We present a novel canonical automaton model for languages over infinite data domains, that is suitable for specifying the behavior of services, protocol components, interfaces, etc. The model is based on register aut...
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Data mining has become an important and active area of research because of theoretical challenges and practical applications associated with the problem of discovering interesting and previously unknown knowledge from...
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In this paper, we show how to fully automatically infer semantic interfaces of data structures on the basis of systematic testing. Our semantic interfaces are a generalized form of Register Automata (RA), comprising p...
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The paper gives an overview of research devoted to developing a semi-automatic methodology of building a semantic model of medical diagnostic knowledge. The methodology is based on natural language processing methods ...
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Data mining has become an important and active area of research because of theoretical challenges and practical applications associated with the problem of discovering interesting and previously unknown knowledge from...
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Data mining has become an important and active area of research because of theoretical challenges and practical applications associated with the problem of discovering interesting and previously unknown knowledge from very large real world database. These databases contain potential gold mine of valuable information, but it is beyond human ability to analyze massive amount of data and elicit meaningful patterns by using conventional techniques. In this study, DNA sequence was analyzed to locate promoter which is a regulatory region of DNA located upstream of a gene, providing a control point for regulated gene transcription. In this study, some supervised learning algorithms such as artificial neural network (ANN), RULES-3 and newly developed keREM rule induction algorithm were used to analyse to DNA sequence. In the experiments different option of keREM, RULES-3 and ANN were used, and according to the empirical comparisons, the algorithms appeared to be comparable to well-known algorithms in terms of the accuracy of the extracted rule in classifying unseen data.
As a virtual experimental device for analysis and calculation of grown-in microdefects formation in undoped silicon dislocation-free single crystals the software is proposed. The software is built on the basis on diff...
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As a virtual experimental device for analysis and calculation of grown-in microdefects formation in undoped silicon dislocation-free single crystals the software is proposed. The software is built on the basis on diffusion model of formation, growth and coalescence of grown-in microdefects. Diffusion model describes kinetics of defect structure changes during cooling after growth on crystallization temperature to room temperature. The software allows the use of personal computer to investigate the defect structure of dislocation-free silicon single crystals with a diameter on 30 mm to 400 mm grown by floating-zone and Czochralski methods.
In this paper, we present the LearnLib, a library of tools for automata learning, which is explicitly designed for the systematic experimental analysis of the profile of available learning algorithms and corresponding...
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Network Intrusion Detection Systems (NIDS) require the ability to generalize from previously observed attacks to detect even new or slight variation records of known attacks. As an intrusion detection system can be re...
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ISBN:
(纸本)1601320752
Network Intrusion Detection Systems (NIDS) require the ability to generalize from previously observed attacks to detect even new or slight variation records of known attacks. As an intrusion detection system can be regarded as classification problem, we use Artificial Neural networks for detection. Using a benchmark study and set from the KDD (Knowledge Data Discovery and Data Mining) competition designed by DARPA and Multi-layered perceptron neural network, this Paper will aim to solve a multi class problem using MLP in to distinguish the attack records from normal ones, and also identify the attack type. In addition, it shows how to use Tikhonov regularization parameter to optimize the optimal network architecture in order to increase the system performance. The results show that the designed system is capable of classifying records with 98.34% accuracy with two hidden layers of neuron. Finally, the performance of the benchmark study is compared with our results.
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