hopefully final commit

thesis/main.tex

@@ -38,7 +38,7 @@
     \begin{titlepage}
         \centering
         {\large \textbf{Leipzig University}}\\[1.5cm]
-        {\large Faculty of Mathematics and Computer Science}\\[2cm]
+        {\large Faculty of Mathematics and Computer Science}\\[1cm]
 
         \includegraphics[width=7cm]{leipzig_university_logo}\\[1cm] % Adjust size as needed
 
@@ -47,7 +47,9 @@
         \textbf{Master’s Thesis}\\[1cm]
 
         \textbf{Author:}\\
-        Alexander Blank\\[0.5cm]
+        Alexander Blank\\ [0.2cm]
+        Matriculation Number: \\
+        3796505\\ [0.5cm]
 
         \textbf{Supervisor:}\\
         Prof. Bogdan Franczyk\\
@@ -62,7 +64,22 @@
 
     \pagebreak
     \begin{abstract}
-        This could be your abstract - call 0800 - ABSTRACT to get your personal quote.
+        This thesis investigates the feasibility of predicting fertility from high-resolution body
+        core temperature data using machine learning.
+        More than 40,000 cycles recorded with an intravaginal sensor were used to train and evaluate recurrent,
+        transformer-based, and convolutional model variants.
+        Results show that while LSTM models achieved the lowest error on standard metrics,
+        transformer-based architectures performed more robustly in use-case evaluations
+        for contraception and natural family planning (NFP).
+        Predictions were substantially more accurate in regular cycles than in irregular ones,
+        underlining both the promise and the limits of temperature-based approaches.
+        A pre-ovulatory temperature drop was identified as a relevant physiological marker influencing fertility probability.
+        In simulated use cases, model predictions approached the effectiveness of established contraceptive
+        methods and enabled more efficient timing for conception efforts.
+        Despite the limitations of retrospective labeling, noisy real-world data,
+        and the restriction to temperature as a single modality,
+        the study demonstrates that machine learning can meaningfully advance individualized fertility prediction.
+        These results provide a foundation for future multimodal approaches and for the development of adaptive, user-tailored fertility support tools.
     \end{abstract}
     \pagebreak
 
@@ -97,6 +114,19 @@
     Responsibility for the content, structure, and conclusions of this thesis remains entirely my own.
     \pagebreak
 
+    I hereby declare that I have written the present thesis independently and only with the use of the sources and aids indicated.
+    In particular, literal or paraphrased quotations are marked as such.
+    I am aware that any violation may lead, even retrospectively,
+    to the revocation of the degree.
+    I further declare that the electronic copy is identical to the printed copies.
+
+    \vspace{2cm}
+
+    \noindent
+    \begin{tabular*}{\textwidth}{@{\extracolsep{\fill}} l c r}
+        Location: & Date: & Signature: \\
+    \end{tabular*}
+
     \include{sections/appendix}
 
     \printbibliography