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Measurement of Non-Technical Skills of Software Development Teams
(North Dakota State University, 2014)
Software Development managers recognize that project team dynamics is a key component of the success of any project. Managers can have a project with well-defined goals, an adequate schedule, technically skilled people and ...
Metrics and Tools to Guide Design of Graphical User Interfaces
(North Dakota State University, 2014)
User interface design metrics assist developers evaluate interface designs in early phase before delivering the software to end users. This dissertation presents a metric-based tool called GUIEvaluator for evaluating the ...
A New Coupling Metric: Combining Structural and Semantic Relationships
(North Dakota State University, 2014)
Maintaining object-oriented software is problematic and expensive. Earlier research has revealed that complex relationships among object-oriented software entities are key reasons that make maintenance costly. Therefore, ...
A Data Mining Approach to Radiation Hybrid Mapping
(North Dakota State University, 2014)
The task of mapping markers from Radiation Hybrid (RH) mapping experiments is typically viewed as equivalent to the traveling-salesman problem, which has combinatorial complexity. As an additional problem, experiments ...
Towards Improving P300-based Brain-Computer Interfaces: From Desktop to Mobile
(North Dakota State University, 2014)
A brain-computer interface (BCI) enables a paralyzed user to interact with an
external device through brain signals. A BCI measures identi es patterns within
these measured signals, translating such patterns into commands. ...
Improved Genetic Programming Techniques For Data Classification
(North Dakota State University, 2014)
Evolutionary algorithms are one category of optimization techniques that are inspired by processes of biological evolution. Evolutionary computation is applied to many domains and one of the most important is data mining. ...
Mapreduce-Enabled Scalable Nature-Inspired Approaches for Clustering
(North Dakota State University, 2014)
The increasing volume of data to be analyzed imposes new challenges to the data mining methodologies. Traditional data mining such as clustering methods do not scale well with larger data sizes and are computationally ...