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Introducing Artificial Neural Networks through a Spreadsheet Model
Author(s) -
Rienzo Thomas F.,
Athappilly Kuriakose K.
Publication year - 2012
Publication title -
decision sciences journal of innovative education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 19
eISSN - 1540-4609
pISSN - 1540-4595
DOI - 10.1111/j.1540-4609.2012.00363.x
Subject(s) - artificial neural network , computer science , artificial intelligence , software , point (geometry) , machine learning , transformation (genetics) , programming language , mathematics , biochemistry , chemistry , geometry , gene
Business students taking data mining classes are often introduced to artificial neural networks (ANN) through point and click navigation exercises in application software. Even if correct outcomes are obtained, students frequently do not obtain a thorough understanding of ANN processes. This spreadsheet model was created to illuminate the roles of the following ANN parameters: weights, learning rates, threshold functions, and transformation functions. The spreadsheet ANN model project is given early in the semester, just after ANN is introduced. Students can see effects of ANN parameters as they make changes to spreadsheet model inputs, greatly enhancing discussion of ANN processes. After working with the spreadsheet model, students have expressed an appreciation for decisions based on patterns of historic data, and they like the ability to peek “behind the curtain” at processes of predictive software packages.