In this chapter we present a BI application delivered as a service on-demand. In particular, it is a data mining service that aims to help instructors involved in distance education to discover their students’ behavior profiles and models about how they navigate and work in their virtual courses offered in Learning Content Management Systems such as Blackboard or Moodle. The main characteristic is that the users do not require data mining knowledge to use the service; they only have to send a data file according to one of the templates provided by the system and request the results. The service carries out the KDD process itself. Furthermore, the service provides an interface based on Web services, which can be called by external software. In short, the chapter talks about the necessity of a service with these characteristics and includes the description of its architecture and its method of operation as well as a discussion about some of the patterns it offers and how these provide instructors valuable knowledge to make decisions.
The present research is aimed at obtaining and experimentally validating an artificial neural network to predict the hardness of TX304HB steel tubes subjected to shot peening. The experimental scope consisted of 228 tubes. Seven variables were considered as input parameters: rotation speed, line speed, material flow, air pressure, the size of the nozzle, and the internal diameter of the tubes; the experimental data demonstrated the need for considering the material bulk hardness as an input variable. One specimen from each tube was taken and subjected to Vickers microhardness tests at a depth of 40 μm from the interior circumference as well as a depth beyond the influence of the shot peening (bulk condition). The hardness was proven to follow Gaussian distribution. Therefore, a neural network was designed and tuned to provide the mean and standard deviation of the hardness for each of the combinations of input variables. The neural networks designed in this way were able to faithfully reproduce the experimental results. Several statistical parameters were determined to measure the goodness of the fitting. Thus, the correlation between experimental and predicted numerical values of mean hardness yields R2 = 0.7651 and a mean absolute percentage error of 1.547 % for the training data set and 0.7402 and 2.054 % for the test data set. The corresponding values for the prediction of the standard deviation are R2 = 0.4713 and 17.946 % for the training set and R2 = 0.6847 and 17.071 % for the test set.
In this chapter we present a BI application delivered as a service on-demand. In particular, it is a data mining service that aims to help instructors involved in distance education to discover their students’ behavior profiles and models about how they navigate and work in their virtual courses offered in Learning Content Management Systems such as Blackboard or Moodle. The main characteristic is that the users do not require data mining knowledge to use the service; they only have to send a data file according to one of the templates provided by the system and request the results. The service carries out the KDD process itself. Furthermore, the service provides an interface based on Web services, which can be called by external software. In short, the chapter talks about the necessity of a service with these characteristics and includes the description of its architecture and its method of operation as well as a discussion about some of the patterns it offers and how these provide instructors valuable knowledge to make decisions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.