2023-2024 Fall Semester Final Exams
This list will be updated as the information about the missing courses are provided.
Kamran Karimov, Predicting the Primary Tissues of Cancers of Unknown Primary Using Machine Learning
Cancers of unknown primary (CUP) pose treatment challenges due to unidentified primary sites, affecting survival rates significantly. Traditional methods falter in pinpointing origins. Gene expression-based studies offer promise; machine learning models trained on annotated cancer data achieve remarkable accuracy, surpassing conventional methods. Our study, utilizing three machine learning models trained on TCGA data, attained competitive accuracy, around 96%.
Date: 18.01.2024 / 11:00 Place: A-108
English
Alper Sarıkaya, A Robust Machine Learning Based IDS Design Against Adversarial Attacks in SDN
Despite impressive achievements made by machine learning algorithms (especially in deep learning), they are easily tricked by modified input data. Adversarial attacks target machine learning models severely. Adversarial training is an effective method against adversarial attacks, but it is not suitable for network domains due to network flow characteristics. In this thesis, the autoencoder's reconstruction error is used for detecting adversarial attacks. The IDS model, RAIDS is proposed and achieves respectful results against adversarial attacks.
Date: 17.01.2024 / 14:00 Place: A-108
English
Serkan Özdemir, Development of a Decision-Support Tool for Managing Drinking Water Reservoir by Using Machine Learning and Deep Learning Methods
Global climate change induces lake level fluctuations, impacted by evolving meteorological factors and water use. Input or output changes swiftly affect the water balance equation. This study explores predictive models for climatic and hydrologic variables, assessing their correlations with lake water level and water quality. Using diverse algorithms—Naive Method, ANN, and RNN—LSTM excels in accuracy by RMSE. Comparisons with the Naïve Method confirm ANN and RNN predictive prowess, especially with extended horizons. Correlations with temperature and evaporation highlight lake water quality impacts. Models and metrics construct a decision support tool for water managers.
Date: 19.12.2023 / 13:30 Place: A-212
English
Toyan Ünal, Predicting Tennis Match Outcome: A Machine Learning Approach Using the SRP-CRISP-DM Framework
This thesis applies machine learning to predict outcomes of men’s singles tennis matches from 2009-2022, utilizing a standardized data mining framework, namely SRP-CRISP-DM, for replicable results. Employing six feature extraction techniques, three models, and two feature selection methods with time-based cross-validation and hyperparameter tuning, the Extreme Gradient Boosting model emerged as the top performer, scoring a Brier score of 0.1913 and an accuracy of 70.5%, with bookmakers' odds as the top predictive feature.
Date: 07.12.2023 Place: A-212
English
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Data Informatics Research Assistant Preliminary Evaluation Results
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Modelling and Simulation Department Research Assistant Preliminary Evaluation Results
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Health Informatics Department Research Assistant Preliminary Evaluation Results
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Announcement about Sticker
Students who want to buy stickers must contact Çetin İnci, one of our institute staff, until October 11, 2023.
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