This chapter critically reviews some of the important methods being used for building quantitative structure-activity relationship (QSAR) models using the artificial neural networks (ANNs). It attends predominantly to the use of multilayer ANNs in the regression analysis of structure-activity data. The highlighted topics cover the approximating ability of ANNs, the interpretability of the resul...

Performance 1 of supervised training of Artificial Neural Networks (ANNs) depends on several factors, including neural network architecture, number of neurons in hidden layers, the neurons' activation functions, and selection of initial network parameters (connection weights). Trial and error is commonly used to select the network parameters and the initial connection weights. Such practice can...

Artificial neural networks (ANNs) are a well-established computational method inspired by the structure and function of biological central nervous systems. Since their conception, ANNs have been utilized in a vast variety of applications due to their impressive information processing abilities. A vibrant field, ANNs have been utilized in bioinformatics, a general term for describing the combina...

In this paper, a framework for testing Deep Neural Network (DNN) design in Python is presented. First, big data, machine learning (ML), and Artificial Neural Networks (ANNs) are discussed to familiarize the reader with the importance of such a system. Next, the benefits and detriments of implementing such a system in Python are presented. Lastly, the specifics of the system are explained, and s...

computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. nowadays, despite the numerous time series forecasting models propos...

Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohon...

The authors have been developing several models based on artificial neural networks, linear regression models, BoxJenkins methodology and ARIMA models to predict the time series of tourism. The time series consist in the “Monthly Number of Guest Nights in the Hotels” of one region. Several comparisons between the different type models have been experimented as well as the features used at the e...

The main purpose of the present paper is to establish an optimum feedforward neural architecture and a well suited training algorithm for financial forecasting. The artificial neural networks (ANNs) ability to extract significant information provides valuable framework for the representation of relationships present in financial data. The evaluation of the computing effort involved in the ANN t...

Artificial neural networks (ANNs) have been applied to time series forecasting. Genetic algorithm (GA) can be used as an optimization search scheme to determine the near optimal architecture and parameters of a neural network, as well. In this study a rich evolutionary connectionist model is proposed, in which GA is used to determine the optimum number of input and hidden nodes of a feedforward...

Artificial neural networks (ANNs) can be utilized to generate predictive models of quantitative structure-activity relationships between a set of molecular descriptors and activity. Evolutionary computation provides a means to appropriately search for the set of weights and bias terms associated with artificial neural networks that minimize selected functions of the error between the actual and...