Two of the most challenging problems that scientists and researchers face when they want to experiment with new cutting-edge algorithms are the time-consuming for encoding and the difficulties for linking them with other technologies and devices. In that sense, this article introduces the artificial organic networks toolkit for LabVIEW™ (AON-TL) from the implementation point of view. The toolkit is based on the framework provided by the artificial organic networks technique, giving it the potential to add new algorithms in the future based on this technique. Moreover, the toolkit inherits both the rapid prototyping and the easy-to-use characteristics of the LabVIEW™ software (e.g., graphical programming, transparent usage of other softwares and devices, built-in programming event-driven for user interfaces), to make it simple for the end-user. In fact, the article describes the global architecture of the toolkit, with particular emphasis in the software implementation of the so-called artificial hydrocarbon networks algorithm. Lastly, the article includes two case studies for engineering purposes (i.e., sensor characterization) and chemistry applications (i.e., blood-brain barrier partitioning data model) to show the usage of the toolkit and the potential scalability of the artificial organic networks technique.
Keywords: artificial hydrocarbon networks; blood-brain barrier partitioning; machine learning; sensor characterization; software implementation.
© 2015 Wiley Periodicals, Inc.