Big Data in Intelligent Information Systems (BDCC 2020)

Big Data in Intelligent Information Systems

Overview:

Big data paradigm is an interdisciplinary field bringing together computer science, mathematics, statistics, and information theory to analyse data for interpretation and prediction. Big data research is characterized by voluminous and incremental datasets and complex data methods. The machine learning methods used in algorithm development are iterative and parallel. These methods can be scaled to handle big data using distributed and parallel computing technologies.

Intelligent and Information Systems provide numerous opportunities for digital transformations including AI, IoT, machine learning, big data, and business intelligence. The amount of data available and its handling has become a centre point for new developments and technologies. Combining AI and machine learning provides organizations with the ability to analyses massive datasets much more reliably and noticeably faster. Big data and its introduction into all areas of business and key industries need a primary way of conducting, and data intelligence provides exactly that. With big data and the volume of information needed to be consumed for different types of investigation and analysis, data intelligence is an extension to the traditional way in which we see and digest data efficiently and extracting the most useful information.

This special issue addresses the issues and challenges posed by several big data problems and give an overview of the state of the art and the future research opportunities. This special issue is opened to submit high quality research contributions from wide range of professions including scholars, researchers, academicians, and Industry people. Original research papers and state of the art reviews will be accepted.

Topics

Topics of interest include, but are not limited to, the following scope:

• Data science for cognitive analysis
• Real-time ubiquitous data science
• Algorithm development for big data analysis in E-health, telemedicine
• Biomedical image processing and big data applications
• Platform for privacy preserving data science
• Social data relationship ranking on internet
• Data applications of cognitive communication Green technologies in information, computing and big communication systems
• Big algorithms and software development

• Networking architecture for big data
• Edge, fog, and mobile edge computing for big data
• Machine learning, data mining and big data analytics in networking
• Smart grid cyber security data analysis
• Intelligent face recognition system using neural learning for smart security
• Data-centric network for big data processing
• Efficient networking for distributed artificial intelligence
• Future Advancements

Important Dates

Manuscript submission deadline: 15 July 2021

Notification of acceptance: 15 September 2021

Submission of final revised paper: 15 October 2021

Publication of special issue (tentative): 15 November 2021

Submission Procedure

Authors should follow the MONET Journal manuscript format described at the journal site. Manuscripts should be submitted on-line through http://www.editorialmanager.com/mone/.

A copy of the manuscript should also be emailed to the Guest Editors at the following email address: anandakumar.psgtech@gmail.com.

Guest Editors:

Dr. Anandakumar Haldorai, Sri Eshwar College of Engineering, Coimbatore, India.

Email: anandakumar.psgtech@gmail.com (Handling Editor)

Dr. Sri Devi Ravana, University of Malaya, Malaysia.

Dr. Joan Lu, University of Huddersfield, United Kingdom.

Dr. Arulmurugan Ramu, Presidency University, Bangalore, India.