## 3 Proposed Schemes
### Deep Extreme Learning Machine (DELM)-Based Model  
In the proposed method, the beneficial features of DL and the ELM are combined, and the approach is called the DELM. Figure 4 shows the model of the DELM; the DELM model uses an input layer of two neurons, and we used five hidden layers and an individual HL consisting of twelve neurons and two output layers. We trained the DELM on historical data to improve the algorithm by taking a different combination of training and testing samples and validating the output results with input data samples. In the training unit of the proposed model, two input parameters were taken, i.e., temperature and humidity values, by a DELM. The model output was also alpha-beta filter values, which were given to the prediction unit as input values. The DELM worked by tuning the parameters to the alpha-beta filter, addressing estimated errors in sensor readings, and intelligently updating the alpha and beta values. The alpha-beta performance was continuously monitored by examining its output results in the training unit. Figure 4: Structure of DELM with alpha-beta filter. Figure 3: The structure model of deep belief network with alpha-beta filter. The alpha values (current temperature values) and the beta values (humidity values) were input into the deep extreme learning machine to the prediction algorithm unit. The prediction algorithm unit was based on alpha and beta filter values, and these values were taken as inputs to predict the desired temperature. The alpha-beta filter does not require all the historical input values, and only the prior outcome values help the algorithm become more intelligent. The system determines the actual state information because of the prior state values; the algorithm is lightweight [11]. In the current research, we tested the dataset with temperature values from noisy temperature-sensor-reading data on the alpha-beta filter. In comparison, noise is always dependent on temperature-sensor readings and other conditions, and the temperature is deeply dependent on the humidity level and is always affected by increases or decreases in humidity in environments. When a filter reads the temperature sensor and removes noise from incoming data, it acquires the temperature with regard to time, T, and estimates the accurate temperature. The performance is mainly measured by tuning the alpha (temperature) and beta (humidity level) parameters in the alpha-beta filter. The tuned parameters are always updated after every iteration. The structure of the DELM is shown in Figure 4.  
The deep ELM is a renowned and exciting method which combines the extreme learning machine (ELM) and deep learning. The standard ANN algorithm needs to be trained more for extensive data, involves extra time consumption, has an inexpensive learning rate, and sometimes may lead to overfitting of the model [2]. The ELM method has been used in classification and regression tasks because of its efficiency; this technique is computationally cheap, and the learning rate is speedy. The model comprises two input layers and five hidden layers, with each HL consisting of 10 neurons and two output layers. Initially, input feature, \(A=[a_{k1}\ a_{k2}\ a_{k3}\ \dots\ \ a_{kZ}]\); a training sample, \([A,B]=\left\{a_{k},\ b_{k}  
\[w= \begin{bmatrix}w_{11}&w_{12}\.\.\.\.\ \ w_{1p}\\ w_{21}&w_{22}\.\.\.\ \ w_{2p}\\ w_{31}&w_{32}\.\.\.\ \ w_{3p}\\ \vdots&\vdots&&\vdots\\ w_{i1}&w_{i2}\.\.\.\ \ w_{ip}\\ \end{bmatrix}  \tag{8}\]  
\[\gamma= \begin{bmatrix}\gamma_{11}&\gamma_{12}\.\.\.\ \ \gamma_{1r}\\ \gamma_{21}&\gamma_{22}\.\.\.\ \ \gamma_{2r}\\ \gamma_{31}&\gamma_{32}\.\.\.\ \ \gamma_{3r}\\ \vdots&\vdots&&\vdots\\ \gamma_{p1}&\gamma_{p2}\.\.\.\ \ \gamma_{pr}\\ \end{bmatrix}  \tag{9}\]  
Then, in the hidden layers, the biases are arbitrarily selected by the extreme learning machine, given by Equation (10). Furthermore, the ELM calculates the \(g(x)\) function, which is the activation function used for the ELM.