Proper analysis of building energy performance requires selecting appropriate models for handling complicated calculations. Machine learning has recently emerged as a promising effective solution solving this problem. The present study proposes novel integrative machine model predicting two parameters residential buildings, namely annual thermal demand (DThE) and weighted average discomfort deg...

In India and across the globe, liver disease is a serious area of concern in medicine. Therefore, it becomes essential to use classification algorithms for assessing the disease in order to improve the efficiency of medical diagnosis which eventually leads to appropriate and timely treatment. The study accordingly implemented various classification algorithms including linear discriminant analy...

We present in this paper, Fourier descriptor and feedforward neural network for face recognition. Analysis is done for various numbers of iterations. Comparison shows that faces are recognized with FFNN more accurately with 50000 iterations. For experiment, FERET database is used.

In this thesis, artificial neural networks (ANNs) are used for prediction of financial and macroeconomic time series. ANNs build internal models of the problem and are therefore suited for fields in which accurate mathematical models cannot be formed, e.g. meteorology and economics. Feedforward neural networks (FFNNs), often trained with backpropagation, constitute a common type of ANNs. Howeve...

Reclaimed asphalt pavement (RAP) is one of the waste materials that highway agencies promote to use in new construction or rehabilitation of highways pavement. Since the use of RAP can affect the resilient modulus and other structural properties of flexible pavement layers, this paper aims to employ two different artificial neural network (ANN) models for modeling and evaluating the effects of ...

Aerodynamic parameter estimation is critical in the aviation sector, especially design and development programs of defense-military aircraft. In this paper, new results application Artificial Neural Networks (ANN) to field aircraft are presented. The performances Feedforward Network (FFNN) with Backpropagation FFNN using Recursive Least Square (RLS) investigated for aerodynamic estimation. meth...

This paper aims to enhance the performance of a cascade-forward neural network (CFNN) model predict output power photovoltaic (PV) module. improvement is conducted by optimizing number hidden neurons using genetic algorithm (GA). The optimization carried out minimize value root mean square error (RMSE) between actual and predicted PV power. CFNN-based GA evaluated five statistical term terms; n...

We examine various and different approaches for the prediction of economic crisis periods of US economy. We examine the traditional econometric discrete choice Logit and Probit models then a feed-forward neural network (FFNN) model and finally we apply an Adaptive Neuro-Fuzzy Inference System (ANFIS). We examine the period 1950-2009, where we take as the in-sample or training period 1950-2005, ...

The purpose of this paper is to develop an appropriate artificial neural network (ANN) model of induction motor bearing (IMB) failure prediction. Acoustic emission (AE) represented the technique of collecting the data that was collected from the IMB and this data were measured in term of decibel (dB) and Distress level. The data was then used to develop the model using ANN for IMB failure predi...

Feedforward neural networks (FFNN) have been utilised for various research in machine learning and they have gained a significantly wide acceptance. However, it was recently noted that the feedforward neural network has been functioning slower than needed. As a result, it has created critical bottlenecks among its applications. Extreme Learning Machines (ELM) were suggested as alternative learn...