國立虎尾科技大學 |

登入

回首頁

Multimodal Depression Detection : = ...

City University of New York.

Multimodal Depression Detection : = An Investigation of Features and Fusion Techniques for Automated Systems.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Multimodal Depression Detection :/
其他題名:	An Investigation of Features and Fusion Techniques for Automated Systems.
作者:	Morales, Michelle Renee.
面頁冊數:	1 online resource (139 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-09(E), Section: A.
Contained By:	Dissertation Abstracts International79-09A(E).
標題:	Linguistics. -
電子資源:	click for full text (PQDT)
ISBN:	9780355865509

Multimodal Depression Detection : = An Investigation of Features and Fusion Techniques for Automated Systems.
Morales, Michelle Renee.

Multimodal Depression Detection :An Investigation of Features and Fusion Techniques for Automated Systems. - 1 online resource (139 pages)

Source: Dissertation Abstracts International, Volume: 79-09(E), Section: A.

Thesis (Ph.D.)--City University of New York, 2018.

Includes bibliographical references

Depression is a serious illness that affects a large portion of the world's population. Given the large effect it has on society, it is evident that depression is a serious health issue. This thesis evaluates, at length, how technology may aid in assessing depression. We present an in-depth investigation of features and fusion techniques for depression detection systems. We also present OpenMM: a novel tool for multimodal feature extraction. Lastly, we present novel techniques for multimodal fusion. The contributions of this work add considerably to our knowledge of depression detection systems and have the potential to improve future systems by incorporating that knowledge into their design.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355865509Subjects--Topical Terms:

557829
Linguistics.
Index Terms--Genre/Form:

554714
Electronic books.

Multimodal Depression Detection : = An Investigation of Features and Fusion Techniques for Automated Systems.
LDR:02010ntm a2200361Ki 4500 001 916866
005 20180928111502.5
006 m o u
007 cr mn||||a|a||
008 190606s2018 xx obm 000 0 eng d
020 $a 9780355865509
035 $a (MiAaPQ)AAI10748603
035 $a (MiAaPQ)minarees:14972
035 $a AAI10748603
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Morales, Michelle Renee. $3 1190722
245 1 0 $a Multimodal Depression Detection : $b An Investigation of Features and Fusion Techniques for Automated Systems.
264 0 $c 2018
300 $a 1 online resource (139 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 79-09(E), Section: A.
500 $a Adviser: Rivka Levitan.
502 $a Thesis (Ph.D.)--City University of New York, 2018.
504 $a Includes bibliographical references
520 $a Depression is a serious illness that affects a large portion of the world's population. Given the large effect it has on society, it is evident that depression is a serious health issue. This thesis evaluates, at length, how technology may aid in assessing depression. We present an in-depth investigation of features and fusion techniques for depression detection systems. We also present OpenMM: a novel tool for multimodal feature extraction. Lastly, we present novel techniques for multimodal fusion. The contributions of this work add considerably to our knowledge of depression detection systems and have the potential to improve future systems by incorporating that knowledge into their design.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Linguistics. $3 557829
650 4 $a Mental health. $3 564038
650 4 $a Computer science. $3 573171
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0290
690 $a 0347
690 $a 0984
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a City University of New York. $b Linguistics. $3 1183909
773 0 $t Dissertation Abstracts International $g 79-09A(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10748603 $z click for full text (PQDT)

筆 0 讀者評論

多媒體

評論

新增評論分享你的心得

Export

取書館別