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Information
| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| PHYSICS (MASTER) (WITH THESIS) | |
| Code | FK743 |
| Name | Artificial Intelligence Applications in Luminescence Dosimetry |
| Term | 2026-2027 Academic Year |
| Term | Fall |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Belirsiz |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. MEHMET YÜKSEL |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The aim of this course is to provide knowledge on the use of artificial intelligence and machine learning techniques for analyzing TL and OSL dosimetry data, determining ionizing radiation dose, and developing AI-based dose prediction models.
Course Content
Fundamental principles of thermoluminescence (TL) and optically stimulated luminescence (OSL) dosimetry methods; luminescence mechanisms and trap–recombination centers; physical characteristics and analysis of TL glow curves and OSL decay curves; determination of ionizing radiation dose from luminescence signals; luminescence data processing and signal analysis methods; fundamental concepts of artificial intelligence and machine learning; artificial neural networks (ANN) and training algorithms; artificial intelligence–based modeling of luminescence data; prediction of TL glow curves and OSL decay curves from ionizing radiation doses; model improvement using optimization algorithms; model validation and performance evaluation methods; error metrics (RMSE, MAE); and uncertainty analysis in dose estimation.
Course Precondition
There is no prerequisite for this course.
Resources
1) Horowitz, Y.S. (1984). Thermoluminescence and Thermoluminescent Dosimetry. CRC Press, 2) Boetter-Jensen, L., McKeever, S.W.S., Wintle, A.G. (2003). Optically Stimulated Luminescence Dosimetry. Elsevier., 3) Bishop, C. (2006). Pattern Recognition and Machine Learning. Springer.
Notes
Lecture Notes, Lecture presentations, Articles and Papers: 1) Yüksel M., Ünsal, E.,Thermoluminescence Properties of Plagioclase Mineral and Modelling of TL Glow Curves with Artificial Neural Networks, Applied Sciences, vol.15(8), 4260, April 2025., 2) Yüksel M., Deniz, F., Ünsal, E.,ANN-Based Prediction of OSL Decay Curves in Quartz from Turkish Mediterranean Beach Sand, Crystals, vol.15(8), 733, August 2025. 3) Yüksel, M., (2022). Radiation Dose Estimation with Artificial Neural Networks. 3rd International Radiation Protection Congress (pp.2). Ankara, Turkey.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explains the fundamental physical principles of thermoluminescence (TL) and optically stimulated luminescence (OSL) dosimetry methods. |
| LO02 | Analyzes and interprets the characteristics of TL glow curves and OSL decay curves. |
| LO03 | Calculates and interprets ionizing radiation doses using data obtained from luminescence measurements. |
| LO04 | Applies data processing and modeling methods to determine radiation dose from TL and OSL signals. |
| LO05 | Models luminescence signals using artificial neural networks (ANN) and machine learning algorithms. |
| LO06 | Develops artificial intelligence models that predict TL glow curves and OSL decay curves from ionizing radiation doses. |
| LO07 | Evaluates and improves the performance of artificial intelligence models using optimization algorithms and model validation methods. |
| LO08 | Interprets the reliability of luminescence dose prediction models using error metrics such as RMSE and MAE and uncertainty analysis. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Has sufficient infrastructure in various subjects of Physics. | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Demonstrate the knowledge of appropriate mathematical techniques used in physics. | 4 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Interpret observational and experimental results. | 3 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Makes use of the conceptual and practical knowledge acquired in the physics field at mastery level. | 3 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Has a knowledge about the logic of scientific research. | 3 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Report the solution of a physics problem, experimental results or projects in a written format or orally. | |
| PLO07 | Bilgi - Kuramsal, Olgusal | Chooses and uses the necessary publications, books and methods for a scientific research. | 3 |
| PLO08 | Bilgi - Kuramsal, Olgusal | Accesses a knowledge about a subject in physics, does literature search and uses other sources for this purpose. | |
| PLO09 | Bilgi - Kuramsal, Olgusal | Provides solutions to the problems encountered in the physics field applying research methods. | 4 |
