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Assisting users in performing their tasks is an important issue in human computer interaction research. A solution to deal with this challenge is to build a personal assistant agent capable to discover the user's habits, abilities, preferences, and goals, ever more accurately anticipating the user's intentions. In order to solve in an intelligent manner this problem, the assistant agent has to continuously...
The problem of identifying a person using biometric data may be of interest. In this paper, EEG signals are used to identify a person as different persons have different EEG patterns. EEG signals can be measured from different locations. Too many signals can degrade the recognition speed and accuracy. A practical technique combining independent component analysis (ICA) for signal cleaning and a supervised...
In the present work an attempt is made to develop a clinical decision support system (CDSS) using the pathological attributes to predict the fetal delivery to be done normal or by surgical procedure. The pathological tests like blood sugar (BR), blood pressure (BP), resistivity index (RI) and systolic-diastolic (S/P) ratio will be recorded at the time of delivery. All attributes lie within a specific...
Epilepsy is a common chronic neurological disorder that is characterized by recurrent unprovoked seizures. About 50 million people worldwide have epilepsy at any one time. This paper presents an Intelligent Diagnostic System for Epilepsy using Artificial Neural Networks (ANNs) and Neuro-Fuzzy technique. In this approach the feed-forward neural network has been trained using Back propagation algorithm...
The aim of this study is to investigate artificial neural network (ANN) for prediction of drug contents. Hydroxypropylmethylcellulose and loratadine specifically were selected as model matrix polymer and drug. All 0, 5, 10, 20 and 40 mg drug loaded in hydroxypropylmethylcellulose films were conditioned at the relative humidity of 25, 50 and 75% each prior to psysicochemical characterization using...
This paper addresses the difficulties brought about by overlapping classes in fuzzy ARTMAP (FAM). Training with such data leads to category proliferation, and classification is made difficult not only by the large number of categories but also the fact that such data can belong to either class. In this paper, changes were proposed to allow more than one class to be predicted during classification,...
Artificial neural networks have been employed in diverse applications ranging from control, to pattern recognition and classification. While password detection can be implemented with a digital electronic circuit with non-volatile memory, this implementation is prone to hacking. In this paper, we present a 3-layer feedforward neural network which we have designed, trained and tested for secure password...
The main objective of this work is to automatically design neural network models with sigmoidal basis units for classification tasks, so that classifiers are obtained in the most balanced way possible in terms of CCR and sensitivity (given by the lowest percentage of examples correctly predicted to belong to each class). We present a memetic Pareto evolutionary NSGA2 (MPENSGA2) approach based on the...
In this paper, I analyzed feed forward network using back propagation learning method with early stopping and radial basis neural network to predict the trend of stock price (i.e. classification) and to predict the stock price (i.e. value prediction). Fundamental data or Technical indicators were not used in this research as basic objective of this research was to determine the usability of artificial...
Because of the complicated interaction of the sludge compost components, it makes the compost quality evaluation system appear the non-linearity and uncertainty. According to the physical circumstances of sludge compost, a compost quality evaluation modeling method based on high speed and precise genetic algorithm neural network is presented. The high speed and precise genetic algorithm neural network...
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