Evaluation of Principal Components Analysis (Pca) and Data Clustering Techniques (Dct) on Medical Data

The present study investigates the performance analysis of PCA filters and six clustering algorithms on the medical data (Hepatitis) which happens to be multidimensional and of high dimension with complexities much more than the conventional data. By Clustering process data reduction is achieved in order to obtain an efficient processing time to mitigate a curse of dimensionality. Usually, in medical diagnosis, the chief guiding symptoms (rubrics) coupled with the clinical tests help in accurate diagnosis of the diseases/disorders. Hence, the primary factors have maximum impact/influence on the detection of the specific disorders. Therefore, the present study is undertaken and the results predict that farthestfirst clustering algorithm happens to be the best clustering algorithm without PCA filter in general, while cobweb clustering algorithm could be preferred with PCA filter in some other medical datasets

A Comparison of different learning models used in data mining for medical data

The present study aims at investigating the different Data mining learning models for different medical data sets and to give practical guidelines to select the most appropriate algorithm for a specific medical data set. In practical situations, it is absolutely necessary to take decisions with regard to the appropriate models and parameters for diagnosis and prediction problems. Learning models and algorithms are widely implemented for rule extraction and the prediction of system behavior. In this paper, some of the well-known Machine Learning(ML) systems are investigated for different methods and are tested on five medical data sets. The practical criteria for evaluating different learning models are presented and the potential benefits of the proposed methodology for diagnosis and learning are suggested. © 2011 American Institute of Physics

Domestication of the Triticeae in the Fertile Crescent

WOS: 000268721700003About 12,000 years ago, humans began the transition from hunter-gathering to a sedentary, agriculture-based society. From its origins ill the Fertile Crescent, farming expanded throughout Europe, Asia and Africa, together with various domesticated plants and animals. Where, how and why agriculture originated is still debated. Progress has been made in Understanding plant domestication in the last few years. New insights were obtained mainly due to (I) the use of comprehensive germplasm collections covering the whole distribution area for each species; (II) the comparison of many wild and domesticated accessions for each species; (III) the identification of the wild progenitor in the wild gene pool and its comparison with domesticate descendants (IV) the use of molecular fingerprinting techniques at many loci and the access to new generation high-throughput sequencing technologies; (V) the identification and cloning of genes involved in domestication, and (VI) excavation campaigns. This chapter reviews the recent knowledge on wheat, barley and rye domestication in the Fertile Crescent and covers several issues concerning the molecular knowledge of the effects induced by domestication and breeding of these crops