DA4IA 2019 - International Workshop on Data Analytics for Intelligent Applications in Future Smart Cities
Topics/Call fo Papers
Internet of things makes it possible for connecting billions of devices to the internet thus providing seamless accessibility, limitless scalability, escalated productivity and a surplus of additional paybacks. Internet of Things enables the realization of connecting the physical world with cyber space this giving birth to the cyber physical systems. The hype surrounding the cyber physical systems and its countless applications is already compelling various stake holders to rapidly upgrade their current infrastructures, processes, tools, and technology to accommodate the massive data aggregation. Since there is a vast amount of data generated by these cyber physical systems, the insights from this voluminous data will enable to make better decisions and facilitate end users.
However, these large-scale cyberphysical systems deployment of brings various issues and challenges; connectivity, data analysis, privacy, security, optimum resource utilization to name a few. The philosophy behind machine learning is to automate the creation of analytical models in order to create algorithms to learn continuously from these large volumes of available data. These models and their respective data will be an omnipotent source for continuous improvement. Similarly, these evolving models produce increasingly positive results, reducing the need for human interaction in significant decision making.
Today's machine learning algorithms comb through data sets that no human could feasibly get through in a year or even a lifetime's worth of work. As the cyber physical systems continue to expand their utility in various aspects of future smart cities, more and more algorithms as well as models are required to keep up with the requirements of various scenarios.
Therefore, this workshop seeks to invite significant contributions in the domain of smart cities and big data analytics to improve various aspects of smart cities such as healthcare, transportation, governance, education, security and urban planning to name a few. We believe machine learning and data analytics play a vital role in the realization of the future cyber physical systems.
However, these large-scale cyberphysical systems deployment of brings various issues and challenges; connectivity, data analysis, privacy, security, optimum resource utilization to name a few. The philosophy behind machine learning is to automate the creation of analytical models in order to create algorithms to learn continuously from these large volumes of available data. These models and their respective data will be an omnipotent source for continuous improvement. Similarly, these evolving models produce increasingly positive results, reducing the need for human interaction in significant decision making.
Today's machine learning algorithms comb through data sets that no human could feasibly get through in a year or even a lifetime's worth of work. As the cyber physical systems continue to expand their utility in various aspects of future smart cities, more and more algorithms as well as models are required to keep up with the requirements of various scenarios.
Therefore, this workshop seeks to invite significant contributions in the domain of smart cities and big data analytics to improve various aspects of smart cities such as healthcare, transportation, governance, education, security and urban planning to name a few. We believe machine learning and data analytics play a vital role in the realization of the future cyber physical systems.
Other CFPs
- International Workshop on Intelligent Systems: Communications, Computing and Networks
- 19th IEEE International Conference on Scalable Computing and Communications (ScalCom 2019)
- The International Workshop on Pervasive Healthcare (Per-Health 2019)
- 1st International Workshop on Security of Ubiquitous Computing (SUC 2019)
- Workshop on Green Cloud for Ubiquitous Intelligence and Computing Applications
Last modified: 2019-03-11 17:01:56