The second International Conference on Advances in Distributed Computing and Machine Learning (ICADCML-2021) is an annual forum that will bring together ideas, innovations, lessons, etc. associated with distributed computing and machine learning, and their application in different areas. The Conference goes virtual due to COVID-19 and will be available on “public domain”. Nowadays, most computing systems are available for parallel and distributed computing. Distributed computing performs an increasingly important role in modern data processing, information fusion, and electronics engineering. Particularly, applying machine learning in distributed environments is becoming an element of high added value and economic potential. Research on Intelligent Distributed Systems has matured during the last decade and many effective applications are now deployed. The machine learning is changing our society. Its application in distributed environments, such as the Internet, electronic commerce, mobile communications, wireless devices, distributed computing, and so on is increasing and is becoming an element of high added value and economic potential, both industrial and research. These technologies are changing constantly as a result of the large research and technical effort being undertaken in both universities and businesses. The exchange of ideas between scientists and technicians from both academic and business areas is essential to facilitate the development of systems that meet the demands of today's society. The technology transfer in this field is still a challenge and for that reason, this type of contributions will be specially considered in this conference . This conference is the forum in which to present the application of innovative techniques to complex problems. The scope of this conference has been kept wide and following are the topics covered (But not limited to):

• Distributed computing, networking,security and applications
• Cloud computing system and network design
• Green computing design and issues
• Cloud storage design and networking
• Cloud system and storage security
• Machine learning, data mining for cloud computing
• Edge, fog, and mobile edge computing
• Security, privacy, trust for cloud computing
• Machine learning approaches and applications
• Machine learning for distributed computing
• Machine learning for traffic engineering and congestion control
• Machine learning for network measurement
• Internet of Things (IoT) Applications and Services
• Security and Privacy for Internet of Things (IoT)
• Blockchain and Decentralized applications
• Blockchain and trust management
• Blockchain implementation and application
• Blockchain architectures and algorithms for heterogeneous networks
• Blockchain based IoT security solutions
• Blockchain in edge and cloud computing
• Blockchain in IoT device identification management
• Blockchain in IoT device authentication, authorization and access control
• Blockchain in IoT data security
• Blockchain in IoT system security
• Deep Learning in Bioinformatics
• Deep learning for hyper spectral image analysis
• Deep Learning in computer vision and pattern recognition
• Deep Learning in games, robots and self-driving cars
• Deep Learning for Natural Language Processing

The Proceedings of ICADCML 2021 is to be published in Springer "Lecture Notes in Networks and Systems (LNNS)" Book series [Confirmed]. The books of this series are submitted to ISI Proceedings, SCOPUS, Google Scholar and Springerlink for indexing.

Extended versions of selected papers presented in ICADCML 2021 are invited and recomended by the conference for submission to the following SCOPUS,DBLP,ACM Digital Library indexed journals:

• International Journal of Computational Science and Engineering [IJCSE]  (Inderscience Publishers)
• International Journal of Embedded Systems [IJES]  (Inderscience Publishers)
• International Journal of Computational Systems Engineering [IJCSysE]  (Inderscience Publishers)
• International Journal of Advanced Intelligence Paradigms [IJAIP]  (Inderscience Publishers)
• International Journal of Knowledge Engineering and Data Mining [IJKEDM]  (Inderscience Publishers)