Titlebook: Artificial Intelligence: Theories and Applications; First International Mohammed Salem,Juan Julián Merelo,Fatima Debbat Conference proceed

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Ekbert Hering,Alexander Schloskebetter fit the input data. The sensitivity to subtle visual details is a key factor for a better facial expression recognition. Furthermore, this method uses the same number of parameters as a convolution layer or a dense layer. The experiments conducted on FER datasets show that the use of our meth

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N. Vonk,B. G. J. Baars,H. Schaller apply the proposed approach on Wisconsin Diagnostic Breast Cancer (WDBC) database. The experimental result improve that the hybridation between RGWO for feature selection and RF classifier increase the accuracy rate of classification and demonstrating it’s robustness in identifying the breast cance

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https://doi.org/10.1007/978-3-642-84136-1od is tested and compared with the conventional method with free and additional disturbances. Simulation results have shown the advantages of automatic tuning of the FSMC‘s parameters to achieve the desired results. The superiority of the tuned controller has been proved to control?the pole angle of

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Entwurf von Fehlertoleranz-Verfahren,t gives the best classification performances in terms of turnaround time and the following metrics: Accuracy, Precision, Recall, and F1 score..The outcomes of the comparison study show that the SVM algorithm is the best classification machine-learning algorithm for automatic ITSC fault detection sin

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