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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 ...
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 ...
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...
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 ...