Browsing 2016 UMKC Theses - Freely Available Online by Thesis Department "Computer Science (UMKC)"
Now showing items 1-8 of 8
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Detection of Tubes in Radiographs Using Canny Edge Detection and Progressive Hough Transforms
(University of Missouri--Kansas City, 2016)An automated method for detecting tubes and catheters in chest radiographs could improve patient safety and healthcare efficiency by helping radiologists to more quickly and accurately identify mal-positioned tubes. We ... -
DMLA: A Dynamic Model-Based Lambda Architecture for Learning and Recognition of Features in Big Data
(University of Missouri--Kansas City, 2016)Real-time event modeling and recognition is one of the major research areas that is yet to reach its fullest potential. In the exploration of a system to fit in the tremendous challenges posed by data growth, several big ... -
iHear – Lightweight Machine Learning Engine with Context Aware Audio Recognition Model
(University of Missouri–Kansas City, 2016)With the increasing popularity and affordability of smartphones, there is a high demand to add machine-learning engines to smartphones. However, Machine Learning with smartphones is typically not feasible due to the heavy ... -
Implementing Product Line Architecture with Code Generation and Separation
(University of Missouri--Kansas City, 2016)Software product line engineering (SPLE) emphasizes high level of reuse and mass customization of the core assets shared by a family of software products. Product line architecture (PLA) is a promising application of ... -
Parallel SPARQL Query Execution using Apache Spark
(University of Missouri--Kansas City, 2016)Semantic Web technologies such as Resource Description Framework (RDF) and SPARQL are increasingly being adopted by applications on the Web, as well as in domains such as healthcare, finance, and national security and ... -
Protected Secret Sharing and its Application to Threshold Cryptography
(University of Missouri--Kansas City, 2016)In the secret reconstruction of Shamir’s (t,n) secret sharing scheme (SS), shares released by shareholders need to be protected otherwise, non-shareholders can also obtain the secret. Key establishment protocol can ... -
SigSpace – Class-Based Feature Representation for Scalable and Distributed Machine Learning
(University of Missouri–Kansas City, 2016)In the era of big data, it is essential to explore the opportunities in discovering knowledge from big data. However, traditional machine learning approaches are not well fit to analyze the full value of big data. ... -
SigsSpace-Text: Parallel and Distributed Signature Learning in Text Analytics
(University of Missouri--Kansas City, 2016)Big data analytics uncover hidden patterns and useful information from big data. It is a complex and time-consuming process. Recent advancements in parallel and distributed approaches have led to the evolution of big data ...