In this study, we propose a multicriteria group decision making (MCGDM) algorithm under uncertainty where data is collected as intervals. The proposed MCGDM algorithm aggregates the data, determines the optimal weights for criteria and ranks alternatives with no further input. The intervals give flexibility to experts in assessing alternatives against criteria and provide an opportunity to gain maximum information. We also propose a novel method to aggregate expert judgements using cloud models. We introduce an experimental approach to check the validity of the aggregation method. After that, we use the aggregation method for an MCGDM problem. Here, we find the optimal weights for each criterion by proposing a bilevel optimisation model. Then, we extend the technique for order of preference by similarity to ideal solution (TOPSIS) for data based on cloud models to prioritise alternatives. As a result, the algorithm can gain information from decision makers with different levels of uncertainty and examine alternatives with no more information from decision-makers. The proposed MCGDM algorithm is implemented on a case study of a cybersecurity problem to illustrate its feasibility and effectiveness. The results verify the robustness and validity of the proposed MCGDM using sensitivity analysis and comparison with other existing algorithms.
Towards interval uncertainty propagation control in bivariate aggregation processes and the introduction of width-limited interval-valued overlap functions
Universidad Pública de Navarra（纳瓦拉公立大学）；Universidade Federal do Rio Grande（里奥格兰德联邦大学）；Universidade Federal do Rio Grande do Norte（北里奥格兰德联邦大学）
Overlap functions are a class of aggregation functions that measure the overlapping degree between two values. Interval-valued overlap functions were defined as an extension to express the overlapping of interval-valued data, and they have been usually applied when there is uncertainty regarding the assignment of membership degrees. The choice of a total order for intervals can be significant, which motivated the recent developments on interval-valued aggregation functions and interval-valued overlap functions that are increasing to a given admissible order, that is, a total order that refines…
Explainable Uncertainty Quantification for Wastewater Treatment Energy Prediction via Interval Type-2 Neuro-Fuzzy System
Jheronimus Academy of Data Science, Eindhoven University of Technology, Eindhoven, The Netherlands（杰罗尼穆斯数据科学学院，埃因霍温理工大学，埃因霍温，荷兰）；Haskoning, Amersfoort, The Netherlands（哈索宁，阿默斯福尔特，荷兰）；School of Industrial Engineering, Eindhoven University of Technology（工业工程学院，埃因霍温理工大学）
ConformaDecompose: Explaining Uncertainty via Calibration Localization
Department of Computer Science and Engineering, University of Bologna, Italy（博洛尼亚大学计算机科学与工程系）；Marketing and Sales, Automobili Lamborghini S.p.A, Sant'Agata Bolognese, Italy（法拉利拉尔布奇公司）；RCT Data Science and Security, Technical University Dortmund, Germany（多特蒙德技术大学数据科学与安全部）；Jönköping AI Lab, Department of Computing, Jönköping University, Sweden（约翰内斯堡大学人工智能实验室）；Jönköping International Business School, Sweden（约翰内斯堡国际商学院）；Department of Software Engineering, Haliç University, Istanbul, Turkey（伊斯坦布尔哈利奇大学软件工程系）；University College Cork, Ireland（科克大学）
Towards Trustworthy Vital Sign Forecasting: Leveraging Uncertainty for Prediction Intervals
College of Computing（计算学院）；Data Science Nanyang Technological University（南洋理工大学数据科学系）；Nanyang Technological University（南洋理工大学）；A*STAR Centre for Frontier AI Research（前沿人工智能研究中心）；School of Computing（计算学院）；University of Utah（犹他大学）；Swee Hock School of Public Health（公共卫生学院）；National University of Singapore（新加坡国立大学）；Lee Kong Chian School of Medicine（李科钦医学院）
Unveil Sources of Uncertainty: Feature Contribution to Conformal Prediction Intervals
Département de Mathématiques, Université du Québec à Montréal（魁北克大学蒙特利尔分校数学系）；Institut Intelligence et Données, Université Laval（拉瓦尔大学智能与数据研究所）；CNRS - Université de Montréal（法国国家科学研究中心-蒙特利尔大学）；CRM – CNRS Aix Marseille Univ, CNRS, AMSE, Marseille, France（法国国家科学研究中心-阿维尼昂大学）
IT2CFNN: An Interval Type-2 Correlation-Aware Fuzzy Neural Network to Construct Non-Separable Fuzzy Rules with Uncertain and Adaptive Shapes for Nonlinear Function Approximation
Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics
Multiple criteria hierarchy process for sorting problems under uncertainty applied to the evaluation of the operational maturity of research institutions
University of Ottawa（渥太华大学）；Methodist University of Piracicaba（皮拉西卡巴卫理公会大学）；University of Campinas（坎皮纳斯大学）；Embrapii-Brazilian Association for Industrial Research and Innovation（Embrapii-巴西工业研究与创新协会）；Mackenzie Presbyterian University（麦肯齐长老会大学）；KMI Software Consulting（KMI软件咨询公司）
