Thesis (M.S.)--Brigham Young University. Dept of Computer Science, 2006. / Includes bibliographical references (p. 39-41).

Generalized Radial Basis Functions were used to construct networks that learn input-output mappings from given data. They are developed out of a theoretical framework for approximation based on regularization techniques and represent a class of three-layer networks similar to backpropagation networks with one hidden layer. A network using Gaussian base functions was implemented and applied to several domains. It was found to perform very well on the two-spirals problem and on the nettalk task. This paper explains what Generalized Radial Basis Functions are, describes the algorithm, its implementation, and the tests that have been conducted. It draws the conclusion that network. implementations using Generalized Radial Basis Functions are a successful approach for learning from examples. / Graduation date: 1991

Thesis (Ph. D.)--Texas A&M University, 2004. / Vita. Includes bibliographical references (p. 171-177).

Thesis (Ph. D.)--Lehigh University, 2000. / Includes vita. Includes bibliographical references (leaves 138-147).

Thesis (M.S.)--West Virginia University, 2002. / Title from document title page. Document formatted into pages; contains xii, 94 p. : ill. (some col.), maps. Includes abstract. Includes bibliographical references (p. 51-53).

Thesis (M.S.)--West Virginia University, 2009. / Title from document title page. Document formatted into pages; contains ix, 83 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 81-83).

Thesis (M.S.)--West Virginia University, 2003. / Title from document title page. Document formatted into pages; contains xiii, 112 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 92-94).

Thesis (M. Phil.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 148-160).

Thesis (Ph. D. (Physics and Electronics))--Rhodes University, 2003.

The elderly population (those aged 65 years or older) in Hong Kong is expected to increase from approximately 13% in 2009 to 28% by 2039. With this rapid growth of elders, it raises attention to prevent and treat chronic diseases of aging. Dementia is particularly concerned because the short term memory loss and other cognitive malfunctions lead to a loss of independent function that has a extensive impact on individuals, families, health and social welfare systems. Currently over 70,000 people endure dementia in Hong Kong and expect quadruple rises by 2036. In order to cope with these diseases, accurate diagnosis is very useful, particular at early stage when treatment outcomes are most effective. Numerous studies have found that AD and other dementias could alter brain metabolism significantly. AD patients usually present the posterior cingulated and parietotemporal cortices hypometabolism and spread into the frontal lobes in advanced disease. In contrast, FTD patients show manifestly hypometabolism in the frontal and anterior temporal cortices, while DLB patients present hypometabolism in the posterior brain comprising primarily the parietoocipital regions. Theoretically, 18F-FDG PET scan can help in the early diagnosis of AD and other dementias by highlighting these decreased FDG uptake cortex regions before MRI or CT scans can detect any structural damage. This is a retrospective chart review study. Patients who had received FDG brain PET-CT scan previously had their regional brain metabolism quantitated using a software call Cortex-ID and clinical laboratory tests. The study is * To develop a Neural Network (NN) that can diagnose the various types of dementia using Brain PET-CT scan, testing accuracy of NN versus an expert and, * To see if the NN can correlate with the clinical severity of the disease as reflected by MMSE score. Finally, three neural networks have been designed and they all fulfill all the required specifications. / published_or_final_version / Diagnostic Radiology / Master / Master of Medical Sciences