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In this paper, comparison of seven backpropagation algorithms used in neural network is made for the three phase power quality assessment. Voltage sag and swell are the two disturbances taken into account for comparing the algorithms. These disturbances are generated with the help of programming in MATLAB. The input data, to train the network is the generated sag and swell disturbances. The backpropagation...
This work proposes an ANN based method for fetal heart rate monitoring. Various measurements are taken and given as input to the ANN based classifier to detect fetal health such as ‘Normal’, ‘Suspect’ and ‘Pathologic’. All the design and simulation works are carried out with MATLAB software. ANN based classifier is trained with data from various recordings of cardiotocography. After the network is...
Security of today communication networks depends also on effective hash function. A cryptographic hash function is used to realize a transformation of input to a fixed-size value. This value is called the hash value. One way hash function could be generated also by an artificial neural network (ANN). Theoretical analysis of the possibility of using artificial neural network and chaotic maps for hashing...
Although Deep Convolutional Neural Networks (CNNs) have liberated their power in various computer vision tasks, the most important components of CNN, convolutional layers and fully connected layers, are still limited to linear transformations. In this paper, we propose a novel Factorized Bilinear (FB) layer to model the pairwise feature interactions by considering the quadratic terms in the transformations...
In conventional echo stat network (ESN), the reservoir are randomly generated, then the spectral radius of the reservoir is scaled to lower than 1. In this method, only the necessary condition for echo state property (ESP) of ESN is satisfied while the sufficient condition is ignored, thus the ESN stability may not be ensured. In this paper, with the predefined singular values (smaller than 1), the...
This paper describes the use of convolutional neural network(CNN) method to classify various image and photo of Indonesia ancient temple. The method itself implements Deep Learning technique designed for Computer Vision task. The idea behind CNN is image pre-processing through a stack of convolution layers to create many patterns that can be easily recognized. The result shows that the learning model...
Effective generation of hash function is very important for an achievement of a security of today networks. A cryptographic hash function is a transformation that takes an input and returns a fixed-size value, which is called the hash value. A recurrent neural network, as a possible approach, could be used for the hash function generation. The performance of the recurrent neural network (RNN) was...
One of the key tasks of Outdoor-type mobile robotics is traversability estimation of underlying surfaces in a in a priori of an unknown heterogeneous environment. The paper presents practical realization of traversability estimation system based on group method of data handling (GMDH). This method is classical technique of data mining and one of the first techniques of Deep Learning. The results of...
In this work, we present a performance comparison of the Multi Layer Perceptron (MLP), Support Vector Machines (SVM) and Voted Perceptron (VP) when applied to a social signal processing task. The signal processing task is in the field of computational politics where the aim is to predict the political parties of American congress members based on their response to certain questions. Using this dataset...
This paper presents a series of experiments on the classification of emergency phone conversation records using artificial neural networks (ANNs). Input data which were processed by ANNs were the features of callers and events taken from emergency phone calls. The authors analyzed four variants of classification: the groups of callers which have specified features, the groups of events which have...
An intelligent system uses machine learning algorithms to provide outputs to every input provided. The introduction of emotions in intelligent systems is required to create systems that are more similar to human beings and thus more reliable. In this paper, the idea of introducing the emotion ‘uncertainty’ in Intelligent Systems is proposed. A Semi-Automated Intelligent System is introduced in this...
The problems arising in loop electrical network system is a relay setting that follows changes in the system such as power source operation, regular maintenance and damage to powers source. To obtain an adaptive relay which is capable of following the changes in the network system, this paper is proposes the modeling of the coordination of the power system network with the cascade forward neural network...
This paper is using artificial neural network (ANN) to predict oxygen content in the water for the fish farm, so that decrease times of starts of oxygen suppliers. In Southern Taiwan, aquaculture is one of major economic industries. Especially, the important issue is how to effectively monitor the oxygen content in the water, so that the fish will not die and start the oxygen suppliers for the minimum...
The safety and reliability of roller bearing always have significant importance in rotating machinery. It is needful to build an efficient and excellent accuracy method to monitoring and diagnosis the baring failure. A novel method is presented in this paper to classify the fault feature by wavelet function and extreme learning machine(ELM) that take into account the high accuracy and efficient. The...
Epileptic seizure source identification involves neurologists combing through a substantial amount of data manually, which sometimes takes weeks per patient. This paper presents a methodology for minimizing the amount of data a neurologist has to analyze to identify the seizure focus. The method keeps the neurologist as the final decision maker and aids in the decision making process. It has to be...
Aiming at the problem that the key water quality parameters in wastewater treatment processing is difficult to detect real-time accurately. An ammonia nitrogen concentration soft measure model based on the artificial neural network(ANN) is proposed in this paper, and utilizing existing data to achieve parameters detection in real-time accurately during the process of wastewater treatment processing...
The system security has turned into an extremely critical worry as system assaults have been extending with the ascent of hacking devices, inconvenience of systems and interruptions in number and brutality. This paper is centered around interruption identification by utilizing Multilayer Perceptron (MLP) with various calculation of backpropagation neural network. In this paper, performance of various...
Microelectrode arrays (MEAs) enable fast and high-throughput readout of cell's electrical signals. MEAs are currently used for phenotype characterization and drug toxicity/efficacy testing with iPSC-derived neurons and cardiomyocytes. A key advantage of MEAs is the capability to record and stimulate individual neurons at multiple sites simultaneously. We will present ongoing advancements of MEA technology,...
Describes the universal approach to the intellectual automated system development of digital signal processing for acoustic testing devices with free vibrations method and the usage of artificial neural networks. The system solves the problem of defects recognition and classification, and enhances performance testing in comparison with traditional instruments.
Stellar Classification is based on their spectral characteristics. In order to improve performance rates previously reported, like those based on statistical analysis or data transformations, classifiers based on computational intelligence provide a high level of accuracy no matter the presented high level of non-linearity or high dimensionality characteristics of data. In this paper, the star's classification...
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