Recent studies have shown the classification and prediction power of the Neural Networks. It has been demonstrated that a NN can approximate any continuous function. Neural networks have been successfully used for forecasting of financial data series. The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and ou...

This paper describes a framework for a statistical anomaly prediction system using ensemble Quickprop neural network forecasting model, which predicts unauthorized invasions of user based on previous observations and takes further action before intrusion occurs. This paper focuses on detecting significant changes of transaction intensity for intrusion prevention. The experimental study is perfo...

Abstract The conventional way of choosing a fashion catalogue has lot drawbacks and is yet to be resolved. Struck in dilemma what choose not to, wearing items that are appropriate, lured by terms misleading such as 50 percent off limited period offer, when reality the prize was never higher first place. These flaws go under radar for humans, but case with machines. proposed system given an imag...

Network traffic prediction (NTP) represents an essential component in planning large-scale networks which are general unpredictable and must adapt to unforeseen circumstances. In small medium-size networks, the administrator can anticipate fluctuations without need of using forecasting tools, but scenario where hundreds new users be added a matter weeks, more efficient tools required avoid cong...

We present a simple approach based on pixel-wise nearest neighbors to understand and interpret the internal operations of state-of-the-art neural networks for pixel-level tasks. Specifically, we aim to understand the synthesis and prediction mechanisms of state-of-the-art convolutional neural networks for pixel-level tasks. To this end, we primarily analyze the synthesis process of generative m...

Neural networks have been gaining a great deal of importance and are used in the areas of prediction and classification; the areas where regression and other statistical models are traditionally being used. In this paper, a comprehensive review of literature comparing feedforward neural networks and traditional statistical methods viz. linear regression with respect to prediction of agricultura...