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003			OSt
005			20250511094958.0
008			250508s2024 ua a|||fr|||| 000 0 eng d
040			_aEG-GICUC _beng _cEG-GICUC _dEG-GICUC _erda
041	0		_aeng _beng _bara
049			_aDeposit
082	0	4	_a005
092			_a005 _221
097			_aM.Sc
099			_aCai01.18.02.M.Sc.2024.Al.E.
100	0		_aAli Othman Ali Muhammad Jahlan, _epreparation.
245	1	0	_aAn Enhanced Machine Learning Approach For Detection of Radioactive Mineralization / _cBy Ali Othman Ali Muhammad Jahlan; Supervised By Prof. Ammar Mohammad, Prof. Mohammad A. S. Youssef.
246	1	5	_a نهج تعلم الآلة المحُسن للكشف عن التمعدنات المشعة /
264		0	_c2024.
300			_a65 leaves : _billustrations ; _c30 cm. + _eCD.
336			_atext _2rda content
337			_aUnmediated _2rdamedia
338			_avolume _2rdacarrier
502			_aأThesis (M.Sc.) -Cairo University, 2024.
504			_aBibliography: pages 60-65.
520			_aThis 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			_aمن 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			_aIssued also as CD
546			_aText in English and abstract in Arabic & English.
650		7	_aComputer Science _2qrmak
653		0	_aAirborne Gamma-Ray Spectrometry _aMineral exploration _aMachine learning _aDBSCAN
700	0		_aAmmar Mohammad _ethesis advisor.
700	0		_aMohammad A. S. Youssef _ethesis advisor.
900			_b01-01-2024 _cAmmar Mohammad _cMohammad A. S. Youssef _UCairo University _FFaculty of Graduate Studies for Statistical Research _DDepartment of Computer Science
905			_aEman El gebaly _eEman Ghareb
942			_2ddc _cTH _e21 _n0
999			_c171956