Browsing Theses (UMKC) by Thesis Department "Computer Science (UMKC)"
Now showing items 21-40 of 108
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A Data mining study of ranking within social networks
(2014-08-27)Social networks have become very popular in the past few years and have become a significant part of our personal and professional lives. As the number of participants in social networks has grown, they have become a ... -
A Data Science Approach to Extracting Insights About Cities and Zones Using Open Government Data
(University of Missouri--Kansas City, 2017)In this research, we introduce a system that utilizes open government data and machine learning algorithms to extract meaningful insights about cities and zones in the United States. It is estimated that 4% of the ... -
Data Structures and Algorithms for Partitioning a Set into Sets of Non-Descending Cardinality
(2015)Data structures have been around since the structured programming era. Algorithms often associate with data structures. An algorithm is a sequence of instructions that accomplishes a task in a finite time period. The ... -
Decision Support System for Pull Requests Review Using Path-based Network Portrait Divergence and Visualization
(2022)Pull requests are widely used in open-source and industrial environments to contribute and assess contributions. Unlike the typical code review process, pull requests provide a more lightweight approach for committing, ... -
Deep Assertion discovery using word embeddings
(University of Missouri -- Kansas City, 2018)In recent years, there has been explosive growth in the amount of biomedical data (e.g., publications, notes from EHRs, clinical trial results), with the majority being unstructured data. As the volume of data is ... -
Deep Learning for Semi-Automated Brain Claustrum Segmentation on Magnetic Resonance (MR) Images
(University of Missouri--Kansas City, 2018)In recent years, Deep Learning (DL) has shown promising results with regard to conducting AI tasks such as computer vision and speech recognition. Specifically, DL demonstrated the state-of-the-art in computer vision ... -
Deep Open Representative Learning for Image and Text Classification
(2020)An essential goal of artificial intelligence is to support the knowledge discovery process from data to the knowledge that is useful in decision making. The challenges in the knowledge discovery process are typically due ... -
DeepSampling: Image Sampling Technique for Cost-Effective Deep Learning
(2020)Deep learning is beneficial from big data while facing computationally expensive, with an increase in data size. Some severe data issues, such as the presence of highly skewed, sparse, and imbalanced data, would substantially ... -
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 ... -
Distributed Collaborative Framework for Deep Learning in Object Detection
(2020)Object detection has gained much attention in recent years because of its ability to localize and classify the objects in videos and images that can be incorporated into many applications. Traditional object detection ... -
Distributed querying of clinical documents modeled as HL7 version 3 standard
(University of Missouri--Kansas City, 2012-01-30)We present a software tool called Collaborative Data Network (CDN) for distributed querying of clinical documents modeled using HL7 v3 standard (e.g., Clinical Document Architecture). HL7 Version 3 standard was developed ... -
Distributed RDF query processing and reasoning for big data / linked data
(2014-08-27)The Linked Data Movement is aimed at converting unstructured and semi-structured data on the documents to semantically connected documents called the "web of data." This is based on Resource Description Framework (RDF) ... -
DL-DI: A Deep Learning Framework for Distributed, Incremental Image Classification
(University of Missouri--Kansas City, 2017)Deep Learning technologies show promise for dramatic advances in fields such as image classification and speech recognition. Deep Learning (DL) is a class of Machine Learning algorithms that involves learning of multiple ... -
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 ... -
Dynamic Activity Predictions using Graph-based Neural Networks for Time Series Forecasting
(2023)Time series forecasting is a vital task in numerous fields, and traditional methods, machine learning models, and neural graph networks have been employed to improve prediction accuracy. However, these techniques need ... -
Dynamic Model Generation and Semantic Search for Open Source Projects using Big Data Analytics
(2015)Open source software is quite ubiquitous and caters to most common software needs developers come across. Many open source projects are considered better than their commercial equivalents as a larger pool of developers ... -
Enabling Large-Scale Storage and Retrieval of Whole Slide Images: A Big Data Approach
(2016)Telepathology has the potential to transform the practice of pathology and be a game changer for patients and pathologists. It can lead to wider, rapid access to expert pathologists across hospitals in the US, improve ... -
An Energy Efficient Addressing Scheme For a Static Wireless Sensor Network
(University of Missouri--Kansas City, 2011-01-20)In this thesis, we propose architecture for a static wireless sensor network where sensors are arranged into a zones with the nodes at each zone interconnected with nodes in sub zones. We also provide an energy efficient ... -
Energy efficient multi-target tracking in heterogeneous wireless sensor networks
(University of Missouri--Kansas City, 2011)Tracking multiple targets in an energy efficient way is an important challenge in wireless sensor networks (WSNs). While most of the prior work consider tracking multiple targets as execution of single target tracking ... -
Event driven querying of semantic sensor web services
(University of Missouri--Kansas City, 2012-05-15)In today's world, there is a tremendous increase in the usage of sensor technology in several fields including agriculture, medicine, and weather. Sensors either in-site or remotely placed are usually deployed in the form ...