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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 ...
Design of Multi-modality Deep Fusion Architecture for Deep Acoustic Analytics
(2021)
significant challenges to conduct accurately deep learning in the environmental and health audio domain. These challenges may occur due to the various field and categories, e.g., environmental, animal sounds, noises, and human body sounds. Specifically...
Second chance competitive autoencoders for understanding textual data
(2021)
Every day, an enormous amount of text data is produced. Sources of text data include news, social media, emails, text messages, medical reports, scientific publications, and fiction. To keep track of this data, there are ...
Software Analytics for Improving Program Comprehension
(2021)
) complex call graphs can become very difficult to understand making call graphs much harder to visualize and interpret by a developer and thus increases the overhead in program comprehension; (2) they are often limited to a single level of granularity...
Dynamic Activity Predictions using Graph-based Neural Networks for Time Series Forecasting
(2023)
Time series forecasting is a vital task in numerous fields, and traditional methods,
machine learning models, and neural graph networks have been employed to improve
prediction accuracy. However, these techniques need ...
Deep Open Representative Learning for Image and Text Classification
(2020)
contributions in machine learning, specifically for image and text classification, i) The unique design of a latent feature vector, i.e., class representative, represents the abstract embedding space projects with the features extracted from a deep neural...
Identifying personality and topics of social media
(2019)
on the Big 5 model. We have built the Big 5 personality classification model using a Twitter dataset that has defined openness, conscientiousness, extraversion, agreeableness, and neuroticism. In this thesis, we (1) conduct personality detection using the Big...
On-chip Voltage Regulator– Circuit Design and Automation
(2021)
-chip regulators. The off-chip regulators become a less attractive choice because of the higher overheads and complexity imposed by the additional wires, pins, and pads. The increased I2R loss makes it challenging to maintain the integrity of different voltage...
Multiple-valued logic: technology and circuit implementation
(2021)
Computing technologies are currently based on the binary logic/number system, which is dependent on the simple on and off switching mechanism of the prevailing transistors. With the exponential increase of data processing ...
StoryNet: A 5W1H-based knowledge graph to connect stories
(2021)
, ELECTRA, and AllenNLP Transformer QA with the datasets - CVE, NVD, SQuAD v1.1, and SQuAD v2.0, and compared them with custom annotations for identifying 5W + 1H. We've presented the performance and accuracy of both approaches in the results section. Our...
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 ...
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 ...
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 ...
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 ...