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Deep Learning with Application in Di...

Chen, Mingshen.

Deep Learning with Application in Disease Prediction and Precipitation Forecasting.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep Learning with Application in Disease Prediction and Precipitation Forecasting.
Author:	Chen, Mingshen.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2020
Description:	113 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-02, Section: B.
Notes:	Advisor: Li, Xiaolin;Yoo, Shinjae.
Contained By:	Dissertations Abstracts International82-02B.
Subject:	Applied mathematics.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27959313
ISBN:	9798662417458

Deep Learning with Application in Disease Prediction and Precipitation Forecasting.
Chen, Mingshen.

Deep Learning with Application in Disease Prediction and Precipitation Forecasting. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 113 p.

Source: Dissertations Abstracts International, Volume: 82-02, Section: B.

Thesis (Ph.D.)--State University of New York at Stony Brook, 2020.

This item must not be sold to any third party vendors.

A deep learning framework that includes convolutional neural network (CNN), self attention mechanism and recurrent neural network (RNN) is built and can be used to study the important scientific problems, including Alzheimer’s Disease (AD) prediction and storm precipitation prediction, in this dissertation. For the AD prediction problem, Single Nucleotide Polymorphisms (SNPs) data from multiple sources are used, which results in very high dimensional input data. To overcome this difficulty, the deep auto-encoder model and supervised auto-encoder model are built to reduce the data dimension so as to improve prediction performance. For the storm precipitation prediction problem, we generate a time series data set that includes precipitation and other related variables along the trajectory of storms since 1998/01/01, which can be further used for other climate related problems. As for the model, convolutional encoder-decoder model is built with position and channel self attention mechanism to extract important information from multiple related variables in the same region, followed by the convolutional network or recurrent network to deal with the temporal dimension.

ISBN: 9798662417458Subjects--Topical Terms:

377601
Applied mathematics.
Subjects--Index Terms:

Deep learning

Deep Learning with Application in Disease Prediction and Precipitation Forecasting.
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245 1 0 $a Deep Learning with Application in Disease Prediction and Precipitation Forecasting.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 113 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-02, Section: B.
500 $a Advisor: Li, Xiaolin;Yoo, Shinjae.
502 $a Thesis (Ph.D.)--State University of New York at Stony Brook, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a A deep learning framework that includes convolutional neural network (CNN), self attention mechanism and recurrent neural network (RNN) is built and can be used to study the important scientific problems, including Alzheimer’s Disease (AD) prediction and storm precipitation prediction, in this dissertation. For the AD prediction problem, Single Nucleotide Polymorphisms (SNPs) data from multiple sources are used, which results in very high dimensional input data. To overcome this difficulty, the deep auto-encoder model and supervised auto-encoder model are built to reduce the data dimension so as to improve prediction performance. For the storm precipitation prediction problem, we generate a time series data set that includes precipitation and other related variables along the trajectory of storms since 1998/01/01, which can be further used for other climate related problems. As for the model, convolutional encoder-decoder model is built with position and channel self attention mechanism to extract important information from multiple related variables in the same region, followed by the convolutional network or recurrent network to deal with the temporal dimension.
590 $a School code: 0771.
650 4 $a Applied mathematics. $3 377601
650 4 $a Artificial intelligence. $3 194058
653 $a Deep learning
653 $a Disease prediction
653 $a Precipitation forecasting
690 $a 0364
690 $a 0800
710 2 $a State University of New York at Stony Brook. $b Applied Mathematics and Statistics. $3 857349
773 0 $t Dissertations Abstracts International $g 82-02B.
790 $a 0771
791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27959313