## 3 Proposed Method
### Applied Algorithm
Long Short-Term (LST) Memory: The LSTM networks are a type of recurrent neural network that can capture dependency over time in serial data and solve the issue of vanishing gradients. Long short-term memory networks add a memory cell and numerous gating methods to regulate data transfer across the network. The LSTM architecture consists of three main components of gates: input, forget, and output. They regulate the information flow by updating or discarding statistics from the memory cell depending on the current and previous inputs. This permits LSTMs to learn long-term reliance by effectively retaining relevant information over many time steps. \[f_{t} =\delta(W_{xf}\times x_{t}+W_{hf}\times h_{t-1}+b_{i}) \tag{6}\] \[i_{t} =\delta(W_{xi}\times x_{t}+W_{hi}\times h_{t-1}+b_{i})\] (7) \[\delta_{t} =\delta(W_{x0}\times x_{t}+W_{h0}\times h_{t-1}+b_{0})\] (8) \[c_{t} =f_{t}.c_{t-1}+i_{t}.\tanh(W_{xf}\times x_{i}+W_{hc}\times h_{t-1} +b_{c})\] (9) \[h_{t} =\delta_{t}\times\tanh(c_{t}) \tag{10}\]  
where the forget gate is \(f_{t}\), input gate is \(i_{t}\), and output gate is \(\delta_{t}\), respectively. Moreover, the term \(c_{t}\) is stating the updated state of a memory cell and the hidden state with respect to time \(t\) is \(h_{t}\). The input with respect to time \(t\) is represented by \(x_{t}\). We measure the matrices of weight with the following symbols:\(W_{xf}\), \(W_{hf}\), \(W_{xi}\), \(W_{hi}\), \(W_{x0}\), \(W_{ho}\), and \(W_{hc}\) correspondingly. The biases are measured by \(b_{i}\), \(b_{o}\) and \(b_{c}\) We measure the sigmoid function for activation by the \(\sigma\) symbol. The LSTM network works as another part of the DL model, which is comprised of memory blocks, or a collection of subnets that are usually connected. Memory blocks consist of an output gate, an input gate, a forget gate, and a memory cell. In contrast to the conventional recurrent unit, which updates its contents at each iteration, the LSTM unit utilizes the introduced gates to determine whether or not to retain the existing memory. LSTM explicitly avoids the long-term dependency conundrum. In contrast to the solitary neural network layer found in recurrent neural networks, the LSTM architecture comprises four interconnected layers. The structure of LSTM is wherein each line represents a complete vector connecting the output of one node to the inputs of the other. LSTM layers are especially designed to collect and represent long-term relationships in sequential data. They possess the capability to retain knowledge from previous inputs and use it to generate estimates for future inputs. Its layers have the ability to handle input sequences that have different values, and the layers provide the ability to automatically adjust and acquire knowledge from sequences of varying lengths, unlike conventional feed-forward neural networks that need inputs of fixed sizes. This model facilitates the acquisition and transmission of significant gradient information over different time steps. This process is crucial for the successful training of deep recurrent neural networks. LSTMsprovide the capability of effectively acquiring and representing information across many time scales when modelling sequential data. This mechanism inherent in LSTM layers enables them to flexibly regulate the amount of past information they keep and the amount of new information to adapt, successfully capturing both short-term and long-term dependencies in the data. This skill is crucial in jobs that involve the varying significance of contextual information throughout various time periods. Its layers are suitable for jobs involving multi-dimensional input data, such as disease identification, image recognition, video analysis, and audio processing. DL models can quickly find temporal links in the input data by using LSTM layers along with convolutional or fully connected layers. LSTMs have been applied in various NLP tasks successfully, including sentiment analysis and recognition.