MARC details
| 000 -LEADER |
|---|
| fixed length control field |
05883namaa22004211i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
OSt |
| 005 - أخر تعامل مع التسجيلة |
|---|
| control field |
20250511094958.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
250508s2024 ua a|||fr|||| 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 |
005 |
| 092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC) |
|---|
| Classification number |
005 |
| Edition number |
21 |
| 097 ## - Degree |
|---|
| Degree |
M.Sc |
| 099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC) |
|---|
| Local Call Number |
Cai01.18.02.M.Sc.2024.Al.E. |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
|---|
| Authority record control number or standard number |
Ali Othman Ali Muhammad Jahlan, |
| Preparation |
preparation. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
An Enhanced Machine Learning Approach For Detection of Radioactive Mineralization / |
| Statement of responsibility, etc. |
By Ali Othman Ali Muhammad Jahlan; Supervised By Prof. Ammar Mohammad, Prof. Mohammad A. S. Youssef. |
| 246 15 - VARYING FORM OF TITLE |
|---|
| Title proper/short title |
نهج تعلم الآلة المحُسن للكشف عن التمعدنات المشعة / |
| 264 #0 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
2024. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
65 leaves : |
| 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 |
أThesis (M.Sc.) -Cairo University, 2024. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Bibliography: pages 60-65. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
This research investigates Airborne Gamma Ray Spectrometry (AGRS) data to detect naturally occurring radioactive anomalies, including potassium, uranium, and thorium, in the Wadi-Biyam area and its surroundings in Egypt's Eastern Desert between latitudes 22° 39' 45'' & 23° 01' N and longitudes 33° 47' & 34° 21' 30'' E. AGRS collects raw, unlabeled data from background radiation and radiation emitted by radioactive elements. When applying statistical methods for anomaly detection to AGRS data, several challenges arise: (1) Inaccuracy: The statistical methods might not accurately identify anomalies due to noise and variability in the data. (2) Time Consumption: Processing and analyzing the extensive data sets generated by AGRS can be time-consuming, delaying results and decision-making. (3) Bias: The statistical methods might be biased, resulting in skewed outcomes that fail to reflect the true distribution of radioactive anomalies accurately. (4) Limited Scope: While useful, traditional statistical methods have limitations regarding the complexity and variety of AGRS data. These limitations can restrict the ability to detect all significant anomalies, necessitating a more comprehensive approach. (5) False Certainty: These methods might produce results with a high degree of apparent certainty not justified by the underlying data, potentially leading to incorrect conclusions. The current challenges in anomaly detection can lead to severe consequences, such as misidentifying areas with high concentrations of radioactive elements or failing to detect significant anomalies. Therefore, the development of a new approach that can effectively address these challenges and improve the precision and reliability of anomaly detection in AGRS data is of utmost importance. We employ two machine learning methods for anomaly detection, DBSCAN and BIRCH, using the Silhouette Coefficient to evaluate results and plan mineral zones. Among the nine rock types studied, we observed significant variations in the concentration of radioactive elements, which we assessed using a Silhouette Score greater than 0.75 to ensure reliable clustering. Notably, the younger granite and wadi deposits stood out for their higher concentrations of these elements. Specifically, the younger granite exhibited a marked anomaly, with 7.55% of its points classified as abnormal, indicating elevated levels of radioactivity. Similarly, the wadi deposits demonstrated a significant presence of radioactive elements, with 4.22% of the points identified as anomalies. These findings suggest that the younger granite and wadi deposits play a crucial role in understanding the distribution of radioactive materials within the studied region, potentially offering insights into geological processes and the environmental implications of these anomalies. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
من بين الأنواع التسعة من الصخور التي تمت دراستها، لاحظنا اختلافات كبيرة في تركيز العناصر المشعة، والتي قمنا بتقييمها باستخدام درجة Silhouette Score أكبر من 0.75 لضمان التجميع الموثوق. والجدير بالذكر أن رواسب الجرانيت والوادي الأصغر سناً برزت لتركيزاتهم الأعلى من هذه العناصر المشعة. وعلى وجه التحديد، أظهر الجرانيت الأصغر سناً شذوذًا ملحوظًا، حيث تم تصنيف 7.55٪ من نقاطه على أنها غير طبيعية، مما يشير إلى مستويات مرتفعة من النشاط الإشعاعي. وبالمثل، أظهرت رواسب الوادي وجودًا كبيرًا للعناصر المشعة، حيث تم تحديد 4.22٪ من النقاط على أنها مناطق مشعة. تشير هذه النتائج إلى ان younger granite وwadi deposits تلعب دورًا حاسمًا في فهم توزيع المواد المشعة داخل المنطقة المدروسة، مما قد يوفر رؤى حول العمليات الجيولوجية والآثار البيئية لهذه العناصر المشعة. |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE |
|---|
| Issues CD |
Issued 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 |
Computer Science |
| Source of heading or term |
qrmak |
| 653 #0 - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
Airborne Gamma-Ray Spectrometry |
| -- |
Mineral exploration |
| -- |
Machine learning |
| -- |
DBSCAN |
| 700 0# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Ammar Mohammad |
| Relator term |
thesis advisor. |
| 700 0# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Mohammad A. S. Youssef |
| Relator term |
thesis advisor. |
| 900 ## - Thesis Information |
|---|
| Grant date |
01-01-2024 |
| Supervisory body |
Ammar Mohammad |
| -- |
Mohammad A. S. Youssef |
| Universities |
Cairo University |
| Faculties |
Faculty of Graduate Studies for Statistical Research |
| Department |
Department of Computer Science |
| 905 ## - Cataloger and Reviser Names |
|---|
| Cataloger Name |
Eman El gebaly |
| Reviser Names |
Eman Ghareb |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Thesis |
| Edition |
21 |
| Suppress in OPAC |
No |
