Capability of biological characteristics in caries prediction for children

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Caries prediction using conventional methods and artificial intelligence neural network

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A decision support system to evaluate the competitiveness of nations

The aim of this chapter is to explore methodological transparency as a viable solution to problems created by existing aggregated indices as well as to conduct a detailed analysis on the ongoing performance of nations’ competitiveness. For this purpose, a methodology composed of three steps is used. To start with, a combined clustering analysis methodology is used to assign countries to appropriate clusters. Unlike the current methods that use a single criterion, the proposed methodology uses 135 criteria for a proper classification of the countries. Relationships between the criteria and classification of the countries are determined using artificial neural networks (ANNs). ANN provides an objective method for determining the criteria weights, which are, for the most part, subjectively specified in existing methods. Finally, the countries are ranked based on weights generated in the previous step. As a final analysis, the dynamic change of the rank of the countries over years has also been investigated

Pattern recognition for bivariate process mean shifts using feature-based artificial neural network

In multivariate quality control, the artificial neural networks (ANN)-based pattern recognition schemes generally performed better for monitoring bivariate process mean shifts and provided more efficient information for diagnosing the source variable(s) compared to the traditional multivariate statistical process control charting. However, these schemes revealed disadvantages in term of reference bivariate patterns in identifying the joint effect and excess false alarms in identifying stable process condition. In this study, feature-based ANN scheme was investigated for recognizing bivariate correlated patterns. Feature-based input representation was utilized into an ANN training and testing towards strengthening discrimination capability between bivariate normal and bivariate mean shift patterns. Besides indicating an effective diagnosis capability in dealing with low correlation bivariate patterns, the proposed scheme promotes a smaller network size and better monitoring capability as compared to the raw data-based ANN scheme