Improving Data Transfer Within Efficient Internet Networks

With the advancement and the development of communications systems and the spread of Internet networks around the world within everyone’s reach, in addition to the importance of efficient and secure data transfer, the process of improving data transfer within effective Internet networks has become an important and critical priority. The classification of digital data for digital communications systems is tested using several modern models by using various smart technologies to ensure its delivery to and from designated places with high accuracy and efficiency. In this study, an improved model has been proposed to improve data transfer over the Internet by using the artificial intelligence neural algorithm (ANN). The content and the quality of information transmitted by digital signals vary in communication systems and Internet networks. As a result, the developed AI algorithm analyzes and classifies this data to speed up the transmission and the reception process, thereby reducing bit error rate (BER), increasing performance efficiency, and reducing losses and loss of information. The machine learning (ML) technology known as artificial neural network (ANN) is a scheme that has evolved to mimic the human brain as a basis for action. The AI Pyramid depicts the stages of development of artificial neural network machine learning technology, leading to the establishment of the principle of deep learning (DL). Due to their ability to circumvent the selectivity problem, distance learning processes are currently attracting much attention from researchers. In this paper, an optimization for data transmission through Internet networks is carried out after examining the network structure and the optimization components the sum of axes, hidden layers, learning rules, and the active function, in order to provide an explanation of ANN. The mechanism of the ANN transmission model has showed high efficiency compared to other machine learning techniques such as SVM and KNN, and it has been effective.