Scientific Programming

Intelligent Decision Support Systems Based on Machine Learning and Multicriteria Decision-Making


Publishing date
01 Jul 2020
Status
Closed
Submission deadline
28 Feb 2020

Lead Editor

1University of Peshawar, Peshawar, Pakistan

2National University of Computing and Emerging Sciences, Islamabad, Pakistan

3Zayed University, Dubai, UAE

4Innopolis University, Innopolis, Russia

This issue is now closed for submissions.
More articles will be published in the near future.

Intelligent Decision Support Systems Based on Machine Learning and Multicriteria Decision-Making

This issue is now closed for submissions.
More articles will be published in the near future.

Description

Intelligent decision support systems (IDSSs) are widely used in various computer science applications for intelligent decision-making. To implement these IDSSs, machine learning algorithms and diverse programming paradigms and frameworks are required. Machine learning and prediction algorithms are abundant in nature and produce variable results. As such, practitioners and decision-makers require intelligent scientific methodologies, such as empirical evaluation and machine learning approaches using multilabel learning, statistical and information-theoretic, landmarking, and complexity methods, to enable them to pick the most appropriate learning and prediction algorithms.

Real-world decision support systems require a consideration and analysis of multiple criteria features which, in turn, affect the final decisions. Criteria are often conflicting in nature: for example, in the case of car selection, factors such as cost, comfort, safety, and fuel economy all come into consideration. Therefore, decision-makers need scientific approaches, such as filter, rapper, and embedded methods, to perform such complex evaluation. Researchers concerned with the design and development of IDSS seek to demonstrate innovative scientific techniques, tools, and models which improve the quality and accuracy of the intended decisions. A few examples of such techniques and tools include multiattribute decision-making (MADM), multiattribute utility theory (MAUT), outranking, sensitivity analysis, rough set exploration system (RSES), adaptive rough sets, and adaptive reasoning methods.

This Special Issue therefore aims to solicit high-quality original research and review articles that cover novel, cutting-edge technologies and methods concerned with the scientific design, development, and implementation of IDSSs. Research that combines the study of IDSSs with machine learning algorithms and multicriteria decision-making (MCDM) software and considers how to improve the quality and accuracy of the decisions generated by these systems across a range of diverse applications is particularly encouraged.

Potential topics include but are not limited to the following:

  • Use of data mining and machine learning techniques and algorithms in the development of IDSSs across a wide range of sectors, such as business, education, and healthcare
  • Comparison analysis, survey, and implementation of different IDSSs and MCDM methods (e.g., collaborative decision-making, knowledge-driven decision-making, (AHP), (ANP), TOPSIS, RSES, ) in various application areas of computer science
  • Use of aggregation operators, multiobjective/criteria optimization, fuzzy MCDM, weighting and ranking criteria, ranking, sorting, and their implementation in real-world application areas
  • Use of MCDM in data mining and data analysis
  • Use of knowledge-driven, data-driven, model-driven, and hybrid decision-making and MCDM
  • Design and development of novel intelligent decision-making methods for social networks analysis, web mining, and crowdsourcing
  • Intelligent decision-making systems for big data analysis
  • Multicriteria-based user identity management
  • Multicriteria-based access control, data curation, fusion, and context awareness

Articles

  • Special Issue
  • - Volume 2020
  • - Article ID 9353120
  • - Research Article

Predicting Politician’s Supporters’ Network on Twitter Using Social Network Analysis and Semantic Analysis

Asif Khan | Huaping Zhang | ... | Lin Dai
  • Special Issue
  • - Volume 2020
  • - Article ID 8616039
  • - Research Article

Failure Prediction of Aircraft Equipment Using Machine Learning with a Hybrid Data Preparation Method

Kadir Celikmih | Onur Inan | Harun Uguz
  • Special Issue
  • - Volume 2020
  • - Article ID 4741963
  • - Research Article

Representation Learning of Knowledge Graphs with Embedding Subspaces

Chunhua Li | Xuefeng Xian | ... | Zhiming Cui
  • Special Issue
  • - Volume 2020
  • - Article ID 7846264
  • - Research Article

FaceFilter: Face Identification with Deep Learning and Filter Algorithm

Mohammed Alghaili | Zhiyong Li | Hamdi A. R. Ali
  • Special Issue
  • - Volume 2020
  • - Article ID 7081653
  • - Research Article

Multiswarm Multiobjective Particle Swarm Optimization with Simulated Annealing for Extracting Multiple Tests

Toan Bui | Tram Nguyen | ... | Tzung-Pei Hong
  • Special Issue
  • - Volume 2020
  • - Article ID 3810261
  • - Research Article

Deep Learning Structure for Cross-Domain Sentiment Classification Based on Improved Cross Entropy and Weight

Rong Fei | Quanzhu Yao | ... | Bo Hu
  • Special Issue
  • - Volume 2020
  • - Article ID 8930387
  • - Research Article

A Grey Target Group Decision Method with Dual Hesitant Fuzzy Information considering Decision-Maker’s Loss Aversion

Yufeng Zhou | Yufeng Li | Zhi Li
  • Special Issue
  • - Volume 2020
  • - Article ID 4748606
  • - Research Article

Data-Driven Decision-Support System for Speaker Identification Using E-Vector System

He Ma | Yi Zuo | ... | C. L. Philip Chen
  • Special Issue
  • - Volume 2020
  • - Article ID 1631869
  • - Research Article

A Novel Linguistic Z-Number QUALIFLEX Method and Its Application to Large Group Emergency Decision Making

Xue-Feng Ding | Li-Xia Zhu | ... | Yi-Qi Feng
  • Special Issue
  • - Volume 2020
  • - Article ID 8403262
  • - Research Article

Object Detection through Modified YOLO Neural Network

Tanvir Ahmad | Yinglong Ma | ... | Amin ul Haq
Scientific Programming
 Journal metrics
Acceptance rate27%
Submission to final decision66 days
Acceptance to publication33 days
CiteScore2.300
Impact Factor0.963
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