in recent years, artificial neural networks (anns) have been widely used for flood esti-mation. in this study, an ann model based on the geomorphologic characteristics of a watershed such as the number of possible paths and their probabilities is developed (gann model). nodes in the input layer are allocated to the surface flows, subsurface flows, excess-rainfall and infiltrated rain. the numbe...

The present investigation entails a procedure by which the surface tension and viscosity of liquids could be redicted.To this end, capillary experiments were performed for porous media by utilizing fifteen different liquids and powders. The time of capillary rise to a certain known height of each liquid in a particular powder was recorded. Two artificial neural networks (ANNs) were...

Curie-point pyrolysis mass spectra were obtained from reference Propionibacterium strains and canine isolates. Artificial neural networks (ANNs) were trained by supervised learning (with the back-propagation algorithm) to recognize these strains from their pyrolysis mass spectra; all the strains isolated from dogs were identified as human wild type P. acnes. This is an important nosological dis...

While Artificial Neural Networks (ANNs) are highly expressive models, they are hard to train from limited data. Formalizing a connection between Random Forests (RFs) and ANNs allows exploiting the former to initialize the latter. Further parameter optimization within the ANN framework yields models that are intermediate between RF and ANN, and achieve performance better than RF and ANN on the m...

Artificial Neural Networks (ANNs) have been applied to predict many complex problems. In this paper ANNs are applied to horse racing prediction. We employed Back-Propagation, Back-Propagation with Momentum, QuasiNewton, Levenberg-Marquardt and Conjugate Gradient Descent learning algorithms for real horse racing data and the performances of five supervised NN algorithms were analyzed. Data colle...

Artificial Neural Networks (ANNs) are based on an abstract and simplified view of the neuron. Artificial neurons are connected and arranged in layers to form large networks, where learning and connections determine the network function. Connections can be formed through learning and do not need to be ’programmed.’ Recent ANN models lack many physiological properties of the neuron, because they ...

Artificial Neural Networks (ANNs) are becoming increasingly useful in numerous areas as they have a myriad of applications. Prior to using ANNs, the network structure needs be determined and ANN trained. The is usually chosen based on trial error. training, which consists finding optimal connection weights biases ANN, done gradient-descent algorithms. It has been found that swarm intelligence a...

The recycled aggregate is an alternative with great potential to replace the conventional concrete alongside other benefits such as minimising usage of natural resources in exploitation produce new concrete. Eventually, this will lead reducing construction waste, carbon footprints and energy consumption. This paper aims study compressive strength using Artificial Neural Network (ANN) which has ...

During recent decades, recognizing letters was a considerable discussion for artificial intelligence researchers and recognize letters due to the variety of languages and different approaches have many challenges. The Artificial Neural Networks (ANNs) are framed based on particular application such as recognition pattern and data classification through learning process is configured. So, it is ...