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Libri sul tema "Multidimensionnal network"

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Autore: Grafiati

Pubblicato: 1 giugno 2024

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1

Murthy, G. Rama. Multidimensional neural networks: Unified theory. New Delhi: New Age International (P) Ltd., Publishers, 2008.

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2

Arena, Paolo, Luigi Fortuna, Giovanni Muscato e Maria Gabriella Xibilia, a cura di. Neural Networks in Multidimensional Domains. London: Springer-Verlag, 1998. http://dx.doi.org/10.1007/bfb0047683.

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3

Melikov, Agassi, e Leonid Ponomarenko. Multidimensional Queueing Models in Telecommunication Networks. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08669-9.

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4

Vlachou, Akrivi. Peer-to-peer query processing over multidimensional data. New York: Springer, 2012.

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5

Henderson, Geraldine R. Brand constructs: The complementarity of consumer associative networks and multidimensional scaling. Cambridge, Mass: Marketing Science Institute, 1998.

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6

Camara, Antonio S. Environmental systems: A multidimensional approach. Oxford: Oxford University Press, 2002.

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7

Henderson, Geraldine R. Brand constructs: The complementarity of consumer associative networks and multidimensional scaling. Cambridge, Mass: Marketing Science Institute, 1998.

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8

Zuzevičiūtė, Vaiva. E-learning as a socio-cultural system: A multidimensional analysis. Hershey, PA: Information Science Reference, an imprint of IGI Global, 2014.

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9

Park, June S. Delay analysis for multidimensional queueing process in CSMA/CD local area networks. Monterey, Calif: Naval Postgraduate School, 1991.

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10

Varlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.

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Abstract (sommario):

The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge". The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

11

Paolo, Arena, a cura di. Neural networks in multidimensional domains: Fundamentals and new trends in modelling and control. London: Springer, 1998.

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12

Varlamov, Oleg. Fundamentals of creating MIVAR expert systems. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1513119.

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Abstract (sommario):

Methodological and applied issues of the basics of creating knowledge bases and expert systems of logical artificial intelligence are considered. The software package "MIV Expert Systems Designer" (KESMI) Wi!Mi RAZUMATOR" (version 2.1), which is a convenient tool for the development of intelligent information systems. Examples of creating mivar expert systems and several laboratory works are given. The reader, having studied this tutorial, will be able to independently create expert systems based on KESMI. The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

13

Ponomarenko, Leonid, e Agassi Melikov. Multidimensional Queueing Models in Telecommunication Networks. Springer, 2014.

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14

Ponomarenko, Leonid, Agassi Melikov e Aghasi Malikov. Multidimensional Queueing Models in Telecommunication Networks. Springer, 2016.

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15

Ponomarenko, Leonid, e Agassi Melikov. Multidimensional Queueing Models in Telecommunication Networks. Springer, 2014.

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16

Vlachou, Akrivi, Kjetil Nørvåg e Christos Doulkeridis. Peer-to-Peer Query Processing over Multidimensional Data. Springer, 2012.

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17

Ashby, F. Gregory, e Fabian A. Soto. Multidimensional Signal Detection Theory. A cura di Jerome R. Busemeyer, Zheng Wang, James T. Townsend e Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.2.

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Abstract (sommario):

Multidimensional signal detection theory is a multivariate extension of signal detection theory that makes two fundamental assumptions, namely that every mental state is noisy and that every action requires a decision. The most widely studied version is known as general recognition theory (GRT). General recognition theory assumes that the percept on each trial can be modeled as a random sample from a multivariate probability distribution defined over the perceptual space. Decision bounds divide this space into regions that are each associated with a response alternative. General recognition theory rigorously defines and tests a number of important perceptual and cognitive conditions, including perceptual and decisional separability and perceptual independence. General recognition theory has been used to analyze data from identification experiments in two ways: (1) fitting and comparing models that make different assumptions about perceptual and decisional processing, and (2) testing assumptions by computing summary statistics and checking whether these satisfy certain conditions. Much has been learned recently about the neural networks that mediate the perceptual and decisional processing modeled by GRT, and this knowledge can be used to improve the design of experiments where a GRT analysis is anticipated.

