## 2 Related Work  
We carried out a broad review of the literature on several renowned performance evaluation and prediction techniques. In recent years, researchers have proposed several techniques to develop and enhance filtering mathematics, and these enhanced models are used in different practical application areas. Discrete data are commonly applied to predict the kinematics of moving objects. These data are related to air traffic control, antisubmarine warfare, missile interceptions, and similar distinguished applications. Methods have been specially designed for radar tracing to estimate velocity and positioning based on noisy data. Some of the filters from this family have been used for applications other than enabling accurate tracking and prediction, comparable to the Kalman filter [11; 12; 13; 14; 15]. The author of Reference [16] used the filters to make predictions and track rate fluctuations. Tracking parameters were also a topic of interest for the author of Reference [17]. The author of Reference [18] aimed to improve the efficiency of the filter family and introduced a new method to improve their performance. Their contribution also shed light on how balanced \(\alpha\)-\(\beta\) filters can be developed in terms of performance. Moreover, \(\alpha\)-\(\beta\)-\(\gamma\) filters have been applied in computer vision for different applications. The author of Reference [19] compared the \(\alpha\)-\(\beta\)-\(\gamma\) filter's performance with that of the Kalman filter. They observed that the Kalman filter converged coefficients to approximately constant levels, and for this reason, the computation of the filter was shown to be ineffective. Due to this inefficiency, the \(\alpha\)-\(\beta\) filter gave a good performance, and these promising results show that, compared with the Kalman filter, this filter has less computational time requirements. The author of Reference [20] also implemented similar filters to forecast target locations within an image plane. The target's position was captured in every iteration to predict one-step-forward locations by putting the data into an \(\alpha\)-\(\beta\)-\(\gamma\) filter. By testing and using this method, the tracking performance was shown to be effective and improved. The author of Reference [21] compared the \(\alpha\)-\(\beta\)-\(\gamma\) filter and the Kalman filter in terms of their performance. They refined the filter parameter values to prove that the \(\alpha\)-\(\beta\)-\(\gamma\) filter performs better than the Kalman filter. The author of Reference [22] proposed the cascaded Proportional Integrative Derivative (PID) law to control motor positions, using an \(\alpha\)-\(\beta\)-\(\gamma\) filter. This method using \(\alpha\)-\(\beta\) filters has a new design and procedure and obtains more precise results. \begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviations** & **Definition** \\ \hline DBN & Deep belief network \\ SVM & Support vector machine \\ ANN & Artificial neural network \\ DELM & Deep extreme learning machine \\ \(\alpha\)-\(\beta\) & Alpha-beta \\ RMSE & Root mean square error \\ MAE & Mean absolute error \\ KF & Kalman filter \\ PID & Proportional Integrative Derivative \\ MLP & Multilayer perceptron \\ BP & Backpropagation \\ HL & Hidden layer \\ AF & Activation Function \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notations with description. Recently, the author of Reference [23] proposed a genetic-based algorithm to determine the best parameter values of the \(\alpha\)-\(\beta\)-\(\gamma\) filter. The noise in the filter was at acceptable levels, and they also achieved a better improvement in performance. The author of Reference [24] proposed a perfect model to track accuracy enhancement, using the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter; this method is named \"the third order filter\" (in which four parameters are used, i.e., the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter). The third temporal derivative of interest value is the added value (called \"jerk\").