Neural networks in business: techniques and applications for the operations researcher

By K. Smith‐Miles, Jatinder N. D. Gupta. Computers & Operations Research, 2000

311 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/731b7025133e4f79a46b414755d2e9cf643b102f

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In Control and Systems Engineering

Citations in the corpus

Machine Learning in Financial Crisis Prediction: A Survey (2012, background): “This corresponds to important survey literature, depicting the popularity of these techniques in many domain problems, [17], [38], [126], [155], [156], [169].”
Neural network ensemble strategies for financial decision applications (2005, background): “The reader is referred to Smith and Gupta [20] for a recent survey of the application of neural networks in a diverse range of operations research problems that include &nancial forecasting and creditworthiness.”
Artificial neural networks applications in economics and management research: An exploratory literature review (2018, background): “Smith and Gupta [13] presented an overview of business applications of neural networks with a focus on marketing, retail, banking and finance, insurance, telecommunications, operations management, and other industries.”
Forecasting and operational research: a review (2008, background): “ANN are frequently employed in a wide range of DM applications (Smith and Gupta, 2000) following their introduction to the OR community by Sharda (1994).”
State-of-the-art in artificial neural network applications: A survey (2018, methodology): “The comparison was made based on (i) author(s)/year of publication (ii) ANN modeling (iii) ANN area of application (iii) studied contribution to human challenges (with references to Supplementary Table 2) [11, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, ”
Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey (2010, unknown): “For instance, Soteriou and Zenios (1999b), Golany and Storbeck (1999) and Athanassopoulos and Giokas (2000) include quality variables in efficiency analysis, whereas Portela and Thanassoulis (2007) use a post hoc analysis to compare service quality index with efficiency measures.”
Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches (2010, background): “This is because approximately 95% of business application studies utilize MLP [48].”
Artificial neural networks in business: Two decades of research (2016, background): “Smith and Gupta [350] presented interesting developments in the use of artificial intelligence for the operations research problems.”