Titlebook: Biomedical Engineering Science and Technology; Second International Bikesh Kumar Singh,G.R. Sinha,Rishikesh Pandey Conference proceedings 2

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,Techniques des ostéotomies tibiales,theta bands extracted using wavelet decomposition. The two different modalities proposed in this work were: (1) using pre-trained network for transfer learning approach (2) pretrained network to extract the image features and classification with the help of support vector machine (SVM). In this expe

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Fallimento della terapia antibiotica,tral spread was obtained and tested on different machine learning models using 10-fold, 5-fold cross validation and 33% hold out. The results obtained under normal and abnormal classification for 10-fold, 5-fold and 33% hold out showed an accuracy of 78.6%, 77.5% and 76.4% for MFCC Delta-Delta featu

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,D’Alembert et la mécanique céleste,ith input representation of the signal, window size for signal segmentation, and various model parameters. It was observed that a very simple time domain representation of the raw signal, segmented by a window of 5s, when combined with a shallow 1D-CNN showed the best performance, with an accuracy s

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,D’Alembert et la mécanique céleste,CV, Recursive Feature Elimination (RFE) and Hybrid selection techniques. Various machine learning classifiers, including Random-Forest (RF), Logistic Regression (LR), Decision-Tree (DT), Gaussian NB (GB), SGD Classifier (SGDC), Nearest-Neighbors (NN), Support Vector Machine (SVM), Adboost (Adb), MLP

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https://doi.org/10.1007/978-2-287-72083-3res obtained from the PCA components are feed to machine learning classifier. The most commonly used classifiers are support vector machine or naive bayes, random forest to classify the epileptic brain signals. In this work, the accuracy, sensitivity obtained with PCA reduction and without PCA reduc

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https://doi.org/10.1007/978-2-287-72083-3evaluating the effectiveness of proposed feature selection algorithms. The results are compared on the performance matrices and it is observed that MIGM algorithm provides the most optimal features compared two other two selection techniques. Also, the model is evaluated for temporal features and fr