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Recognition ofsome arabic syllables sets using deep neural networks / (Record no. 170871)

MARC details
000 -LEADER
fixed length control field	04173namaa22004451i 4500
003 - CONTROL NUMBER IDENTIFIER
control field	OSt
005 - أخر تعامل مع التسجيلة
control field	20250303121434.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field	250216s2024 \|\|\|a\|\|\|f m\|\|\| 000 0 eng d
040 ## - CATALOGING SOURCE
Original cataloguing agency	EG-GICUC
Language of cataloging	eng
Transcribing agency	EG-GICUC
Modifying agency	EG-GICUC
Description conventions	rda
041 0# - LANGUAGE CODE
Language code of text/sound track or separate title	eng
Language code of summary or abstract	eng
--	ara
049 ## - Acquisition Source
Acquisition Source	Deposit
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number	610.28
092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC)
Classification number	610.28
Edition number	21
097 ## - Degree
Degree	Ph.D
099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC)
Local Call Number	Cai01.13.03.Ph.D.2024.Da.R
100 0# - MAIN ENTRY--PERSONAL NAME
Authority record control number or standard number	Dahlia Mohammad Ismail Omran,
Preparation	preparation.
245 10 - TITLE STATEMENT
Title	Recognition ofsome arabic syllables sets using deep neural networks /
Statement of responsibility, etc.	by Dahlia Mohammad Ismail Omran ; Supervisors Prof. Ahmed Hisham Kandil, Prof. Ahmed Mohammed El-Bialy, Dr. Sherif Ahmed Sami.
246 15 - VARYING FORM OF TITLE
Title proper/short title	التعرف على بعض مجموعات المقاطع اللفظية العربية باستخدام الشبكات العصبية العميقة /
264 #0 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Date of production, publication, distribution, manufacture, or copyright notice	2024.
300 ## - PHYSICAL DESCRIPTION
Extent	63 pages :
Other physical details	illustrations ;
Dimensions	30 cm. +
Accompanying material	CD.
336 ## - CONTENT TYPE
Content type term	text
Source	rda content
337 ## - MEDIA TYPE
Media type term	Unmediated
Source	rdamedia
338 ## - CARRIER TYPE
Carrier type term	volume
Source	rdacarrier
502 ## - DISSERTATION NOTE
Dissertation note	Thesis (Ph.D)-Cairo University, 2024.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note	Bibliography: pages 59-63.
520 ## - SUMMARY, ETC.
Summary, etc.	This work focused on applying the Convolutional Neural Networks (CNN) to recognize some recitation rules of the Holy Quran, the Qalqalah rule which is applied to five letters of the Arabic Alphabet and it implies vibration of these letters when there is absence of vowels on them with Sukon, and Hams or Whisper rule which is applied to other ten letters of the Arabic alphabet and it implies breathing flow when pronouncing those letters. The used dataset consists of 4406 Quranic audios each of 500ms length which were extracted off continuous audio records for professional readers. Each sample represent one CVC, CVCC or CV:C Arabic syllables which contain one of Qalqalah or Hams sounds. The feature extraction technique used in the proposed system was the Mel Frequency Cepstral Coefficients (MFCC). Recognition process for Qalqalah achieved 93% accuracy while it achieved 91% for Hams rule.
520 ## - SUMMARY, ETC.
Summary, etc.	ركز هذا العمل على تطبيق الشبكات العصبية التلافيفية (CNN) للتعرف على بعض قواعد تلاوة القرآن الكريم، وقاعدة القلقلة المطبقة على خمسة حروف من الأبجدية العربية وتعني اهتزاز هذه الحروف في حالة السكون. أما قاعدة الهمس والتي تنطبق على الحروف العشرة الأخرى من الأبجدية العربية و التي تتضمن تدفق التنفس عند نطق تلك الحروف. و تتكون مجموعة البيانات المستخدمة من 4406 مقطع صوتي قرآني طول كل منها 500 مللي ثانية تم استخراجها من التسجيلات الصوتية المستمرة لأربعة من القراء المحترفين. تمثل كل عينة مقطعًا لفظيًا عربيًا واحدًا من CVC أو CVCC أو CV:C يحتوي على أحد أصوات القلقلة أو الهمس. كانت تقنية استخراج المعالم المستخدمة في النظام المقترح هي معاملات (MFCC). وحققت عملية التعرف باستخدام الشبكة العصبية التلافيفية للقلقلة على دقة 93%، بينما حققت 91% لحكم الهمس.
530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE
Issues CD	Issues also as CD.
546 ## - LANGUAGE NOTE
Text Language	Text in English and abstract in Arabic & English.
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element	Biomedical Engineering
Source of heading or term	qrmak
653 #0 - INDEX TERM--UNCONTROLLED
Uncontrolled term	Convolutional Neural Networks
--	MFCC
--	Recitation Rules
--	characteristics of Arabic letters
--	keyword spotting
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name	Ahmed Hisham Kandil
Relator term	thesis advisor.
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name	Ahmed Mohammed El-Bialy
Relator term	thesis advisor.
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name	Sherif Ahmed Sami
Relator term	thesis advisor.
900 ## - Thesis Information
Grant date	01-01-2024
Supervisory body	Ahmed Hisham Kandil
--	Ahmed Mohammed El-Bialy
--	Sherif Ahmed Sami
Discussion body	Tamer Yousef Basha
--	Mohamed Aly El Dosouky
Universities	Cairo University
Faculties	Faculty of Engineering
Department	Department of Biomedical Engineering and Systems
905 ## - Cataloger and Reviser Names
Cataloger Name	Shimaa
905 ## - Cataloger and Reviser Names
Cataloger Name	Eman Ghareeb
Reviser Names	Revisor
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme	Dewey Decimal Classification
Koha item type	Thesis
Edition	21
Suppress in OPAC	No

Holdings
Source of classification or shelving scheme	Home library	Current library	Date acquired	Inventory number	Full call number	Barcode	Date last seen	Effective from	Koha item type
Dewey Decimal Classification	المكتبة المركزبة الجديدة - جامعة القاهرة	قاعة الرسائل الجامعية - الدور الاول	16.02.2025	90681	Cai01.13.03.Ph.D.2024.Da.R	01010110090681000	16.02.2025	16.02.2025	Thesis

جامعة القاهرة

المكتبة المركزية الجديدة

مكتبة جامعة القاهرة الأهلية

Recognition ofsome arabic syllables sets using deep neural networks / (Record no. 170871)