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Full-Text Articles in Physical Sciences and Mathematics
A Statistical Approach To Characterize And Detect Degradation Within The Barabasi-Albert Network, Mohd-Fairul Mohd-Zaid
A Statistical Approach To Characterize And Detect Degradation Within The Barabasi-Albert Network, Mohd-Fairul Mohd-Zaid
Theses and Dissertations
Social Network Analysis (SNA) is widely used by the intelligence community when analyzing the relationships between individuals within groups of interest. Hence, any tools that can be quantitatively shown to help improve the analyses are advantageous for the intelligence community. To date, there have been no methods developed to characterize a real world network as a Barabasi-Albert network which is a type of network with properties contained in many real-world networks. In this research, two newly developed statistical tests using the degree distribution and the L-moments of the degree distribution are proposed with application to classifying networks and detecting degradation …
Determining The Optimal Work Breakdown Structure For Government Acquisition Contracts, Brian J. Fitzpatrick
Determining The Optimal Work Breakdown Structure For Government Acquisition Contracts, Brian J. Fitzpatrick
Theses and Dissertations
The optimal level of Government Contract Work Breakdown Structure (G-CWBS) reporting for the purposes of Earned Value Management was inspected. The G-Score Metric was proposed, which can quantitatively grade a G-CWBS, based on a new method of calculating an Estimate At Completion (EAC) cost for each reported element. A random program generator created in R replicated the characteristics of DOD program artifacts retrieved from the Cost Analysis Data Enterprise (CADE) system. The generated artifacts were validated as a population, however validation at the demographic combination level using an artificial neural network was inconclusive. Comparative WBS forms were created for a …
A Recommendation System For Meta-Modeling: A Meta-Learning Based Approach, Can Cui, Mengqi Hu, Jeffery D. Weir, Teresa Wu
A Recommendation System For Meta-Modeling: A Meta-Learning Based Approach, Can Cui, Mengqi Hu, Jeffery D. Weir, Teresa Wu
Faculty Publications
Various meta-modeling techniques have been developed to replace computationally expensive simulation models. The performance of these meta-modeling techniques on different models is varied which makes existing model selection/recommendation approaches (e.g., trial-and-error, ensemble) problematic. To address these research gaps, we propose a general meta-modeling recommendation system using meta-learning which can automate the meta-modeling recommendation process by intelligently adapting the learning bias to problem characterizations. The proposed intelligent recommendation system includes four modules: (1) problem module, (2) meta-feature module which includes a comprehensive set of meta-features to characterize the geometrical properties of problems, (3) meta-learner module which compares the performance of instance-based …