## 2 Related Theory and Work  
### Related Theory  
1. Convolutional Neural Networks (CNNs)  
Convolutional neural networks are extensively accepted, DL-based feed-forward networks inspired by the work of [PERSON] and [PERSON] in neuroscience [17]. [PERSON] first  
Figure 3: Methodology implemented.  
proposed them for image processing [18]. The CNN architectures were developed and employed widely after the significant overview of AlexNet [19], and they have replaced the outdated classification methods. A typical CNN mainly consists of an input layer, some convolutional layers, activation units, a pooling layer, and a fully connected layer [20]. The convolutional and pooling layers can be used to capture deep feature maps of two-dimensional inputs. The convolution and pooling operations and parameter sharing phenomenon of CNNs enable them to learn features from images and to run on any device [21]. The convolution operation can be interpreted as sliding a filter over data and, for each position, applying a dot product between the filter and the data at this position [22]. The convolution layers are designated for convolving the local input regions with the kernel filters, which generate the feature maps or activation maps by the activation unit. The next is the pooling or sub-sampling layer for downsampling the features and merging semantically similar features into one. This layer reduces the dimension and parameter of the network [23]. The two commonly applied pooling operations are average pooling, which determines the average value of each patch on the activation map, and maximum pooling (or max pooling), which determines the maximum value for each patch of the feature map. The next step is converting the output of the convolutional block into a one-dimensional array for inputting it into the next layer, called a fully connected layer. The last layer is the classification layer, which categorizes the objects into the respective class [24]. Figure 4a shows the general 2-D CNN model consisting of an input image and feature extraction block (convolution, activation unit, and pooling layer) followed by the fully connected layer/s and, finally, the classification stage. CNNs have immensely improved the performance and efficiency in computer vision, object detection, natural language processing, and speech recognition, with a gradual increment in the production and memory of GPU. Hence, the use of CNNs has proliferated within computer vision [13; 25].  
Moreover, to deal with one-dimensional data, 1-D CNNs have been developed. In brief, 1-D CNNs are the modified version of 2-D CNNs, in which forward-propagation (FP) and back-propagation (BP) are simple array operations rather than matrix operations, which makes them more efficient for specific applications in dealing with 1-D signals [13]. In a 1-D CNN, i.e., Conv1D, the kernel slides along one dimension. In addition, the relatively shallow structure of a 1-D CNN makes it able to learn challenging tasks involving 1-D signals, and they are also suited for real-time applications [26]. Figure 4b shows the common structure of a 1-D CNN. Recent studies show that compact 1-D CNNs have provided superior performance in applications related to one-dimensional data [27]. The applications of a 1-D CNN include the analysis of 1-D sensor and accelerometer data, natural language processing, and speech recognition.