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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. ...
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 ...
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 ...
Multi-Modal Topic Sentiment Analytics for Twitter
(University of Missouri -- Kansas City, 2018)
Sentiment analysis has proven to be very successful in text applications. Social media
is also considered a quite rich source to get data regarding user’s behaviors and
preference. Identifying social context would make ...
SigsSpace-Text: Parallel and Distributed Signature Learning in Text Analytics
(University of Missouri--Kansas City, 2016)
Big data analytics uncover hidden patterns and useful information from big data. It is a complex and time-consuming process. Recent advancements in parallel and distributed approaches have led to the evolution of big data ...
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 ...
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 ...
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 ...
PPDQ-BG: Parallel Partition and Distributed Query Processing for Big Graphs
(University of Missouri--Kansas City, 2016)
In recent years, there has been an explosive growth of the linked data of a global
information space that often requires expensive computations to perform big graph analysis
and query processing. Graph data represent ...
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 ...
Distributed Collaborative Framework for Deep Learning in Object Detection
(2020)
Object detection has gained much attention in recent years because of its ability to localize and classify the objects in videos and images that can be incorporated into many applications. Traditional object detection ...
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 ...
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 ...
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 ...
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 ...
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 ...
AudioCNN: Audio Event Classification With Deep Learning Based Multi-Channel Fusion Networks
(2020)
In recent years, there is growing interest in environmental sound classification with a plethora of real-world applications, especially in audio fields like speech and music. Recent research works have proven spectral ...