Titlebook: Network Data Analytics; A Hands-On Approach K. G. Srinivasa,Siddesh G. M.,Srinidhi H. Book 2018 Springer International Publishing AG 2018

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K. G. Srinivasa,Siddesh G. M.,Srinidhi H.iables. It accounts for the question which knowledge states have to be expected when specified analysis-based learning mechanisms are applied to given instructional information. The Sepia model shows which and how qualitative physics knowledge facilitates quantitative physics problem solving. Sepia

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lerance for free dimensions and mating dimensions. Finally, it explains the classification of materials, material mechanical properties, definitions of mechanical failures, typical failure modes, and typical failure theories for ductile and brittle materials. Therefore, this chapter is a concise sum

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K. G. Srinivasa,Siddesh G. M.,Srinidhi H.nteraction by the acquisition of usage patterns. Thus, the three Sects. ., ., and . will present the state-of-the-art which is related to Chap. .. Section . will describe existing approaches for describing interactions between human users and interactive dialogue systems. Existing approaches for the

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K. G. Srinivasa,Siddesh G. M.,Srinidhi H.e of low-fidelity modeling, including the low-fidelity model setup and finding a proper balance between the model speed and accuracy. More detailed exposition of various response correction methods and their applications for solving computationally expensive design problems in various engineering fi

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K. G. Srinivasa,Siddesh G. M.,Srinidhi H.on using response correction techniques can be enhanced with adjoint sensitivities. In particular, we start by discussing how to incorporate derivative data into the surrogate modeling and optimization process. Then, we provide the formulations for adjoint-enhanced versions of space mapping (Chap. .

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ty. This chapter provides a firefly algorithm-driven simulation-optimization approach for MGA that can efficiently create multiple solution alternatives to problems containing significant stochastic uncertainties that satisfy required system performance criteria and yet are maximally different in th

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Introduction to Data Analyticse analytical architecture is first discussed. It is later followed by the different phases that are involved in the lifecycle of the data analytics project and the interconnection of Big data and Hadoop ecosystem.