## 3 The Proposed Model
### Intent Detection Using the DLMNN Model  
At this stage, the CL-DLBIDC technique makes use of the DLMNN model for intent detection and classification. In the DLMNN model, all of the inputs are offered to separate nodes that are common from the input region of classification [23]. The weight signifies a randomly allocated value and can be connected with all of the inputs. The following is the Hidden Layer (HL). The nodes in this HL are termed \"hidden nodes\", and they apply the function of summing the product of input values and the weighted vector of every input node which is connected to it.  
In DLMNN, the weighted value is optimized by employing the MFO algorithm and DLMNN. An arbitrary weighted value offers a further backpropagation (BP) procedure for achieving the outcome in an optimized manner. The activation function is then executed, and the resultant layer is carried to following layer. This weight is an utmost control on the classification result. The algorithmic steps of DLMNN classification are as follow:  
Step 1: Allocate score value of selective features and its corresponding weight as:  
\[E_{i}=\{E_{1},E_{2},E_{3}\ldots E_{n}\}, \tag{7}\]  
\[W_{i}=\{W_{1},\;W_{2},\;W_{3}\ldots W_{n}\}, \tag{8}\]  
Step 2: Multiply the input with the weighted vectors, which are arbitrarily chosen, and then, it can be entirely summed as:  
\[R=\sum_{i=1}^{n}E_{i}W_{i}, \tag{9}\]  
where \(R\) refers to the summed value, \(E_{i}\) denotes the input entropy value, and \(W_{i}\) stands for the weight value.  
Step 3: Estimate the activation function (AF), employing  
\[AF_{i}=f\left(\sum_{i=1}^{n}E_{i}W_{i}\right), \tag{10}\]  
Step 4: Measure the resultant HL as:  
\[Y_{i}=A_{i}+\sum G_{i}W_{i}, \tag{11}\]where \(A_{i}\) defines the bias value, \(W_{i}\) demonstrates the weight between the input and HLs, and \(G_{i}\) refers to the values that are changed by application of \(AF\).  
Step 5: Re-perform the earlier three steps on all of the layers of the DLMNN. Last, estimate the resultant unit by adding up every input signal weight to attain the resultant layer neuron value,  
\[Ou_{i}=A_{i}+\sum P_{i}W_{i}, \tag{12}\]  
where \(P_{i}\) signifies the value of the layer which leads to the output 1, \(W_{j}\) represents the weight of HL, and \(Ou_{i}\) demonstrates the output unit.  
Step 6: Contrast the network outcome with the target values. The variance among these two values produces the error signal. This value can be demonstrated mathematically as:  
\[E_{r}=Ta_{i}-Ou_{i}, \tag{13}\]  
where, \(E_{r}\) represents the error signal, \(Ta_{i}\) indicates the aimed target output, and \(Ou_{i}\) stands for the classification current output.  
Step 7: At this point, the resultant unit is weighted against the target values. Accordingly, the relative error is defined. According to this error, a value \(\delta_{i}\) has been calculated, and it can be utilized for allocating the error at the resultant layer back to every other unit on network:  
\[\delta_{i}=E_{r}[f(Ou_{i})], \tag{14}\]  
Step 8: The weighted correction is measured by employing the BP approach. This relation is:  
\[wc_{i}=\beta\delta_{i}(E_{i}), \tag{15}\]  
where \(wc_{i}\) defines the weighted correction, \(\beta\) signifies the momentum term, \(E_{i}\) implies the input vector, and \(\delta_{i}\) stands for the error that is distributed from the network.