Artificial Intelligence: A Guide to Intelligent Systems

By Michael Negnevitsky. 2001

2,523 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7c4a9643c701c0c91ea50fd587038f79187a0a5e

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Citations in the corpus

Application of analytical hierarchy process, frequency ratio, and certainty factor models for groundwater potential mapping using GIS (2015, methodology): “The ROC curves were then obtained by considering cumulative percentage of potential maps (on the x axis) and the cumulative percentage of groundwater occurrence (on the y axis) (Negnevitsky 2002; Pourghasemi et al. 2012b, 2012c, 2012d).”
A comparative assessment between linear and quadratic discriminant analyses (LDA-QDA) with frequency ratio and weights-of-evidence models for forest fire susceptibility mapping in China (2017, background): “The ROC curve plots the specificity (SPF) and sensitivity (SST) on the x and y axis, respectively (Negnevitsky 2002; Pourtaghi and Pourghasemi 2014).”
Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy (2019, background): “AI can be thought of as a computational algorithm that is capable of learning and identifying patterns in a given voluminous data set and then able to apply this learning to new unseen set of data in order to make an autonomous decision without any human supervision (Negnevitsky, 2011; Stone, 2016).”
Multilayer perceptron and neural networks (2009, methodology): “The algorithm for a multilayer perceptron with a hidden layer is the following [ 8 ]:”
The Efficacy of Neural Networks and Simple Technical Indicators in Predicting Stock Markets (2007, methodology): “We use supervised learning [ 11 ] to train our neural network prediction system.”
GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran (2015, background): “It represents the trade‐off between the two rates (Negnevitsky 2002).”
Cluster-head election using fuzzy logic for wireless sensor networks (2005, methodology): “3 Linguistic variables and hedges A linguistic variable is a fuzzy variable [8].”
Qualitative decision making with correlation coefficients of hesitant fuzzy linguistic term sets (2015, background): “In addition, they also use ambiguous terms to express their cognition [1].”