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