Oskooi behzad (6 resultados)

Taschenbuch. Condición: Neu. Improving Medical Data Classification Accuracy Using Ensemble methods | An Application of Ensemble Learning Methods | Behzad Oskooi (u. a.) | Taschenbuch | 108 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783844358223 | Verantwortliche Person für die EU: LAP Lambert Academic Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. …

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Currently, electronic medical instruments are widely used in hospitals to gather vital information about patients bodies. This medical data is used by professionals to distinguish the causes of illnesses and the EEG is a well known test for this purpose. The EEG has many features and thus the volume of gathered data can increase dramatically if the number of samples or patients is enlarged. Interpreting this huge amount of data is not a simple task as the analysis is very time consuming. The application of machine learning methods can reduce this time for analysis as well as increase the accuracy of such analyses. In this publication, experiments are designed to take advantage of ensemble learning methods to classify large amounts of medical information which have many input vectors. The aim of these experiments is to differentiate between two groups of EEG data, collected during two separate Reiki and placebo operations. The analysis should help shed some light on this new and exciting field of study, and should be especially useful for professionals in the fields of Computer Science, or anyone else considering utilizing machine learning methods for clinical purposes. 108 pp. Englisch.…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Currently, electronic medical instruments are widely used in hospitals to gather vital information about patients bodies. This medical data is used by professionals to distinguish the causes of illnesses and the EEG is a well known test for this purpose. The EEG has many features and thus the volume of gathered data can increase dramatically if the number of samples or patients is enlarged. Interpreting this huge amount of data is not a simple task as the analysis is very time consuming. The application of machine learning methods can reduce this time for analysis as well as increase the accuracy of such analyses. In this publication, experiments are designed to take advantage of ensemble learning methods to classify large amounts of medical information which have many input vectors. The aim of these experiments is to differentiate between two groups of EEG data, collected during two separate Reiki and placebo operations. The analysis should help shed some light on this new and exciting field of study, and should be especially useful for professionals in the fields of Computer Science, or anyone else considering utilizing machine learning methods for clinical purposes.…

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Oskooi BehzadThe Author graduated with a Masters in Computer Science from Uppsala University of Sweden. He is an expert in Siemens, Cisco and Huawei telecommunication systems in Telecom of Iran. He teaches Computer Security course in.…

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Currently, electronic medical instruments are widely used in hospitals to gather vital information about patients¿ bodies. This medical data is used by professionals to distinguish the causes of illnesses and the EEG is a well known test for this purpose. The EEG has many features and thus the volume of gathered data can increase dramatically if the number of samples or patients is enlarged. Interpreting this huge amount of data is not a simple task as the analysis is very time consuming. The application of machine learning methods can reduce this time for analysis as well as increase the accuracy of such analyses. In this publication, experiments are designed to take advantage of ensemble learning methods to classify large amounts of medical information which have many input vectors. The aim of these experiments is to differentiate between two groups of EEG data, collected during two separate Reiki and placebo operations. The analysis should help shed some light on this new and exciting field of study, and should be especially useful for professionals in the fields of Computer Science, or anyone else considering utilizing machine learning methods for clinical purposes.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 108 pp. Englisch.…