Deep-BDB: Call for Papers

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 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 enable decentralization, immutability, and transparency of data and applications. It has been exploited in modern research and industrial domains in order to achieve 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 Blockcain (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 focuses on specific challenges in deep (and machine) learning, big data and blockchain. Topics of interest include (but not limited to):

Deep/Machine learning based models
Statistical models and learning
Data analysis, insights and hidden pattern
Data analysis and decision making
Data wrangling, munching and cleaning
Data integration and fusion
Data visualization
Data and information quality
Security threat detection
Visualizing security threats
Enhancing privacy and trust
Data mining; Information extraction;
Sentiment analysis
Data classification and clustering
Knowledge acquisition and learning
Clustering, classification and regression
Supervised and unsupervised learning
Blockain security and trust
Blockchain data management
Data & application reliability
Blockchain and data distribution
Blockain and finacial transactions
Blockchain and Bitcoin applications
Blockain and NoSQL databases
Protocols for blockchain
Cryptography, Cryptocurrency
Fraud detection and prevention
Blockchain and Internet of Things
Scalability of blockchains

Application areas:
Finance, business and retail
Intelligent transportation
Healthcare and clinical decision support
Bioinformatics and biomedical informatics
Computer vision
Human activity recognition
Cybersecurity
Natural language processing
Recommender systems
Social media and networks