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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 an efficient and timely manner. Processing observation data is surpassed by semantically annotating data and using rule based reasoning as an inference tool. ECA enables a shift of the main focus from a large cluster section to a precise smaller section...
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 ...
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 ...
Explainable AI framework through Multi-Context Multi-Dimensional Graph Neural Network
(2023)
In this research, we explored the multifaceted realm of digital communication, emphasizing social media channels such as Twitter and Reddit, complemented by conventional data-gathering techniques like focus group discussions and structured digital...
Software Analytics for Improving Program Comprehension
(2021)
Program comprehension is an essential part of software development and maintenance. Traditional methods of program comprehension, such as reviewing the codebase and documentation, are still challenging for understanding ...
Semantic Frameworks for Document and Ontology Clustering
(University of Missouri--Kansas City, 2011-01-20)
methods of clustering has focused mainly on the algorithm per se, with relatively less emphasis on feature selection and similarity measures. The latter can significantly impact the accuracy of clustering, as well as the runtime of clustering. Also...
Domain Playground: Extending Deep Learning Models to Open Domain Boundaries
(2021)
Deep learning models have demonstrated monumental performance in classification tasks but require extensive data and training procedures to converge. Additionally, the performance is only guaranteed when there is no domain ...
ADInsight: A Multimodal and Explainable Framework for Alzheimer's Disease Progression and Conversion Prediction
(2023)
an exploration into cognitive focus and visual identification, which are essential elements in the development of Alzheimer's disease. Benefiting both clinicians and patients, CVRT paves the way for innovative treatment strategies. In summary, our ADInsight...
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 ...
Deep Open Representative Learning for Image and Text Classification
(2020)
models makes it hard to interpret what they produce. It is essential to bridge the gap between the models and their support in decisions with something potentially understandable and interpretable. To address the gap, we focus on designing critical...
On-chip Voltage Regulator– Circuit Design and Automation
(2021)
as most promising for on-chip implementation: (i) the low-drop-out (LDO) regulator and (ii) the switched-capacitor (SC)regulator. The first part of our research mainly focused on the LDO regulator. Inspired by the recent surge of interest for cap...
GraphEvo: Evaluating Software Evolution Using Machine Learning Based Call Graph Analytics And Network Portrait Divergence
(2022)
Understanding software evolution is essential for software development tasks, including debugging, maintenance, and testing. Unfortunately, as software changes, it becomes more prominent and more complicated, which makes ...
Topic Sentiment Trend Detection and Prediction for Social Media
(2020)
Social media often plays a crucial role in disseminating information to warn the public about health concerns. Opioid addiction has become of the significant outbreaks in the United States. Studying opioid issues in social ...
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 ...
Automated End-to-End Management of the Deep Learning Lifecycle
(2020)
Deep learning has improved the state-of-the-art results in an ever-growing number of domains. This success heavily relies on the development of deep learning models--an experimental, iterative process that produces tens ...
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 ...
Second chance competitive autoencoders for understanding textual data
(2021)
Every day, an enormous amount of text data is produced. Sources of text data include news, social media, emails, text messages, medical reports, scientific publications, and fiction. To keep track of this data, there are ...
Class Representative Projection for Text-based Zero-Shot Learning
(2020)
There have been significant advances in supervised machine learning and enormous benefits from deep learning for a range of diverse applications. Despite the success of deep learning, in reality, very few works have shown ...
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 ...