In this paper, an automatic classifier has been developed using Feed Forward Neural Network (FFNN) to classify the ECG signals between different heartbeats. Here, the classifier is trained independently bymorphological, heartbeat interval features and temporal features using Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). The trained classifier then classifies the be...

The use of Artificial Intelligence methods is becoming increasingly common in the modeling and forecasting of hydrological and water resource processes. In this study, applicability of Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) methods, Generalized Regression Neural Networks (GRNN) and Feed 5 Forward Neural Networks (FFNN), for forecasting of daily river f...

In this paper the RWTH large vocabulary continuous speech recognition (LVCSR) systems developed for the IWSLT2016 evaluation campaign are described. This evaluation campaign focuses on transcribing spontaneous speech from Skype recordings. State-of-the-art bidirectional long shortterm memory (LSTM) and deep, multilingually boosted feed-forward neural network (FFNN) acoustic models are trained a...

Some results from a method for generating recurrent neural networks (RNN) for prediction of financial and macroeconomic time series are presented. In the presented method, a feedforward neural network (FFNN) is first obtained using backpropagation. While backpropagation is usually able to find a fairly good predictor, all FFNN are limited by their lack of short-term dynamic memory. RNNs, by con...

this paper intends to offer a new iterative method based on arti cial neural networks for finding solution of a fuzzy equations system. our proposed fuzzi ed neural network is a ve-layer feedback neural network that corresponding connection weights to output layer are fuzzy numbers. this architecture of arti cial neural networks, can get a real input vector and calculates its corresponding fu...

A new hybrid clustering algorithm based on a three-layer feed forward neural network (FFNN), a distribution density function, and a cluster validity index, is presented in this paper. In this algorithm, both feature weighting and sample weighting are considered, and an optimal cluster number is automatically determined by the cluster validity index. Feature weights are learnt via FFNN based on ...

In this study, applicability of Artificial Neural Network (ANN) methods for river flow estimation is investigated. Accurate modeling and forecasting of hydrological processes such as rainfall, rainfall-run off relationship, runoff, is important for management and planning of water resources. To illustrate the capability of ANN for modeling of water resources, Great Menderes River, locate in the...

Brain is well protected inside the hard and bony skull that hampers the study of its functions as well as makes the diagnosis of brain diseases more difficult and challenging. In this paper we perform review study on brain tumor detection from Magnetic Resonance Image (MRI). Stages for brain tumor detection using MR image are Pre-processing, Segmentation, Feature Extraction, Classification. The...

This paper aims to determine suitable seasonal autoregressive integrated moving average (SARIMA) and feed-forward neural network (FFNN) models forecast the total non-coincidental monthly system peak demand in Philippines. To satisfy stationary requirement of SARIMA model, differencing, first-differencing were applied. The findings reveal that (0,1,1)(0,1,1)12 is appropriate model. All model par...

BACKGROUND In the past several years, there has been increasing interest and enthusiasm in molecular biomarkers as tools for early detection of cancer. Liquid chromatography tandem mass spectrometry (LC/MS/MS) based plasma proteomics profiling technique is a promising technology platform to study candidate protein biomarkers for early detection of cancer. Factors such as inherent variability, p...