## 3 Materials and Methods
### AI-Based Models Employed
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Order** & **Method** & **Description** & **Key Parameters** & **Advantages** & **Disadvantages** & **Reference** \\ \hline \multirow{3}{*}{1} & Correlation-Based & Selects features with a high & \multirow{3}{*}{Correlation} & Simple and \multirow{3}{*}{} & \multirow{3}{*}{} & \multirow{3}{*}{} & \multirow{3}{*}{} \\  & Feature Selection (CFS) & correlation to the target and & low inter-feature & threshold & & computationally \\  & & correlation. & & inexpensive. & relationships. & \\ \hline \multirow{3}{*}{2} & \multirow{3}{*}{ANOVA F-Test} & Measures statistical & \multirow{3}{*}{\(p\)-value threshold} & Well-suited for \multirow{3}{*}{} & Limited to linear \multirow{3}{*}{} \\  & & significance of each feature & \multirow{3}{*}{\(p\)-value threshold} & & \multirow{3}{*}{} \\  & with the target variable. & & significant features. & & \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{Variance Threshold} & Removes features with low & \multirow{3}{*}{Variance} & Simple and \multirow{3}{*}{} & \multirow{3}{*}{} \\  & variance. & threshold & uninformative & & \\  & & & & & the target variable. & \\ \hline \multirow{3}{*}{4} & \multirow{3}{*}{Chi-Square Test} & Measures dependence & \multirow{3}{*}{None} & Effective for \multirow{3}{*}{} & \multirow{3}{*}{} \\  & between categorical features and the target variable. & & categorical data. & & \\ \hline \multirow{3}{*}{5} & \multirow{3}{*}{Lasso Regularization} & Shrinks coefficients of less & \multirow{3}{*}{} & \multirow{3}{*}{} & \multirow{3}{*}{} \\  & important features to zero. & parameter \(\lambda\) & & & \\ \hline \multirow{3}{*}{6} & Principal Component & Reduces dimensionality by \multirow{3}{*}{} & \multirow{3}{*}{} & \multirow{3}{*}{} & \multirow{3}{*}{} \\  & Analysis (PCA) & projecting data onto \multirow{3}{*}{} & \multirow{3}{*}{} & \multirow{3}{*}{} \\  & principal components. & & & & \\ \hline \multirow{3}{*}{7} & Particle Swarm & Simulates particle & Number of \multirow{3}{*}{} & \multirow{3}{*}{} \\  & Optimization (PSO) & movement to optimize & particles, inertia & & \\ \cline{3-5}  & & feature subsets. & weight & & \\ \hline \multirow{3}{*}{8} & Genetic Algorithm & Uses evolutionary & \multirow{3}{*}{} & \multirow{3}{*}{} & \multirow{3}{*}{} \\  & (GA) & principles like crossover and \multirow{3}{*}{} & \multirow{3}{*}{} & \multirow{3}{*}{} \\  & mutation. & & & & \\ \hline \multirow{3}{*}{9} & Ant Colony & Simulates pheromone-based \multirow{3}{*}{} & \multirow{3}{*}{} & \multirow{3}{*}{} \\  & Optimization (ACO) & movement to optimize & & & \\ \cline{3-5}  & & feature selection. & & & \\ \hline \multirow{3}{*}{10} & Coyote Optimization & Model the social behavior of \multirow{3}{*}{} & \multirow{3}{*}{} & \multirow{3}{*}{} \\  & Algorithm (COA) & coyotes in packs. & & & \\ \hline \multirow{3}{*}{11} & Imperialist & Simulates imperialistic \multirow{3}{*}{} & \multirow{3}{*}{} & \multirow{3}{*}{} \\  & Competitive & competition among nations & nations, & & \\ \cline{3-5}  & Algorithm (ICA) & to optimize selection. & & & \\ \hline \multirow{3}{*}{12} & Whale Optimization & Mimics humpback whales\({}^{\prime}\) & Number of \multirow{3}{*}{} & Balances & \multirow{3}{*}{} \\  & Algorithm (WOA) & bubble-net hunting & whales, search & exploration and & \\ \cline{3-5}  & & behavior. & space size & exploitation. & \\ \hline \multirow{3}{*}{13} & Grey Wolf Optimizer & Simulates leadership \multirow{3}{*}{} & \multirow{3}{*}{} & \multirow{3}{*}{} \\  & (GWO) & hierarchy and hunting & & & \\ \cline{3-5}  & mechanism of wolves. & & & & \\ \hline \multirow{3}{*}{14} & Simulated Annealing & Combines stochastic \multirow{3}{*}{} & Initial \multirow{3}{*}{} & \multirow{3}{*}{} \\  & exploration with a cooling & temperature, & & \\ \cline{3-5}  & (SA) & schedule. & & & \\ \cline{3-5}  & & & & \\ \hline \multirow{3}{*}{15} & Firefly Algorithm (FA) & Mimics fireflies’ light-based \multirow{3}{*}{} & Light absorption, randomness & Balances exploration and exploitation. & May be slower for \multirow{3}{*}{} \\  & & attraction. & factor & exploitation. & \\ \hline \multirow{3}{*}{16} & \multirow{3}{*}{Harmony Search (HS)} & Inspired by the process of \multirow{3}{*}{} & \multirow{3}{*}{} & \multirow{3}{*}{} \\  & musical improvisation to & memory size, & & \\ \cline{3-5}  & find optimal solutions. & pitch adjustment & & \\ \cline{3-5}  & & rate & & \\ \hline \multirow{3}{*}{17} & \multirow{3}{*}{Bat Algorithm (BA)} & Mimics echolocation \multirow{3}{*}{} & \multirow{3}{*}{} & \multirow{3}{*}{} \\  & behavior of bats for \multirow{3}{*}{} & \multirow{3}{*}{} & \multirow{3}{*}{} \\  & optimization. & & & \\ \cline{3-5}  & & rate, frequency & & \\ \hline \multirow{3}{*}{18} & Artificial Bee Colony \multirow{3}{*}{} & Simulates the foraging \multirow{3}{*}{} & Number of bees, \multirow{3}{*}{} & \multirow{3}{*}{} \\  & (ABC) & behavior of honeybees. & & & \\ \cline{3-5}  & & & & \\ \hline \hline \end{tabular}
\end{table}