Physical Sciences and Mathematics Commons^™

Sector Expansion And Elliptical Modeling Of Blue-Gray Ovoids For Basal Cell Carcinoma Discrimination In Dermoscopy Images, Pelin Guvenc, Robert W. Leander, Serkan Kefel, William V. Stoecker, Ryan K. Rader, Kristen A. Hinton, Sherea Monica Stricklin, Harold S. Rabinovitz, Margaret C. Oliviero, Randy Hays Moss

Chemistry Faculty Research & Creative Works

Background: Blue-gray ovoids (B-GOs), a critical dermoscopic structure for basal cell carcinoma (BCC), offer an opportunity for automatic detection of BCC. Due to variation in size and color, B-GOs can be easily mistaken for similar structures in benign lesions. Analysis of these structures could afford accurate characterization and automatic recognition of B-GOs, furthering the goal of automatic BCC detection. This study utilizes a novel segmentation method to discriminate B-GOs from their benign mimics.

Methods: Contact dermoscopy images of 68 confirmed BCCs with B-GOs were obtained. Another set of 131 contact dermoscopic images of benign lesions possessing B-GO mimics provided a …

Improving Database Quality Through Eliminating Duplicate Records, Mingzhen Wei, Andrew H. Sung, Martha E. Cather

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Redundant or duplicate data are the most troublesome problem in database management and applications. Approximate field matching is the key solution to resolve the problem by identifying semantically equivalent string values in syntactically different representations. This paper considers token-based solutions and proposes a general field matching framework to generalize the field matching problem in different domains. By introducing a concept of String Matching Points (SMP) in string comparison, string matching accuracy and efficiency are improved, compared with other commonly-applied field matching algorithms. The paper discusses the development of field matching algorithms from the developed general framework. The framework and corresponding …

Hydrogeophysical Investigation At Luxor, Southern Egypt, Ahmed Ismail, Neil Lennart Anderson, J. David Rogers

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Over the past 35 years, the exposed stone foundations of the ancient Egyptian monuments at Luxor have deteriorated at an alarmingly accelerated rate. Accelerated deterioration is attributable to three principal factors: 1) excavation and exposure of foundation stone; 2) construction of the Aswan High Dam; and 3) changes in the regional groundwater regime. In an effort to better elucidate the hydrostratigraphy in the Luxor study area that extends from the River Nile to the boundaries of the Nile Valley and covers about 70 km², a geophysical/hydrological investigation was conducted. Forty Schlumberger vertical electrical soundings (VES), two approximately 6 …

Identification Of Cutting Force In End Milling Operations Using Recurrent Neural Networks, Q. Xu, K. Krishnamurthy, Bruce M. Mcmillin, Wen Feng Lu

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The problem of identifying the cutting force in end milling operations is considered in this study. Recurrent neural networks are used here and are trained using a recursive least squares training algorithm. Training results for data obtained from a SAJO 3-axis vertical milling machine for steady slot cuts are presented. The results show that a recurrent neural network can learn the functional relationship between the feed rate and steady-state average resultant cutting force very well. Furthermore, results for the Mackey-Glass time series prediction problem are presented to illustrate the faster learning capability of the neural network scheme presented here

A Recursive Least Squares Training Algorithm For Multilayer Recurrent Neural Networks, Q. Xu, K. Krishnamurthy, Bruce M. Mcmillin, Wen Feng Lu

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Recurrent neural networks have the potential to perform significantly better than the commonly used feedforward neural networks due to their dynamical nature. However, they have received less attention because training algorithms/architectures have not been well developed. In this study, a recursive least squares algorithm to train recurrent neural networks with an arbitrary number of hidden layers is developed. The training algorithm is developed as an extension of the standard recursive estimation problem. Simulated results obtained for identification of the dynamics of a nonlinear dynamical system show promising results.