Applications of Soft Computing Techniques for Cancer Prognosis: Artificial Intelligence Techniques in Survival Analysis - Softcover
Inhaltsangabe
The study done in this book was designed as a focused preliminary exploration into the power of the genetic algorithm and fuzzy logic system to the prediction of cancer survival, The results of the experiments were very positive, comparing the outcome of the GA model with that of FL it shows the robustness of the GA model as prediction system.The two principal designs indicate that the use of genetic algorithms and fuzzy logic in NPC is definitely a fruitful endeavour. The results would suggest that genetic algorithms as standalone classifier models are better (based on the system designed in this research) for this sort of task than a fuzzy logic model.
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Über die Autorin bzw. den Autor
Dr.Oras F. Baker is the head of Research and Industrial Collaboration Dept in UCSI University and currently he is a faculty member in UKH University. He obtained his PhD in Artificial Intelligence from the University of Malaya in 2009. He is a member in IEEE, ACM, ACS, and ECIE. Editorial Board Member for IJCSIS, IJWMC,IJASA journals.
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Applications of Soft Computing Techniques for Cancer Prognosis : Artificial Intelligence Techniques in Survival Analysis
Verlag:
LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3847319663
ISBN 13: 9783847319665
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Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | The study done in this book was designed as a focused preliminary exploration into the power of the genetic algorithm and fuzzy logic system to the prediction of cancer survival, The results of the experiments were very positive, comparing the outcome of the GA model with that of FL it shows the robustness of the GA model as prediction system.The two principal designs indicate that the use of genetic algorithms and fuzzy logic in NPC is definitely a fruitful endeavour. The results would suggest that genetic algorithms as standalone classifier models are better (based on the system designed in this research) for this sort of task than a fuzzy logic model. Artikel-Nr. 11808012/1
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