Wyniki wyszukiwania - BazTech

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Rockburst prediction in kimberlite using decision tree with incomplete data

Pu Y., Apel D. B., Lingga B.

Journal of Sustainable Mining

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2018

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Vol. 17, iss. 3

158--165

EN

A rockburst is a common engineering geological hazard. In order to predict rockburst potential in kimberlite at an underground diamond mine, a decision tree method was employed. Based on two fundamental premises of rockburst occurrence, σθ, σc, σt, WET are determined as indicators of rockburst, which are also partition attributes of the decision tree. 132 training samples (with 24 incomplete samples) were obtained from real rockburst cases from all over the world to build the decision tree. The decision tree based on 108 complete samples was built with an accuracy of 73% for 15 validation samples while another decision tree based on 132 samples (with 24 groups of incomplete data) shows an accuracy of 93% for validation samples. Hence, the second decision tree was employed for kimberlite burst prediction. 12 samples from lab tests and a numerical model were used as test samples. The results indicate a moderate burst liability which matches real situations at the diamond mind in question.

2

Development of a fuzzy-driven system for ovarian tumor diagnosis

Żywica P., Dyczkowski K., Wójtowicz A., Stachowiak A., Szubert S., Moszyński R.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 4

632--643

EN

In this paper we present OvaExpert, an intelligent system for ovarian tumor diagnosis. We give an overview of its features and main design assumptions. As a theoretical framework the system uses fuzzy set theory and other soft computing techniques. This makes it possible to handle uncertainty and incompleteness of the data, which is a unique feature of the developed system. The main advantage of OvaExpert is its modular architecture which allows seamless extension of system capabilities. Three diagnostic modules are described, along with examples. The first module is based on aggregation of existing prognostic models for ovarian tumor. The second presents the novel concept of an Interval-Valued Fuzzy Classifier which is able to operate under data incompleteness and uncertainty. The third approach draws from cardinality theory of fuzzy sets and IVFSs and leads to a bipolar result that supports or rejects certain diagnoses.

3

OvaExpert: an intelligent medical diagnosis support system for ovarian tumor

Żywica P., Apolinarski S., Kubiński B.

Journal of Medical Informatics & Technologies

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2015

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Vol. 24

183--190

EN

In this paper we present OvaExpert, an intelligent system for ovarian tumor diagnosis. We give an overview of its features and main design assumptions. As a theoretical framework the system uses fuzzy set theory and other soft computing techniques. This makes it possible to handle uncertainty and incompleteness of the data which is an unique feature of developed system. The main advantage of OvaExpert is its modular architecture which allows seamless extension of system capabilities. Two diagnostic modules are described in the paper along with examples. First module is based on aggregation of existing prognostic models for ovarian tumor. Second, on novel concept of Interval– Valued Fuzzy Classifier which is able to operate under data incompleteness and uncertainty.