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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...
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 ...
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...
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...
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...
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...
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 ...
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 ...
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 and storage needs...
RUPEE: A Big Data Approach to Indexing and Searching Protein Structures
(2021)
not based on structure alone. In the case of sequence clustering, strong structure similarities are often hidden behind cluster representatives. Existing protein structure searches that focus on better quality results often perform full pairwise protein...
A Novel Deep Learning-Based Framework for Context Aware Semantic Segmentation in Medical Imaging
(2023)
automated screening system based on a unified modeling approach of diagnosis. The system can extract multiple ocular features with a novel semantic segmentation network to early detect the symptoms of retinal disease. We proposed a novel technique of dynamic...
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 ...
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 ...