Aims & Scope:

The aim of this issue is to explore new technologies enable us to collect more data than ever before. With an overwhelming amount of web-based, mobile, and sensor-generated data arriving at a terabyte and even zettabyte-scale, new science and insights can be discovered from the highly detailed and domain-specific information which can contain useful information about problems such as national intelligence, cyber security, fraud detection, financial trading, personalised medicine and treatments, personalised information and recommendations and personalised athletic training. Machine learning algorithms, particularly deep learning plays a vital role in big data analysis. Deep Learning algorithms extracts high-level and complex abstractions by discovering intricate structure in large data sets. Deep learning techniques are nowadays the leading approaches to solve complex machine learning and pattern recognition problems such as speech and image understanding, semantic indexing, data tagging and fast information retrieval. Despite of that, there is a limited understanding of how to design computationally efficient and effective learning algorithms.

This special session focuses on all aspects of big data analytics, with a particular emphasis on the analysis and learning of massive volume of unstructured data and developing effective and efficient large-scale learning algorithms. In addition, this session aims to bring leading scientists, researchers and experts together to discuss and share the current and new research topics and ideas, to provide a platform to present and discuss recent advancements as well as to increase international collaborations between academic institutions and industries.

Keywords:
Big data, Security, privacy, deep Learning, Cyber Security, Techniques and Application, Cloud Computing, Mobile Computing, Data Analysis, Data Science.

Subtopics:  Big Data Processing Algorithms  Knowledge Discovery, Integration and Transformation  Big Data Discovery and Analysis Patterns  Big Data Classification and Clustering Techniques  Data-Driven Reasoning, Learning, decision making and planning  Big Data Security and Privacy  Big Data in Cyber security, Finance, Healthcare and Transportation  Applications Learning in Uncertainty Labelled Data  Sentiment Analysis and Opinion Mining  Optimisation Methods for Deep Learning, semantic analysis  Deep Learning Applications  Future Directions and Challenges in Big Data Analytics  Data Science  Analysis of Big Data Cloud and Mobile  Cyber Security  Big Data Mining


Papers submitted for publication for this special issue will be peer reviewed and selected on basis of their quality and relevance to the theme of this special issue. Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page (http://benthamscience.com/journals/recent-patents-onengineering/author-guidelines/#top ).

Please submit your manuscript via email to raghvendraagrawal7@gmail.com , brojokishoremishra@gmail.com, raghv1987@gmail.com.


Schedule:

Manuscript submission deadline: Jun. 15, 2017
Peer Review Due: July. 15, 2017
Revision Due: Aug. 10, 2017
Notification of Acceptance by the Guest Editor: Sept 25, 2017
Final Manuscripts Due: Oct., 2017