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Enhancing cell-phones{u2019} received signal strength prediction using deep learning / (Record no. 82708)

MARC details
000 -LEADER
fixed length control field 02193cam a2200325 a 4500
003 - CONTROL NUMBER IDENTIFIER
control field EG-GiCUC
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250223032833.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 211020s2021 ua dh f m 000 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency EG-GiCUC
Language of cataloging eng
Transcribing agency EG-GiCUC
041 0# - LANGUAGE CODE
Language code of text/sound track or separate title eng
049 ## - LOCAL HOLDINGS (OCLC)
Holding library Deposite
097 ## - Thesis Degree
Thesis Level M.Sc
099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC)
Classification number Cai01.13.08.M.Sc.2021.Am.E
100 0# - MAIN ENTRY--PERSONAL NAME
Personal name Amr Saleh Fouad Hussein Nassar
245 10 - TITLE STATEMENT
Title Enhancing cell-phones{u2019} received signal strength prediction using deep learning /
Statement of responsibility, etc. Amr Saleh Fouad Hussein Nassar ; Supervised Mohsen Rashwan
246 15 - VARYING FORM OF TITLE
Title proper/short title تحسين دقة التنبؤ بمستوى قوة الإشارة المستقبلية فى الهواتف الخلوية باستخدام التعلم العميق
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Cairo :
Name of publisher, distributor, etc. Amr Saleh Fouad Hussein Nassar ,
Date of publication, distribution, etc. 2021
300 ## - PHYSICAL DESCRIPTION
Extent 83 P. :
Other physical details charts , facsimiles ;
Dimensions 30cm
502 ## - DISSERTATION NOTE
Dissertation note Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Electronics and Communications
520 ## - SUMMARY, ETC.
Summary, etc. While mobile operators invest in providing the best quality of service (QOS) to its customers, a live visibility on the actual QOS at the customer end is often needed. Mobile operators rely on drive test to measure QOS at user level thus identify the service level. When this visibility is inaccurate or not live, detecting and acting on customer problems can take lengthy timeframes.The thesis proposes machine learning models using huge historical dataset collected from actual filed readings to predict the QOS received at the customer level indifferent locations. Five ML approaches are examined, and the results were compared to identify the ML model that can offer higher prediction accuracy for QOS.Then Clustering ML model was built to divide the coverage area into small areas such that probe devices can be used to collect field readings from specific locations to improve the predictive model
530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE
Additional physical form available note Issued also as CD
653 #4 - INDEX TERM--UNCONTROLLED
Uncontrolled term Machine Learning
653 #4 - INDEX TERM--UNCONTROLLED
Uncontrolled term Reference Signal Strength
653 #4 - INDEX TERM--UNCONTROLLED
Uncontrolled term Telecom Optimization
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Mohsen Rashwan ,
Relator term
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="http://172.23.153.220/th.pdf">http://172.23.153.220/th.pdf</a>
905 ## - LOCAL DATA ELEMENT E, LDE (RLIN)
Cataloger Nazla
Reviser Revisor
905 ## - LOCAL DATA ELEMENT E, LDE (RLIN)
Cataloger Shimaa
Reviser Cataloger
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Thesis
Holdings
Source of classification or shelving scheme Not for loan Home library Current library Date acquired Full call number Barcode Date last seen Koha item type Copy number
Dewey Decimal Classification   المكتبة المركزبة الجديدة - جامعة القاهرة قاعة الرسائل الجامعية - الدور الاول 11.02.2024 Cai01.13.08.M.Sc.2021.Am.E 01010110084486000 22.09.2023 Thesis  
Dewey Decimal Classification   المكتبة المركزبة الجديدة - جامعة القاهرة مخـــزن الرســائل الجـــامعية - البدروم 11.02.2024 Cai01.13.08.M.Sc.2021.Am.E 01020110084486000 22.09.2023 CD - Rom 84486.CD