Have a personal or library account? Click to login
An Environment for Collective Perception based on Fuzzy and Semantic Approaches Cover

An Environment for Collective Perception based on Fuzzy and Semantic Approaches

Open Access
|Feb 2018

References

  1. [1] F. Antunes and J. Costa, Integrating decision support and social networks, Advances in Human-Computer Interaction, vol. 2012, no. 574276, 2012.
  2. [2] Smarter Cities - New cognitive approaches to long-standing challenges, http://www.ibm.com/smarterplanet/us/en/smarter_cities/overview/, accessed: 2017-02-10.
  3. [3] D. Doran, K. Severin, S. Gokhale, and A. Dagnino, Social media enabled human sensing for smart cities, AI Communications, vol. 29, no. 1, pp. 57–75, 2016.10.3233/AIC-150683
  4. [4] G. P. Hancke, G. P. Hancke Jr et al., The role of advanced sensing in smart cities, Sensors, vol. 13, no. 1, pp. 393–425, 2012.10.3390/s130100393
  5. [5] G. R. Ceballos and V. M. Larios, A model to promote citizen driven government in a smart city: Use case at gdl smart city, in 2016 IEEE International Smart Cities Conference (ISC2), pp. 1–6, 2016.10.1109/ISC2.2016.7580873
  6. [6] P. Zeile, B. Resch, L. Dörrzapf, J.-P. Exner, G. Sagl, A. Summa, and M. Sudmanns, Urban emotions–tools of integrating people perception into urban planning, in REAL CORP 2015. PLAN TOGETHER–RIGHT NOW–OVERALL. From Vision to Reality for Vibrant Cities and Regions. Proceedings of 20th International Conference on Urban Planning, Regional Development and Information Society. CORP–Competence Center of Urban and Regional Planning, pp. 905–912, 2015.
  7. [7] A. Vakali, D. Chatzakou, V. A. Koutsonikola, and G. Andreadis, Social data sentiment analysis in smart environments-extending dual polarities for crowd pulse capturing. in DATA, pp. 175–182, 2013.
  8. [8] D. Toti and M. Rinelli, On the road to speed-reading and fast learning with CONCEPTUM, in Proceedings - 2016 International Conference on Intelligent Networking and Collaborative Systems, IEEE INCoS 2016, pp. 1–6, 2016.10.1109/INCoS.2016.30
  9. [9] S. Baccianella, A. Esuli, and F. Sebastiani, Senti- WordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining, in Proceedings of the Seventh Conference on International Language Resources and Evaluation (LREC’10). Valletta, Malta: European Language Resources Association (ELRA), 2010.
  10. [10] G. D’Aniello, A. Gaeta, M. Gaeta, V. Loia, and M. Reformat, Collective awareness in Smart City with Fuzzy Cognitive Maps and Fuzzy sets, in 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2016.10.1109/FUZZ-IEEE.2016.7737875
  11. [11] R. R. Yager and M. Z. Reformat, Looking for likeminded individuals in social networks using tagging and e fuzzy sets, Fuzzy Systems, IEEE Transactions on, vol. 21, no. 4, pp. 672–687, 2013.10.1109/TFUZZ.2012.2227263
  12. [12] B. Kosko, Fuzzy cognitive maps, International journal of man-machine studies, vol. 24, no. 1, pp. 65–75, 1986.10.1016/S0020-7373(86)80040-2
  13. [13] G. D’Aniello, V. Loia, and F. Orciuoli, A multiagent fuzzy consensus model in a situation awareness framework, Applied Soft Computing, vol. 30, pp. 430 – 440, 2015.10.1016/j.asoc.2015.01.061
  14. [14] M. Olazabal and U. Pascual, Use of fuzzy cognitive maps to study urban resilience and transformation, Environmental Innovation and Societal Transitions, 2015.10.1016/j.eist.2015.06.006
  15. [15] U. Özesmi and S. L. Özesmi, Ecological models based on peoples knowledge: a multi-step fuzzy cognitive mapping approach, Ecological Modelling, vol. 176, no. 1, pp. 43–64, 2004.10.1016/j.ecolmodel.2003.10.027
  16. [16] F. Habib and A. Shokoohi, Classification and resolving urban problems by means of fuzzy approach, World Academy of Science, Engineering and Technology, vol. 36, pp. 894–901, 2009.
  17. [17] D. Toti, AQUEOS: A system for question answering over semantic data, in Proceedings - 2014 International Conference on Intelligent Networking and Collaborative Systems, IEEE INCoS 2014, pp. 716–719, 2014.10.1109/INCoS.2014.13
  18. [18] D. Milne and I. H. Witten, An open-source toolkit for mining wikipedia, Artif. Intell., vol. 194, pp. 222–239, Jan. 2013. [Online]. Available: http://dx.doi.org/10.1016/j.artint.2012.06.00710.1016/j.artint.2012.06.007
  19. [19] N. Capuano, C. De Maio, S. Salerno, and D. Toti, A methodology based on commonsense knowledge and ontologies for the automatic classification of legal cases, in ACM International Conference Proceeding Series, 2014.10.1145/2611040.2611048
  20. [20] N. Capuano, A. Longhi, S. Salerno, and D. Toti, Ontology-driven generation of training paths in the legal domain, International Journal of Emerging Technologies in Learning, vol. 10, no. 7, pp. 14–22, 2015.10.3991/ijet.v10i7.4609
  21. [21] V. Basile and M. Nissim, Sentiment analysis on Italian tweets, in Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Atlanta, Georgia: Association for Computational Linguistics, pp. 100–107, 2013. [Online]. Available: http://www.aclweb.org/anthology/W13-1614
  22. [22] R. R. Yager, On ordered weighted averaging aggregation operators in multicriteria decisionmaking, Systems, Man and Cybernetics, IEEE Transactions on, vol. 18, no. 1, pp. 183–190, 1988.10.1109/21.87068
  23. [23] T.-A. Shiau and J.-S. Liu, Developing an indicator system for local governments to evaluate transport sustainability strategies, Ecological Indicators, vol. 34, pp. 361 – 371, 2013.10.1016/j.ecolind.2013.06.001
Language: English
Page range: 191 - 210
Submitted on: Sep 5, 2017
Accepted on: Aug 30, 2017
Published on: Feb 9, 2018
Published by: SAN University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2018 Giuseppe D’Aniello, Matteo Gaeta, Francesca Loia, Marek Reformat, Daniele Toti, published by SAN University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.