New parallel algorithms for solving the decomposed linear programs are developed. Direct parallelization of the sequential algorithm results in very limited performance improvement using multiple processors. By redesigning the algorithm, we achieved more than 2*P times performance improvement over the sequential algorithm, where P is the number of processors used...
This dissertation incorporates coalition formation and probabilistic planning towards a domain-independent automated planning solution scalable to multiple heterogeneous robots in complex domains. The first research direction investigates the effectiveness of Task Fusion and introduces heuristics that improve task allocation and result in better quality plans, while requiring lower computational cost...
Although machine learning systems are often effective in real-world applications, there are situations in which they can be even better when provided with some degree of end user feedback. This is especially true when the machine learning system needs to customize itself to the end user's preferences, such as in...
Most of today’s Internet of Things (IoT) applications assume that data will be moved offdevices into centralized cloud platforms. While existing IoT systems leverage cloud-based analytics for meaningful data reasoning, the assumption that data should always be moved off the devices is problematic. The amount of data to be moved...
Frailty is a clinical syndrome characterized by decreased resilience to stressors, resulting from dysregulation across multiple physiological systems. Frailty is prevalent in elders and is associated with a wide range of adverse outcomes including death, disability, hip fracture, and falls. In the absence of a gold standard, there is a...
In the United States, falls are the leading cause of fractures and accidental death in the elderly; 70% of these fractures are women. Hispanic women account for a large population in the US, however data on falls and fractures for these women is lacking. The purpose of this systematic review...
Linear programming is a recent development in the field of Mathematics having its origin in the past seventy-five years. The purpose of this study was to identify several methods for solving linear programming problems. The algorithm for each of the methods is described in detail along with an analysis of...
Parallel solutions for two classes of linear programs are
presented. First we parallelized the two-phase revised simplex
algorithm and showed that it is possible to get linear improvement in
performance. The simplex algorithm is the best known algorithm for
solving linear programs, and we claim our result is the best...
Several problems relating to linear programming modeling have
been identified: (1) model formulation, validation and explanation
are difficult and time consuming; (2) models must be frequently
updated and debugged; (3) flexibility is needed to specify the model
schema for a class of problems as a group; and (4) significant
differences...
The goal of Inductive Learning is to produce general rules from a set of seen examples, which can then be applied to other unseen examples. ID3 is an inductive learning algorithm that can be used for the classification task. The input to the algorithm is a set of tuples of...