## 3 Methodology
### Sparrow Search Algorithm  
The Sparrow Search Intelligence Sparrow Search Algorithm is a novel swarm intelligence optimization algorithm that was presented in 2020. It is primarily motivated by the foraging and anti-predatory actions of sparrows [58]. The SSA optimization algorithm is ideally suited for application in artificial intelligence machine learning models. Several investigations have been conducted on improving prediction models using the SSA algorithm. With the aim of enhancing the precision of fall detection, ref. [59] built a fall prediction model based on the SSA-BP neural network, adjusted it to pick the \begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt}} \hline \hline \multicolumn{2}{c}{**Advantage**} & \multicolumn{2}{c}{**Disadvantage**} \\ \hline
1. & Decision tree that is straightforward to grasp and interpret. & 1. & Prone to overfitting and overly complicated, which makes it hard to generalize, but this can be fixed by setting a minimum number of samples for each node and limiting the depth of the decision tree. \\
2. & Working knowledge of quantitative and categorical data, alongside discrete and continuous values. & 2. & Unstable and even minor changes in the sample can have an enormous effect. This can be handled by integrated learning. \\
3. & A white-box model with clearly interpretable outcomes. If the model shows the circumstance, Boolean logic can comprehend it. & 3. & Decision tree learning maximizes the local optimum, that is, the optimum at each node, and hence does not ensure the return of a globally optimal decision tree. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Decision tree algorithm pros and cons. appropriate number of sliding windows, and attained the accurate fall detection process. In an attempt to enhance oil recovery, ref. [60] constructed an SSA-ANN model, utilizing SSA, and the weights, biases, and hyperparameters of the ANN model were optimized; their experimental results generate a significant enhancement in accuracy in terms of oil recovery [61; 62; 63]. Ref. [64] proposed the SSA-LSTM model, which employs SSA for waveform fitting and achieves an accuracy of more than 95% for each LSTM network. As a result, multiple studies have indicated the efficiency of SSA in improving the model's hyperparameters. The primary notion of the SSA algorithm is as follows:  
1. Sparrow foraging behavior simulations: When foraging, sparrows migrate in various areas and directions in search of food. The SSA algorithm represents the solution space of an optimization problem as a distribution of food, where individual organisms search for optimal solutions, resembling the foraging behavior of sparrows in search of food. 2. Individual sparrows and pops: Through their unique actions and interactions with others, every individual symbolizes a solution and looks for the best one. There are plenty of individuals in the pops who cooperate to enhance search results. 3. Flight and position updates: In each iteration, each individual flies according to its position and speed, and the direction and distance of the flight are impacted by individual and global optimal solutions. The individual's position will be modified based on the outcome of the flight, which will progressively approach the best solution. To come closer to the ideal solution, the location of the individual is adjusted to reflect the outcomes of the flight. 4. Adaptation assessments: The value of the objective function is used to assess each individual's fitness. The higher the fitness value, the closer the individual is to the most appropriate response. 5. Knowledge transfer and updates: Individuals monitor the actions of others' locations and fitness values to try to update their behavior in an attempt to broaden their search through knowledge transmission and updating. In the sparrow algorithm, the basic parameters such as the number of iterations needed for the population and the population size are specified, and the proportion of discoverers is also set.