Future prospects: AI and machine learning in cloud-based SIP trunking

Abstract

The relevance of the study lies in the consideration of artificial intelligence and machine learning as one of the most important technologies that determine the future of the telecommunications industry. Integration of artificial intelligence and machine learning into cloud-based Session Initiative Protocol trunking solutions can potentially significantly improve the efficiency, performance, and security of these solutions. The purpose of the study was to analyse the possibilities of integrating artificial intelligence and machine learning in cloud-based Session Initiation Protocol trunking solutions. The analysis and the case study methods were applied. The study found that in the modern world, artificial intelligence and machine learning can no longer be considered separately from many aspects of human activity. These technologies are widely used in the telecommunications sector. The integration of artificial intelligence and machine learning in this sector is a key to solving various problems. The findings underline that artificial intelligence and machine learning have the potential to significantly improve the efficiency, performance, and security of cloud-based Session Initiation Protocol trunking solutions. In particular, it was found that these technologies can be successfully used for intelligent call routing, optimising resource allocation, and providing a higher level of security. The results of the study are an important contribution to improving intelligent call routing, optimising resource allocation, and improving the level of security for data and network protection. In addition, the results of the study have the potential to increase the competitiveness of telecommunication companies and ensure the sustainable development of this industry