MARC details
| 000 -LEADER |
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| fixed length control field |
03394ntm a2200361 i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
vtls000104342 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
KUKTEM |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251117113438.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
180719s2018 my a f a m 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0008051(Local) |
| 039 #9 - LEVEL OF BIBLIOGRAPHIC CONTROL AND CODING DETAIL [OBSOLETE] |
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| Level of rules in bibliographic description |
201906181320 |
| Level of effort used to assign nonsubject heading access points |
amy2 |
| Level of effort used to assign subject headings |
201808131529 |
| Level of effort used to assign classification |
fateeha |
| -- |
201807191610 |
| -- |
fateeha |
| 040 ## - CATALOGING SOURCE |
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| 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) |
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| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FKEE .N87 2018 r Thesis |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Nurdiyana Zahed, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
Intelligent odour profile classification of honey using Case Based Reasoning and K-Nearest Neighbors / |
| Statement of responsibility, etc. |
Nurdiyana Zahed |
| 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 |
2018 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xii, 102 pages : |
| Other physical details |
illustrations (some color) ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD ROM |
| 336 ## - CONTENT TYPE |
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| Content type term |
text |
| Source |
rdacontent |
| 336 ## - CONTENT TYPE |
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| Content type term |
text |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
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| Media type term |
unmediated |
| Source |
rdamedia |
| 337 ## - MEDIA TYPE |
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| Media type term |
computer |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
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| 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 |
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| General note |
Faculty of Electrical and Electronics Engineering |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (Master of Science) -- Universiti Malaysia Pahang – 2018 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
Honey produced by sting bee contains several important nutrients that can be used as food additive, medicinal food and food preservative. In Malaysia, three most common types of pure honey are Gelam, Acacia, and Tualang which can be verified from Department of Agriculture Malaysia. Honey can be classified using several methods and one of them is by referring to its botanical origin (botanical place where the honey is collected) which influence the quality of honey, its market price and benefits. Various techniques can be found in classifying honey where odour profile is amongst them. In order to establish honey odour profile, electronic nose (E-nose) is one of the significant instrument that can be used for honey odour profile measurement and detection. E-nose comprises of three major components; chamber, microcontroller and sensor arrays that capable of establishing odour profile pattern. In order to get E-nose response measurement, it has been validated using Gas Chromatography Mass Spectrometry (GC-MS) which was pre-processed using Solid Phase Micro-extraction (SPME). Three datasets of three different groups of honey from E-nose were measured in electrical signal for odour detection. The measured resistance data was pre-processed using normalization technique to standardize the scale of sample features. Mean values were extracted as selected feature and statistical methods using proximity matrix and boxplot data pattern graphical representation according to types of honey. Mean features for Case-based Reasoning (CBR) cases that have been extracted were employed into two types of classifiers; intelligent classification using CBR and K-Nearest Neighbors (KNN). Both CBR and KNN performances have been evaluated. The results have shown 100% rate of accuracy, sensitivity and specificity of classification from CBR classifier and KNN using weigh (k=1), ratio 90:10 and Euclidean distance. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
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| Corporate name or jurisdiction name as entry element |
Faculty of Electrical and Electronics Engineering |
| 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 |
Disertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Theses |