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

Cybersecurity Orientation Meeting

Dear Students,

In the 2023-2024 Academic Year Fall Term,our introduction and information meeting with our students who have been accepted to the Cyber Security program will be held face to face on September 27, 2023 at 10:00 at Conference-2 (II-06 Classroom) in Informatics Institute.

We congratulate our students who have been accepted to the program and wish them success.

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