Mood recognition as customer’s feedback using fuzzy inference system / Siti 'Aisyah Sa'dan ... [et al.]
Within the last several years, human mood recognition has been actively explored in the computer vision research. Human mood recognition is widely applied in education, psychology and customer service management. This study has been prepared for the customer service management. Nowadays, customer se...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universiti Teknologi MARA, Perak
2018
|
| Subjects: | |
| Online Access: | http://ir.uitm.edu.my/id/eprint/39741/1/39741.pdf http://ir.uitm.edu.my/id/eprint/39741/ https://mijournal.wixsite.com/index/copy-of-vol-2-no-1-june-2019 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.uitm.ir.39741 |
|---|---|
| record_format |
eprints |
| spelling |
my.uitm.ir.397412020-12-27T14:33:24Z http://ir.uitm.edu.my/id/eprint/39741/ Mood recognition as customer’s feedback using fuzzy inference system / Siti 'Aisyah Sa'dan ... [et al.] Sa'dan, Siti 'Aisyah Yusof, Noor Hazira Mohd Bahrin, Ummu Fatihah Hamzah, Siti Salbiah Algorithms Fuzzy logic Within the last several years, human mood recognition has been actively explored in the computer vision research. Human mood recognition is widely applied in education, psychology and customer service management. This study has been prepared for the customer service management. Nowadays, customer service is usually conducted through a manual survey to measure the customer’s satisfaction. Manual customer’s satisfaction survey is subjective and the customer’s response may be less accurate. The objective of this study is to develop a mood recognition prototype as customer’s satisfaction feedback using fuzzy inference system and to measure its effectiveness. This study explores the recognition of domain-specific mood using a fuzzy inference system to detect three categories of mood; negative and positive and neutral with the accuracy of 78% matched based on the mouth measurement and computation. The future study focus on adding more feature points and improve rules and combine other classifiers to the fuzzy inference system for better performance. Universiti Teknologi MARA, Perak 2018-09-20 Article PeerReviewed text en http://ir.uitm.edu.my/id/eprint/39741/1/39741.pdf Sa'dan, Siti 'Aisyah and Yusof, Noor Hazira and Mohd Bahrin, Ummu Fatihah and Hamzah, Siti Salbiah (2018) Mood recognition as customer’s feedback using fuzzy inference system / Siti 'Aisyah Sa'dan ... [et al.]. Multidisciplinary Informatics Journal (MIJ), 1 (2). pp. 77-85. ISSN 2637-0042 https://mijournal.wixsite.com/index/copy-of-vol-2-no-1-june-2019 |
| institution |
Universiti Teknologi Mara |
| building |
Tun Abdul Razak Library |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Teknologi Mara |
| content_source |
UiTM Institutional Repository |
| url_provider |
http://ir.uitm.edu.my/ |
| language |
English |
| topic |
Algorithms Fuzzy logic |
| spellingShingle |
Algorithms Fuzzy logic Sa'dan, Siti 'Aisyah Yusof, Noor Hazira Mohd Bahrin, Ummu Fatihah Hamzah, Siti Salbiah Mood recognition as customer’s feedback using fuzzy inference system / Siti 'Aisyah Sa'dan ... [et al.] |
| description |
Within the last several years, human mood recognition has been actively explored in the computer vision research. Human mood recognition is widely applied in education, psychology and customer service management. This study has been prepared for the customer service management. Nowadays, customer service is usually conducted through a manual survey to measure the customer’s satisfaction. Manual customer’s satisfaction survey is subjective and
the customer’s response may be less accurate. The objective of this study is to develop a mood recognition prototype as customer’s satisfaction feedback using fuzzy inference system and to measure its effectiveness. This study explores the recognition of domain-specific mood using a fuzzy inference system to detect three categories of mood; negative and positive and neutral with the accuracy of 78% matched based on the mouth measurement and computation. The future study focus on adding more feature points and improve rules and combine other
classifiers to the fuzzy inference system for better performance. |
| format |
Article |
| author |
Sa'dan, Siti 'Aisyah Yusof, Noor Hazira Mohd Bahrin, Ummu Fatihah Hamzah, Siti Salbiah |
| author_facet |
Sa'dan, Siti 'Aisyah Yusof, Noor Hazira Mohd Bahrin, Ummu Fatihah Hamzah, Siti Salbiah |
| author_sort |
Sa'dan, Siti 'Aisyah |
| title |
Mood recognition as customer’s feedback using fuzzy
inference system / Siti 'Aisyah Sa'dan ... [et al.] |
| title_short |
Mood recognition as customer’s feedback using fuzzy
inference system / Siti 'Aisyah Sa'dan ... [et al.] |
| title_full |
Mood recognition as customer’s feedback using fuzzy
inference system / Siti 'Aisyah Sa'dan ... [et al.] |
| title_fullStr |
Mood recognition as customer’s feedback using fuzzy
inference system / Siti 'Aisyah Sa'dan ... [et al.] |
| title_full_unstemmed |
Mood recognition as customer’s feedback using fuzzy
inference system / Siti 'Aisyah Sa'dan ... [et al.] |
| title_sort |
mood recognition as customer’s feedback using fuzzy
inference system / siti 'aisyah sa'dan ... [et al.] |
| publisher |
Universiti Teknologi MARA, Perak |
| publishDate |
2018 |
| url |
http://ir.uitm.edu.my/id/eprint/39741/1/39741.pdf http://ir.uitm.edu.my/id/eprint/39741/ https://mijournal.wixsite.com/index/copy-of-vol-2-no-1-june-2019 |
| _version_ |
1687396810534944768 |
| score |
13.211869 |