ITU-T L Supplement 52
ITU International Telecommunication Union
Recommendation ITU‑T Y.3602 describes a model and operations for big data provenance. This Recommendation also provides the functional requirements for…
ITU-T L.1040
Effects of information and communication technology-enabled autonomy on vehicles longevity and waste creation
Recommendation ITU-T L.1040 establishes guidelines and requirements for information and communication technology original equipment manufacturer vendors providing equipment to autonomous…
Recommendation ITU-T P.1402 introduces machine-learning techniques and their application for quality of service (QoS) and quality of experience (QoE) prediction…
ITU-T M.3382
Requirements for work order processing in telecom management with artificial intelligence
Recommendation ITU-T M.3382 provides the requirements for work order processing in telecom management with artificial intelligence (AI). Based on AI…
ITU-T Y.3180
Mechanism of traffic awareness for application-descriptor-agnostic traffic based on machine learning
Recommendation ITU-T Y.3180 specifies the mechanism of traffic awareness for application_x0002_descriptor-agnostic traffic based on machine learning. This Recommendation specifies the…
Recommendation ITU-T Y.3654 specifies the mechanisms of machine learning in big data driven networking (bDDN). A set of related aspects…
Recommendation ITU-T Y.4471 defines a reference functional architecture of network-based driving assistance (NDA) for autonomous vehicles. It clarifies the concept…
ITU-T F.748.12
Deep learning software framework evaluation methodology
A deep learning software framework provides an easy and fast way for manufactures to develop their own artificial intelligence (AI)…
ITU-T M.3381
Requirements for energy saving management of 5G radio access network (RAN) systems with artificial intelligence (AI)
Recommendation ITU-T M.3381 provides requirements for energy saving management of a 5G radio access network (RAN) system with artificial intelligence…
Recommendation ITU-T P.565 provides the output of the framework which is a machine learning based speech quality prediction model that…
