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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) ...
CR-GAN: Content-Based Recommender System with Conditional Generative Adversarial Networks
(University of Missouri--Kansas City, 2018)
Recommender systems have become increasingly popular by providing a wide range
of products with a variety of styles. This trend has resulted in consumers expecting more
intelligent and highly dynamic recommenders. The ...
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. ...
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 ...
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...
RUPEE: A Big Data Approach to Indexing and Searching Protein Structures
(2021)
Given the close relationship between protein structure and function, protein structure searches have long played an established role in bioinformatics. Despite their maturity, existing protein structure searches either ...
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 ...
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 ...
Siamese Network-Based Multi-Modal Deepfake Detection
(2020)
Deep learning widely applies to solve various problems in healthcare, robotics, and computer vision. Presently, an emerging deep learning application called "deepfake" has raised concerns about the multiple types of security ...
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 ...
Topic-Based Video Classification and Retrieval Using Machine Learning
(University of Missouri--Kansas City, 2017)
Machine learning has made significant progress for many real-world problems. The
Deep Learning (DL) models proposed primarily concentrate on object detection, image
classification, and image captioning. However, very ...
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 ...
Modeling and simulation of silicon photonics based optical ring resonator biosensor
(2021)
In the photonic technological platforms, the signal is carried by light rather than an electron as in conventional electronic technologies. Electronic processing of the signals is becoming restricted, particularly in the ...
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 ...
ADInsight: A Multimodal and Explainable Framework for Alzheimer's Disease Progression and Conversion Prediction
(2023)
ADInsight represents the crux of this dissertation, introducing an integrated and explainable framework centered on predicting Alzheimer's disease (AD) conversion, particularly for those at the early stage of mild cognitive ...
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. ...
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 ...
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 ...
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 ...
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 ...