International Journal of Computational Intelligence Systems (SCIE Indexing, IF: 2.153)

Title: Advanced Developments in Machine Learning and Optimization for Heterogeneous Data Analytics

Guest Editors:
Prof. Nian Zhang, University of the District of Columbia, Washington, D.C., USA. nzhang@udc.edu
Prof. Qingshan Liu, Southeast University, Nanjing, Jiangsu, China. qsliu@seu.edu.cn
Prof. Zhishan Guo, University of Central Florida, Orlando, Florida, USA. zsguo@ucf.edu

Aims and Scope

Recent advances in storage, hardware, information technology, communication, and networking have resulted in increasingly large and complex heterogeneous data. This has powered the demand to extract useful and actionable insights from data in an automatic, reliable and scalable way. Neural networks are widely used learning machines with powerful learning ability and adaptability, which have achieved remarkable performance in the data analytical tasks, such as computer vision, face/speech recognition, video surveillance, document summarization, distributed and/or real-time resource allocation, etc. Recently there is a surge of research activities devoted to theoretical development of scalable and robust learning models on deep neural networks, neurodynamics, and combinatorial optimization techniques.

This special issue aims to present the latest theoretical and technical advancements in the broad area of neural networks and learning systems for heterogeneous data computing. Potential topics include but are not limited to the following:  

• Neural networks model and learning algorithms;
• Classification, clustering, and regression;
• Imbalanced data learning;
• Deep neural networks and learning;
• Neurodynamic optimization;
• Distributed optimization.
• Applications of neural networks in data analytics and time series forecasting and analysis

Main topics and quality control

This special issue is dedicated to a set of best papers in the field of machine learning and optimization for data analytics. Full papers will be subject to a strict review procedure for final selection to this special issue based on the following criteria:

1. Quality and originality in theory and methodology of machine learning and optimization for data analytics;

2. Relevance to machine learning and optimization research area, such as

• Neural networks model and learning algorithms;
• Classification, clustering, and regression;
• Imbalanced data learning;
• Deep neural networks and learning;
• Neurodynamic optimization;
• Distributed optimization.

3. Application orientation which exhibits originality and machine learning and optimization theory, such as

• Neural networks and learning systems for specialized problem-solving and various applications in big data analysis and time series forecasting and analysis.

4. If there is an implementation, the details of the implementation must be provided;

5. Extended papers must contain at least 40% new material (qualitative) relative to the conference paper.

Important Dates
Submission of papers: 30 April, 2020
Notification of review results: 30 June, 2020
Submission of revised papers: 30 August, 2020
Notification of final review results: 30 September, 2020

Charge
To publish an open access article in this journal, Authors are requested to pay an Article Publication Charge (APC) of EUR 850 per accepted paper. Submission of articles is free of charge.