| PLO10 | Bilgi - Kuramsal, Olgusal | Can perform an independent research. | 3 |
| PLO11 | Bilgi - Kuramsal, Olgusal | Can perform group work effectively in a research or industrial projects. | 3 |
| PLO12 | Bilgi - Kuramsal, Olgusal | Becomes conscious of the necessity of lifelong learning. | |
| PLO13 | Bilgi - Kuramsal, Olgusal | To keep track of the developments in physics and updates himself/herself invariably. | 3 |
| PLO14 | Bilgi - Kuramsal, Olgusal | Shares his/her ideas and suggestions for solutions to the physical problems with experts and non-experts by supporting them with quantitative and qualitative data. | |
| PLO15 | Bilgi - Kuramsal, Olgusal | Can make an effective written or oral presentation of the results obtained in a study. | 3 |
| PLO16 | Bilgi - Kuramsal, Olgusal | Makes use of the knowledge, problem solving and / or application skills acquired in the physics field in interdisciplinary studies. | 4 |
| PLO17 | Bilgi - Kuramsal, Olgusal | Has a foundation necessary to work in a research and development organizations. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to luminescence dosimetry, TL and OSL dosimetry principles, Data acquisition processes | Study from textbooks and lecture notes, literature review | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 2 | Luminescence mechanisms, energy traps and recombination centers, data characteristics | Study from textbooks and lecture notes, literature review | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Alıştırma ve Uygulama |
| 3 | TL glow curves, glow curve analysis and numerical data representation | Study from textbooks and lecture notes, literature review | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Bireysel Çalışma |
| 4 | OSL decay curves, signal components and analysis of data characteristics | Study from textbooks and lecture notes, literature review | Öğretim Yöntemleri: Tartışma, Deney / Laboratuvar, Benzetim |
| 5 | Preprocessing of luminescence data, data normalization and feature extraction | Study from textbooks and lecture notes, literature review | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Alıştırma ve Uygulama |
| 6 | Introduction to machine learning, dataset creation and data splitting methods | Study from textbooks and lecture notes, literature review | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 7 | Structure of artificial neural networks (ANN), training algorithms, and their application to luminescence data | Study from textbooks and lecture notes, literature review | Öğretim Yöntemleri: Soru-Cevap, Anlatım, Tartışma |
| 8 | Mid-Term Exam | Study from textbooks and lecture notes | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Radiation dose estimation using artificial intelligence based on TL and OSL data | Study from textbooks and lecture notes, literature review | Öğretim Yöntemleri: Anlatım, Tartışma, Alıştırma ve Uygulama, Bireysel Çalışma |
| 10 | Prediction of TL glow curves from ionizing radiation dose using artificial intelligence | Study from textbooks and lecture notes, literature review | Öğretim Yöntemleri: Anlatım, Benzetim, Gösterip Yaptırma, Soru-Cevap |
| 11 | Prediction of OSL decay curves from ionizing radiation dose using artificial intelligence | Study from textbooks and lecture notes, literature review | Öğretim Yöntemleri: Anlatım, Benzetim, Gösterip Yaptırma, Soru-Cevap |
| 12 | Improvement of artificial intelligence models using optimization algorithms | Study from textbooks and lecture notes, literature review | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Bireysel Çalışma |
| 13 | Model validation methods, training–test datasets, and cross-validation | Study from textbooks and lecture notes, literature review | Öğretim Yöntemleri: Alıştırma ve Uygulama, Benzetim, Problem Çözme |
| 14 | Model performance analysis, RMSE and MAE error metrics | Study from textbooks and lecture notes, literature review | Öğretim Yöntemleri: Tartışma, Alıştırma ve Uygulama, Benzetim, Bireysel Çalışma |
| 15 | Uncertainty analysis in dose prediction models and artificial intelligence-based applications | Study from textbooks and lecture notes, literature review | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Bireysel Çalışma |
| 16 | Term Exams | Study from textbooks and lecture notes | Ölçme Yöntemleri: Yazılı Sınav, Proje / Tasarım |
| 17 | Term Exams | Study from textbooks and lecture notes | Ölçme Yöntemleri: Yazılı Sınav, Proje / Tasarım |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 4 | 56 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 12 | 12 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 12 | 12 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 152 | ||
| Total Workload / 25 (h) | 6,08 | ||
| ECTS | 6 ECTS | ||