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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 ...
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 ...
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 ...
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 ...
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 ...
StoryNet: A 5W1H-based knowledge graph to connect stories
(2021)
Stories are a powerful medium through which the human community has exchanged information since the dawn of the information age. They have taken multiple forms like articles, movies, books, plays, short films, magazines, ...
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 ...
Multi-modal emotion detection using deep learning for interpersonal communication analytics
(2019)
In recent years, deep learning technologies have been increasingly applied to generate meaningful data for advanced research in humanities and sciences. Interpersonal communication skills are crucial to success in science. ...
Identifying personality and topics of social media
(2019)
Twitter and Facebook are the renowned social networking platforms where users post, share, interact and express to the world, their interests, personality, and behavioral information. User-created content on social media ...
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 ...
SAF-DL: Semantic Analysis Framework for Deep Learning Open Source Projects
(University of Missouri--Kansas City, 2018)
There are a lot of open source projects available on the internet. Specifically, due to the
increasing interest of Deep Learning (DL), the number of DL open source projects is also
increased. This project is motivated ...
MDRED: Multi-Modal Multi-Task Distributed Recognition for Event Detection
(University of Missouri -- Kansas City, 2018)
Understanding users’ context is essential in emerging mobile sensing applications, such
as Metal Detector, Glint Finder, Facefirst. Over the last decade, Machine Learning (ML)
techniques have evolved dramatically for ...
3D Hand Pose Estimation Via a Lightweight Deep Learning Model
(University of Missouri -- Kansas City, 2018)
Deep Learning with depth cameras has enabled 3D hand pose estimation from RGBD
images. Commercial solutions like Leap Motion and Intel RealSense™ use stereoscopic sensors or
IR illumination-based methods to capture the ...
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 ...
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 ...
Context-Aware Adaptive Model for Smart Energy
(2013)
Building energy awareness and providing feedback on energy use is a vital component in transforming the behavior of individuals and communities towards a more efficient use of electric power. An enormous amount of energy ...
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 ...
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 ...
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 ...