## 3 Proposed Schemes
### The Deep Belief Network (DBN)-Based Learning Model  
We proposed a deep-belief-network-based (DBN) alpha-beta filter. The model was first developed by [PERSON] [30]. It is also a very popular prediction method which uses real-world data to predict unseen data. To boost the model performance, we combined this model with other typical time-series prototypes. The model contains different learning components with less complexity and restructures the inputs concerning the probability. The model is mainly based on the restricted Boltzmann machine (RBM) and pre-trains the network with the help of unsupervised learning for every couple of layers. The model of the RBM comprises a hidden layer, Boolean hidden units, a visible layer, and a two-fold-layer neural network. Figure 2: The complete architecture of our proposed scheme. In the learning module, we used the deep belief network. Inputs to the DBN are the temperature-sensor values and humidity-sensor values. The outputs of the DBN are the alpha-beta values in the prediction module. This model comprises two inputs, and the probability distribution can learn various sets of data. Furthermore, the model consists of a visible layer of units with symmetrical connections, and the model has only one HL of hidden components. Inside the same layer, there are no interconnections. However, it still requires a bipartite graph and the development of its neurons. The hidden-layer probability distribution and layer-wise configuration of the learning process [31] are displayed as follows:  
\[EE(v,h)=-\sum_{i=1}^{n_{\text{r}}}aivi-\sum_{y=1}^{n_{\text{r}}}byhy-\sum_{i= 1}^{n_{\text{r}}}\cdot\sum_{y=1}^{n_{\text{r}}}h_{y}vy,iv_{i} \tag{1}\]  
\[P(v,h)=\left(\frac{e^{-E(v,h)}}{\Sigma_{v}\Sigma_{h}e^{-E(v,h)}}\right) \tag{2}\]  
Meanwhile, in the visible layer, \(vi\) is the \(i\)th neuron binary state. The other representations are as follows: The visible layer, \(n_{\text{r}}\), is the number of neurons. The \(y\)th is the Boolean neuron inside the hidden layer of the binary state, and \(hy\) is the number of neurons, \(n_{\text{r}}\), in the hidden layer. The \(vy\) and \(i\) are the weight matrices between the HL and the visible layer. The \(ai\) is the bias vector for the hidden layers and the visible layer, by:  
\[P(h_{i}=1|v)=\sigma\Bigg{(}bj+\sum_{j=1}^{nv}\omega j,\ ivi\Bigg{)} \tag{3}\]  
\[P(v_{i}=1|h)=\sigma\Bigg{(}ai+\sum_{j=1}^{nh}\omega j,\ i\hbar j\Bigg{)} \tag{4}\]  
The above two equations symbolize the activation functions of the HL and the visible layer, where the sigmoid activation function [2] is referred to as \(\sigma\). Now, we explain how the final output predicts the DBN regression model, and different models are created to train each terminal node. Normalization was completed for each feature's train and test datasets to adjust the original data between 0 and 1. The Min-Max scaler function was used, and it was fed into the training model. The new input feature xi means the original value; the input feature minimum value is \(\min(x)\), and \(\max(x)\) represents the maximum value, which is the new rescaled value of \(x_{i}\). The prediction performance for the test data was measured by the error metrics. Equation (5) shows how the data are normalized:  
\[new\ \varkappa_{i}=\frac{x_{i}-\min(x)}{\max(x)-\min(x)} \tag{5}\]  
In the prediction module, we used the alpha-beta filter. The inputs to the filter are the temperature sensor values. The sensor-reading module obtains the sensor-temperature-reading data and inputs it to the compute delta temperature module. The compute delta temperature module outputs are used as inputs to the predicted actual temperature module and updated velocity. The predicted accurate temperature and initial state module values are input to the previous updated state module. The previous updated velocity module takes the updated value module and the initial velocity state values as inputs.