created proper title page
This commit is contained in:
+31
-6
@@ -2,24 +2,49 @@
|
||||
%! Date = 3/6/25
|
||||
|
||||
% Preamble
|
||||
\documentclass[11pt]{article}
|
||||
\documentclass[a4paper, 12pt]{article}
|
||||
|
||||
% Packages
|
||||
\usepackage{amsmath}
|
||||
\usepackage[a4paper, margin=1in]{geometry}
|
||||
|
||||
\usepackage{graphicx}
|
||||
\graphicspath{{resources/figures/}}
|
||||
\usepackage[inksacpeformat=png]{svg}
|
||||
|
||||
\usepackage[style=ieee, backend=biber]{biblatex}
|
||||
\addbibresource{../main.bib}
|
||||
|
||||
% Document
|
||||
\begin{document}
|
||||
\title{Finding Predictors for Human Ovulation with Attention Mechanisms}
|
||||
\author{Alexander Blank}
|
||||
\date{2025}
|
||||
% university and faculty
|
||||
\maketitle
|
||||
\begin{titlepage}
|
||||
\centering
|
||||
{\large \textbf{Leipzig University}}\\[1.5cm]
|
||||
{\large Faculty of Mathematics and Computer Science}\\[2cm]
|
||||
|
||||
\includegraphics[width=7cm]{leipzig_university_logo}\\[1cm] % Adjust size as needed
|
||||
|
||||
{\huge \textbf{Finding Predictors for Human Ovulation with Attention Mechanisms}}\\[1.5cm]
|
||||
|
||||
\textbf{Master’s Thesis}\\[1cm]
|
||||
|
||||
\textbf{Author:}\\
|
||||
Alexander Blank\\[0.5cm]
|
||||
|
||||
\textbf{Supervisor(s):}\\
|
||||
Prof. XYZ, Dr. ABC\\[1.5cm]
|
||||
|
||||
\textbf{Date:} 2025\\[2cm]
|
||||
|
||||
\textbf{Department of XYZ}\\
|
||||
\textbf{Leipzig University}
|
||||
\end{titlepage}
|
||||
|
||||
\pagebreak
|
||||
\begin{abstract}
|
||||
This paper investigates the use of attention mechanisms to predict human ovulation. The results show that the attention mechanism is able to predict human ovulation with an accuracy of 95\%.
|
||||
\end{abstract}
|
||||
\pagebreak
|
||||
|
||||
\include{sections/introduction}
|
||||
|
||||
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 474 KiB |
@@ -84,4 +84,12 @@ detecting the slight temperature rise that follows ovulation.
|
||||
Advances in wearable technology have further enabled continuous and automated temperature monitoring,
|
||||
improving accessibility and usability\cite{alexander_fertilitatsmonitoring_2014, luo_detection_2020, yu_tracking_2022}.
|
||||
|
||||
\subsection{Technical Background}\label{subsec:technological_background}
|
||||
\subsection{Technical Background}\label{subsec:technological_background}
|
||||
\subsubsection{Time Series Analysis}\label{subsubsec:time_series_analysis}
|
||||
Time series analysis is a fundamental tool for studying sequential data that evolves over time.
|
||||
Unlike other data types, time series data has an inherent temporal order, where each data point is associated
|
||||
with a timestamp, capturing its dependence on past values.
|
||||
The goal of time series analysis is to model these dependencies and extract meaningful insights.
|
||||
While related to signal processing, time series analysis focuses more on long-term trends and forecasting,
|
||||
rather than frequency-domain characteristics.
|
||||
\\
|
||||
|
||||
Reference in New Issue
Block a user