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A Semantic Approach for Automatic Recovery of Software Architecture
(2014)
and reusing open source software systems. The limitation is primarily due to the lack of an automatic approach to recovering architecture models from source code. In particular, the increasing size of most open source systems makes it a challenge to manually...
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 ...
Topic Sentiment Trend Detection and Prediction for Social Media
(2020)
to analyze the social media data from the public, social media news, and newspapers. We have further extended the framework for the successful prediction of topic trends for given the current issues. For the topic trend prediction model, the deep neural...
Second chance competitive autoencoders for understanding textual data
(2021)
for selecting neurons that are eligible to enter the learning competition provided by the SCAT approach. This process prevents neurons from a high-similarity score to more than k/2 other neurons from entering the competition. We hypothesize that eliminating...
Identifying personality and topics of social media
(2019)
5 model, (2) extract the topics from Facebook and Twitter data based on each personality, (3) analyze the top essential topics, and (4) find the relation between topics and personalities. The personality would be useful to identify what kind...
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 ...
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. ...
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 ...
StoryNet: A 5W1H-based knowledge graph to connect stories
(2021)
the information from all around the globe. Even though there have been efforts to consolidate the information on a large scale like Wikipedia, Wiki Data, etc, they are devoid of any real-time happenings. With the recent advances in Natural Language Processing (NLP...
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 to the following reasons...
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 sensor data is a...
Semantic Frameworks for Document and Ontology Clustering
(University of Missouri--Kansas City, 2011-01-20)
prove the effectiveness of this model in extracting citation semantics. For the clustering stage, the Citonomy framework offers two approaches: (1) CS-VS: Combining Citation Semantics and VSM (Vector Space Model) Measures and (2) CS2CS: From Citation...
Domain Playground: Extending Deep Learning Models to Open Domain Boundaries
(2021)
the limitations of existing domain adaptation approaches. Based on our previous work, Class Representatives (CRs), the DP framework extracts features from pre-trained models and aggregates them to build a model dynamically in the form of discrete representations...
Evidence based medical query system on large scale data
(2014-07-30)
query systems based on Ontology, Medical Subject Headings (MeSH), or keyword searches are available, query systems based on evidence and effective retrieval of data from large collections of clinical data are not sufficiently available. This thesis...
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 ...
AI-based Edge Computing System for Event Based Analytics
(2021)
In recent years, the Internet of Things (IoT) has received lots of attention due to its promising applications. Along with IoT evolution, we have witnessed advanced research for edge computing and its potential benefits ...
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 ...
Distributed Collaborative Framework for Deep Learning in Object Detection
(2020)
that is designed with a single-class-single-model mechanism for multiple objects in a distributed manner. For useful grouping, we made use of the intraclass correlation from existing models during inferencing. Results from the case studies with Pascal VOC 2007...
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. ...
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 ...