The 2nd International Conference on Deep Learning, Big Data and Blockchain

Deep and machine learning are the state-of-the-art at providing models, methods, tools and techniques for developing autonomous and intelligent systems which can revolutionize industrial and commercial applications in various fields such as online commerce, intelligent transportation, healthcare and medicine, security, manufacturing, education, games, and various other industrial applications. All such fields produce and consume a massive amount of big data, which include, for example, online commerce data (marketing data, customer reviews, customer relationship), transportation data (road sensors, cameras, GPS), and data about healthcare, social media, and various other applications. Deep learning techniques and big data techniques yield useful outputs in predicting, discovering and acquiring insights and deeper knowledge about events for better and efficient decision making. The groundbreaking technology of blockchain technology also enables decentralization, immutability, and transparency of data and applications. It has been exploited in modern research and industrial domains in order to achieve a high level of trust, security and reliable execution of applications and data which are shared across a network of computers.

The International Conference on Deep Learning, Big Data and Blockchain (DEEP-BDB) aims to enable synergy between these areas and to provide a leading forum for researchers, developers, practitioners, and professional from public sectors and industries in order to meet and share latest solutions and ideas in solving cutting edge problems in modern information society and economy. The conference comprises a set of tracks that focus on specific challenges in deep (and machine) learning, big data and blockchain.

All papers accepted for this conference are to be published by Springer in the Advances in Intelligent Systems and Computing series. The proceedings (books) of this series are submitted to ISI Proceedings, EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink.