MARC details
| 000 -LEADER |
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| fixed length control field |
03683ntm a2200337 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251125110924.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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t||||fr|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
240911t20242024my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0009956 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMPSA |
| 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) |
FTKEE .S66 2024 r Thesis |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Son Ali Akbar, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
Enhancement of hybrid-deep learning model for microscopic bacterial image classification / |
| Statement of responsibility, etc. |
Son Ali Akbar |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMPSA, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2024 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2024 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xiv, 148 pages ; |
| Other physical details |
illustration ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD-ROM |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
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| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
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| Source |
rdacarrier |
| Carrier type term |
volume |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Faculty of Electrical & Electronics Engineering Technology |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2024 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
Bacterial infection is a health problem caused by bacteria attacking all body organs (i.e., Burkholderia pseudomallei sp., Hemophilus influenzae sp., Klebsiella pneumoniae sp., Pseudomonas aeruginosa sp., Staphylococcus aureus sp., Streptococcus pneumoniae sp.). Early identification of bacterial infection significantly influences clinical microbiology diagnosis performance and decision treatment. In addition, the ability of visual microbiologists influences the traditional method based on the gold standard used to classify bacterial species through microscopy. Computer-aided diagnosis (CAD) based on image processing and machine learning is one of the modern technologies that would help clinical microbiology assessments. The current issues on the CAD implementation include the efficiency model development, and time consumption during species classification. Traditional image processing and machine learning are ineffective work its involve handcrafted feature extraction. The existing scheme applies CNN-based deep learning to achieve high-accuracy predictions. However, existing CNN algorithms face the obstacle of avoiding overfitting and computation, simultaneously taking longer. Developing a hybrid method that combines deep learning and machine learning with the efficient design of models proposes a solution to this problem. In this approach, the CNN layers are utilized for deep feature extraction. This study has proposed the enhancement of hybrid method based on deep convolution neural networks (EHDCNN) for classification the bacterial species. The EHDCNN scheme extracted the feature on the earlier CNN layer by the skip connection technique which can be efficient in the training process. Then, the SVM-based error-correcting output codes (ECOC) model was used to classify the feature map. The experiment results show that the proposed method based on an EHDCNN model produces a high prediction score, such as overall accuracy of 99.61%, recall of 99.58%, specificity of 99.97%, precision of 99.58%, F-score of 0.9958, and FPR of 0.03%, respectively. Additionally, the computational time was achieved by a rapid processing of 05.08 minutes. The high computational hardware-based GPU can ensure the speed of deep processing. In addition, GPU-based high-computing hardware can ensure deep processing speed. Therefore, the proposed model supported by highgrade devices has the potential to be applied in CAD systems. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
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| Corporate name or jurisdiction name as entry element |
Faculty of Electrical & Electronics Engineering Technology |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| 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 |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |