Computer Sciencehttp://hdl.handle.net/10464/61402024-03-29T06:04:18Z2024-03-29T06:04:18ZParameter selection for modeling of epidemic networksDube, MichaelHoughten, SheridanAshlock, Danielhttp://hdl.handle.net/10464/171402023-01-07T01:27:54Z2018-05-01T00:00:00ZParameter selection for modeling of epidemic networks
Dube, Michael; Houghten, Sheridan; Ashlock, Daniel
The accurate modeling of epidemics on social contact networks is difficult due to the variation between different epidemics and the large number of parameters inherent to the problem. To reduce complexity, evolutionary computation is used to create a generative representation of the epidemic model. Previous gains from the use of local, verses global, operators are further explored to better balance exploration and exploitation of the genetic algorithm. A typical parameter study is conducted to test this new local operator and the new method of point packing is utilized as a proof of concept to perform a better search of the parameter space. All experiments from both approaches are tested against nine epidemic profiles. The point-packing driven parameter search demonstrates that the algorithm parameters interact substantially and in a non-linear fashion, and also shows that the good parameter settings are problem specific.
2018-05-01T00:00:00ZEdit metric decoding: Return of the side effect machinesHoughten, SheridanCollins, Tyler K.Hughes, James AlexanderBrown, Joseph Alexanderhttp://hdl.handle.net/10464/171392023-01-07T01:26:48Z2018-05-01T00:00:00ZEdit metric decoding: Return of the side effect machines
Houghten, Sheridan; Collins, Tyler K.; Hughes, James Alexander; Brown, Joseph Alexander
Side Effect Machines (SEMs) are an extension of finite state machines which place a counter on each node that is incremented when that node is visited. Previous studies examined a genetic algorithm to discover node connections in SEMs for edit metric decoding for biological applications, namely to handle sequencing errors. Edit metric codes, while useful for decoding such biologically created errors, have a structure which significantly differentiates them from other codes based on Hamming distance. Further, the inclusion of biologically- motivated restrictions on allowed words makes development of decoders a bespoke process based on the exact code used. This study examines the use of evolutionary programming for the creation of such decoders, thus allowing for the number of states to be evolved directly, not witnessed in previous approaches which used genetic algorithms. Both direct and fuzzy decoding are used, obtaining correct decoding rates of up to 95% in some SEMs.
2018-05-01T00:00:00ZA deep learning pipeline to classify different stages of Alzheimer's disease from fMRI dataKazemi, YosraHoughten, Sheridanhttp://hdl.handle.net/10464/171382023-01-06T02:31:22Z2018-05-01T00:00:00ZA deep learning pipeline to classify different stages of Alzheimer's disease from fMRI data
Kazemi, Yosra; Houghten, Sheridan
Alzheimer's disease (AD) is an irreversible, progressive neurological disorder that causes memory and thinking skill loss. Many different methods and algorithms have been applied to extract patterns from neuroimaging data in order to distinguish different stages of Alzheimer's disease (AD). However, the similarity of the brain patterns in older adults and in different stages makes the classification of different stages a challenge for researchers. In this paper, convolutional neuronal network architecture AlexNet was applied to fMRI datasets to classify different stages of the disease. We classified five different stages of Alzheimer's using a deep learning algorithm. The method successfully classified normal healthy control (NC), significant memory concern (SMC), early mild cognitive impair (EMCI), late cognitive mild impair (LMCI), and Alzheimer's disease (AD). The model was implemented using GPU high performance computing. Before applying any classification, the fMRI data were strictly preprocessed. Then, low to high level features were extracted and learned using the AlexNet model. Our experiments show significant improvement in classification. The average accuracy of the model was 97.63%. We then tested our model on test datasets to evaluate the accuracy of the model per class, obtaining an accuracy of 94.97% for AD, 95.64% for EMCI, 95.89% for LMCI, 98.34% for NC, and 94.55% for SMC.
2018-05-01T00:00:00ZHierarchical clustering and tree stabilitySaunders, AmandaAshlock, DanielHoughten, Sheridanhttp://hdl.handle.net/10464/171372023-01-06T02:31:08Z2018-05-01T00:00:00ZHierarchical clustering and tree stability
Saunders, Amanda; Ashlock, Daniel; Houghten, Sheridan
Hierarchical clustering via neighbor joining, widely used in biology, can be quite sensitive to the addition or deletion of single taxa. In an earlier study it was found that neighbor joining trees on random data were commonly quite unstable in the sense that large re-arrangements of the tree occurred when the tree was reconstructed after the deletion of a single data point. In this study, we use an evolutionary algorithm to evolve extremely stable and unstable data sets for a standard neighbor-joining algorithm and then check the stability using a novel type of clustering called bubble clustering. Bubble clustering is an instance of associator clustering. The stability measure used is based on the size of the subtree containing each pair of taxa, a quantity that provides an objective measure of a given trees hypothesis about the relatedness of taxa. It is shown experimentally that even in data sets evolved to be stable for a standard neighbor joining algorithm, bubble clustering is a significantly more stable algorithm.
2018-05-01T00:00:00Z