Abstract: Interest in (STEM) Science Technology Engineering
Mathematics education especially Computer Science education has
seen a drastic increase across the country. This fuels effort towards
recruiting and admitting a diverse population of students. Thus the
changing conditions in terms of the student population, diversity
and the expected teaching and learning outcomes give the platform
for use of Innovative Teaching models and technologies. It is
necessary that these methods adapted should also concentrate on
raising quality of such innovations and have positive impact on
student learning. Light-Weight Team is an Active Learning Pedagogy,
which is considered to be low-stake activity and has very little or
no direct impact on student grades. Emotion plays a major role in
student’s motivation to learning. In this work we use the student
feedback data with emotion classification using surveys at a public
research institution in the United States. We use Actionable Pattern
Discovery method for this purpose. Actionable patterns are patterns
that provide suggestions in the form of rules to help the user achieve
better outcomes. The proposed method provides meaningful insight
in terms of changes that can be incorporated in the Light-Weight team
activities, resources utilized in the course. The results suggest how
to enhance student emotions to a more positive state, in particular
focuses on the emotions ‘Trust’ and ‘Joy’.
Abstract: Spatial outliers in remotely sensed imageries represent
observed quantities showing unusual values compared to their
neighbor pixel values. There have been various methods to detect the
spatial outliers based on spatial autocorrelations in statistics and data
mining. These methods may be applied in detecting forest fire pixels
in the MODIS imageries from NASA-s AQUA satellite. This is
because the forest fire detection can be referred to as finding spatial
outliers using spatial variation of brightness temperature. This point is
what distinguishes our approach from the traditional fire detection
methods. In this paper, we propose a graph-based forest fire detection
algorithm which is based on spatial outlier detection methods, and test
the proposed algorithm to evaluate its applicability. For this the
ordinary scatter plot and Moran-s scatter plot were used. In order to
evaluate the proposed algorithm, the results were compared with the
MODIS fire product provided by the NASA MODIS Science Team,
which showed the possibility of the proposed algorithm in detecting
the fire pixels.
Abstract: Typical Intelligent Decision Support System is 4-based, its design composes of Data Warehouse, Online Analytical Processing, Data Mining and Decision Supporting based on models, which is called Decision Support System Based on Data Warehouse (DSSBDW). This way takes ETL,OLAP and DM as its implementing means, and integrates traditional model-driving DSS and data-driving DSS into a whole. For this kind of problem, this paper analyzes the DSSBDW architecture and DW model, and discusses the following key issues: ETL designing and Realization; metadata managing technology using XML; SQL implementing, optimizing performance, data mapping in OLAP; lastly, it illustrates the designing principle and method of DW in DSSBDW.
Abstract: The vast amount of information hidden in huge
databases has created tremendous interests in the field of data
mining. This paper examines the possibility of using data clustering
techniques in oral medicine to identify functional relationships
between different attributes and classification of similar patient
examinations. Commonly used data clustering algorithms have been
reviewed and as a result several interesting results have been
gathered.