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This paper describes the implementation of the Open Source machine learning package AZOrange.
Calling the algorithms was done using the Scikit-learn machine learning package [79, 80].
The dialogic dimensions of a hypermedia learning package are often perceived as the interactivity of its design.
This research adopts the quantitative approach and the ADDIE model is used in the development of this multimedia learning package.
We employed information gain attribute evaluator, relief attribute evaluator, and correlation-based feature selection (CFS) from Weka machine learning package [62] for the gene selection.
The implementation of the algorithm was adapted from the WEKA machine learning package [ 52].
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The approach involved the development of an e-learning package which included simulations and interactivity, then experimental testing in a case study workplace environment with the collection of both quantitative and qualitative data to assess the effectiveness of the package.
This paper discusses our modular approach to atomistic machine learning through the development of the open-source Atomistic Machine-learning Package (Amp), which allows for representations of both the total and atom-centered potential energy surface, in both periodic and non-periodic systems.
In the first instance training will be delivered during the site initiation visit but staff will also be able to access an e-learning package containing a video at any time throughout the study.
The intervention used was the Anderson Peak Performance e-learning package 'Managing Employee Pressure at Work', an established e-learning health promotion programme for managers with a focus on the six management standards domains: Change, Control, Demands, Relationship, Role and Support (http://www.andersonpeakperformance.co.uk).co.uk
Clinicians will also view an e-learning package that will be developed and issued to all sites, detailing study procedures.
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