Quantitative structure-property relationships (QSPRs) for estimating aqueous solubility of organic compounds at 25 degrees C were developed based on a fuzzy ARTMAP and a back-propagation neural networks using a heterogeneous set of 515 organic compounds. A set of molecular descriptors, developed from PM3 semiempirical MO-theory and topological descriptors (first-, second-, third-, and fourth-or...

Quantitative structure-property relationships (QSPRs) for estimating a dimensionless Henry's Law constant of organic compounds at 25 degrees C were developed based on a fuzzy ARTMAP and back-propagation neural networks using a heterogeneous set of 495 organic compounds. A set of molecular descriptors developed from PM3 semiempirical MO-theory and topological descriptors (second-order molecular ...

Adaptive Resonance Theory (ART) models arc real-lime neural networks for category learning, pattern recognition, and prediction. Unsupervised fuzzy ART and supervised fuzzy ARTMAP networks synthesize fuzzy logic and ART by exploiting the formal similarity between tile computations of fuzzy subsethood and the dynamics of ART category choice, search, and learning. Fuzzy ART self-organizes stable ...

Automatic pattern classifiers that allow for on-line incremental learning can adapt internal class models efficiently in response to new information without retraining from the start using all training data and without being subject to catastrophic forgeting. In this paper, the performance of the fuzzy ARTMAP neural network for supervised incremental learning is compared to that of supervised b...

Quantitative structure-property relationships (QSPRs) for estimating the logarithm octanol/water partition coefficients, logK(ow), at 25 degrees C were developed based on fuzzy ARTMAP and back-propagation neural networks using a heterogeneous set of 442 organic compounds. The set of molecular descriptors were derived from molecular connectivity indices and quantum chemical descriptors calculate...

The economic and social values of breast cancer diagnosis are very high. This study explores the predictive abilities of Fuzzy ARTMAP neural networks for breast cancer diagnosis. The data used is a combination of 39 mammographic, sonographic, and other descriptors, which is novel for the field. By using feature selection techniques we propose a subset of 21 descriptors that outperform the full ...

One of the major problems in computational biology is the inability of existing classification models to incorporate expanding and new domain knowledge. This problem of static classification models is addressed in this paper by the introduction of incremental learning for problems in bioinformatics. Many machine learning tools have been applied to this problem using static machine learning stru...

The economic and social values of breast cancer diagnosis are very high. This study explores the predictive abilities of Fuzzy ARTMAP neural networks for breast cancer diagnosis. The data used is a combination of 39 mammographic, sonographic, and other descriptors, which is novel for the field. By using feature selection techniques we propose a subset of 21 descriptors that outperform the full ...

Abstract: In this paper, the gain in LD-CELP speech coding algorithm is predicted using three neural models, that are equipped by genetic and particle swarm optimization (PSO) algorithms to optimize the structure and parameters of neural networks. Elman, multi-layer perceptron (MLP) and fuzzy ARTMAP are the candidate neural models. The optimized number of nodes in the first and second hidden la...

A new approach is presented for the development of quantitative structure–property relations (QSPR) based on the extraction of relevant molecular features with self-organizing maps and the use of a modified fuzzy-ARTMAP classifier for variable prediction. The present methodology is demonstrated for the development of a QSPR for the aqueous-phase infinite dilution activity coefficient , based on...