The most important challenges in electric load forecasting is to find the accurate electricity load forecasting. Because, it is volatile in nature and has to be consumed immediately. Fuzzy Decision Tree is applied to predict the annual electricity requirement in India. Population and Per Capital gross domestic product (GDP) are taken as input variables and the electricity consumption is predict...

The aim of this paper is to develop the design of classifier using Artificial Neural Network for patients survival analysis based on echocardiography dataset. Survival analysis can be considered a classification problem in which the application of machine learning methods is appropriate. Survival analysis plays an important role not only for health care policy markers, but also for the clinicia...

The neural network (NN)-based direct uncertainty quantification (UQ) methods have achieved the state of art performance since first inauguration, known as lower–upper-bound estimation (LUBE) method. However, currently-available cost functions for guided NN training are not always converging, and all converged NNs do generate optimized prediction intervals (PIs). In recent years researchers prop...

porosity is one of the most important properties for comprehensive studies of hydrocarbon reservoirs. for determination of porosity in a rock, that is the ratio of volume of voids to the total volume of the rock, there are two conventional methods: in the first method, direct measurement of porosity is carried out by testing drilling cores. in the second method, porosity is determined indirectl...

Multi-task learning (MTL) with neural networks leverages commonalities in tasks to improve performance, but often suffers from task interference which reduces the benefits of transfer. To address this issue we introduce the routing network paradigm, a novel neural network and training algorithm. A routing network is a kind of self-organizing neural network consisting of two components: a router...

A stacked generalization data mining approach was applied to a simplified version of the KDD Cup 2001 protein localization task. Four level-0 models were developed: an Artificial Neural Network, a Decision Tree, a Nearest Neighbor Classifier, and a Hybrid model that trained an Artificial Neural Network using those inputs selected as important by the Decision Tree. These models were developed fr...

Multi-task learning (MTL) with neural networks leverages commonalities in tasks to improve performance, but often suffers from task interference which reduces the benefits of transfer. To address this issue we introduce the routing network paradigm, a novel neural network and training algorithm. A routing network is a kind of self-organizing neural network consisting of two components: a router...

A major drawback associated with the use of artificial neural networks for data mining is their lack of explanation capability. While they can achieve a high predictive accuracy rate, the knowledge captured is not transparent and cannot be verified by domain experts. In this paper, Artificial Neural Network Tree (ANNT), i.e. ANN training preceded by Decision Tree rules extraction method is pres...

In this paper, we explore some basic questions on the complexity of training neural networks with ReLU activation function. We show that it is NP-hard to train a two-hidden layer feedforward network. If dimension input data and network topology fixed, then there exists polynomial time algorithm for same problem. also if sufficient over-parameterization provided in first hidden network, which fi...