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Semantic Frameworks for Document and Ontology Clustering
(University of Missouri--Kansas City, 2011-01-20)
The Internet has made it possible, in principle, for scientists to quickly find research papers of interest. In practice, the overwhelming volume of publications makes this a time consuming task. It is, therefore, important ...
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) ...
CSISE: cloud-based semantic image search engine
(2014-03-27)
Due to rapid exponential growth in data, a couple of challenges we face today are how to handle big data and analyze large data sets. An IBM study showed the amount of data created in the last two years alone is 90% of the ...
Evidence based medical query system on large scale data
(2014-07-30)
As huge amounts of data are created rapidly, the demand for the integration and analysis of such data has been growing steadily. It is especially essential to retrieve relevant and accurate evidence in healthcare and ...
Semantic code search and analysis
(2014-07-28)
As open source software repositories have been enormously growing, the high quality source codes have been widely available. A greater access to open source software also leads to an increase of software quality and reduces ...
A Semantic Approach for Automatic Recovery of Software Architecture
(2014)
Open source projects have been continuously growing in popularity. As a result, a number of open source projects begin to play an important role in current software development. In practice, limited assistance has been ...
Situation Aware Mobile Apps Framework
(University of Missouri--Kansas City, 2012-10-03)
Mobile devices, like smart phones or tablets, have become ubiquitous, with their adoption being driven by their immediacy and sensing capabilities. Applications, or apps, that run on portable computing devices have surged ...
Active Mobile Interface for smart health
(2013)
Computer interfaces are rapidly evolving beyond the traditional keyboard-mouse-monitor triad. The widespread availability of touch screens and voice recognition software has made it possible to execute commands in many ...
Topic network: a semantic model for effective learning
(University of Missouri--Kansas City, 2011-06-08)
There has been tremendous interest in sharing and retrieving information through the Web. A
search engine can be used to retrieve relevant web documents. However, the sheer volume of
results returned often requires ...
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 ...
VirtualMindTrial: An Intelligent Questionnaire System for Clinical Trail Recruitment
(University of Missouri--Kansas City, 2010)
The recruitment of human subjects for clinical trials research is a critically important step in the discovery of new cures for diseases. Volunteers are subjected to an elaborate questionnaire process in current recruitment ...
Social Bridge: searching beyond Friend of a Friend networks
(University of Missouri--Kansas City, 2012-06-11)
Social networking has turned into an integral constituent in our lives. There appears
to be an imperative demand for finding and linking with others to share one's day-to-day
activities. However, currently available ...
A Graph Analytics Framework for Knowledge Discovery
(2016)
In the current data movement, numerous efforts have been made to convert and normalize
a large number of traditionally structured and unstructured data to semi-structured data
(e.g., RDF, OWL). With the increasing number ...
A Pervasive Middleware for Activity Recognition with Smartphones
(2015)
Activity Recognition (AR) is an important research topic in pervasive computing. With the rapid increase in the use of pervasive devices, huge sensor data is generated from diverse devices on a daily basis. Analysis of the ...
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 ...
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. ...
A semantic framework for event-driven service composition
(University of Missouri--Kansas City, 2011-09-14)
Service Oriented Architecture (SOA) has become a popular paradigm for designing
distributed systems where loosely coupled services (i.e. computational entities) can be
integrated seamlessly to provide complex composite ...
KB4DL: Building a Knowledge Base for Deep Learning
(University of Missouri -- Kansas City, 2019)
Deep Learning (DL) has received considerable attention from the AI community. However, we
suffer from the lack of ability in interpretation and annotation of the outcomes from
extensive and exhausting learning efforts. ...
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 ...
Feature-based Analysis for Open Source using Big Data Analytics
(2015)
The open source code base has increased enormously and hence understanding the functionality of the projects has become extremely difficult. The existing approaches of feature discovery that aim to identify functionality ...