A Cotton Disease Diagnosis Method Using a Combined Algorithm of Case-Based Reasoning and Fuzzy Logic

2020 ◽  
Author(s):  
Yuhong Dong ◽  
Zetian Fu ◽  
Stevan Stankovski ◽  
Yaoqi Peng ◽  
Xinxing Li
Keyword(s):  
Fuzzy Logic ◽  
Disease Diagnosis ◽  
Case Based Reasoning ◽  
Cotton Production ◽  
Scoring Method ◽  
Disease Symptoms ◽  
Diagnosis Method ◽  
And Control ◽  
Combined Algorithm ◽  
Case Based

Abstract In this study, a cotton disease diagnosis method that uses a combined algorithm of case-based reasoning (CBR) and fuzzy logic was designed and implemented. It focuses on the prevention, diagnosis and control of diseases affecting cotton production in China. Conventional methods of disease diagnosis are primarily based on CBR with reference to user-provided symptoms; however, in most cases, user-provided symptoms do not fully meet the requirements of CBR. To address this problem, fuzzy logic is incorporated into CBR to allow for more flexible and accurate models. With the help of CBR and fuzzy reasoning, three diagnostic results can be obtained by the cotton disease diagnosis system (CDDS) constructed in this study: success, success but not exact and failure. To verify the reliability of the CDDS and its ability to diagnose cotton diseases, its diagnostic accuracy and stability were analyzed and compared with the results obtained by the traditional expert scoring method. The analysis results reveal that the CDDS can achieve a high diagnostic success rate (above 90%) and better diagnostic stability than the traditional expert scoring method when at least four disease symptoms are input. The CDDS provides an independent and objective source of information to assist farmers in the diagnosis and prevention of cotton diseases.

Soft case-based reasoning

2005 ◽  
Vol 20 (3) ◽  
pp. 267-269 ◽  
Author(s):  
WILLIAM CHEETHAM ◽  
SIMON SHIU ◽  
ROSINA O. WEBER
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Network Theory ◽  
Probabilistic Reasoning ◽  
Case Based Reasoning ◽  
Case Representation ◽  
Soft Computing Techniques ◽  
Neural Network Theory ◽  
Case Based

The aim of this commentary is to discuss the contribution of soft computing—a consortium of fuzzy logic, neural network theory, evolutionary computing, and probabilistic reasoning—to the development of case-based reasoning (CBR) systems. We will describe how soft computing has been used in case representation, retrieval, adaptation, reuse, and case-base maintenance, and then present a brief summary of six CBR applications that use soft computing techniques.

scholarly journals Emergency Response for COVID-19 Prevention and Control in Urban Rail Transit Based on Case-Based Reasoning Method

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Yuning Wang ◽  
Yingzi Liang ◽  
Hui Sun ◽  
Yufei Yang
Keyword(s):  
Emergency Response ◽  
Prevention And Control ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Case Based Reasoning ◽  
Historical Case ◽  
Urban Rail ◽  
Emergency Responses ◽  
And Control ◽  
Case Based

As an important public travel mode, urban rail transit has the characteristics of crowded passengers and closed operation. Safe management of urban rail transit is an important research topic that attracted attention in recent years. This article proposes a decision analysis method based on case-based reasoning, which aims to solve the emergency response problems for the prevention and control of corona virus disease 2019 (COVID-19) in urban rail transit. In this method, first, the historical cases are extracted and filtered by calculating the similarity between the target case and the historical case. A set of similar historical cases is constructed by setting the similarity threshold in advance. Second, comprehensive utility value of emergency response of each similar case is calculated referring to the utility evaluation of emergency response effect and response cost of each similar historical case. On this basis, the emergency plan of the target case is generated by selecting the emergency plans of the similar historical cases corresponding to the maximum comprehensive utility values of the emergency responses. Finally, with the emergency responses of COVID-19 in Tianjin rail transit as the background, this paper explains the feasibility and effectiveness of the proposed method within a case study.

INTRODUCTION OF A MULTIAGENT PARADIGM TO OPTIMIZE A CASE-BASED REASONING SYSTEM DESIGNED TO PERSONALIZE THREE-DIMENSIONAL NUMERICAL REPRESENTATIONS OF HUMAN ORGANS

2014 ◽  
Vol 26 (05) ◽  
pp. 1450060 ◽  
Author(s):  
Julien Henriet ◽  
Christophe Lang
Keyword(s):  
Three Dimensional ◽  
Processing System ◽  
Case Based Reasoning ◽  
Distributed Architecture ◽  
Accidental Exposure ◽  
Human Organs ◽  
Reasoning System ◽  
Numerical Representations ◽  
And Control ◽  
Case Based

The case-based reasoning (CBR) approach consists of retrieving solutions from similar past problems and adapting them to new problems. Interpolation tools can easily be used as adaptation tools in CBR systems. The accuracies of interpolated results depend on the set of known solved problems with which the interpolation tools are previously trained. EquiVox is a CBR-based system designed for retrieving and adapting three-dimensional numerical representations of human organs called phantoms. EquiVox uses an interpolation tool as an adaptation process. These phantoms are used by radiation protection experts to establish dosimetric reports in case of accidental overexposure to radiation. In addition, medical physicians need these phantoms to compute and control exposure to radiation used to treat diseases such as cancerous tumors in hospitals. The present work aims at proposing a distributed architecture for EquiVox so that a user may find a solution as quickly as possible based on the most recent available knowledge of a given community. We have designed a distributed architecture based on a multiagent paradigm and studied the theoretical performance of the new version. The ability of the new system to quickly provide and adapt solutions using the most up-to-date knowledge has been analyzed from a probabilistic angle. In the case of limited and accidental exposure to radiation, the proposed parallel processing system improves the previous and sequential version of EquiVox. Improvements are also obtained in some cases of massive exposure to radiation.

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