Modeling neutrosophic variables based on particle swarm optimization and information theory measures for forest fires
Author:
Publisher
Springer Science and Business Media LLC
Subject
Hardware and Architecture,Information Systems,Theoretical Computer Science,Software
Link
http://link.springer.com/article/10.1007/s11227-018-2512-5/fulltext.html
Reference47 articles.
1. Hutto L et al (2016) Toward a more ecologically informed view of severe forest fires. Ecosphere 7(2):01255
2. Fernandes PM et al (2017) On the reactive nature of forest fire-related legislation in Portugal: a comment on Mourão and Martinho (2016). Land Use Policy 60:12–15
3. El Aziz MA, Hemdan AM, Ewees AA, Elhoseny M, Shehab A, Hassanien AE, Xiong S (2017) Prediction of Biochar yield using adaptive neuro-fuzzy inference system with particle swarm optimization. In: IEEE PES PowerAfrica Conference, June 27–30. IEEE, Accra, pp 115–120. https://doi.org/10.1109/powerafrica.2017.7991209
4. Ewees AA, El Aziz MA, Elhoseny M (2017) Social-spider optimization algorithm for improving ANFIS to predict Biochar yield. In: 8th International Conference on Computing, Communication and Networking Technologies (8ICCCNT), July 3–5. IEEE, Delhi
5. Alblowi SA, Salama AA, Eisa M (2013) New concepts of neutrosophic sets. Int J Math Comput Appl Res (IJMCAR) 3(4):95–102
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