Sentence boundary identification is an important step for text processing tasks, e.g., machine translation, POS tagging, text summarization etc., in this paper, we present an approach comprising of Feed Forward Neural Network (FFNN) along with part of speech information of the words in a corpus. Proposed adaptive system has been tested after training it with varying sizes of data and threshold ...

Compared to mechanical signals that are used for estimating human limb motion intention, non-invasive surface electromyography (sEMG) is a preferred signal in human-robotic systems. However, noise interference, crosstalk from adjacent muscle groups, and an inability measure deeper tissues disadvantageous sEMG’s reliable use. In this work, we hypothesize fusion between sEMG vivo ultrasound (US) ...

EEG analysis aims to help scientists better understand the brain, physicians diagnose and treatment choices of brain-computer interface. Artificial neural networks are among most effective learning algorithms perform computing tasks similar biological neurons in human brain. In some problems, network model's performance might significantly degrade overfit due irrelevant features that negatively...

Work had always been under process to design efficient algorithms for image compression based on various conventional and soft computing methodologies. This paper aims at exploring the application of multi layered perceptron (MLP) feed forward neural networks (FFNN), wavelet transforms and their combination architectures for image compression. Initially two neural network architectures for imag...

One of the main obstructions in Multi-Access systems is the Multi-Access Interference (MAI) between signals that sharing the same channel. Specially, in the CDMA systems where all users share the same channel all the time. The objective of this work is to compare three different Artificial Neural Network (ANN)-based multiuser detectors for Wideband Code Division Multiple Access WCDMA system bui...

<p>Predictive maintenance system (PdM) is a new concept that helps operators evaluate the current status of their systems, and it also assists in predicting future quality these systems scheduling action. This paper proposes PdM model utilizes machine learning to predict system’s operational after M active steps based on L previous observations implemented by feedforward neural network (F...

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...

Feature extraction methods and subsequent neural network performances are explored in this paper. Object recognition method ‘regionprops’ and moment invariants are used to extract basic characteristics from acquired bloodstain images. The extracted features are in return fed into a neural network for the purpose of pattern recognition. The blood drop in the image is first detected using sobel e...

Neural networks (NN) have demonstrated to be useful for estimating software development effort. A NN can be classified depending of its architecture. A Feedforward neural network (FFNN) and a General Regression Neural Network (GRNN) have two kinds of architectures. A FFNN uses randomization to be trained, whereas a GRNN uses a spread parameter to the same goal. Randomization as well as the spre...

Patients age has been estimated in healthy population by means of the heart rate variability (HRV) parameters to assess the potentiality of HRV indexes as a biomarker of age. A long-term analysis of HRV has been performed, computing linear time and frequency domain parameters as well as non-linear metrics, in a dataset of 113 healthy subjects (age range 20-85 years old). The principal component...