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Books on the topic 'Network extraction'

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Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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1

Trigueiros, Duarte. Neural network based methods in the extraction of knowledge from accounting and financial data. Norwich: University of East Anglia, 1991.

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2

Mokhlesabadifarahani, Bita, and Vinit Kumar Gunjan. EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-320-0.

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3

Berry, R. H. The application of neural network based methods to the extraction of knowledge from accounting reports: A classificationstudy. Norwich: School of Information Systems, University of East Anglia, 1991.

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4

Supervised and unsupervised pattern recognition: Feature extraction and computational intelligence. Boca Raton, Fla: CRC Press, 2000.

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5

Semantic network analysis: Techniques for extracting, representing and querying media content. Charleston, SC: BookSurge, 2008.

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6

DNIS 2010 (2010 Aizu Daigaku). Databases in networked information systems: 6th international workshop, DNIS 2010, Aizu-Wakamatsu, Japan, March 29-31, 2010 : proceedings. Berlin: Springer, 2010.

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7

Gunjan, Vinit Kumar, and Bita Mokhlesabadifarahani. EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction. Springer, 2015.

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8

Gunjan, Vinit Kumar, and Bita Mokhlesabadifarahani. EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction. Springer London, Limited, 2015.

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9

Bianconi, Ginestra. Multilayer Networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.001.0001.

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Multilayer networks are formed by several networks that interact with each other and co-evolve. Multilayer networks include social networks, financial markets, transportation systems, infrastructures and molecular networks and the brain. The multilayer structure of these networks strongly affects the properties of dynamical and stochastic processes defined on them, which can display unexpected characteristics. For example, interdependencies between different networks of a multilayer structure can cause cascades of failure events that can dramatically increase the fragility of these systems; spreading of diseases, opinions and ideas might take advantage of multilayer network topology and spread even when its single layers cannot sustain an epidemic when taken in isolation; diffusion on multilayer transportation networks can significantly speed up with respect to diffusion on single layers; finally, the interplay between multiplexity and controllability of multilayer networks is a problem with major consequences in financial, transportation, molecular biology and brain networks. This field is one of the most prosperous recent developments of Network Science and Data Science. Multilayer networks include multiplex networks, multi-slice temporal networks, networks of networks, interdependent networks. Multilayer networks are characterized by having a highly correlated multilayer network structure, providing a significant advantage for extracting information from them using multilayer network measures and centralities and community detection methods. The multilayer network dynamics (including percolation, epidemic spreading, diffusion, synchronization, game theory and control) is strongly affected by the multilayer network topology. This book will present a comprehensive account of this emerging field.
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10

Irarrazaval, Felipe, and Martín Arias-Loyola. Resource Peripheries in the Global Economy: Networks, Scales, and Places of Extraction. Springer International Publishing AG, 2021.

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11

Irarrázaval, Felipe, and Martín Arias-Loyola. Resource Peripheries in the Global Economy: Networks, Scales, and Places of Extraction. Springer International Publishing AG, 2022.

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12

Folino, Antonietta, and Roberto Guarasci, eds. Knowledge Organization and Management in the Domain of Environment and Earth Observation (KOMEEO). Ergon – ein Verlag in der Nomos Verlagsgesellschaft, 2022. http://dx.doi.org/10.5771/9783956508752.

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The volume contains the proceedings of the KOMEEO (Knowledge Organization and Management in the domain of Environment and Earth Observation) international conference, organized in the field of the European ERA-PLANET (The European Network for observing our changing Planet) H2020 program. Papers present research projects and experiences related to different aspects of organizing knowledge in the environmental domain, which nowadays is receiving great attention from the European Union. In particular, they address topics related to Knowledge Organization Systems (KOSs), to their application in specific contexts, to the extraction of metadata, to the achievement of semantic interoperability. With contributions by Richard Absalom, Prof. Stefano Allegrezza, Dr. Giovanna Aracri, Armando Bartucci, Dr. Assunta Caruso, Prof. Eugenio Casario, Dr. Maria Teresa Chiaravalloti, Sergio Cinnirella, Martin Critelli, Sabina Di Franco, Prof. Antonietta Folino, Dr. Claudia Lanza, Francesca M.C. Messiniti, Prof. Alexander Murzaku, Dr. Anna Perri, Dr. Erika Pasceri, Paolo Plini, Prof. Anna Rovella and Rosamaria Salvatori.
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13

Falkow, Sally. Social Media Intelligence: Extracting Knowledge from the Fire Hose of Conversations. Pearson Education, Limited, 2013.

