Artificial iIntelligence for Big Data: issues and challenges

Silva, Adriana Artificial iIntelligence for Big Data: issues and challenges., 2020 [Preprint]

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English abstract

Artificial intelligence (AI) concerns the study and development of intelligent ma-chines and software. The associated ICT research is highly technical and specialized, and its focal problems include the developments of software that can reason, gather knowledge, plan intelligently, learn, communicate, perceive and manipulate objects. AI also allows users of big data to automate and enhance complex descriptive and predictive analytical tasks that, when performed by humans, would be extremely la-bour intensive and time consuming. Thus, unleashing AI on big data can have a sig-nificant impact on the role data plays in deciding how we work, how we travel and how we conduct business. This paper explores how Artificial Intelligence, in conjunc-tion with Big Data technologies, can help organizations to bring about operational and business transformation.Deep learning will also be connected to other major learning frameworks such as reinforcement learning and transfer learning. A thorough survey of the literature on deep learning for wireless communication networks is provided, followed by a detailed description of several novel case-studies wherein the use of deep learning proves extremely useful for network design. For each case-study, it will be shown how the use of (even approximate) mathematical models can significantly reduce the amount of live data that needs to be acquired/measured to implement data-driven approaches.

Item type:	Preprint
Keywords:	big data, AI,ML, wireless
Subjects:	L. Information technology and library technology > LA. Telecommunications. L. Information technology and library technology > LB. Computer networking. L. Information technology and library technology > LC. Internet, including WWW. L. Information technology and library technology > LH. Computer and network security.
Depositing user:	Ms Adriana Silva
Date deposited:	28 Jul 2020 08:35
Last modified:	28 Jul 2020 08:35
URI:	http://hdl.handle.net/10760/40180

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