Search
Now showing items 21-40 of 49
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 ...
On-chip Voltage Regulator– Circuit Design and Automation
(2021)
With the increase of density and complexity of high-performance integrated circuits and systems, including many-core chips and system-on-chip (SoC), it is becoming difficult to meet the power delivery and regulation ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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. ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
Evidence based medical query system on large scale data
(2014-07-30)
As huge amounts of data are created rapidly, the demand for the integration and analysis of such data has been growing steadily. It is especially essential to retrieve relevant and accurate evidence in healthcare and ...
SigSpace – Class-Based Feature Representation for Scalable and Distributed Machine Learning
(University of Missouri–Kansas City, 2016)
In the era of big data, it is essential to explore the opportunities in discovering knowledge
from big data. However, traditional machine learning approaches are not well fit
to analyze the full value of big data. ...