Gharehbaghi A. Deep Learning in Time Series Analysis 2023 [PDF] [English]Info:✔Title: Deep Learning in Time Series Analysis✔Pages: 208✔Author: Arash Gharehbaghi✔Year: 2023✔Subjects: ✔Category: ✔Publisher: CRC Press; 1st edition✔ISBN: 0367321785Description:Deep learning is an important element.
Beyond being used to solve conventional challenges such as delivery lead time, transport costs, inventory waste or poor decision-making, ML can even predict users’ intent through cognitive search.
In a recent study published in the journal Hypertension, researchers examined blood pressure (BP) outcomes in individuals with hypertension during the coronavirus disease 2019 (COVID-19) pandemic.
Constructing and analyzing functional brain networks (FBN) has become a promising approach to brain disorder classification. However, the conventional successive construct-and-analyze process would limit the performance due to the lack of interactions and adaptivity among the subtasks in the process. Recently, Transformer has demonstrated remarkable performance in various tasks, attributing to its effective attention mechanism in modeling complex feature relationships. In this paper, for the first time, we develop Transformer for integrated FBN modeling, analysis and brain disorder classification with rs-fMRI data by proposing a Diffusion Kernel Attention Network to address the specific challenges. Specifically, directly applying Transformer does not necessarily admit optimal performance in this task due to its extensive parameters in the attention module against the limited training samples usually available. Looking into this issue, we propose to use kernel attention to replace the o
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