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VSI: Expert DM 2020 : Applied Soft Computing: Special Issue on Expert Decision Making for Data Analytics with Applications


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Submission Deadline Sep 30, 2020
Categories    machine learning   data analytics   decision making   soft computing

Call For Papers

Applied Soft Computing
Special Issue on Expert Decision Making for Data Analytics with Applications
(JCR IF 2018: 4.873; Q1; Ranking: Artificial Intelligence 20/134; Computer Science-Interdisciplinary Applications 11/106.)

Kevin Kam Fung Yuen, Singapore University of Social Sciences, Singapore,

Jenq-Shiou Leu, National Taiwan University of Science and Technology, Taiwan,

Alessio Ishizaka, NEOMA Business School, France,

Hissam Tawfik, Leeds Beckett University, United Kingdom,

Frans Coenen, University of Liverpool, Liverpool, United Kingdom,

Decisions can be made using human judgements, data analytics, or a combination of the two. With the rapid growth of data, various data analytics techniques have been adopted to explore data to find meaningful patterns to support decision making. On the other hand, a lot of decision problems are without past data, or the related data exists but is very difficult and/or expensive to obtain; in which case formulation of a suitable decision model based on ‘expert’ judgements is the main solution for decision making. Whilst many decision problems are supported with partial data or are not merely based on historical data to find patterns, hybrid techniques integrating Expert Decision Models (EDMs) into Data Analytics Algorithms (DAAs) present a promising solution for complex decision and data analytics problems.
Data analytics techniques use modern statistical and machine learning mechanisms to analyze diverse kinds of data, on either a small or big scale, to discover information or knowledge for better decision making. Data analytics techniques may refer to clustering, regression, classification, association learning, reinforcement learning, evolutionary learning, deep learning, or statistical learning.
EDMs are concerned with decision making techniques based on expert judgements, preferences, or opinions as inputs. EDM may refer to the research areas of multi-criteria decision making, recommender systems, user preference engineering, knowledge engineering and expert systems.
This special issue aims to bring together academia and practitioners of both applied decision science and applied data science to report on the recent developments to integrate decision models based on expert judgements into data analytics algorithms to form sophisticated approaches for solving complex decision problems for various application domains.
Relevant applications using Expert Decision Making for Data Analytics include (but are not limited to) the following:
• Industrial engineering and Operational research
• Recommender system
• Social network
• Financial and economic applications
• Internet computing and Cybersecurity
• Bioinformatics and computational biology
• Medicine, health, and Wellbeing.
• Natural Language Processing
• Intelligent Transportation and Logistic
• Image/Video Recognition
• Self-organization Network
• Smart city
• Industry 4.0.

• Open Submission: 30 April 2020
• Submission Deadline: 30 September 2020
• Author Notification incl. revisions: Within 10 weeks after submission
• Final Acceptance Deadline: Feb 2021

Paper submissions for the special issue should follow the submission format and guidelines for regular papers and submitted at All the papers will be peer-reviewed following Applied Soft Computing reviewing procedures. Guest editors will make an initial assessment of the suitability and scope of all submissions. Papers will be evaluated based on their originality, presentation, relevance and contributions, as well as their suitability to the special issue. Papers that either lack originality, clarity in presentation or fall outside the scope of the special issue will not be sent for review. Authors should select "VSI: Expert DM" when they reach the "Article Type" step in the submission process. The submitted papers must propose original research that has not been published nor currently under review in other venues.

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