Articles listed below give examples of ART-based systems being used outside the CNS department at Boston University.
| Categories |
Topics:
Machine Learning,
Applications:
Biological Classification,
Models:
ARTMAP, |
| Author(s) |
Cohen, G.M. |
Ham, F.H. |
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| Abstract |
The concentration of a substance, such as glucose, in a biological sample, such as human tissue (e.g. the skin of an index finger) is non-invasively determined by directing the output beam of a laser diode onto and into the ... |
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| Categories |
Topics:
Machine Learning,
Models:
ARTMAP,
Fuzzy ARTMAP, |
| Author(s) |
Huang, H.H. |
Knapp, G.M. |
Lin, C.C. |
Lin, S.S. |
Spoerre, J.K. |
Wang, H.P. |
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| Abstract |
The invention provides a machine fault diagnostic system to help ensure effective equipment maintenance. The major technique used for fault diagnostics is a fault diagnostic network (FDN) which is based on a modified ARTMAP ... |
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| Categories |
Topics:
Machine Learning,
Applications:
Medical Diagnosis,
Models:
Fuzzy ARTMAP, |
| Author(s) |
Cross, S.S. |
Downs, J. |
Harrison, R.F. |
Kennedy, R.L. |
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| Abstract |
This paper presents research into the application of the fuzzy ARTMAP neural network model to medical pattern classification tasks. A number of domains, both diagnostic and prognostic, are considered. Each such domain ... |
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| Categories |
Topics:
Machine Learning,
Models:
ART 2 / Fuzzy ART,
Fuzzy ARTMAP, |
| Author(s) |
Bebis, G. |
Fernlund, H. |
Georgiopoulos, M. |
Heileman, G.L. |
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| Abstract |
This paper focuses on two ART architectures, the Fuzzy ART and the Fuzzy ARTMAP. Fuzzy ART is a pattern clustering machine, while Fuzzy ARTMAP is a pattern classification machine. Our study concentrates on the order ... |
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| Categories |
Topics:
Machine Learning,
Applications:
Medical Diagnosis,
Models:
Fuzzy ARTMAP, |
| Author(s) |
Bortolozzi, F. |
Murshed, N. |
Sabourin, R. |
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| Abstract |
This work investigates the use of a fuzzy ARTMAP neural network for detecting cancerous cells, based on the one-class problem approach. This approach is inspired by the way human beings perform pattern recognition. We all ... |
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| Categories |
Topics:
Machine Learning,
Applications:
Industrial Control,
Models:
ART 1, |
| Author(s) |
Chen, S.J. |
Cheng, C.S. |
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| Abstract |
The Adaptive Resonance Theory (ART) neural network is a novel method for the cell formation problem in group technology (GT). The advantages of using an ART network over other conventional methods are its fast computation ... |
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| Categories |
Topics:
Machine Learning,
Applications:
Industrial Control,
Models:
ART 1, |
| Author(s) |
Dagli, C.H. |
Bahrami, A. |
Lynch, M. |
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| Abstract |
We describe a hybrid intelligent design retrieval and packaging system by utilizing techniques such as fuzzy associative memory, backpropagation neural networks, and adaptive resonance theory. As an illustrative example, a ... |
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| Categories |
Topics:
Machine Learning,
Models:
ART 1, |
| Author(s) |
Dagli, C.H. |
|
| Abstract |
The ART1 neural network paradigm employs a heuristic where new vectors are compared with group representative vectors for classification. ART1 is adapted for the cell formation problem by reordering input vectors and by ... |
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| Categories |
Topics:
Image Analysis,
Applications:
Character Recognition,
Human-Machine Interface,
Models:
ART 2 / Fuzzy ART, |
| Author(s) |
Dimitriadis, Y.A. |
Coronado, J.L. |
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| Abstract |
A new mathematical editor, based on the recognition of run-on discrete handwritten symbols, is proposed. The tested laboratory prototype of the system, modular and adaptable to the user habits and site requirements, uses a ... |
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| Categories |
Topics:
Machine Learning,
Models:
ART 1,
Modified ART, |
| Author(s) |
Chong, C.W. |
Hwarng, H.B. |
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| Abstract |
An adaptive resonance theory (ART) based, general-purpose control chart pattern recognizer (CCPR) which is capable of fast and cumulative learning is presented. The implementation of this ART-based CCPR was made possible by ... |
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