Human-Centric Intelligent Systems - Call for Papers for the Special Issue: Open-world Machine Learning for Improving Human Decision-Making
Aims and Scope
Traditional machine learning follows the assumptions of closed-world learning. However, in a real-world scenario, interactive and automated applications work in a dynamic environment, and data from new classes arrive regularly. Open-world learning aims to fill the learning style gap between human beings and machines., which can be generally defined as a model that can learn new things that are not learned before while performing its anticipated job. The purpose of this Special Issue is to gather a collection of the latest studies on various topics in open-world machine learning for improving human decision-making, whether theoretical or empirical, that address but are not limited to the following topics: (i) continual learning; (ii) incremental learning; (iv) data-driven decision making; (v) knowledge-enhanced machine learning; (vi) multi-granularity cognitive computing.
Main topics and quality control
This special issue intends to bring together the most recent developments in research, industry best practises, problems, and challenges related to open-world machine learning for improving human decision-making in various sectors. We welcome unpublished, original submissions on any topic linked to open-world machine learning for improving human decision-making, not only the ones listed here.
• Continual Learning
• Incremental Learning
• Data-driven Decision Making
• Knowledge-enhanced Machine Learning
• Multi-granularity Cognitive Computing
Image Credit: [M] Grafissimo / Getty Image / iStock
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 the special issue.
2. Relevance to the topic of the special issue.
3. Application orientation which exhibits novelty.
4. If there is an implementation, the details of the implementation must be provided.
5. Presence of the following statements (if applicable): disclosure of potential conflicts of interest, research involving human participants and/or animals, informed consent.
Important date
Open date: 22 July 2024
Close date: 30 May 2025
Submit your paper
All papers have to be submitted via the Editorial Manager online submission and peer review system. Instructions will be provided on screen and you will be stepwise guided through the process of uploading all the relevant article details and files associated with your submission. During submission authors should indicate that their manuscript belongs to the special issue “SI: Open-world Machine Learning for Improving Human Decision-Making” (this question will appear at “Additional Information” step). All manuscripts must be in the English language.
To access the online submission site for the journal, please visit https://www.editorialmanager.com/hcin/default1.aspx. Note that if this is the first time that you submit to the Human-Centric Intelligent Systems, you need to register as a user of the system first.
NOTE: Before submitting your paper, please make sure to review the journal's Author Guidelines first.
After Acceptance
This special issue will be published as a virtual collection that will be accessible at SpringerLink.
Accepted papers will be published online within about 20 days after acceptance, fully citable by DOI (Digital Object Identifier), prior to publication in the issue.”
This Collection supports and amplifies research related to SDG 9: Industry, Innovation & Infrastructure.
Introduction of the guest editor(s)
Dr. Chuan Luo
Sichuan University, Chengdu, China
Dr. Chuan Luo is currently an Associate Professor with the College of Computer Science, Sichuan University, Chengdu, China. He received the Ph.D. degree in Computer Science from Southwest Jiaotong University, Chengdu, China, in 2015. His current research interests include granular computing, cloud computing, and incremental learning. He serves as an Editor of Human-Centric Intelligent Systems, Area Editor of International Journal of Computational Intelligence Systems, Member of Special Committee of CAAI Granular Computing and Knowledge Discovery.
Prof. Dun Liu
Southwest Jiaotong University, Chengdu, China
Dun Liu received his B.S. degree and Ph.D. degree from the Southwest Jiaotong University, China in 2005 and 2011, respectively. He is presently a Professor in School of Economics and Management, Southwest Jiaotong University, China. He was also a postdoctor researcher in School of Economics and Management, Tsinghua University. His research interests include Data Mining and Knowledge Discovery, Rough Sets, Granular Computing, Decision Support Systems and Management Information System. He is a member of ACM and IEEE, a senior member of IRSS and a senior member of China Computer Federation.
Prof. Xin Yang
Southwestern University of Finance and Economics, China
Xin Yang received the M.S. degree in electronic engineering from Sichuan University, Chengdu, China, in 2010, and the Ph.D. degree in computer science from Southwest Jiaotong University, Chengdu, China, in 2019. He is currently a Professor with the Department of Artificial Intelligence, School of Economic Information and Engineering, Southwestern University of Finance and Economics, Chengdu. He has authored more than 40 research papers in refereed journals and conferences. His research interests include data mining, three-way decisions, granular computing, and rough sets.