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Full-Text Articles in Other Engineering
A Direct Data-Cluster Analysis Method Based On Neutrosophic Set Implication, Florentin Smarandache, Sudan Jha, Gyanendra Prasad Joshi, Lewis Nkenyereya, Dae Wan Kim
A Direct Data-Cluster Analysis Method Based On Neutrosophic Set Implication, Florentin Smarandache, Sudan Jha, Gyanendra Prasad Joshi, Lewis Nkenyereya, Dae Wan Kim
Branch Mathematics and Statistics Faculty and Staff Publications
Raw data are classified using clustering techniques in a reasonable manner to create disjoint clusters. A lot of clustering algorithms based on specific parameters have been proposed to access a high volume of datasets. This paper focuses on cluster analysis based on neutrosophic set implication, i.e., a k-means algorithm with a threshold-based clustering technique. This algorithm addresses the shortcomings of the k-means clustering algorithm by overcoming the limitations of the threshold-based clustering algorithm. To evaluate the validity of the proposed method, several validity measures and validity indices are applied to the Iris dataset (from the University of California, Irvine, Machine …
Multidimensional Feature Engineering For Post-Translational Modification Prediction Problems, Norman Mapes Jr.
Multidimensional Feature Engineering For Post-Translational Modification Prediction Problems, Norman Mapes Jr.
Doctoral Dissertations
Protein sequence data has been produced at an astounding speed. This creates an opportunity to characterize these proteins for the treatment of illness. A crucial characterization of proteins is their post translational modifications (PTM). There are 20 amino acids coded by DNA after coding (translation) nearly every protein is modified at an amino acid level. We focus on three specific PTMs. First is the bonding formed between two cysteine amino acids, thus introducing a loop to the straight chain of a protein. Second, we predict which cysteines can generally be modified (oxidized). Finally, we predict which lysine amino acids are …
Toward Mining Massive And Multi-Dimensional Data For Extreme Hydrometeorological And Climate Event Analyses, Ling Qiu, Francisco Munoz-Arriola, Carlos M. Carrilo
Toward Mining Massive And Multi-Dimensional Data For Extreme Hydrometeorological And Climate Event Analyses, Ling Qiu, Francisco Munoz-Arriola, Carlos M. Carrilo
UCARE Research Products
This poster presents the research on the main patterns of spatial distribution and temporal variability of precipitation and temperature in the Missouri River Basin (MRB). MRB has 117 million acres in cropland, produces of 46%, 22%, and 34% US's wheat, corn, and cattle, respectively. Also, MRB is known for intense weather and extreme climate variability. The approach is to identify different patterns of spatial distribution and temporal variability of precipitation and temperature through the use of a Principal Component Analysis. We have found that precipitation and temperature are the ideal meteorological variable to test spatiotemporal variability of extreme events using …