Osteoporosis is an essential index of health and economics in every country. Recognizing asymptomatic elderly population with high risks of osteoporosis remains a difficult challenge. For this purpose, we developed and validated an artificial neural network (ANN) to identify the osteoporotic subjects in the elderly. The study population consisted of 1403 elderly adults (mean age 63.50 +/- 0.24 ...

Time series analysis is a major mathematical tool in hydrology, with the moving average being most popular model type for this purpose due to its simplicity. During last 20 years, various studies have focused on an important statistical characteristic, namely long-term persistence and simultaneous consistency at all timescales, when different timescales are involved simulation. Though these iss...

Owing to the rapid increase in construction and demolition (C&D) waste, information of waste generation (WG) has been advantageously utilized as a strategy for C&D management. Recently, artificial intelligence (AI) strategically employed obtain accurate WG information. Thus, this study aimed manage (DW) by combining three algorithms: neural network (multilayer perceptron) (ANN-MLP), sup...

In the past, we attempted to use a multilayer perceptron neural network as a means to prevent those unknown language inputs from being misidentified as one of the target languages in language identification system. However, the use of multilayer perceptron neural network could not utilize the temporal information from the utterances. Results show that with the use of phonemic unigram as input f...

purpose of this paper is to assess the value of neural networks for classification of cancer and noncancer prostate cells. Gauss Markov Random Fields, Fourier entropy and wavelet average deviation features are calculated from 80 noncancer and 80 cancer prostate cell nuclei. For classification, artificial neural network techniques which are multilayer perceptron, radial basis function and learni...

Metaplasticity property of biological synapses is interpreted in this paper as the concept of placing greater emphasis on training patterns that are less frequent. A novel implementation is proposed in which, during the network learning phase, a priority is given to weight updating of less frequent activations over the more frequent ones. Modeling this interpretation in the training phase, the ...