國立高雄大學圖資館 |

Language: English

Back

An introduction to artificial psychologyapplication fuzzy set theory and deep machine learning in psychological research using R /

Record Type:	Electronic resources : Monograph/item
Title/Author:	An introduction to artificial psychologyby Hojjatollah Farahani ... [et al.].
Reminder of title:	application fuzzy set theory and deep machine learning in psychological research using R /
other author:	Farahani, Hojjatollah.
Published:	Cham :Springer International Publishing :2023.
Description:	1 online resource (xx, 252 p.) :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	PsychologyResearch
Online resource:	https://doi.org/10.1007/978-3-031-31172-7
ISBN:	9783031311727$q(electronic bk.)

An introduction to artificial psychologyapplication fuzzy set theory and deep machine learning in psychological research using R /
An introduction to artificial psychologyapplication fuzzy set theory and deep machine learning in psychological research using R /[electronic resource] :by Hojjatollah Farahani ... [et al.]. - Cham :Springer International Publishing :2023. - 1 online resource (xx, 252 p.) :ill., digital ;24 cm.

Introduction Chapter 1: After Method -- Chapter 2: Overview on Mathematical Basis of Fuzzy Set Theory. - Chapter 3: Fuzzy Inference Systems (FIS) -- Chapter 4: Fuzzy Cognitive Maps(FCM) -- Chapter 5: Network analysis -- Chapter 6: Association Rules Mining and Associative Classification -- Chapter 7: Artificial Neural Network -- Chapter 8: Feature Selection -- Chapter 9: Cluster analysis.

Artificial Psychology (AP) is a highly multidisciplinary field of study in psychology. AP tries to solve problems which occur when psychologists do research and need a robust analysis method. Conventional statistical approaches have deep rooted limitations. These approaches are excellent on paper but often fail to model the real world. Mind researchers have been trying to overcome this by simplifying the models being studied. This stance has not received much practical attention recently. Promoting and improving artificial intelligence helps mind researchers to find a holistic model of mental models. This development achieves this goal by using multiple perspectives and multiple data sets together with interactive, and realistic models. In this book, the methodology of approximate inference in psychological research from a theoretical and practical perspective has been considered. Quantitative variable-oriented methodology and qualitative case-oriented methods are both used to explain the set-oriented methodology and this book combines the precision of quantitative methods with information from qualitative methods. This is a book that many researchers can use to expand and deepen their psychological research and is a book which can be useful to postgraduate students. The reader does not need an in-depth knowledge of mathematics or statistics because statistical and mathematical intuitions are key here and they will be learned through practice. What is important is to understand and use the new application of the methods for finding new, dynamic and realistic interpretations. This book incorporates theoretical fuzzy inference and deep machine learning algorithms in practice. This is the kind of book that we wished we had had when we were students. This book covers at least some of the most important issues in mind research including uncertainty, fuzziness, continuity, complexity and high dimensionality which are inherent to mind data. These are elements of artificial psychology. This book implements models using R software.

ISBN: 9783031311727$q(electronic bk.)

Standard No.: 10.1007/978-3-031-31172-7doiSubjects--Topical Terms:

942877
Psychology
--Research

LC Class. No.: BF76.6.I57

Dewey Class. No.: 150.72

An introduction to artificial psychologyapplication fuzzy set theory and deep machine learning in psychological research using R /
LDR:03549nmm a2200325 a 4500 001 636408
003 DE-He213
005 20230518131959.0
006 m d
007 cr nn 008maaau
008 230815s2023 sz s 0 eng d
020 $a 9783031311727$q(electronic bk.)
020 $a 9783031311710$q(paper)
024 7 $a 10.1007/978-3-031-31172-7 $2 doi
035 $a 978-3-031-31172-7
040 $a GP $c GP
041 0 $a eng
050 4 $a BF76.6.I57
072 7 $a JM $2 bicssc
072 7 $a PSY000000 $2 bisacsh
072 7 $a JM $2 thema
082 0 4 $a 150.72 $2 23
090 $a BF76.6.I57 $b I61 2023
245 0 3 $a An introduction to artificial psychology $h [electronic resource] : $b application fuzzy set theory and deep machine learning in psychological research using R / $c by Hojjatollah Farahani ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a 1 online resource (xx, 252 p.) : $b ill., digital ; $c 24 cm.
505 0 $a Introduction Chapter 1: After Method -- Chapter 2: Overview on Mathematical Basis of Fuzzy Set Theory. - Chapter 3: Fuzzy Inference Systems (FIS) -- Chapter 4: Fuzzy Cognitive Maps(FCM) -- Chapter 5: Network analysis -- Chapter 6: Association Rules Mining and Associative Classification -- Chapter 7: Artificial Neural Network -- Chapter 8: Feature Selection -- Chapter 9: Cluster analysis.
520 $a Artificial Psychology (AP) is a highly multidisciplinary field of study in psychology. AP tries to solve problems which occur when psychologists do research and need a robust analysis method. Conventional statistical approaches have deep rooted limitations. These approaches are excellent on paper but often fail to model the real world. Mind researchers have been trying to overcome this by simplifying the models being studied. This stance has not received much practical attention recently. Promoting and improving artificial intelligence helps mind researchers to find a holistic model of mental models. This development achieves this goal by using multiple perspectives and multiple data sets together with interactive, and realistic models. In this book, the methodology of approximate inference in psychological research from a theoretical and practical perspective has been considered. Quantitative variable-oriented methodology and qualitative case-oriented methods are both used to explain the set-oriented methodology and this book combines the precision of quantitative methods with information from qualitative methods. This is a book that many researchers can use to expand and deepen their psychological research and is a book which can be useful to postgraduate students. The reader does not need an in-depth knowledge of mathematics or statistics because statistical and mathematical intuitions are key here and they will be learned through practice. What is important is to understand and use the new application of the methods for finding new, dynamic and realistic interpretations. This book incorporates theoretical fuzzy inference and deep machine learning algorithms in practice. This is the kind of book that we wished we had had when we were students. This book covers at least some of the most important issues in mind research including uncertainty, fuzziness, continuity, complexity and high dimensionality which are inherent to mind data. These are elements of artificial psychology. This book implements models using R software.
650 0 $a Psychology $x Research $x Data processing. $3 942877
650 0 $a Artificial intelligence. $3 194058
650 0 $a Fuzzy sets. $3 182529
650 0 $a Machine learning. $3 188639
650 1 4 $a Behavioral Sciences and Psychology. $3 911851
650 2 4 $a Cognitive Psychology. $3 273717
650 2 4 $a Cognitive Science. $3 304630
650 2 4 $a Machine Learning. $3 833608
650 2 4 $a Artificial Intelligence. $3 212515
700 1 $a Farahani, Hojjatollah. $3 942876
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-31172-7
950 $a Behavioral Science and Psychology (SpringerNature-41168)