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A Novel Deep Learning-Based Framework for Context Aware Semantic Segmentation in Medical Imaging
(2023)
. Our research also proposed an architecture inspired by the generative adversarial network. The network enhanced the base model with residual, dense, and attention mechanisms. The residual mechanism helped extend the network depth without falling...
Topic Sentiment Trend Detection and Prediction for Social Media
(2020)
in social media. The proposed framework was designed to cope with the following tasks: topic trend detection, sentiment analysis, and topic prediction. The VADER-based time series sentiment analysis and the KATE-based topic modeling methods were applied...
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 images based on deep learning...
GraphEvo: Evaluating Software Evolution Using Machine Learning Based Call Graph Analytics And Network Portrait Divergence
(2022)
propose a graph-based software framework called GraphEvo based on deep learning modeling for graphs. We applied the recent network comparison advancement to software networks via information theory-based metric Network portrait divergence (NPD). NPD...
DMLA: A Dynamic Model-Based Lambda Architecture for Learning and Recognition of Features in Big Data
(University of Missouri--Kansas City, 2016)
the components of the DMLA architecture. In the DMLA framework, a dynamic predictive model, learned from the training data in Spark, is loaded from the context information into a Storm topology to recognize/predict the possible events. The event-based context...
AI-based Edge Computing System for Event Based Analytics
(2021)
, desirable availability, and privacy protection. However, cloud-based AI solutions are not readily deployable to the edge in IoT's data-driven world because of the difficulties of dealing with diverse sources, lack of availability, and network traffic...
Deep Open Representative Learning for Image and Text Classification
(2020)
representatives of the discovery process from data to the knowledge that can be used to perform reasoning. In this dissertation, a novel model named Class Representative Learning (CRL) is proposed, a class-based classifier designed with the following unique...
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 ...
StoryNet: A 5W1H-based knowledge graph to connect stories
(2021)
to any field. We have evaluated two approaches for generating results - training-based and rule-based. For the rule-based approach, we used Stanford NLP parsers to identify patterns for the 5W + 1H terms, and for the training based approach, BERT...
Second chance competitive autoencoders for understanding textual data
(2021)
three novel autoencoders, SCAT (Second Chance Autoencoder for Text), SSCAT (Similarity-based SCAT), and CSCAT (Coherent-based SCAT). Our autoencoders utilize competitive learning among the k winner neurons in the bottleneck layer, which become...
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 ...
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)
-feature multi-modality fusion-based audio classification to solve these problems in acoustic classification. We have proposed the multi-modality fusion architecture with Deep Acoustics (DA) and Multimodal Deep Acoustics (MDA). The contributions are (1...
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 ...
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 ...
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 ...
H3DNET: A Deep Learning Framework for Hierarchical 3D Object Classification
(University of Missouri--Kansas City, 2017)
Deep learning has received a lot of attention in the fields such as speech recognition and
image classification because of the ability to learn multiple levels of features from raw data.
However, 3D deep learning is ...