## 2 Literature Review  
In this section, the research work carried out by the authors is reviewed. A fundamental ANN model is presented in [4] and is shown in Figure 1. The structure has one input layer that has an input feature vector of length 'n'. At the output, there is also one layer with output variables of length'm'. The input features are processed through to the output layer via a hidden layer of neurons of length 'k'. The raw data, i.e., the data to be processed, is entered through the input layer. This layer only provides an interface for the data that is required to be processed. The processing was then carried out using the hidden layer. The hidden layer is the key to this network, where all computational processing of the characteristics acquired from the input layer is done. The processed data are then forwarded to the end user via the output layer. The data sent to the output layer are the solution to the problem interfaced from the input layer. An application of the Mobile Net is presented in [5]. This is a streamlined architecture that comprises lightweight CNNs that use depth-wise separable convolutions. The first manuscript [6] presents different recognition systems for sign languages. The authors in [7] presented a communication framework for DnM people. They used an automated Sign Language Interpreter (SLI), where they acquired data using motion sensors fitted to a glove. The sensor data are acquired using an Arduino board. The acquired data are then processed using a machine-learning technique. The system achieves an accuracy rate of 93%. In [8], a speech recognition system is presented that identifies a registered speaker's voice. This is a computer-based implementation in which language patterns are stored. The information was processed and acquired using a questionnaire created by the authors in [9]. They collected data from various organizations set up for DnM people. They concluded that the average age when hearing loss is discovered is 2.8 years, while a hearing aid is normally used from 7.6 years of age. A system is presented in [10], which is referred to as AWAAZ. DnM people can use this system, where their gestures are acquired using image processing. The main components of the gesture recognition system are image acquisition, segmentation, morphological erosion, and feature extraction. In [11], a system that supports two-way communication between  
Figure 1: Scope of this research within the artificial intelligence domain. DnM and NDnM people is presented that uses an automatic speech recognition technique and a mobile application based on visualization. The authors developed an Android-based application in [12]. This uses the Principal Component Analysis (PCA) approach and the Video Relay Service (VRS). The VRS works as a manual interpreter, converting speech to hand signals and vice versa. The hand gesture is recognized by the PCA algorithm. A web cam-based application is developed in [13], where the images are first acquired using a webcam, then PCA is applied to extract features and the characters are recognized using training sets. The authors surveyed [14] and published the issues faced by DnM children. They concluded that it is possible to apply a common solution due to various parameters. Some solutions are presented in this paper. They also concluded that Hand-Gesture-Recognition (HGR) systems have contributed significantly to a shift in the way people interact with computers. There is a lot of focus on improving sensor technology, which is expected to have a significant impact on how DnM people can communicate. The Leap-Motion-Controller (LMC) [15] is one such device. Despite the success of developing cutting-edge algorithms, there are still limitations because these algorithms have not focused on how to quickly process sequential hand gesture data and are unable to characterize the discriminative representation of different classes of hand gestures.