## 3 Proposed Method
### Applied Algorithm  
Convolutional Neural Networks (CNNs): The CNNs are mostly employed in the field of computer visualization, namely for tasks involving image and video recognition. However, they may also be effectively utilized for sequential data analysis, including text processing. The CNNs are specifically built to learn and analyze spatial hierarchies of features from input data. This enables them to efficiently capture and represent patterns and structures within the data. The essential components of CNNs encompass the following three layers, namely--convolutional, pooling and fully connected layers. Convolutional neural networks also employ filtering techniques to apply them to input datasets, aiming to extract local features and generate feature maps. The sharing layers effectively reduce the spatial dimensions of the feature maps while retaining crucial information. The interconnected layers of the neural network combine the extracted features and produce predictions based on the learned demonstrations. The representation of the output of a convolutional layer can be achieved by the utilization of the convolution process, which is commonly termed as such,  
\[H_{ij}=(K*X)=\sum_{m=1}^{M}\sum_{n=1}^{N}K_{n,n}.X_{i+m-1,j+n-1} \tag{1}\]  
where, \(H_{ij}=\) \"element at position (_i,j_) is fed as input to achieve output feature map\"; \(K=\) \"CNN kernel\"; \(X=\) \"input data of a feature map\"; \(M\) and \(N=\) \"extents of the kernel\". The outcome of the CNN is then voted for a triggering function called ReLU (Rectified Linear Unit). This presents non-linearity,  
\[Y=\text{ReLU}(H)=\text{max}(0,H) \tag{2}\]  
where, \(Y=\) \"output after the activation function is applied to the feature map \(H^{\prime\prime}\). Recurrent Neural Networks (RNN): The RNNs are designed to efficiently handle sequential data by capturing temporal dependencies and maintaining information across time steps. These techniques seem to be particularly advantageous in jobs that need the examination of sequential patterns, such as natural language processing (NLP), speech recognition, and investigation of time series data. Recurrent neural networks are characterized by the presence of feedback connections, which facilitate the transmission of information from one temporal step to the subsequent one. This stands in contrast to the unidirectional flow of information in feed-forward neural networks. The utilization of this architectural design allows RNNs to effectively preserve an internal memory, hence enhancing their ability to capture and represent long-term data dependencies. The essential component of an RNN is the recurrent hidden layer, responsible for processing sequential data. Furthermore, each hidden unit within the recurrent layer is equipped with a recurrent connection, enabling it to receive its own prior output as an input. This facilitates the network's ability to retain and integrate knowledge from preceding iterations, hence enhancing its computational process in the present iteration. The computation within an RNN can be denoted by the subsequent mathematical expressions. \[h_{t} =f(W_{x}x_{t}+W_{h}h_{t-1}+b_{h}) \tag{3}\] \[h^{\prime}_{t} =f(W^{\prime}_{x}h_{t}+W_{h^{\prime}}h^{\prime}_{t-1}+b^{\prime}_{ h})\] (4) \[y_{t} =f(W_{y}h^{\prime}_{t}+b_{y}) \tag{5}\]  
where \(h_{t}\) = hidden layer at time \(t\), \(x_{t}\) is input at time \(t\), \(W_{x}\) = \"weighted matrix for the RNN connections\", \(W^{\prime}_{x}\) = \"weight matrix for the input connections\", \(W_{y}\) = \"weight matrix for the output connections\", \(b_{h}\) and \(b_{y}\) are \"bias terms\", and \(f\) = \"activation function\". RNNs are suitable for emotion recognition tasks that involve sequential data, such as analyzing the temporal patterns in speech or text data. However, standard RNNs have the lack of the disappearing \"gradient problem\" because of their limitations in capturing long-term dependence.