MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03773ntm a2200373 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20251125105734.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
|---|
| fixed length control field |
t||||fr|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
|---|
| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
201014t2 2 m a|||fram|| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
THE0008908(Local) |
| Qualifying information |
hardback |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
UMP |
| Language of cataloging |
eng |
| Transcribing agency |
UMP |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FTKPM .A97 2019 r Thesis |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Mohd Azraai Mohd Razman, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Hunger behaviour classification of lates calcarifer using machine learning for automatic demand feeder through image processing / |
| Statement of responsibility, etc. |
Mohd Azraai Mohd Razman |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMP, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2019 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2019 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xv, 140 pages : |
| Other physical details |
illustrations (some color) ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD-ROM |
| 336 ## - CONTENT TYPE |
|---|
| Content type term |
text |
| Source |
rdacontent |
| 336 ## - CONTENT TYPE |
|---|
| Content type term |
text |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
|---|
| Media type term |
unmediated |
| Source |
rdamedia |
| 337 ## - MEDIA TYPE |
|---|
| Media type term |
computer |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
|---|
| Carrier type term |
volume |
| Source |
rdacarrier |
| 338 ## - CARRIER TYPE |
|---|
| Carrier type term |
computer disc |
| Source |
rdacarrier |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| File type |
text file |
| Encoding format |
PDF |
| Source |
rda |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Faculty of Manufacturing and Mechatronic Engineering Technology |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Thesis (Doctor of Philosophy in Manufacturing and Mechatronic Engineering Technology) -- Universiti Malaysia Pahang – 2019 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
The understanding and identification of fish hunger behaviour are non-trivial in the aquaculture industry. This thesis aims at classifying the hunger state of Lates Calcarifer via the integration of computer vision and machine learning. Prior to the classification of the hunger states, the hunger state of the fish is identified through the k-means clustering technique and it was established that the hunger state could be demarcated into either ‘Hungry’ or ‘Satiated’. Upon the identification of the hunger state, significant features that could contribute towards the accurate classification of the states are investigated. The aforesaid features are analysed by the box plot analysis and the Principal Component Analysis (PCA). The established features are COG x, COG y and the moving summation of the pixel. Different machine learning models were investigated by incorporating the identified features, i.e., Discriminant Analysis (DA), Support Vector Machine (SVM) and k-Nearest Neighbours (k-NN) and it was demonstrated that the SVM trained model is able to classify up to 99.00%, suggesting that the developed system is viable for fish farming. A supplementary analysis was further carried out to understand the circadian rhythm of the fish by evaluating the time-series features. Different window sizes ranging from 0.5 min, 1.0 min, 1.5 min and 2.0 min coupled with the mean, maximum, minimum and variance for each of the distinctive temporal window sizes are investigated. PCA and PCA varimax rotation was employed in order to identify the best features through classifying it via SVM and k-NN. It was shown that the mean and variance of all temporal sizes are significant. In addition, the efficacy of different models based on the identified secondary features, namely DA, SVM, k-NN, Decision Tree (Tree), Logistic Regression (LR), Random Forest Tree (RF) and Neural Network (NN) are evaluated. It was found that the k-NN yielded the highest classification accuracy with 96.47% from the test sets. In order to further refine the k-NN model developed, hyperparameter optimization by means of Bayesian Optimization was carried out. Through the optimization process, the best hyperparameters that could attain a classification accuracy of 97.16% are the Standardized Euclidean distance metric with a k value of one. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
Faculty of Manufacturing and Mechatronic Engineering Technology |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Theses |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |