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Brain-Computer Interface (BCI) can be realized by translating user's thoughts into control commands to assist paralyzed persons to communicate and control electronic devices. In this work, Electroencephalographic (EEG) signals were recorded from four subjects while they perform different mental states. We present an Artificial-Neural-Network-based approach for the purpose of classifying Electroencephalographic...
In present paper, authors develop a model for estimation of earth slope stability based on the artificial neural networks. For this purpose, authors engage multi-layer feed-forward network with Levenberg-Marquardt learning algorithm and 14 hidden nodes, using existing experimental data, and the results of traditional limit equilibrium analyzes of 57 different cases according to the predefined experimental...
Nowadays, as information systems are more open to the Internet, the importance of secure networks is tremendously increased. Interconnected systems such as web server data servers are now under the threats of network attackers. Intrusion Detection System (IDS) is the most powerful system that can handle the intrusions of the computer environments by triggering alerts to make the analysts take actions...
This paper classifies noisy English Alphabets with the help of Artificial Neural Network. Here a supervised single layer perceptron learning algorithm for the classification of noisy English alphabets has been proposed. The presented algorithm requires very few number of input neurons for training. The algorithm is capable of classification of English alphabets for different scenario of noise. Simulation...
A flood is an extremely dangerous disaster that can wipe away an entire city, coastline, and rural area. The flood can cause wide destrotion to property and life that has the supreme corrosive force and can be highly damaging. In order to decrease the damages caused by the flood, an Artificial Neural Network (ANN) model has been established to predict flood in Sungai Isap, Kuantan, Pahang, Malaysia...
Thermal power of nuclear reactor needs to be carefully maintained to produce desired electrical power. While in-core measurement system has a higher safety risk, ex-core measurement has been employed to increase safety. Artificial neural network with multi-layer perceptron architecture and Bayesian regularization algorithm has been trained and tested for estimating the thermal power at G.A. Siwabessy...
Nowadays Opinion mining is given more important, since it provides decision makers to estimate the success of a newly proposed techniques, novel ad campaign or novel product launch. In general, supervised methods such as Support Vector Machine (SVM) and Artificial Neural Network (ANN) are used to classify the opinions. In some cases SVM performs better classification and some cases ANN performs better...
In order to search the abnormal data of seismic satellite quickly, we extract the ultra low frequency electric field waveform data of 10 days before the Wenchuan earthquake in this paper. The back propagation neural network classification model is designed and the self-organizing feature map network clustering model is used to verify back propagation network using the mean, variance, skewness and...
The trajectory tracking system of particle motion on sieve surface was designed by the combination of the analysis of image sequences based on binocular stereo vision and three-dimensional position reconstruction based on artificial neural network. Firstly, the calibration plane with uniformly distributed solid circles was placed in multiple positions within the effective field of view. The images...
Use of the error correcting codes (ECC) in a multiclass audio emotion recognition problem is proposed to improve the emotion recognition accuracy. We visualize the emotion recognition system as a noisy communication channel, thus motivating the use of ECC. We assume the emotion recognition process consists of an audio feature extractor followed by an artificial neural network (ANN) for emotion classification...
Language or pattern recognition is an ever-growing field in research. The motivation behind this work is the growing importance of foreign languages in everyday life. It is specifically focused on groups who might have to face a foreign language somewhere and can't seem to understand it. This will also be beneficial for partially visually impaired persons. It will help in recognizing the alphabets...
Knowledge of characteristics of the transmission channel is advantageous for the selection of a suitable location of transmitting and receiving antennas, choice of the carrier frequency and the transmission parameters such as bit rate, modulation type, coding, etc. However, the description of properties of the transmission channel can be computationally time consuming, and the computational complexity...
Along with the rapid expansion of the MW sized big wind turbine sector, the small wind turbine industry is also growing. Understanding the power response of these systems to the variations in wind velocity is essential for the optimal selection and efficient management of these turbines. This is defined by the power curves of wind turbines. In this paper, we propose nonparametric models for the power...
Several theoretical and experimental studies show that the characterization of a tube can be done through the cut-off frequencies of the anti-symmetric circumferential waves A1 propagating around the tube of various radius ratio b/a (a: outer radius and b: inner radius). This work investigates the abilities of Artificial Neural Networks ANN to predict the thickness of a tube immersed in water for...
DNA microarrays are normally used to measure the expression values of thousands of several genes simultaneously in the form of large matrices. This raw gene expression data may contain some missing cells. These missing values may affect the analysis performed subsequently on these gene expression data. Several imputation methods, like K-Nearest Neighbor Imputation (KNNImpute), Singular Value Decomposition...
Detecting cyber-attacks in cloud infrastructures is essential for protecting cloud infrastructures from cyber-attacks. It is difficult to detect cyber-attacks in cloud infrastructures due to the complex and distributed natures of cloud infrastructures. In addition, various computing and storage devices, both mobile and stationary, are connected to cloud infrastructures to facilitate users access,...
In this work, we develop an artificial neural network model to predict the potential of solar power in Libya. We use multilayered, feed-forward, back-propagation neural networks for the mean monthly solar radiation using the data of 25 cities spread over Libya for the period of 6 years (2010–2015). Meteorological and geographical data (longitude, latitude, and altitude, month, mean sunshine duration,...
Based on the relationship between porosity (or lithological facies) and other petrophysical properties, Artificial neural networks (ANN) are respectively trained for porosity estimation and lithological facies classification, using core porosity (CPOR) data and core lithological facies interpretation results of part of core interval together with some well logs (petrophysical properties). After the...
After some studies on the HMLP neural network output equation, it was found out that parts of the equation resemble matrix multiplication operation. Therefore, an approach to calculate the output equation of the HMLP using matrix multiplication method was proposed. The proposed approach was simulated and compared with another approach to calculate HMLP output using loops. The result proved that the...
This paper presents a relaying algorithm based on Artificial Neural Network (ANN) technique for the protection of transmission line. A feed forward ANN with six inputs and eleven outputs has been developed for the detection and classification of faults. Data was generated by simulating a 400 kV, 50Hz, 100 km transmission line in PSCAD/EMTDC at a sampling frequency of 2 kHz. Three ANN configurations...
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