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Erratum to: Theoretical foundation of a decision network for urban development

Shih-kung Lai, Jhong-you Huang,lai@tongji.edu.cn,jhongyouhuang@gmail.com

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 10,   Pages 1677-1677 doi: 10.1631/FITEE.15e0000

Abstract: Erratum to: , 2017 18(8):1033-1039. doi:

Keywords： Decision making     Linked decisions     Decision network     Planning    

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Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4,   Pages 814-828 doi: 10.1007/s11465-021-0650-6

Abstract: To address this issue, this paper explores a decision-tree-structured neural network, that is, the deepconvolutional tree-inspired network (DCTN), for the hierarchical fault diagnosis of bearings.The proposed model effectively integrates the advantages of convolutional neural network (CNN) and decisiontree methods by rebuilding the output decision layer of CNN according to the hierarchical structuralcharacteristics of the decision tree, which is by no means a simple combination of the two models.

Keywords： bearing     cross-severity fault diagnosis     hierarchical fault diagnosis     convolutional neural network     decision tree    

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Erratum to: Theoretical foundation of a decision network for urban development Perspective

Shih-kung LAI, Jhong-you HUANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 10,   Pages 1677-1677 doi: 10.1631/FITEE.1510000

Abstract: 规划问题具有挑战性和复杂性，因为它们通常涉及多重属性偏好和多个利益相关者。因此，很少有规划工具能帮助规划者解决这些问题。传统决策分析在处理规划问题时无法达到预期，因为它主要侧重于为特定决策者制定单一决策。本文介绍了一个名为“决策网络”的规划工具的理论基础，其目的是帮助规划者在面对具有多属性偏好的多个利益相关者时，作出多重且相互关联的决定。本研究提供了一个成熟科学技术的起点，将有助于处理复杂的规划问题。我们首先提出决策网络模式所欲处理的规划问题的一般化模式。然后我们针对这个规划问题提出一个有效的解决演算法，再使用数值示例来演示该演算法是如何运作。所提出的解决演算法被证明是有效的，亦使该规划工具的计算机化成为可能。同时，我们指明“决策网络”的图形表示较“决策树”的图形表示来得更为有效。因此，在处理具有挑战性和复杂性的城市规划问题时，通过决策网络模式所作出的多重的且相关的决策，将会比独立地做出这些决策产生更多的好处。

Keywords： Decision making     Linked decisions     Decision network     Planning    

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Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients in industrial crystallization

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 4,   Pages 523-535 doi: 10.1007/s11705-021-2083-5

Abstract: ingredients (APIs) in ethanol were considered in the prediction models, which were constructed by random decisionforests and artificial neural network with optimized data structure and model accuracy.

Keywords： solubility prediction     machine learning     artificial neural network     random decision forests    

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Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting concrete

Frontiers of Structural and Civil Engineering 2023, Volume 17, Issue 2,   Pages 284-305 doi: 10.1007/s11709-022-0901-6

Abstract: Artificial neural network, random forest, and categorical gradient boosting (CatBoost) models were optimized

Keywords： compressive strength     self-compacting concrete     artificial neural network     decision tree     CatBoost    

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Virtual Urban Transportation System and Decision Support Mode for Transportation Development

Wang Wei, Zhao De, Hua Xuedong, Zhou Wei

Strategic Study of CAE 2021, Volume 23, Issue 3,   Pages 163-172 doi: 10.15302/J-SSCAE-2021.03.020

Abstract: style="text-align: justify;">Developing a localized virtual urban transportation system (VUTS) and a decisionFurthermore, a decision support mode for transportation development is proposed; it covers the applicationSubsequently, we built the VUTS of Chongqing and provided plans for the municipal government regarding its decisionThe case results suggest that the decision support mode based on VUTS is effective in the demonstration

Keywords： urban transportation system     road network     traffic analysis model     virtual transportation system     decision    

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The research on structural damage identification using rough set and integrated neural network

Juelong LI, Hairui LI, Jianchun XING, Qiliang YANG

Frontiers of Mechanical Engineering 2013, Volume 8, Issue 3,   Pages 305-310 doi: 10.1007/s11465-013-0259-5

Abstract: efficiently solve this problem, a new damage identification method based on rough set and integrated neural networkAnd then the reduced attributes will be put into the sub-neural network.The sub-neural network can give the preliminary diagnosis from different aspects of damage.The decision fusion network will give the final damage identification results.identification examples show that this method can simplify the redundant information to reduce the neural network

Keywords： rough set     integrated neural network     damage identification     decision making fusion    

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Convergence to real-time decision making

James M. TIEN

Frontiers of Engineering Management 2020, Volume 7, Issue 2,   Pages 204-222 doi: 10.1007/s42524-019-0040-5

Abstract: Real-time decision making reflects the convergence of several digital technologies, including those concernedMore specifically, real-time decision making can be depicted in terms of three converging dimensions:Internet of Things, decision making, and real-time.Decision making includes model-based analytics (since before 1990), information-based Big Data (sinceCentral to decision making, especially real-time decision making, is the ServGood concept, which the

Keywords： real-time decision making     services     goods     ServGoods     Big Data     Internet of Things     artificial intelligence    

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Current applications of artificial intelligence for intraoperative decision support in surgery

