MARC details
| 000 -LEADER |
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| fixed length control field |
04128nam a22003257a 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 |
20251125105525.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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| fixed length control field |
a||||fr|||| 001 0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
200303t20192019my ||||f ma|| 001 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0008544(Local) |
| 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) |
FSKKP .O83 2019 r Thesis |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Osama Nayel Ahmad Al Sayaydeh, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
Modern fuzzy min max neural networks for pattern classification / |
| Statement of responsibility, etc. |
Osama Nayel Ahmad Al Sayaydeh |
| 264 01 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| 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 04 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
© 2019 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xiii, 115 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 |
| 337 ## - MEDIA TYPE |
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| Media type term |
unmediated |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
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| Carrier type term |
volume |
| Source |
rdacarrier |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
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| File type |
text file |
| Encoding format |
PDF |
| Source |
rda |
| 500 ## - GENERAL NOTE |
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| General note |
Faculty of Computer Systems & Software Engineering |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2019 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
In the recent years, the world has demonstrated an increasing interest in soft computing techniques to deal with complex real world problems. Neural network and fuzzy logic are considered to be one of the most popular soft computing techniques that applied in pattern classification domain. To build an efficient classifier model, researchers have introduced hybrid models that combine both fuzzy logic and artificial neural networks. Among these algorithms, Fuzzy Min Max (FMM) neural network algorithm has been proven to be one of the premier neural networks for undertaking the pattern classification problems. Although the FMM has many important features with the ability to provide online learning process and can handle the forgetting problem, it suffers from a number of limitations, especially in its learning process i.e., expansion process, overlapping test process, and contraction process. Therefore, Modern Fuzzy Min Max neural network is introduced with aim of overcoming the specified limitations of the original FMM. The MDFMM introduces a number of contributions in addition to modify the original FMM expansion activation function by replace it with that from the Enhanced Fuzzy Min Max (EFMM) to eliminate the overlapping cases. First, this study proposed a new expansion technique to overcome both overlap leniency and irregularity of hyperbox expansion problems, as a result, reducing the number of contraction processes. Secondly, proposing a new overlapping test formula that simplify the FMM/EFMM overlap test process with perfectly covers all the possible overlapped cases. Thirdly, proposing a new contraction process that provides more accurate hyperboxes description and avoid data distortion problem (hyperbox information losses). Fourthly, proposing a new prediction strategy in the test phase by integrating the distance equation with membership function in order to solve the randomization decision making problem, which helps to provide more accurate prediction when input sample has same fitness values with different classes. To overcome the network structure complexity of MDFMM, a further improvement is introduced by improving the selection of the winning hyperbox during the expansion process using the k-nearest neighbours algorithm (MDFMM-Kn). The performance of MDFMM and MDFMM-Kn was evaluated using different UCI benchmark datasets and 2D artificial intelligence dataset. Furthermore, three statistical analysis techniques, namely, bootstrap method, k-fold cross-validation and the Wilcoxon signed-rank test, were utilized to statistically quantify the performances. From the empirical evaluation, the proposed MDFMM is better than the recent existing model modified FMM network (MFMMN) in terms of accuracy at an improvement percentage of 35.42%. Furthermore, the average performance of the MDFMM-Kn against the FMM and MDFMM models is better than that of the existing techniques in terms of complexity at a percentage of 62%. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
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| Corporate name or jurisdiction name as entry element |
Faculty of Computer Systems & Software Engineering |
| 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 |
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| Topical term or geographic name entry element |
Theses |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |