## 2 Related Work  
The asteroseismology research-related literature work is examined in this section. Several past research approaches are analyzed in the context of asteroseismology classification. The learning techniques were utilized and evaluated for the classification task.  
The classical deep-learning-based 1D CNN [19] supervised learning model was applied to classify the red giant's branch in asteroseismology [20]. The 1D CNN model was trained using the asteroseismology dataset. The visual features [21] from the oscillation spectra were utilized in this study. These features help define the oscillating properties of stars and the beginning of helium core burning. The model was tested for classifying the helium-burning red giants in asteroseismology.  
A new method was proposed to determine the evolutionary phase of HeB and RGB stars for classification [22]. The asteroseismic data help to achieve the information necessary for classification. The proposed research presents an autonomous way of finding the evolutionary state through the data analysis of the power spectrum. The proposed method was efficient in providing a large number of star results. The dipole-mode oscillations structure was used that has a mixed character in RGB. These oscillation structures were utilized for classification.  
Network intrusion detection using the deep-learning-based model [23] was proposed in this study [24]. The Convolutional Neural Network (CNN) and Gated Recurrent Units (GRU) based model was applied for the network intrusion detection task. The ADASYN technique [21] was utilized to balance the employed datasets. The dataset feature selection was made using the Random Forest model [25]. The proposed model was evaluated on three NSL-KDD, UNSW_NB15, and CIC-IDS2017 datasets.  
The Twitter depression classification using recurrent neural networks (RNN) was proposed [26]. The Twitter tweets are scraped for model training and testing. The depression analysis was carried out based on textual data. The findings protect people from disorders by developing a diagnostic classification system.  
The literature summary and comparative analysis of our proposed research study with the existing research approaches are expressed in Table 1. The analysis contains the year of publication of the literature, the dataset used for model building, the type of learning techniques utilized, proposed approaches, and the analysis remarks.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Literature**} & **Year of** & \multirow{2}{*}{**Data Set**} & **Learning** & **Proposed** & \multirow{2}{*}{**Research Aim**} \\  & **Publication** & & **Type** & **Technique** & \\ \hline
[22] & 2016 & Kepler satellite & Data Analysis & Novel Method & A new method was proposed for classification to determine the evolutionary phase of HeB and RGB stars. \\
[20] & 2017 & Asteroseismology & Deep Learning & 1D CNN & Simple deep-learning-based 1D CNN was proposed for classification in asteroseismology. \\
[24] & 2022 & UNSW\_NB15 & Deep Learning & CNN-GRU & The deep-learning-based CNN and GRU models were proposed for network intrusion classification. \\ \hline \end{tabular}
\end{table}
Table 1: The literature summary and comparative analysis with other state-of-the-art studies with our proposed research.