## 4 Proposed Model Design
### Learning Algorithms Performance Comparison  
The proposed DNN model is compared to that of other famous ML algorithms such as Probabilistic Neural Network (PNN) [64], k-Nearest Neighbor (KNN) [65], Random Forest (RF) [66], Support Vector Machine (SVM) [67], MLP (Multilayer Perceptron) [68], Nave Bayes (NB) [69], and logistic regression [70]. Table 6 shows the conventional machine learning methods with optimized values of their parameters. Table 7 compares the performance of the several learning methods. From Table 7, the DNN model beats other machine learning techniques. The DNN model, for example, had the best accuracy of 83.40%, while the RF model had the second-best accuracy of 82.21%. With an accuracy of 64.35%, the Logistic regression model is the least accurate.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Optimized Values** \\ \hline Learning rate & 0.001 \\ Iteration & 128 \\ Seed & 1234 L \\ Number of hidden layers & 12-10-2-1 \\ Number of Neurons & 5 \\ Activation Function & ReLu \\ Regularization.l2 & 0.001 \\ Weight initialization & XAVIER function \\ Optimizer & 0.1 \\ Dropout & SGD Method \\ Update & ADAGRAD function \\ \hline \hline \end{tabular}
\end{table}
Table 4: DNN model hyper-parameter list with optimum values.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Processor** & **3.20 GHz \(\times\) 4** \\ \hline \multirow{4}{*}{Specification of Single node} & CPU & Intel CoreTM \\  & Connectivity & 100 Mbps Ethernet LAN \\  & Hard disk & 480.4 GB \\  & Memory & 16 GB \\ \hline \multirow{4}{*}{Software} & Operating system & Ubuntu18.04 LTS \\  & Hadoop & 2.7.3 \\ \cline{1-1}  & Spark & 2.0 \\ \cline{1-1}  & OS Type & 64-bit \\ \cline{1-1}  & JDK & 1.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Apache Spark cluster configuration details.  
\begin{table}
\begin{tabular}{l l l} \hline
**Classifier** & **Parameters** & **Optimized Values** \\ \hline \hline \multirow{6}{*}{SVM} & Cost & 10 \\  & Kernel Type & Linear \\  & Kernel Degree & 2 \\  & Coef0 & 9 \\  & Gamma & 0.01 \\  & Shrinkage & TRUE \\ \hline \hline RF & Mtry & 8 \\  & No. of trees & 350 \\ \hline KNN & Weighting & 10 \\  & k-neighbors & Distance \\ \hline Logistic Regression & solver & \(1\times 10^{4}\) \\  & C & Sag \\ \hline \multirow{6}{*}{MLP} & Solver & Adam \\  & Hidden layer size & 100 \\  & Learning rate & Constant \\  & Activation & Relu \\  & Shuffle & True \\  & Batch size & auto \\ \hline \multirow{2}{*}{NB} & Binarize & 0.0 \\  & Class\_prior & None \\  & alpha & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance Comparison of Machine Learning Method.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Method** & **MCC** & **SN (\%)** & **SP (\%)** & **ACC (\%)** \\ \hline DNN & 0.711 & 89.01 & 82.35 & 83.40 \\ RF & 0.626 & 74.65 & 87.24 & 82.21 \\ SVM & 0.522 & 61.44 & 88.17 & 77.50 \\ MLP & 0.607 & 88.75 & 70.79 & 81.37 \\ KNN & 0.621 & 96.61 & 58.67 & 81.44 \\ Logistic & 0.229 & 77.38 & 44.80 & 64.35 \\ Regression & 0.438 & 78.10 & 63.39 & 72.23 \\ PNN & 0.293 & 13.55 & 100 & 65.47 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Impact of different learning rates and activation functions.  
\begin{table}
\begin{tabular}{l l l} \hline
**LR** & **Tanh ACC (\%)** & **Relu ACC (\%)** \\ \hline
0.009 & 78.83\% & 80.24\% \\
0.008 & 81.78\% & 81.85\% \\
0.007 & 80.45\% & 81.22\% \\
0.006 & 81.29\% & 81.36\% \\
0.005 & 81.64\% & 81.44\% \\
0.004 & 81.86\% & 81.36\% \\
0.003 & 82.00\% & 82.41\% \\
0.002 & 81.16\% & 82.56\% \\
0.001 & **82.00\%** & **83.40\%** \\
0.01 & 77.07\% & 81.63\% \\
0.02 & 64.63\% & 73.21\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Impact of different learning rates and activation functions.  
back-propagation (weight optimization). In contrast, other machine learning approaches (conventional machine learning approach) use single-stack processing layers.