Question generation is the task of generating good quality assessment item automatically from a given input corpus to examine the content knowledge of the learner (Heilman & Smith, 2010b; Kunichika, Katayama, Hirashima, & Takeuchi, 2001).
A well designed identification experiment can generate good quality models while incurring significant costs.
The HMM-based text-to-speech (SSS) approach has been shown to generate good quality and intelligible speech [1].
Image analysis is an important part of the process of generating good quality microarray data, prior to data mining.
In order to generate good quality element while keeping the updated problem still solvable, the design and definition of the neural network is more complex than those in the traditional classification problems.
A number of factors may explain the difficulties in generating good quality information.
In our study, for example, around 30% of samples collected were suitable for flow cytometry while 40% were suitable for microarray which demonstrates the difficulty in obtaining sample sizes to generate good quality data.
We have generated good quality draft genome sequences for a strain of Pae recently isolated from a bleeding canker on diseased European horse chestnut in Britain (strain 2250) as well as the Indian type strain of Pae that causes a leaf-spot disease on Indian horse chestnut [8], [9].
Thus, 232 primer sets generated good quality of sequences both for Forrest and Williams 82.
Overall, the PGM and MiSeq approaches generated good quality sequence data rapidly with relatively high coverage.
Public views have been found to generate good quality, clinically relevant research [ 31– 31].
