% \documentclass[a4paper,fleqn]{cas-dc} % \documentclass[sn-mathphys-num]{sn-jnl} \documentclass{article} \usepackage{booktabs} \usepackage{graphicx} % Add this to your preamble %\usepackage{silence} %\WarningFilter{latex}{'h' float specifier changed to 'ht'} % line numbers %\usepackage[mathlines, switch]{lineno} %\usepackage[right]{lineno} %\usepackage{cite} %\theoremstyle{thmstyleone}% \newtheorem{theorem}{Theorem}% meant for continuous numbers %%\newtheorem{theorem}{Theorem}[section]% meant for sectionwise numbers %% optional argument [theorem] produces theorem numbering sequence instead of independent numbers for Proposition \newtheorem{proposition}[theorem]{Proposition}% %%\newtheorem{proposition}{Proposition}% to get separate numbers for theorem and proposition etc. %\theoremstyle{thmstyletwo}% \newtheorem{example}{Example}% \newtheorem{remark}{Remark}% %\theoremstyle{thmstylethree}% \newtheorem{definition}{Definition} %\usepackage{cite} \usepackage{natbib} \setcitestyle{square,numbers,sort&compress} \renewcommand{\thetable}{S\arabic{table}} \renewcommand{\thefigure}{S\arabic{figure}} %\renewcommand{\tablename}{TableS} \begin{document} %\DeclareCaptionLabelFormat{addS}{#1 S#2} %\captionsetup[table]{labelformat=addS} %~\vspace{0.1cm} %~\\ \begin{table}[ht] \raggedright \caption{Evaluation of traditional machine learning algorithms as classifier on DS2.\\} \label{tab5} \setlength{\tabcolsep}{10pt} % Adjust the spacing between columns \begin{tabular}{@{}ccccccc@{}} \toprule \textbf{Model} & \textbf{ACC} & \textbf{AUROC} & \textbf{AUPRC} & \textbf{F\_score} & \textbf{Recall} & \textbf{Precision} \\ \midrule RF & 49.42 & 70.00 & 31.00 & 22.81 & 17.02 & 56.54 \\ KNN & 47.72 & 68.00 & 30.00 & 28.98 & 26.28 & 39.18 \\ LR & 37.79 & 54.00 & 24.00 & 8.93 & 7.47 & 20.58 \\ DT & 29.98 & 42.00 & 18.00 & 15.81 & 16.18 & 16.25 \\ \textbf{NN} & \textbf{93.47} & \textbf{99.55} & \textbf{97.50} & \textbf{93.47} & \textbf{93.47} & \textbf{93.47} \\ \bottomrule \end{tabular} \end{table} %%%% \begin{table}[ht] \caption{Evaluation of traditional machine learning algorithms as classifier on DS3.\\} \label{tab6} \setlength{\tabcolsep}{10pt} % Adjust the spacing between columns \begin{tabular}{@{}ccccccc@{}} \toprule \textbf{Model} & \textbf{ACC} & \textbf{AUROC} & \textbf{AUPRC} & \textbf{F\_score} & \textbf{Recall} & \textbf{Precision} \\ \midrule RF & 41.72 & 59.00 & 26.00 & 5.03 & 4.83 & 9.35 \\ KNN & 37.79 & 53.00 & 24.00 & 6.54 & 6.53 & 9.96 \\ LR & 38.81 & 55.00 & 24.00 & 5.84 & 4.88 & 11.36 \\ DT & 24.00 & 34.00 & 15.00 & 4.21 & 4.93 & 4.00 \\ \textbf{NN} & \textbf{92.21} & \textbf{98.16} & \textbf{96.12} & \textbf{91.67} & \textbf{91.67} & \textbf{91.67} \\ \bottomrule \end{tabular} \end{table} \begin{table}[ht] % \raggedright \caption{Assessment of ADEP against other methods on DS2.\\} \label{tab8} \setlength{\tabcolsep}{9pt} % Adjust the spacing between columns \resizebox{1.2\textwidth}{!