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14

Yang, Sijia, and Sandra González-Bailón. Semantic Networks and Applications in Public Opinion Research. Edited by Jennifer Nicoll Victor, Alexander H. Montgomery, and Mark Lubell. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190228217.013.14.

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Semantic networks represent and model messages and discourse as a relational structure, emphasizing patterns of interdependence among semantic units or actors-concepts. This chapter traces the epistemological roots of semantic networks, then illustrates with examples how this approach can contribute to the study of political rhetoric or opinions. It focuses on three levels of analysis: cognitive mapping at the individual level, discourse analysis at the interpersonal level, and framing and salience at the collective level. Drawing from the rich literature on natural language processing and machine learning, the chapter introduces readers to essential methodological considerations when extracting and building up semantic networks from textual data. It also offers a discussion on the relevance of semantic networks to analyzing public opinion, especially as it manifests in discursive and deliberative theories of democracy.
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15

Find it fast: Extracting expert information from social networks, big data, tweets, and more. 6th ed. CyberAge Books/Information Today, Inc., 2015.

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16

Tjoa, A. Min, Andreas Holzinger, Edgar Weippl, and Peter Kieseberg. Machine Learning and Knowledge Extraction: First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, ... Springer, 2017.

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17

Christopherson, Susan. Outside Regional Paths: Constructing an Economic Geography of Energy Transitions. Edited by Gordon L. Clark, Maryann P. Feldman, Meric S. Gertler, and Dariusz Wójcik. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198755609.013.52.

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Moving beyond theories of socio-technical adaptation, a new economic geography of energy transitions is developing that contributes to a deeper understanding of adaptation and change in energy systems. This new geography of energy transitions draws on concepts in evolutionary economic geography but moves beyond regional analysis to recognize the nation state as a critical venue for strategic action by firms. The dependence on the nation state for access to the resource; financing of exploration and production; favourable regulatory oversight; and the infrastructure to transport the commodity to profitable markets, make it the essential venue for strategic action. Drawing on the US case of shale gas and oil extraction, this chapter argues that, despite the emergence of global production networks in the oil and gas industry, national-scale governance remains central to understanding energy transitions.
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18

Tjoa, A. Min, Andreas Holzinger, Edgar Weippl, and Peter Kieseberg. Machine Learning and Knowledge Extraction: Third IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2019, ... Springer, 2019.

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19

Tjoa, A. Min, Andreas Holzinger, Edgar Weippl, and Peter Kieseberg. Machine Learning and Knowledge Extraction: Second IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International Cross-Domain Conference, CD-MAKE 2018, ... Springer, 2018.

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20

Holzinger, Andreas, Edgar Weippl, Peter Kieseberg, and A. Min Tjoa. Machine Learning and Knowledge Extraction: 6th IFIP TC 5, TC 12, WG 8. 4, WG 8. 9, WG 12. 9 International Cross-Domain Conference, CD-MAKE 2022, Vienna, Austria, August 23-26, 2022, Proceedings. Springer International Publishing AG, 2022.

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21

Tjoa, A. Min, Andreas Holzinger, Edgar Weippl, and Peter Kieseberg. Machine Learning and Knowledge Extraction: 5th IFIP TC 5, TC 12, WG 8. 4, WG 8. 9, WG 12. 9 International Cross-Domain Conference, CD-MAKE 2021, Virtual Event, August 17-20, 2021, Proceedings. Springer International Publishing AG, 2021.

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22

Machine Learning and Knowledge Extraction: 4th IFIP TC 5, TC 12, WG 8. 4, WG 8. 9, WG 12. 9 International Cross-Domain Conference, CD-MAKE 2020, Dublin, Ireland, August 25-28, 2020, Proceedings. Springer International Publishing AG, 2020.

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