Allison J. Navarrete-Welton, Daniel A. Hashimoto

Frontiers of Medicine 2020, Volume 14, Issue 4,   Pages 369-381 doi: 10.1007/s11684-020-0784-7

Abstract: This scoping review investigated AI-based decision support systems targeted at the intraoperative phasethat a number of opportunities exist for future researchers and clinicians to work on AI for surgical decision

Keywords： artificial intelligence     decision support     clinical decision support systems     intraoperative     deep learning    

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Challenges of human–machine collaboration in risky decision-making

Frontiers of Engineering Management 2022, Volume 9, Issue 1,   Pages 89-103 doi: 10.1007/s42524-021-0182-0

Abstract: purpose of this paper is to delineate the research challenges of human–machine collaboration in risky decision-makingIn risky decision-making, a human decision-maker is vulnerable to cognitive biases when judging the possibleoutcomes of a risky event, whereas a machine decision-maker cannot handle new and dynamic contexts withWe first summarize features of risky decision-making and possible biases of human decision-makers thereinAfterward, we review the literature on human–machine collaboration in a general decision context, from

Keywords： human–machine collaboration     risky decision-making     human–machine team and interaction     task allocation    

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CORRECTION to: Special issue: Decision, risk analytics and data intelligence

Xiaozhe ZHAO, Desheng Wu

Frontiers of Engineering Management   Pages 697-697 doi: 10.1007/s42524-020-0139-8

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An overview on the applications of the hesitant fuzzy sets in group decision-making: Theory, support

Zeshui XU, Shen ZHANG

Frontiers of Engineering Management 2019, Volume 6, Issue 2,   Pages 163-182 doi: 10.1007/s42524-019-0017-4

Abstract: element (HFE), which is the basic component of HFSs, can express the evaluation values of multiple decisionThus, the HFS has its unique advantages in group decision making (GDM).the consensus process under hesitant fuzzy environment, which is an important support for a complete decision-makingaspect, we introduce seven hesitant fuzzy GDM approaches, which can be applied in GDM under different decision-making

Keywords： hesitant fuzzy set     hesitant fuzzy preference relation     group decision-making    

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Clinical decision-making by the emergency department resident physicians for critically ill patients

Tengda Xu, Jun Xu, Xuezhong Yu, Sui Ma, Zhong Wang

Frontiers of Medicine 2012, Volume 6, Issue 1,   Pages 89-93 doi: 10.1007/s11684-012-0183-9

Abstract:

The application of main methodologies for clinical decision-making by residents in emergency medicalMedical College Hospital of were analyzed by independent investigators who evaluated the main clinical decision-makingThe application of decision-making strategies by PG1 and PG3 groups, which means the residents in firstThe decision-making processes applied by junior and senior residents were significantly different, althoughInclusion of clinical decision-making in medical curricula is needed to improve decision-making in critical

Keywords： clinical decision-making     emergency medicine     critically ill patient     resident     methodology    

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Both-Branch Fuzzy Decision and Problems on Decision Discernment

Shi Kaiquan,Li Qiqiang

Strategic Study of CAE 2001, Volume 3, Issue 1,   Pages 71-77

Abstract: (X*≠{x}), and the optimal decision analysis model.In addition, the paper proposes decision judgement theorem, decision discernment theorem, decision surplusage-discardingBoth-branch fuzzy decision has such characteristics as decision two-direction dependence character, decisionpiling-synthesis character, decision branch-separating character, decision branch-degeneration character, and decision non-fault character.

Keywords： both-branch fuzzy decision     decision model     judgement theorem     discernment theorem     surplusage-discarding    

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Title Author Date Type Operation

Erratum to: Theoretical foundation of a decision network for urban development

Shih-kung Lai, Jhong-you Huang,lai@tongji.edu.cn,jhongyouhuang@gmail.com

Journal Article

Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical

Journal Article

Erratum to: Theoretical foundation of a decision network for urban development

Shih-kung LAI, Jhong-you HUANG

Journal Article

Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients in industrial crystallization

Journal Article

Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting concrete

Journal Article

Virtual Urban Transportation System and Decision Support Mode for Transportation Development

Wang Wei, Zhao De, Hua Xuedong, Zhou Wei

Journal Article

The research on structural damage identification using rough set and integrated neural network

Juelong LI, Hairui LI, Jianchun XING, Qiliang YANG

Journal Article

Li Haoran: Research on the Transformation Decision of "Replacing Coal With Gas" Based on Peer Effectand Network Effect (2020-7-11)

19 May 2022

Conference Videos

Convergence to real-time decision making

James M. TIEN

Journal Article

Current applications of artificial intelligence for intraoperative decision support in surgery

Allison J. Navarrete-Welton, Daniel A. Hashimoto

Journal Article

Challenges of human–machine collaboration in risky decision-making

Journal Article

CORRECTION to: Special issue: Decision, risk analytics and data intelligence

Xiaozhe ZHAO, Desheng Wu

Journal Article

An overview on the applications of the hesitant fuzzy sets in group decision-making: Theory, support

Zeshui XU, Shen ZHANG

Journal Article

Clinical decision-making by the emergency department resident physicians for critically ill patients

Tengda Xu, Jun Xu, Xuezhong Yu, Sui Ma, Zhong Wang

Journal Article

Both-Branch Fuzzy Decision and Problems on Decision Discernment

Shi Kaiquan,Li Qiqiang

Journal Article