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Incremental functional diagnosis aims at minimising the number of tests to be executed to perform the diagnosis, to limit efforts and costs. Iteratively the test to be executed is selected and based on the collected outcome, either the faulty component is identified or a new test is performed. This paper proposes a novel approach based on the syndromes occurrence probability, that defines how i) to...
Functional diagnosis for complex electronic boards is a time-consuming task that requires big expertise to the diagnosis engineers. In this paper we propose a new engine for board-level adaptive incremental functional diagnosis based on decision trees. The engine incrementally selects the tests that have to be executed and based on the test outcomes it automatically stops the diagnosis as soon as...
We investigate how to use the scripts with automatically generated fast-performing analytic SQL statements to speed up the KDD-related tasks of attribute selection and decision tree induction. We base our framework on the entity-attribute-value data model in order to seamlessly scale the required queries with respect to the amounts of attributes involved in the given task's specification. We note...
The important goal in data mining is to reveal hidden knowledge from data and various algorithms have been proposed so far. But the problem is that typically not all rules are interesting - only small fractions of the generated rules would be of interest to any given user. Hence, numerous measures such as confidence, support, lift, information gain, and so on, have been proposed to determine the best...
Signature-based anti-viruses are very accurate, but are limited in detecting new malicious code. Dozens of new malicious codes are created every day, and the rate is expected to increase in coming years. To extend the generalization to detect unknown malicious code, heuristic methods are used; however, these are not successful enough. Recently, classification algorithms were used successfully for...
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