## 3 The Proposed Model
### Image Classification  
In the final stage, the ALSTM model is exploited for the classification of BT into distinct classes. The RNN is a class of NNs where the output of feed-forward traditional ANN is provided as novel input to neurons dependent upon novel input value. The resultant value at some neurons \((z+1)\) is dependent upon their input at moment \(z\). This improves the dynamism of the network method. Considering there is a connection between two input values, this method was determined as a memory network method [4; 7; 25; 26]. In RNN, input data were considered connected to everyone. The LSTM is also the most famous RNN network method, whereas the structure was established to vanish gradient problems. At this point, \(w_{z}\) implies the input value at time \(z\), and \(0_{z}\) signifies the resultant value at time \(z\). Figure 2 depicts the framework of the LSTM technique.  
The structure of the LSTM network node contains 3 fundamental gates, namely the forget gate \(f_{z}\), input gate \(i_{z}\), and output gate \(0_{z}\). However, the input, as well as output gates, signify the data entered and data exit the node at time \(z\) correspondingly. The forgetting gate chooses the data to be forgotten related to the preceding status data \((h_{z-1})\) and existing input \((x_{z})\). These 3 gates choose that upgrade the present memory cell \(c_{z}\) and present latency \(h_{z}\) value. During the LSTM node, the connections amongst the gates were computed mathematically utilizing the following formula:  
\[i_{z}=\sigma(w_{i}.\ [h_{z-1},\ x_{z}]+b_{i}) \tag{11}\]  
\[f_{z}=\sigma\Big{(}w_{f}.\ [h_{z-1},\ x_{z}]+b_{f}\Big{)} \tag{12}\]  
\[o_{z}=\sigma(w_{o}.\ [h_{z-1},\ x_{z}]+b_{o}) \tag{13}\]  
\[c_{z}^{\prime}=tanh(w_{c}.\ [h_{z-1},\ x_{z}]+b_{c}) \tag{14}\]  
\[c_{z}=f_{z}*c_{z-1}+i_{z}*c_{z}^{\prime} \tag{15}\]  
\[h_{z}=0_{z}*tanh(C_{z}) \tag{16}\]  
In the LSTM network structure procedures, the representation vectors take as an input in the primary data to the final data. Consider \(H\in\mathbb{R}^{d\times N}\) represent a matrix consisting of a hidden vector \([h_{1},\ \dots,\ h_{N}]\) that the LSTM produced, where the size of the hidden layer is represented as \(d\) and the length of the given data is denoted by \(N\). Moreover, \(e_{N}\in\mathbb{R}^{N}\) represents a vector of ls and \(v_{a}\) embedding vector. The attention model produces an attention a weighted hidden representation \(r\) and weight vector \(\alpha\).  
\[M=tanh\bigg{(}\left[\begin{array}{c}W_{h}H\\ W_{v}v_{a}\otimes e_{N}\end{array}\right]\bigg{)} \tag{17}\]  
\[\alpha=softmax\Big{(}w^{\top}M\Big{)} \tag{18}\]  
\[r=H\alpha^{T} \tag{19}\]  
In the equation, the projection parameters are characterized by \(M\in\mathbb{R}^{(d+d_{a})\times N}\), \(\alpha\in\mathbb{R}^{N}\), \(r\in\mathbb{R}^{d}\). \(W_{h}\in\mathbb{R}^{d\times d}\), \(W_{v}\in\mathbb{R}^{d_{a}\times d_{a}}\) and \(w\in\mathbb{R}^{d+d_{a}}\). \(r\) signifies a weighted  
Figure 2: Architecture of LSTM.  
representation of data with a given aspect, and a vector consisting of attention weight is indicated as \(\alpha\). The attention model enables the model to capture the essential part of data while considering different aspects.