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Heart disease is a deadly disease that large population of people around the world suffers from. When considering death rates and large number of people who suffers from heart disease, it is revealed how important early diagnosis of heart disease. Traditional way of diagnosis is not sufficient for such an illness. Developing a medical diagnosis system based on machine learning for prediction of heart...
The purpose of this paper is to develop and analyses device capable of identifying sign language. The recognition is performed using Multilayer Perceptron and all the input data are signals from flex sensors, accelerometers and gyroscopes. Artificial Neural Network is tested modifying parameters as: a) number of neurons in only middle layer, b) learning rate between input and middle layers and c)...
In this paper, a Multilayer Perceptron artificial neural network is modeled to estimate complete photonic band-gaps (C-PBGs) of bi-dimensional photonic crystals. Unit cells of square lattice photonic crystals, composed of two silicon round rods and embedded in air, have been designed by an artificial immune network algorithm, and their geometries have been stored in a database along with their C-PGBs...
Nowadays the security of computer devices is growing significantly. This is due to more and more devices areconnected to the network. For this reason, optimize the performance of systems able to detect intrusions (IDS) is a goalof common interest. The following work consists of use thegeneralizing power of neural networks to classify the attacks. In particular, we will use multilayer perceptron (MLP)...
Anterior Cruciate Ligament (ACL) injury is an injury of knee joints happened to many athletes. It has the significant impact on the patients' movement in their daily life and sport activities. Thus, it is important to detect the ACL injury in an early stage. So that, the proper treatment can be operated in time. This paper proposes a novel method to seek out differences of gait patterns between the...
Artificial neural networks (ANN) are one of the dominant learning techniques used in the field of artificial intelligence and have significant assets as their properties imitate the behavior of neurons in human brain. In this paper is presented the research focused on ANN, specifically Multilayer perceptron (MPL) with the aim of detection of human face in the still image. This system was implemented...
The transistor dimensions of devices in MOS based circuit highly influence the circuit characteristics and evaluation parameters. Choosing the dimensions while being critical is also extremely complex primarily due to complex mathematical formulations at the submicron level. Thus, it is required to employ techniques to simplify the dimensioning process while still keeping the parameters under check...
A hybrid algorithm for short-term load forecasting is proposed. The particle swarm optimization algorithm used in the training phase of the artificial neural network is optimized by combining it with the gravitational search algorithm. In this paper, we have combined the exploitation of PSO and exploration of GSA to form a single algorithm that can be used to get more accurate results for load forecast...
We describe a method for circuit synthesis that determines the parameter values by using a set of artificial neural networks (ANNs) that learn in sequence. Each ANN is optimized to output only one design parameter, and the latter constrains the learning/recall of its successor(s). Two competing ANN architectures are considered, the multilayer perceptron (MLP) and the radial basis functions (RBF) network,...
This paper proposes an Intelligent Handwriting Thai Signature Recognition System base on Multilayer Perceptron and Radial Basis Network. The proposed system compose of three main processes, i.e. image pre-processing, feature extraction and Thai signature recognition. In the recognition processes the neural network is used into two stage. First, Multilayer Perceptron (MLP) and Radial Basis Function...
This paper compares the development of neural network based prediction models for sustainable insurance using gradient techniques for training of the network. Convergence of different gradient algorithms is compared on data sets taken from life insurance in the rural sector. For applying these gradient based algorithms, prediction models based on neural networks are simulated in MATLAB Neural Network...
We demonstrate that visual (geometric) patterns can be robustly recognized by an artificial retina composed of a chaotic sensitive system where the coding of the patterns is by attractor features and an artificial neural network is used to classify the attractors. This opens the door to sensorial systems that mimic the biological ones. The specificity of solutions of chaotic systems to their parameters...
Classification is one-out-of several applications in the neural network (NN) world. Multilayer perceptron (MLP) is the common neural network architecture which is used for classification tasks. It is known for its error back propagation (EBP) algorithm, which opened the new way for solving classification problems given a set of empirical data. In this paper, we performed experiments using three different...
This paper explores complex-valued multilayer perceptrons (MLPs) with the mechanism of stochastic resonance (SR). SR is a phenomenon such that a weak periodic signal in the system can be enhanced and detected in the presence of noises. It is expected that the combination of complex-valued encoding and SR mechanism will improve the performance of MLPs, rather than MLPs with either of them. The performances...
In the proposed work, a change detection technique is developed using a combination of multilayer perceptrons (MLPs). At the onset, the different MLPs are trained with the labeled patterns. Then, the support values (or, the output values) for the unlabeled patterns are obtained from these trained MLPs. At last, decision regarding the class assignment for the unlabeled patterns has been made by fusing...
Wavelet is basically a filtering technique based on digital signal processing which have been widely applied in image processing. Recently, wavelet has been applied as a pre-processing element as they are good in analyzing signals. In this study, we have tested the wavelet filtering technique in temperature time series prediction for Batu Pahat region, ranging from 2005 – 2009. In this work, we proposed...
The hearing impaired is afraid of walking along a street and living a life alone. Since, it is difficult for hearing impaired to hear and judge sound information and they often encounter risky situations while they are in outdoors. The sound produced by moving vehicle in outdoor situation cannot be moderated wisely by profoundly hearing impaired community. They also cannot distinguish the type and...
A marine electromagnetic survey is an engineering endeavour to discover the location and dimension of a hydrocarbon layer under an ocean floor. In this kind of survey, an array of electric and magnetic receivers are located on the sea floor and record the scattered, refracted and reflected electromagnetic wave, which has been transmitted by an electric dipole antenna towed by a vessel. The data recorded...
This paper introduces the sparse multilayer perceptron (SMLP) which learns the transformation from the inputs to the targets as in multilayer perceptron (MLP) while the outputs of one of the internal hidden layers is forced to be sparse. This is achieved by adding a sparse regularization term to the cross-entropy cost and learning the parameters of the network to minimize the joint cost. On the TIMIT...
In this paper, a novel collective network of binary classifiers (CNBC) framework is presented for content-based audio classification. The topic has been studied in several publications before, but in many cases the number of different classification categories is quite limited and needed to be fixed a priori. We focus our efforts to increase both the classification accuracy and the number of classes,...
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