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Missouri University of Science and Technology
Electrical and Computer Engineering Faculty Research & Creative Works
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Full-Text Articles in Engineering
Abnormal Cell Detection Using The Choquet Integral, R. Joe Stanley, James M. Keller, Charles William Caldwell, Paul D. Gader
Abnormal Cell Detection Using The Choquet Integral, R. Joe Stanley, James M. Keller, Charles William Caldwell, Paul D. Gader
Electrical and Computer Engineering Faculty Research & Creative Works
Automated Giemsa-banded chromosome image research has been largely restricted to classification schemes associated with isolated chromosomes within metaphase spreads. In normal human metaphase spreads, there are 46 chromosomes occurring in homologous pairs for the autosomal classes 1-22 and the X chromosome for females. Many genetic abnormalities are directly linked to structural and/or numerical aberrations of chromosomes within metaphase spreads. Cells with the Philadelphia chromosome contain an abnormal chromosome for class 9 and for class 22, leaving a single normal chromosome for each class. A data-driven homologue matching technique is applied to recognizing normal chromosomes from classes 9 and 22. Homologue …
A Committee Of Neural Networks For Automatic Speaker Recognition (Asr) Systems, Viresh Moonasar, Ganesh K. Venayagamoorthy
A Committee Of Neural Networks For Automatic Speaker Recognition (Asr) Systems, Viresh Moonasar, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
This paper describes how the results of speaker verification systems can be improved and made robust with the use of a committee of neural networks for pattern recognition rather than the conventional single-network decision system. It illustrates the use of a supervised learning vector quantization neural network as the pattern classifier. Linear predictive coding and cepstral signal processing techniques are utilized to form hybrid feature parameter vectors to combat the effect of decreased recognition success with increased group size (number of speakers to be recognized)
A Parallel Computer-Go Player, Using Hdp Method, Donald C. Wunsch, Xindi Cai
A Parallel Computer-Go Player, Using Hdp Method, Donald C. Wunsch, Xindi Cai
Electrical and Computer Engineering Faculty Research & Creative Works
The game of Go has simple rules to learn but requires complex strategies to play well, and, the conventional tree search algorithm for computer games is not suited for Go program. Thus, the game of Go is an ideal problem domain for machine learning algorithms. This paper examines the performance of a 19x19 computer Go player, using heuristic dynamic programming (HDP) and parallel alpha-beta search. The neural network based Go player learns good Go evaluation functions and wins about 30% of the games in a test series on 19x19 board