Language:
English
繁體中文
Help
回圖書館首頁
手機版館藏查詢
Login
Back
Switch To:
Labeled
|
MARC Mode
|
ISBD
Neuro fuzzy hybrid models for classi...
~
Melin, Patricia.
Linked to FindBook
Google Book
Amazon
博客來
Neuro fuzzy hybrid models for classification in medical diagnosis
Record Type:
Electronic resources : Monograph/item
Title/Author:
Neuro fuzzy hybrid models for classification in medical diagnosis/ by Patricia Melin, Juan Carlos Guzman, German Prado-Arechiga.
Author:
Melin, Patricia.
other author:
Guzman, Juan Carlos.
Published:
Cham :Springer International Publishing : : 2021.,
Description:
ix, 103 p. :ill., digital ;24 cm.
Contained By:
Springer Nature eBook
Subject:
Artificial intelligence - Medical applications. -
Online resource:
https://doi.org/10.1007/978-3-030-60481-3
ISBN:
9783030604813
Neuro fuzzy hybrid models for classification in medical diagnosis
Melin, Patricia.
Neuro fuzzy hybrid models for classification in medical diagnosis
[electronic resource] /by Patricia Melin, Juan Carlos Guzman, German Prado-Arechiga. - Cham :Springer International Publishing :2021. - ix, 103 p. :ill., digital ;24 cm. - SpringerBriefs in applied sciences and technology. - SpringerBriefs in applied sciences and technology..
This book is focused on the use of intelligent techniques, such as fuzzy logic, neural networks and bio-inspired algorithms, and their application in medical diagnosis. The main idea is that the proposed method may be able to adapt to medical diagnosis problems in different possible areas of the medicine and help to have an improvement in diagnosis accuracy considering a clinical monitoring of 24 hours or more of the patient. In this book, tests were made with different architectures proposed in the different modules of the proposed model. First, it was possible to obtain the architecture of the fuzzy classifiers for the level of blood pressure and for the pressure load, and these were optimized with the different bio-inspired algorithms (Genetic Algorithm and Chicken Swarm Optimization) Secondly, we tested with a local database of 300 patients and good results were obtained. It is worth mentioning that this book is an important part of the proposed general model; for this reason, we consider that these modules have a good performance in a particular way, but it is advisable to perform more tests once the general model is completed.
ISBN: 9783030604813
Standard No.: 10.1007/978-3-030-60481-3doiSubjects--Topical Terms:
900591
Artificial intelligence
--Medical applications.
LC Class. No.: R859.7.A78 / M45 2021
Dewey Class. No.: 610.28563
Neuro fuzzy hybrid models for classification in medical diagnosis
LDR
:02253nmm a2200325 a 4500
001
2236029
003
DE-He213
005
20201027203449.0
006
m d
007
cr nn 008maaau
008
211111s2021 sz s 0 eng d
020
$a
9783030604813
$q
(electronic bk.)
020
$a
9783030604806
$q
(paper)
024
7
$a
10.1007/978-3-030-60481-3
$2
doi
035
$a
978-3-030-60481-3
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
R859.7.A78
$b
M45 2021
072
7
$a
UYQ
$2
bicssc
072
7
$a
TEC009000
$2
bisacsh
072
7
$a
UYQ
$2
thema
082
0 4
$a
610.28563
$2
23
090
$a
R859.7.A78
$b
M522 2021
100
1
$a
Melin, Patricia.
$3
855558
245
1 0
$a
Neuro fuzzy hybrid models for classification in medical diagnosis
$h
[electronic resource] /
$c
by Patricia Melin, Juan Carlos Guzman, German Prado-Arechiga.
260
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2021.
300
$a
ix, 103 p. :
$b
ill., digital ;
$c
24 cm.
490
1
$a
SpringerBriefs in applied sciences and technology
520
$a
This book is focused on the use of intelligent techniques, such as fuzzy logic, neural networks and bio-inspired algorithms, and their application in medical diagnosis. The main idea is that the proposed method may be able to adapt to medical diagnosis problems in different possible areas of the medicine and help to have an improvement in diagnosis accuracy considering a clinical monitoring of 24 hours or more of the patient. In this book, tests were made with different architectures proposed in the different modules of the proposed model. First, it was possible to obtain the architecture of the fuzzy classifiers for the level of blood pressure and for the pressure load, and these were optimized with the different bio-inspired algorithms (Genetic Algorithm and Chicken Swarm Optimization) Secondly, we tested with a local database of 300 patients and good results were obtained. It is worth mentioning that this book is an important part of the proposed general model; for this reason, we consider that these modules have a good performance in a particular way, but it is advisable to perform more tests once the general model is completed.
650
0
$a
Artificial intelligence
$x
Medical applications.
$3
900591
650
0
$a
Blood pressure
$x
Measurement
$x
Data processing.
$3
3487047
650
1 4
$a
Computational Intelligence.
$3
1001631
650
2 4
$a
Biomedical Engineering and Bioengineering.
$3
3381533
700
1
$a
Guzman, Juan Carlos.
$3
1906482
700
1
$a
Prado-Arechiga, German.
$3
3295512
710
2
$a
SpringerLink (Online service)
$3
836513
773
0
$t
Springer Nature eBook
830
0
$a
SpringerBriefs in applied sciences and technology.
$3
1565541
856
4 0
$u
https://doi.org/10.1007/978-3-030-60481-3
950
$a
Intelligent Technologies and Robotics (SpringerNature-42732)
based on 0 review(s)
Location:
ALL
電子資源
Year:
Volume Number:
Items
1 records • Pages 1 •
1
Inventory Number
Location Name
Item Class
Material type
Call number
Usage Class
Loan Status
No. of reservations
Opac note
Attachments
W9397914
電子資源
11.線上閱覽_V
電子書
EB R859.7.A78 M45 2021
一般使用(Normal)
On shelf
0
1 records • Pages 1 •
1
Multimedia
Reviews
Add a review
and share your thoughts with other readers
Export
pickup library
Processing
...
Change password
Login