Walker H. Land

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This research describes a non-interactive process that applies several forms of computational intelligence to classifying biopsy lung tissue samples. Three types of lung cancer evaluated (squamous cell carcinoma, adenocarcinoma, and bronchioalveolar carcinoma) together account for 65–70% of diagnoses. Accuracy achieved supports hypothesis that an accurate(More)
This research consisted of evaluating diagnostic performance results using SVM outputs previously obtained from an integrated Duke/DDSM USF data set and the GRNN oracle. The SVM kernels used in this research included Additive, Multiplicative, S2000, and Spline kernels. GRNN results are presented for the following combinations of gate variables: age, mass(More)
The automated decision paradigms presented in this work address the false positive (FP) biopsy occurrence in diagnostic mammography. An EP/ES stochastic hybrid and two kernelized Partial Least Squares (K-PLS) paradigms were investigated with following studies: methodology performance comparisonsautomated diagnostic accuracy assessments with two data sets.(More)
The need for rapid and accurate detection systems is expanding and the utilization of cross-reactive sensor arrays to detect chemical warfare agents in conjunction with novel computational techniques may prove to be a potential solution to this challenge. We have investigated the detection, prediction, and classification of various organophosphate (OP)(More)