18

Webb, Benjamin, e Leonid Bunimovich. Isospectral Transformations: A New Approach to Analyzing Multidimensional Systems and Networks. Springer, 2014.

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19

Webb, Benjamin, e Leonid Bunimovich. Isospectral Transformations: A New Approach to Analyzing Multidimensional Systems and Networks. Springer, 2016.

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20

Webb, Benjamin, e Leonid Bunimovich. Isospectral Transformations: A New Approach to Analyzing Multidimensional Systems and Networks. Springer, 2014.

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21

Webb, Benjamin, e Leonid Bunimovich. Isospectral Transformations: A New Approach to Analyzing Multidimensional Systems and Networks. Springer, 2014.

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22

Krasnopolsky, Vladimir M. Application of Neural Networks in the Earth System Sciences: Neural Networks Emulations for Complex Multidimensional Mappings. Springer, 2013.

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23

Krasnopolsky, Vladimir M. Application of Neural Networks in the Earth System Sciences: Neural Networks Emulations for Complex Multidimensional Mappings. Springer London, Limited, 2013.

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24

Krasnopolsky, Vladimir M. The Application of Neural Networks in the Earth System Sciences: Neural Networks Emulations for Complex Multidimensional Mappings. Springer, 2014.

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25

(Editor), Paolo Arena, Luigi Fortuna (Editor), Giovanni Muscato (Editor) e Maria Gabriella Xibilia (Editor), a cura di. Neural Networks in Multidimensional Domains: Fundamentals and New Trends in Modelling and Control (Lecture Notes in Control and Information Sciences). Springer, 1998.

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26

Rafaeli, Sheizaf, e Yaron Ariel. Assessing interactivity in computer-mediated research. A cura di Adam N. Joinson, Katelyn Y. A. McKenna, Tom Postmes e Ulf-Dietrich Reips. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199561803.013.0006.

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Abstract (sommario):

Interactivity is not unique to computers or networks and cannot be reserved solely for the discussion of so-called ‘New Media’. Restricting analysis of interactivity to the domain of computerized and new technology alone problematizes comparisons with traditional media as well as with further developments of the new media. This article begins by reviewing some of the leading definitions and by highlighting the primary conceptual development of interactivity. It then discusses the correlates of interactivity and the different ways studies have measured it, and looks into some of the effects of interactivity by surveying empirical findings. The article also suggests treating interactivity as a unidimensional variable rather than a multidimensional construct. It is argued that expected, actual, and perceived interactivity are the relevant frameworks when examining the variable. Finally, the importance of information, meaning, and value, and their relation to interactivity, are highlighted.

27

Mockaitis, Thomas R. Violent Extremists. ABC-CLIO, LLC, 2019. http://dx.doi.org/10.5040/9798216032472.

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Abstract (sommario):

Written for the general reader as well as the professional, this succinct but comprehensive work examines the hybrid nature of the two violent extremist movements threatening the United States: Islamist extremism and white nationalism. Scholarship as well as popular discourse on terrorism often focuses disproportionately on specific groups without paying sufficient attention to the ideology that motivates them. This book emphasizes understanding and countering the ideology that fuels extremism over preoccupation with specific organizations such as Al Qaeda or ISIS. It sets contemporary terrorist threats in perspective, avoiding fear-mongering and political rhetoric. The book examines the nature of violent extremism today in all its forms, including lone wolves and cyber threats. Focusing on the threats posed by both international and domestic terrorism, it analyzes each in depth as a multidimensional hybrid phenomenon: each threat exists as an ideology, as distinct groups espousing that ideology, and as a network of followers. Short, easy-to-read chapters take readers through the subject matter in a clear, methodical manner. Written in an accessible style by an author who has studied terrorism for more than 30 years and provided extensive media coverage on the subject, the work is a valuable addition to the literature on violent extremism.

28

The greatest cosmic revolution in human history and the greatest philosophical thinking cognition revolution--Smith-Fangruida's book intensive reading 2018v.1. 4 Mobile version Network speed reading version (Wels/dell): Fangruida-smith space tunnel and philosophical revolution 3D multidimensional structure of philosophy Consciousness and bidirectional and multiple structures of the nervous system. Paris Newyork: internet, 2019.

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