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Applications of Intelligent Optimization in Biology and Medicine Current Trends and Open Problems /

This volume provides updated, in-depth material on the application of intelligent optimization in biology and medicine. The aim of the book is to present solutions to the challenges and problems facing biology and medicine applications. This Volume comprises of 13 chapters, including an overview cha...

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Detaylı Bibliyografya
Müşterek Yazar: SpringerLink (Online service)
Diğer Yazarlar: Hassanien, Aboul-Ella (Editör), Grosan, Crina (Editör), Fahmy Tolba, Mohamed (Editör)
Materyal Türü: e-Kitap
Dil:İngilizce
Baskı/Yayın Bilgisi: Cham : Springer International Publishing : 2016.
Imprint: Springer,
Edisyon:1st ed. 2016.
Seri Bilgileri:Intelligent Systems Reference Library, 96
Konular:
Online Erişim:Full-text access
İçindekiler:
  • A simplex Nelder Mead Genetic Algorithm for Minimizing Molecular Potential Energy Function
  • A Survey of Metaheuristics Methods for Bioinformatics Applications
  • DNA Based Steganography: Survey and Analysis for Parameters Optimization
  • Dental Image Registration Using Particle Swarm Optimized for Thin Plate Splines from Semi-Automatic
  • A Modified Particle Swarm Optimization Algorithm for Solving Capacitated Maximal Covering Location Problem in Healthcare Systems
  • Optimization Methods for Medical Image Super Resolution Reconstruction
  • PCA-PNN and PCA-SVM based CAD Systems for Breast Density Classification
  • Retinal Blood Vessels Segmentation Based on Bio-Inspired Algorithm
  • Systematic Analysis of Applied Data Mining Based Optimization Algorithms in Clinical Attribute Extraction and Classification for Diagnosis of Cardiac Patients
  • Particle Swarm Optimization Based Fast Fuzzy C-Means Clustering for Liver CT Segmentation
  • Enhanced Prediction of DNA-Binding Proteins and Classes
  • MEDLINE Text Mining: An Enhancement Genetic Algorithm based Approach for Document Clustering
  • Optimized Tumor Breast Cancer Classification Using Combining Random Subspace and Static Classifiers Selection Paradigms.