[PDF] Top 20 Electroencephalography Analysis Using Neural Network and Support Vector Machine during Sleep
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Electroencephalography Analysis Using Neural Network and Support Vector Machine during Sleep
... the sleep stage has a big correlation with the EEG frequency band, gave frequency analysis of the EEG ...characteristic vector that matches the predetermined data level, divided in the units of 30 ... See full document
5
Comparison of School Building Construction Costs Estimation Methods Using Regression Analysis, Neural Network, and Support Vector Machine
... the neural net- work cost estimating model is superior to the regression analysis estimation model, many have also demonstrated not only the superiority of NN but the problems associ- ated with using ... See full document
7
Image Denoising using Neural Network with SVM (Support Vector Machine) and LDA (Linear Discriminant Analysis)
... presents neural networks as an efficient and robust tool for noise reduction ...use Neural Network; SVM and LDA as the learning algorithm which follows the supervised ... See full document
5
Speaker Recognition and Gender Identification using Artificial Neural Network and Support Vector Machine
... 2) Support Vector Machine: In machine learning, support-vector machines (SVM) are supervised learning models with associated learning algorithms that analyze data used for ... See full document
6
Applying ANN, ANFIS, and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO
... artificial neural network, Multi-layer Perceptron artificial neural network, Least squares support vector machine and adaptive neuro-fuzzy inference system are developed ... See full document
36
Spatial Prediction of Landslides using Time Series Analysis and Support Vector Machine
... and support vector machine, which is the SVM- NN ...Perceptron Neural Networks (MLP Neural Nets) and Naïve Bayes ...MLP Neural Nets showed better accuracy than the other two ... See full document
6
Leaves Classification Using SVM and Neural Network for Disease Detection
... diseases using neural network analysis and support vector ...captured using a digital camera with specific calibration procedure under controlled ...classification ... See full document
8
Comparison of Breast Cancer Detection using Probabilistic Neural Network and Support Vector Machine
... The researchers selected the move towards for finding malignancy in a mammogram image. As it improved the SNR portion of the lesion being identified and eradicated the false-positive conclusion [9]. Thus an algorithm is ... See full document
9
Analysis of various Machine Learning Techniques to Detect Phishing Email
... of machine realizing, that can separate the follow into groups of traffic where each bunch has different traffic ...few machine learning techniques including Logistic Regression (LR), Classification and ... See full document
9
Comparative Study of Classification Techniques on Breast Cancer FNA Biopsy Data
... implement Support Vector Machine (SVM) and Bayesian network solution on the benchmark ...back-propagation neural network) and these two newly developed modeling approaches will ... See full document
8
Mango Leaf Ailment Detection using Neural Network Ensemble and Support Vector Machine
... The quality inspection of leaves consists of two main aspects, internal and external examinations. The internal quality inspection is usually achieved by human sensory, smoking test or chemical analysis, whereas ... See full document
6
Color and Texture based Feature Extraction for Classifying Skin Cancer using Support Vector Machine and Convolutional Neural Network
... detection using color and texture ...classification using Support Vector Machine ...cancer Analysis and Melanoma is developed in 2017 by Ashwini and ... See full document
6
Disease Identification in Cotton Plants Using Spatial FCM & PNN Classifier
... on support vector machines [4] for developing weather based prediction models of plant diseases is proposed by Rakesh & ...artificial neural network (back propagation neural ... See full document
7
Dimension Reduction and Classification of Hyperspectral Images based on Neural Network Sensitivity Analysis and Multi-instance Learning
... sensitivity analysis for dimension ...addressed using multi-instance learning for ...combined neural network sensitivity analysis with a multi- instance learning algorithm based on a ... See full document
26
Effective Classification Techniques for Melanoma Classification on Dermoscopy Images
... image analysis techniques have become important tools in this research area due to the difficulty and subjectivity of human ...the analysis of dermoscopy images using Artificial Neural ... See full document
6
Deep Learning For Anticipation Of Cardiovascular Disease: A Practical Approach
... Artificial Neural Network ...for analysis of cardiac ...the analysis efficiency of cardiac arrest to 92 percent and has shown to be a great source for anticipating heart attacks in ... See full document
6
Optimum Parameters for Fault Detection in Bioreactor Using Support Vector Machine and Neural Network
... A neural network consists of three layers, ...multilayer network can also be used, which has more than one hidden ...artificial neural network, consisting of a single ... See full document
13
Cancer detection using aritifical neural network and support vector machine: a comparative study
... techniques. This technique can also examine medical data in a shorter time and more precisely [3]. The aim of the classification is to develop a set of models that are able to correctly classify the class of different ... See full document
9
Performance comparison of three artificial neural network methods for classification of electroencephalograph signals of five mental tasks
... In this paper, performance of three classifiers for classification of five mental tasks were investigated. Wavelet Packet Transform (WPT) was used for fea- ture extraction of the relevant frequency bands from raw ... See full document
6
Machine Learning Methods of Effort Estimation and It’s Performance Evaluation Criteria
... results. Machine learning approaches could be appropriate at this field because they can increase the accuracy of estimation by training rules of estimation and repeating the run ...artificial neural ... See full document
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