Evaluation of Breast Cancer Tumor Classification with Unconstrained Functional Networks Classifier

This paper proposes functional networks as an unconstrained classifier scheme for multivariate data to diagnose the breast cancer tumor. The performance of this new technique is measured using two well known databases under the minimum description length criterion, the results are compared with the most common existing classi- fiers in both computer science and statistics literatures. This new classifier shown reliable and efficient results with better correct classification rate, and much less computational time

Prediction of Pile Capacity Parameters using Functional Networks and Multivariate Adaptive Regression Splines

The soil is found to vary spatially everywhere in nature. As such, it’s generally a difficult task to predict the nature of soil for any particular application with traditional methods like experimental, empirical, finite element or finite difference analysis. Analysis with traditional methods taking into factor all the varying inputs makes it a complex problem, which is difficult to solve and comprehend. This necessitates the use of statistical modelling tool for the solution to problems concerning soil. Pile foundations are widely used in civil engineering construction. However, owing to the variable behavior of soil and the dependence of vertical pile load capacity on numerous factors, there does not exist a definite equation which can estimate the pile load accurately and include all the factors comprehensively. Artificial intelligence techniques are known to successfully develop accurate prediction models with the obtained input and output data form laboratory experiments or field data. Therefore, in the present study, Functional Network (FN) and multivariate adaptive regression splines (MARS) were used to develop prediction models for the lateral load capacity of piles, vertical capacity of driven piles in cohesionless soil, friction capacity of piles in clay, axial capacity of piles and pullout capacity of ground anchors. In all the cases, prediction equations were provided for the developed models which were found to be simple and can be easily used by practicing geotechnical engineers. A standalone application was also developed to facilitate the calculation of required pile capacity parameters based on the prediction equations. The prediction models built by FN and MARS were compared with different artificial intelligence (AI) techniques and empirical models available in the literature and FN and MARS were found to invariably outperform other AI techniques and empirical methods