Comparative Analysis of Neural Networks and Deep Learning using Wireless Communication

Scientists in recent times have experienced a broad utilization of both deep learning approaches and neural networks through wireless communication. However, these deep learning approaches can offer better decision-making and predictions for future activities. On the other hand, neural networks can learn regularly by themselves and provide relevant output that is unlimited to the offered input. With the application of both DL and NN via wireless communication, reaping the merits of the modern-day digital economy has become easier than before. Researchers in the research paper has successfully conducted a comparative analysis between DL and NN. While NN can be used to transmit data in both input-output values via wireless connections, DL at once enhances the data transformation scopes for a better and more sustainable future. Primary research has been conducted with 60 individuals to understand their practical knowledge on the topic. The data was collected in a way to support the comparative analysis of this research. After the primary research analysis, secondary data has been analyzed as well to support the primary research findings. Findings showed that Deep Learning (DL) has outcompeted the Neural Network (NN) in terms of benefits in wireless communication. The DL has the capacity to manage enormous amounts of data which is not possible using NN. Moreover, DL is effective in storing historical data and can process complex information better than NN.