-Signals of the Electroencephalogram (EEG) can reflect the electrical background activity of the brain generated by the cerebral cortex nerve cells. This has been the mostly utilized signal, which helps in effective analysis of brain functions by supervised learning methods. In this paper, an approach for improving the accuracy of EEG signal classification is presented to detect epileptic seizures. Moreover, Independent Component Analysis (ICA) is incorporated as a preprocessing step and Short Time Fourier Transform (STFT) is used for denoising the signal adequately. Feature extraction of EEG signals is accomplished on the basis of three parameters namely, Standard Deviation, Correlation Dimension and Lyapunov Exponents. The Artificial Neural Network (ANN) is trained by incorporating Levenberg-Marquardt(LM) training algorithm into the backpropagation algorithm that results in high classification accuracy. Experimental results reveal that the methodology will improve the clinical service of the EEG recording and also provide better decision making in epileptic seizure detection than the existing techniques. The proposed EEG signal classification using feed forward Backpropagation Neural Network performs better than to the EEG signal classification using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier in terms of accuracy, sensitivity, and specificity.
The major part of the underlying idea is going to
detect the fire from upcoming smoke and the shade color of the
smoke using convolutional neural network. The fire detection
followed by the smoke detection is going to depend on the shade
and the direction vector analysis in this paper. Image processing
from the available set of data is very vague ideation so in order to
strengthen the idea we are incorporating two main features that
is the smoke shade and direction vector. For this major process
we will involve data preprocessing through bi-variate hypothesis
to select two variables as the color of smoke and the direction of
the smoke and hence do the further analysis on other features
that how are they going to help in the upcoming detection
neurons for the robust algorithm of fire detection
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