supervised learning. The neural network in this system accepts 13 clinical features as input and predicts that there is a presence or absence of heart disease in the patient, along with different performance measures. Keywords: Heart

INTRODUCTION: One of the most common malignancy among women is Breast cancer. Many neoplastic deaths have been witnessed because of the Breast cancer. It holds the stats of effecting about 10% of the women all over the world at some stage of their life [1]. The threat of Breast cancer starts from the puberty stage and rises gradually till the peri- menopausal years when it has reached to its maximum level of occurrence [2] . Speaking about the statistics in United States alone one in eight women

As mentioned in the previous section of this chapter trend analysis, analyze just changes in the past years in electricity demand and utilize it to predict future electricity demand, but there is no process to explain why these changes happened. End users and behavior of end user are not important in this model. But in end use method of forecasting, statistical information related to customers along with amount of change act as the basis for the forecast. While in Economical methods, the results