9783330335912 - liver cancer analysis using supervised machine learning classifiers: artificial intelligence in medical diagnosis de tiwari, manish; chakrabarti, prasun; chakrabarti, tulika (5 resultados)
- Tapa blanda
Librería: preigu, Osnabrück, Alemaniapreigu
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 61,05
Envío por EUR 70,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 5 disponibles
Taschenbuch. Condición: Neu. Liver Cancer Analysis Using Supervised Machine Learning Classifiers | Artificial intelligence in medical diagnosis | Manish Tiwari (u. a.) | Taschenbuch | 176 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9783330335912 | Verantwortliche Person für die EU: BoD - Books on Demand, In de T…arpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.
- Tapa blanda
- Impresión bajo demanda
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 71,90
Envío por EUR 23,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The research work has a notable social impact as it facilitates liver cancer diagnosis on the basis of statistical approaches and experimental performance of machine learning classifiers on ILPD(Indian Liver Patient Dataset) and BUP…A liver datasets. The work embodies certain discovered facts. The liver cancer diagnosis can be governed by concept learning, artificial neural modeling, geometric distribution and Cobb-Douglas model. The augmentation or expansion of features indicating liver cancer growth can be quantified and realized based on Markov property based state transition. Liver cancer detection can also be analyzed based upon the fundamental principle of information gain. The realibility and mean time to failure of liver cancer testing system can be carried out in the light of parallel system configuration. The factor leading to liver cancer can be sensed on the basis of weighted majority algorithms.The present objective is also to propose a method using supervised machine learning that can help the physician for accurate diagnosis of liver cancer. For experimental analysis two liver cancer datasets and six diverse classifiers in machine learning have been used. 176 pp. Englisch.
- Tapa blanda
- Impresión bajo demanda
Librería: moluna, Greven, Alemaniamoluna
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 59,05
Envío por EUR 48,99Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Tiwari ManishMr. Manish Tiwari is Assistant Professor, CSE Dept, GITS Udaipur - Prof. P. Chakrabarti is Dean(R&D) and Senior Professor, Dept of CSE, ITM Universe Vadodara - Dr. Tulika Chakrabarti is Ass…istant Professor (Senior Grade.
- Tapa blanda
- Impresión bajo demanda
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 71,90
Envío por EUR 60,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The research work has a notable social impact as it facilitates liver cancer diagnosis on the basis of statistical approaches and experimental performance of machine learning classifiers on ILPD(Indian Liver Patient Dataset) and BUPA li…ver datasets. The work embodies certain discovered facts. The liver cancer diagnosis can be governed by concept learning, artificial neural modeling, geometric distribution and Cobb-Douglas model. The augmentation or expansion of features indicating liver cancer growth can be quantified and realized based on Markov property based state transition. Liver cancer detection can also be analyzed based upon the fundamental principle of information gain. The realibility and mean time to failure of liver cancer testing system can be carried out in the light of parallel system configuration. The factor leading to liver cancer can be sensed on the basis of weighted majority algorithms.The present objective is also to propose a method using supervised machine learning that can help the physician for accurate diagnosis of liver cancer. For experimental analysis two liver cancer datasets and six diverse classifiers in machine learning have been used.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 176 pp. Englisch.
- Tapa blanda
- Impresión bajo demanda
Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 71,90
Envío por EUR 61,40Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The research work has a notable social impact as it facilitates liver cancer diagnosis on the basis of statistical approaches and experimental performance of machine learning classifiers on ILPD(Indian Liver Patient Dataset) and BUPA liv…er datasets. The work embodies certain discovered facts. The liver cancer diagnosis can be governed by concept learning, artificial neural modeling, geometric distribution and Cobb-Douglas model. The augmentation or expansion of features indicating liver cancer growth can be quantified and realized based on Markov property based state transition. Liver cancer detection can also be analyzed based upon the fundamental principle of information gain. The realibility and mean time to failure of liver cancer testing system can be carried out in the light of parallel system configuration. The factor leading to liver cancer can be sensed on the basis of weighted majority algorithms.The present objective is also to propose a method using supervised machine learning that can help the physician for accurate diagnosis of liver cancer. For experimental analysis two liver cancer datasets and six diverse classifiers in machine learning have been used.