}{ % Resize the table to 95% of the text width \begin{tabular}{@{}ccccccccc@{}} \toprule \textbf{Model} & \textbf{ACC} & \textbf{AUROC} & \textbf{AUPRC} & \textbf{F\_score} & \textbf{Recall} & \textbf{Precision} & \textbf{FN} & \textbf{FP} \\ \midrule \textbf{ADEP} & 92.16 & 99.83 & \textbf{97.03} & \textbf{92.16} & \textbf{92.16} & \textbf{92.16} & \textbf{5072} & \textbf{5072} \\ GGI-DDI & 90.09 & 99.06 & 95.14 & 89.60 & 89.60 & 89.60 & 16813 & 16813 \\ SSF-DDI & \textbf{92.32} & 99.10 & 96.48 & 89.97 & 89.97 & 89.97 & 16225 & 16225 \\ LSFC & 92.26 & 99.13 & 96.50 & 89.98 & 89.98 & 89.98 & 16202 & 16202 \\ DPSP & 90.36 & \textbf{99.93} & 96.33 & 89.90 & 89.90 & 89.90 & 16339 & 16339 \\ GNN-DDI & 90.20 & 99.91 & 96.19 & 89.00 & 89.00 & 89.00 & 17779 & 17779 \\ MSTE & 84.09 & 99.68 & 89.59 & 80.50 & 80.50 & 80.50 & 31526 & 31529 \\ MDF-SA-DDI & 90.18 & 99.91 & 95.93 & 88.24 & 88.24 & 88.24 & 19008 & 19008 \\ NNPS & 90.06 & 99.90 & 96.16 & 87.77 & 87.77 & 87.77 & 19769 & 19769 \\ DDIMDL & 90.19 & 99.87 & 94.60 & 89.15 & 89.15 & 89.15 & 17536 & 17536 \\ RF & 69.48 & 78.91 & 97.00 & 77.99 & 77.99 & 77.99 & 35590 & 35590 \\ KNN & 59.23 & 52.62 & 88.66 & 74.08 & 74.08 & 74.08 & 41915 & 41915 \\ LR & 72.75 & 78.30 & 98.51 & 77.55 & 77.55 & 77.55 & 36302 & 36302 \\ DT & 62.65 & 45.21 & 79.39 & 59.20 & 59.20 & 59.20 & 95800 & 95800 \\ \bottomrule \end{tabular} } \end{table} \begin{table}[ht] % \raggedright \caption{Assessment of ADEP against other methods on DS3.\\} \label{tab9} \setlength{\tabcolsep}{9pt} % Adjust the spacing between columns \resizebox{1.2\textwidth}{!}{ % Resize the table to 95% of the text width \begin{tabular}{@{}ccccccccc@{}} \toprule \textbf{Model} & \textbf{ACC} & \textbf{AUROC} & \textbf{AUPRC} & \textbf{F\_score} & \textbf{Recall} & \textbf{Precision} & \textbf{FN} & \textbf{FP} \\ \midrule \textbf{ADEP} & \textbf{92.38} & \textbf{98.61} & \textbf{95.52} & \textbf{90.89} & \textbf{90.89} & \textbf{90.89} & \textbf{5773} & \textbf{5773} \\ GGI-DDI & 91.01 & 98.30 & 93.56 & 85.49 & 85.49 & 85.49 & 9206 & 9206 \\ SSF-DDI & 92.15 & 98.52 & 95.14 & 87.05 & 87.05 & 87.05 & 8215 & 8215 \\ LSFC & 91.24 & 98.33 & 93.93 & 85.64 & 85.64 & 85.64 & 9113 & 9113 \\ DPSP & 91.00 & 98.49 & 94.65 & 85.58 & 85.58 & 85.58 & 9147 & 9147 \\ GNN-DDI & 89.89 & 98.24 & 91.32 & 84.83 & 84.83 & 84.83 & 9623 & 9623 \\ MSTE & 83.25 & 98.06 & 90.04 & 83.19 & 83.19 & 83.19 & 10664 & 10664 \\ MDF-SA-DDI & 89.16 & 97.99 & 91.93 & 82.15 & 82.15 & 82.15 & 11324 & 11324 \\ NNPS & 84.13 & 95.15 & 89.90 & 83.24 & 83.24 & 83.24 & 10632 & 10632 \\ DDIMDL & 87.71 & 97.41 & 91.92 & 82.39 & 82.39 & 82.39 & 11172 & 11172 \\ RF & 70.07 & 88.14 & 76.45 & 74.08 & 74.08 & 74.08 & 16446 & 16446 \\ KNN & 69.99 & 90.15 & 73.38 & 70.79 & 70.79 & 70.79 & 18535 & 18535 \\ LR & 72.14 & 78.26 & 89.98 & 73.55 & 73.55 & 73.55 & 16783 & 16783 \\ DT & 59.12 & 64.38 & 55.16 & 60.34 & 60.34 & 60.34 & 38299 & 38299 \\ \bottomrule \end{tabular} } \end{table} \end{document}