Big Data Analytics in the Internet-Of-Things And Cyber-Physical Systems

Lv, Z.; Song, H.; Lloret, J.; Kim, D.; De Souza, J. (2019). Big Data Analytics in the Internet-Of-Things And Cyber-Physical Systems. IEEE Access. 7:18070-18075. https://doi.org/10.1109/ACCESS.2019.2895441S1807018075

Emerging Trends, Issues, and Challenges in Big Data and Its Implementation toward Future Smart Cities

(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other worksHan, G.; Guizani, M.; Lloret, J.; Chan, S.; Wan, L.; Guibene, W. (2017). Emerging Trends, Issues, and Challenges in Big Data and Its Implementation toward Future Smart Cities. IEEE Communications Magazine. 55(12):16-17. https://doi.org/10.1109/MCOM.2017.8198795S1617551

Editor’s Note

The International Journal of Interactive Multimedia and Artificial Intelligence - IJIMAI (ISSN 1989 - 1660) provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances on Artificial Intelligence (AI) tools or tools that use AI with interactive multimedia techniques

Guest editorial : intelligent ubiquitous computing and advanced learning systems for biomedical engineering

The health monitoring for disease diagnosis and prognosis in a desired smart medical structure is realized by interpreting the health data. The advances in sensor technologies and biomedical data acquisition tools have led to a new era of big data, where different sensors collect massive amounts of medical data every day. This Special Issue explores the latest development in emerging technologies of biomedical engineering, including big medical data, artificial intelligence, cloud/fog computing, federated learning, ubiquitous computing and communication, internet of things, wireless technologies, and security and privacy. The biological wearable sensors can enhance the decision-making and early disease diagnosis processes by intelligently investigating and collecting large amounts of biomedical data (i.e. big health data). Hence, there is a need for scalable advanced learning, and intelligent algorithms that lead to reliable and interoperable solutions to make effective decisions in emergency medicine technologies. The optimization algorithms can be used in order to acquire the sensor data from multiple sources for fast and accurate health monitoring.peer-reviewe

Spartan Daily, February 10, 1998

Volume 110, Issue 13https://scholarworks.sjsu.edu/spartandaily/9229/thumbnail.jp

Volume 19, Number 2, June 1999 OLAC Newsletter

Digitized June 1999 issue of the OLAC Newsletter