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

TL;DR: The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems and proposed feedforwardand feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance.

TL;DR: A comprehensive review of 196 studies which employ operational research (O.R.) and artificial intelligence (A.I.) techniques in the assessment of bank performance is presented in this article.

TL;DR: Knowledge Management: Systems and Processes as mentioned in this paper is a complete introduction to the subject of knowledge management, and incorporates technical as well as social aspects, concepts and principles, and practical examples.

TL;DR: A classification of these articles by application area reveals that neural networks are being used for a diverse range of corporate functional activities, particularly in the areas of production/operations and finance.

TL;DR: The authors demonstrate that neural networks should be another tool in the telecommunications engineer's toolbox, and offer the computational power of nonlinear techniques, while providing a natural path to efficient massively-parallel hardware implementations.

TL;DR: It is found that ANN models produce switching signals that could have been exploited by investors in an out-of-sample context to achieve superior cumulative and risk-adjusted returns when compared to either regression or a simple buy-and-hold strategy in the market indices.

TL;DR: The importance of adding self-coupling terms to the energy functions which are redundant from the encoding point of view but beneficial when it comes to ignoring local minima and to stabilizing the good solutions in the annealing process is stressed.