Advanced Methods in Optimization and Machine Learning for Heterogeneous Data Analytics

  in Special Issue   Posted on June 15, 2019

Information for the Special Issue

Submission Deadline: Sun 30 Jun 2019
Journal Impact Factor : 3.317
Journal Name : Neurocomputing
Journal Publisher:
Website for the Special Issue:
Journal & Submission Website:

Special Issue Call for Papers:

1. Summary and Scope

Recent advances in storage, hardware, information technology, communication, and networking have resulted in a large amount of heterogeneous data. This has powered the demand to extract useful and actionable insights from such data in an automatic, reliable and scalable way. Machine learning, which aims to construct algorithms that can learn from and make predictions on data intelligently, has attracted increasing attention in the recent years and has been successfully applied to many data analytical tasks, such as image processing, face recognition, video surveillance, document summarization, etc. Since a lot of machine learning algorithms formulate the learning tasks as linear, quadratic or semi-definite mathematical programming problems, optimization becomes a crucial tool and plays a key role in machine learning and multimedia data analysis tasks. On the other hand, machine learning and the applications in heterogeneous data analytics are not simply the consumers of optimization technology but a rapidly evolving interdisciplinary research field that is itself promoting new optimization ideas, models, and solutions. 

This special issue aims to seek the high-quality papers from academics and industry-related researchers in the areas of applied mathematics, machine learning, artificial intelligence, pattern recognition, data mining, multimedia processing, and big data to show the most recently advanced methods, e.g. neural networks and learning systems, in optimization and machine learning for heterogeneous data computing. 

Scope and Topics

The topics of the special issue include, but are not limited to: • Adversarial Machine Learning

• Concept Drift

• Domain Adaptation

• Distributed /Parallel Algorithms in Machine Learning

• Kernel Methods

• Evolutionary Computation

• Graph-based Learning

• Imbalanced Data Learning

• Manifold Learning

• Metric Learning

• Multi-task Learning 

• Multiview Learning

• Neural Networks and Deep Learning

• Reinforcement Learning

• Representation Learning

• Sequential Learning for Video and Audio Data

• Supervised Learning

• Support Vector Machines

• Transfer Learning

• Unsupervised Learning 

2. Submission Guidelines

Authors should prepare their manuscripts according to the \”Instructions for Authors\” guidelines of “Neurocomputing” outlined at the journal website All papers will be peer-reviewed following a regular reviewing procedure. Each submission should clearly demonstrate evidence of benefits to society or large communities. Originality and impact on society, in combination with a media-related focus and innovative technical aspects of the proposed solutions will be the major evaluation criteria. 

3. Important Dates

Submission Deadline: June 30, 2019

First Review Decision: August 31, 2019

Revisions Due: October 15, 2019, 2019

Final Manuscript: December 15, 2019

Expected publication date: March 31, 2020 

4. Guest Editors

1. Prof. Yiu-ming Cheung (Leading Guest Editor)

2. Prof. Yuping Wang 

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