Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 3 of 3
Full-Text Articles in Entire DC Network
Refined Genetic Algorithms For Polypeptide Structure Prediction, Charles E. Kaiser Jr.
Refined Genetic Algorithms For Polypeptide Structure Prediction, Charles E. Kaiser Jr.
Theses and Dissertations
Accurate and reliable prediction of macromolecular structures has eluded researchers for nearly 40 years. Prediction via energy minimization assumes the native conformation has the globally minimal energy potential. An exhaustive search is impossible since for molecules of normal size, the size of the search space exceeds the size of the universe. Domain knowledge sources, such as the Brookhaven PDB can be mined for constraints to limit the search space. Genetic algorithms (GAs) are stochastic, population based, search algorithms of polynomial (P) time complexity that can produce semi-optimal solutions for problems of nondeterministic polynomial (NP) time complexity such as PSP. Three …
Analysis Of Linkage-Friendly Genetic Algorithms, Laurence D. Merkle
Analysis Of Linkage-Friendly Genetic Algorithms, Laurence D. Merkle
Theses and Dissertations
Evolutionary algorithms (EAs) are stochastic population-based algorithms inspired by the natural processes of selection, mutation, and recombination. EAs are often employed as optimum seeking techniques. A formal framework for EAs is proposed, in which evolutionary operators are viewed as mappings from parameter spaces to spaces of random functions. Formal definitions within this framework capture the distinguishing characteristics of the classes of recombination, mutation, and selection operators. EAs which use strictly invariant selection operators and order invariant representation schemes comprise the class of linkage-friendly genetic algorithms (lfGAs). Fast messy genetic algorithms (fmGAs) are lfGAs which use binary tournament selection (BTS) with …
Clustered Microcalcification Detection Using Optimized Difference Of Gaussians, Edward M. Ochoa
Clustered Microcalcification Detection Using Optimized Difference Of Gaussians, Edward M. Ochoa
Theses and Dissertations
The objective of this thesis is to design an automated microcalcification detection system to be used as an aid in radiologic mammogram interpretation. This research proposes the following methodology for clustered microcalcification detection. First, preprocess the digitized film mammogram to reduce digitization noise. Second, spatially filter the image with a difference of Gaussians (DoG) kernel. To detect potential microcalcifications, segment the filtered image using global and local thresholding. Next, cluster and index these detections into regions of interest (ROIs). Identify ROIs on the digitized image (or hardcopy printout) for final radiologic diagnosis.