Call for Papers
Post date: Dec 20, 2017 11:20:51 AM
IJCNN-2018 Special Session on: Machine learning for big data: scalable algorithms and applications
Organized by
Ahmad Taher Azar, ahmad_t_azar@ieee.org
Faculty of Computers and Information, Benha University, Egypt
Website: http://www.bu.edu.eg/staff/ahmadazar14
Robi Polikar, polikar@rowan.edu
Website: http://users.rowan.edu/~polikar/
Edwin Lughofer, edwin.lughofer@jku.at
Department of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz Altenbergerstrasse 69 A-4040 Linz
Website: http://www.flll.jku.at/staff/edwin
Description
Technology is advancing overtime and sizes of data sets generated are considered big and complex. The current database management tools and methods used to process data are inadequate; this paves way for big data analytics evolution and innovation. There is a growing need to develop big data tools and techniques to build capabilities to solve problems better than ever before. In current practices a number of industries are readily leveraging big data to their benefit. Big data research has empowered the success of many applications in urban computing, social science, e-commerce, computer vision, natural language processing, speech recognition, bioinformatics, education, physics, chemistry, biology, and engineering. On the other hand, in order to enable learning with big data, scalable algorithms have attracted much attention in machine learning and data mining. Big data computing needs advanced technologies or methods to solve the issues of computational time to extract valuable information, in a realistic and practical time frame, without compromising the models’ quality. Numerous computational techniques for Big Data have been proposed, including stochastic optimization, parallel and distributed optimization, randomization, and GPU computing.
Scope and Topics
The aim of this special session is to provide an opportunity for international researchers to share and review recent advances in the emerging topic of machine learning with big data, with an emphasis on applications and scalable algorithms. The special session aims to solicit original, full length original articles on new findings and developments from researchers, academicians and practitioners from industries, in the area of machine learning with big data and scalable algorithms.
The topics of interest include, but are not limited to:
Active Learning with Big Data
Algorithmic paradigms, models, and analysis of Big Data
Big Data Clustering
Cloud computing for Big Data models and paradigms
Data Stream Mining Techniques for Big Data
Dimensional Reduction Techniques
Evolving modeling Techniques for Big Data
Kernel Machines " Large or Sequence Data Processing
Machine Learning Algorithms for Big Data
Meta-Heuristics Algorithms
Multi-dimensional Big Data
Non-linear Pattern Analysis
Optimization on Manifolds
Scalable and Incremental Algorithms
Stochastic optimization
Visualization of Big Data Technology
IMPORTANT DATES
January 15, 2018: Deadline for paper submission.
March 15, 2018: Notification of acceptance for papers
May 01, 2018: Deadline for camera-ready paper submission