A Prescriptive Stock Market Investment Strategy for the Restaurant Industry using an Artificial Neural Network Methodology

Weckman, Gary R.; Dravenstott, Ronald W.; Young, William A.; Ardjmand, Ehsan; Millie, David F.; Snow, A. J.

doi:10.4018/ijban.2016010101

Cited by 9 publications

(2 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Las redes neuronales artificiales proporcionaron un 93,13% de precisión entre los clientes investigados, en comparación con el 54% obtenido con las máquinas vectores de soporte. Weckman et al (2020) implementaron un modelo de predicción de acciones por medio de una red neuronal artificial para la industria restaurantera; con este estudio obtuvieron mejores resultados en comparación con otros métodos, como la predicción del analista, regresión lineal múltiple y estrategias de comercio de compra y retención.…”

Section: Estudios Turísticos Y Las Redes Neuronales Artificialesunclassified

“…Por otro lado, la metodología propuesta en este documento también complementa los trabajos realizados por Zheng et al (2013) y Weckman et al (2020), ya que, además de poder realizar predicciones con las redes neuronales artificiales, otra aplicación se fundamenta en el análisis de la importancia de los factores de un fenómeno de estudio (Valle-Cruz et al, 2020).…”

Section: Principales Hallazgosunclassified

See 1 more Smart Citation

Percepción de la calidad en restaurantes: un análisis mixto con redes neuronales

López-Chau

Muñoz-Chávez²,

Valle-Cruz

2022

estud.gerenc.

View full text Add to dashboard Cite

Este estudio se centra en identificar los factores que influyen en la percepción de la calidad del consumidor en restaurantes de servicio a mesa en el pueblo mágico Real del Monte, Hidalgo, México. La metodología se basa en dos perspectivas, en primer lugar, en el análisis de los factores más importantes de los resultados de una encuesta de 22 ítems por medio de redes neuronales artificiales aplicada a 320 comensales y, en segundo lugar, en la aplicación de entrevistas semiestructuradas a ocho comensales. Los hallazgos muestran que los aspectos fundamentales que influyen en la percepción de los consumidores son la capacidad del personal para responder preguntas, la música de fondo, así como la calidad y el sabor de los alimentos.

show abstract

Section: Estudios Turísticos Y Las Redes Neuronales Artificialesunclassified

Section: Principales Hallazgosunclassified

Percepción de la calidad en restaurantes: un análisis mixto con redes neuronales

López-Chau

Muñoz-Chávez²,

Valle-Cruz

2022

estud.gerenc.

View full text Add to dashboard Cite

show abstract

Artificial Neural Networks and Their Applications in Business

Bihl

Young

Weckman

2018

Encyclopedia of Information Science and Technology, Fourth Edition

View full text Add to dashboard Cite

Despite the natural advantage humans have for recognizing and interpreting patterns, large and complex datasets, as in Big Data, preclude efficient human analysis. Artificial neural networks (ANNs) provide a family of pattern recognition approaches for prediction, clustering and classification applicable to KDD with ANN model complexity ranging from simple (for small problems) highly complex (for large issues). To provide a starting point for readers, this chapter first describes foundational concepts that relate to ANNs. A listing of commonly used ANN methods, heuristics, and criteria for initializing ANNs is then discussed. Common pre- and post- data processing methods for dimensionality reduction and data quality issues are then described. The authors then provide a tutorial example of ANN analysis. Finally, the authors list and describe applications of ANNs to specific business related endeavors for further reading.

show abstract

Artificial Neural Networks and Their Applications in Business

Bihl

Young

Weckman

2019

Advanced Methodologies and Technologies in Artificial Intelligence, Computer Simulation, and Human-Computer Interaction

View full text Add to dashboard Cite

Despite the natural advantage humans have for recognizing and interpreting patterns, large and complex datasets, as in big data, preclude efficient human analysis. Artificial neural networks (ANNs) provide a family of pattern recognition approaches for prediction, clustering, and classification applicable to KDD with ANN model complexity ranging from simple (for small problems) to highly complex (for large issues). To provide a starting point for readers, this chapter first describes foundational concepts that relate to ANNs. A listing of commonly used ANN methods, heuristics, and criteria for initializing ANNs are then discussed. Common pre- and post-data processing methods for dimensionality reduction and data quality issues are then described. The authors then provide a tutorial example of ANN analysis. Finally, the authors list and describe applications of ANNs to specific business-related endeavors for further reading.

show abstract

A Prescriptive Stock Market Investment Strategy for the Restaurant Industry using an Artificial Neural Network Methodology

Cited by 9 publications

References 35 publications

Percepción de la calidad en restaurantes: un análisis mixto con redes neuronales

Percepción de la calidad en restaurantes: un análisis mixto con redes neuronales

Artificial Neural Networks and Their Applications in Business

Artificial Neural Networks and Their Applications in Business

Contact Info

Product

Resources

About