語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Deep Learning with Application in Di...
~
Chen, Mingshen.
FindBook
Google Book
Amazon
博客來
Deep Learning with Application in Disease Prediction and Precipitation Forecasting.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Deep Learning with Application in Disease Prediction and Precipitation Forecasting./
作者:
Chen, Mingshen.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:
113 p.
附註:
Source: Dissertations Abstracts International, Volume: 82-02, Section: B.
Contained By:
Dissertations Abstracts International82-02B.
標題:
Applied mathematics. -
電子資源:
https://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:
2122814
Applied mathematics.
Subjects--Index Terms:
Deep learning
Deep Learning with Application in Disease Prediction and Precipitation Forecasting.
LDR
:02304nmm a2200337 4500
001
2277313
005
20210521101654.5
008
220723s2020 ||||||||||||||||| ||eng d
020
$a
9798662417458
035
$a
(MiAaPQ)AAI27959313
035
$a
AAI27959313
040
$a
MiAaPQ
$c
MiAaPQ
100
1
$a
Chen, Mingshen.
$3
3555626
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
2122814
650
4
$a
Artificial intelligence.
$3
516317
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
1684041
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
https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27959313
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9429047
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入