Agriculture Crop Suitability Prediction Using Rough Set on Intuitionistic Fuzzy Approximation Space and Neural Network
A. Anitha,D. P. Acharjya +1 more
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In this article, the authors considered the overall geographical space verses population in India, 7% of population is chronicled in Tamilnadu, with 3% of water and 4% of land.About:
This article is published in Fuzzy Information and Engineering.The article was published on 2019-01-02 and is currently open access. It has received 1 citations till now. The article focuses on the topics: Population & Rough set.read more
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Precipitation prediction by integrating Rough Set on Fuzzy Approximation Space with Deep Learning techniques
Tishya Manna,A. Anitha +1 more
TL;DR: In this paper , the Rough Set on Fuzzy Approximation Space (RSFAS) with a deep learning (DL) technique was used to predict the precipitation level in the southern coastal areas of India in a seasonal way.
References
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MonographDOI
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
Journal ArticleDOI
Intuitionistic fuzzy sets
TL;DR: Various properties are proved, which are connected to the operations and relations over sets, and with modal and topological operators, defined over the set of IFS's.
Journal ArticleDOI
An introduction to computing with neural nets
TL;DR: This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification and exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components.
Journal ArticleDOI
Rough fuzzy sets and fuzzy rough sets
Didier Dubois,Henri Prade +1 more
TL;DR: It is argued that both notions of a rough set and a fuzzy set aim to different purposes, and it is more natural to try to combine the two models of uncertainty (vagueness and coarseness) rather than to have them compete on the same problems.
Proceedings ArticleDOI
Theory of the backpropagation neural network
TL;DR: A speculative neurophysiological model illustrating how the backpropagation neural network architecture might plausibly be implemented in the mammalian brain for corticocortical learning between nearby regions of the cerebral cortex is presented.