This research suggests an add-on working compatibly with Google Forms to construct multiple-choice questions (MCQs). This add-on comprises two main components including software for constructing MCQs and a data storage for collecting data used to compose MCQs. I demonstrated effectiveness of the add-on by applying it in the context of repetition online learning in which students could learn relearn and take an exam of which MCQs generated by the add-on. At the last step of this research I suggest using a sentiment analysis method – based on natural language processing and machine learning to find environmental factors and teaching strategies suitable for the repetition online learning.

The data fusion is implement to take the survey data remotely from difference sources and have various looks to be combined to get a new agent. To get better quality of data we will present a method of fusion by wavelet transform with IHS Transformation method or PCA Transformation method. From the results showed that both IHS and PCA Transformation method able to be gotten high quality but the specifications of wave from damage is different.

Genetic algorithms are applied to the multiple products and multiple periods inventory lot-sizing problem with supplier selection under storage capacity. The objective of this research is to calculate the optimal inventory lot-sizing for each supplier and minimize the total inventory cost which includes joint purchase cost of the products transaction cost for the suppliers and holding cost for remaining inventory. It is assumed that demand of multiple products is known over a planning horizon. The problem is formulated as a mixed integer programming and is solved with genetic algorithms. Finally numerical example is provided to illustrate the solution procedure. The results determine what products to order in what quantities with which suppliers in which periods.

Accurate efficient and robust travel time prediction is crucial to the development of advanced traveler information systems for providing route guidance information. To achieve this goal this paper proposed a travel time prediction through the matching of the current speed interval pattern to that in a historical database. Speed intervals instead of speeds are considered in this study to simplify the structure of matching patterns for improving matching efficiency. In this study speed interval patterns are defined by sets of link speed intervals that are either spatially or temporarily correlated with the link considered. With the speed interval patterns the algorithm is developed for searching the historical pattern(s) that is/are the closest match with the current one. Then link speeds from these matched patterns are combined for travel time prediction. By using the GPS probe taxi data which the collected speeds are aggregated in every 5 minutes the proposed travel time prediction system is implemented in Bangkok. With the speed data from probe taxi this paper has chosen four links/paths with different geometric and flow characteristics for testing the performance of the proposed travel time prediction system. From these tests it is found that the optimal speed interval pattern should include: 1) speeds of the studied link within three preceding time intervals and; 2) speeds of links in the first connection level of the studied link. Also while the computational time is capable of real-time application the proposed prediction algorithm is more accurate under uninterrupted flow conditions.