Deep learning and case-based reasoning for predictive and adaptive traffic emergency management

2020 ◽  
Author(s):  
Ali Louati ◽  
Hassen Louati ◽  
Zhaojian Li
Keyword(s):  
Deep Learning ◽  
Emergency Management ◽  
Case Based Reasoning ◽  
Case Based

scholarly journals The 25th International Conference on Case-Based Reasoning

AI Magazine ◽  
2018 ◽  
Vol 39 (2) ◽  
pp. 79-80
Author(s):  
David W. Aha ◽  
Kerstin Bach ◽  
Odd Erik Gundersen ◽  
Jean Lieber
Keyword(s):  
Deep Learning ◽  
Poster Session ◽  
Case Based Reasoning ◽  
International Conference ◽  
Process Oriented ◽  
Case Based

ICCBR-2017, the 25th International Conference on Case-Based Reasoning, was held in Trondheim (Norway) in June 2017. The conference included 27 original contributions presented in oral sessions and in a poster session. In addition to three invited talks, the meeting also included workshops on CBR and Deep Learning, Computer Analogy, and Process-Oriented CBR, as well as a Doctoral Consortium, the Computer Cooking Contest, and the first CBR Video Competition.

scholarly journals A fusion method based on Deep Learning and Case-Based Reasoning which improves the resulting medical image segmentations

2020 ◽  
Vol 147 ◽  
pp. 113200 ◽  
Author(s):  
Lisa Corbat ◽  
Mohammad Nauval ◽  
Julien Henriet ◽  
Jean-Christophe Lapayre
Keyword(s):  
Deep Learning ◽  
Medical Image ◽  
Case Based Reasoning ◽  
Fusion Method ◽  
Case Based

A Case Similarity Calculation Model Based on the Urban Flooding Case with Stratified Data Characteristics

2018 ◽  
Vol 6 (2) ◽  
pp. 134-151
Author(s):  
Xiaoyu Zhu ◽  
Yuxiang Fan ◽  
Junguang Gao
Keyword(s):  
Emergency Management ◽  
Real Data ◽  
Calculation Model ◽  
Case Based Reasoning ◽  
Urban Flooding ◽  
Entropy Weight ◽  
Entropy Weight Method ◽  
Weight Method ◽  
Stratified Data ◽  
Case Based

Abstract As the pace of urbanization is accelerating, increasing amount of floodplain has been projected as the future cities. Subsequently, urban flooding is being studied by global emergency management exports due to its increasingly significant impact on us. Some existing research on flooding emergency management based on the case-based reasoning (CBR) method have made tremendous progress, but the urban flooding case with its stratified data characteristics is required a new methodology which is different from the ones applied to flash floods. So, based on the case-based reasoning (CBR) method, this paper proposed a CPIE-CBR model with four layers, classification filtration, punctiform similarity, interval similarity and entropy weight method, to calculate the case similarity among the urban flooding case with stratified data characteristics. Then we carry out the numerical simulation with the real data about China and conduct some comparison with original ways so that we observe the validity and efficiency of our model in the end.

Case Reuse Based on Fuzzy Reasoning – Adaption for Emergency Management

2013 ◽  
Vol 333-335 ◽  
pp. 1324-1327
Author(s):  
Chao Huang ◽  
Quan Yi Huang ◽  
Shao Bo Zhong ◽  
Jian Guo Chen
Keyword(s):  
Text Mining ◽  
Emergency Management ◽  
Incomplete Information ◽  
Fuzzy Sets ◽  
Association Rules ◽  
Text Classification ◽  
Fuzzy Reasoning ◽  
Experiential Knowledge ◽  
Case Based Reasoning ◽  
Case Based

Emergency management is such a domain where experiential knowledge could be easily collected, and is quite suitable for the application of case based reasoning. However, in practice there are two problems limiting the effectiveness of CBR, the he incomplete information and changing situations. This paper proposed an approach based on fuzzy sets and text mining to solve those two problems, which contains four steps: a) represent the attributes with fuzzy sets, b) extract solution texts with text classification, c) establish connections of attributes and solutions with association rules, and d) adjust the solution with fuzzy reasoning. An example shows the adaption for emergency management and illustrates the improvement for CBR with the approach.

scholarly journals A Case-Based Reasoning Model Powered by Deep Learning for Radiology Report Recommendation

2021 ◽  
Vol In Press (In Press) ◽  
pp. 1
Author(s):  
Elvira Amador-Domínguez ◽  
Emilio Serrano ◽  
Daniel Manrique ◽  
Javier Bajo
Keyword(s):  
Deep Learning ◽  
Case Based Reasoning ◽  
Radiology Report ◽  
Case Based
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