scholarly journals A CASE-BASED REASONING SYSTEM FOR GENOTYPIC PREDICTION OF HIV-1 CO-RECEPTOR TROPISM

2013 ◽  
Vol 11 (04) ◽  
pp. 1350006 ◽  
Author(s):  
MARK C. EVANS ◽  
AGNES C. PAQUET ◽  
WEI HUANG ◽  
LAURA NAPOLITANO ◽  
ARNE FRANTZELL ◽  
...  
Keyword(s):  
Prediction Algorithm ◽  
Clinical Samples ◽  
Support Vector ◽  
Case Based Reasoning ◽  
V3 Loop ◽  
Prediction Tool ◽  
Enhanced Sensitivity ◽  
Scoring Matrices ◽  
Tropism Prediction ◽  
Case Based

Accurate co-receptor tropism (CRT) determination is critical for making treatment decisions in HIV management. We created a genotypic tropism prediction tool by utilizing the case-based reasoning (CBR) technique that attempts to solve new problems through applying the solution from similar past problems. V3 loop sequences from 732 clinical samples with diverse characteristics were used to build a case library. Additional sequence and molecular properties of the V3 loop were examined and used for similarity assessment. A similarity metric was defined based on each attribute's frequency in the CXCR4-using viruses. We implemented three other genotype-based tropism predictors, support vector machines (SVM), position specific scoring matrices (PSSM), and the 11/25 rule, and evaluated their performance as the ability to predict CRT compared to Monogram's enhanced sensitivity Trofile®assay (ESTA). Overall concordance of the CBR based tropism prediction algorithm was 81%, as compared to ESTA. Sensitivity to detect CXCR4 usage was 90% and specificity was at 73%. In comparison, sensitivity of the SVM, PSSM, and the 11/25 rule were 85%, 81%, and 36% respectively while achieving a specificity of 90% by SVM, 75% by PSSM, and 97% by the 11/25 rule. When we evaluated these predictors in an unseen dataset, higher sensitivity was achieved by the CBR algorithm (87%), compared to SVM (82%), PSSM (76%), and the 11/25 rule (33%), while maintaining similar level of specificity. Overall this study suggests that CBR can be utilized as a genotypic tropism prediction tool, and can achieve improved performance in independent datasets compared to model or rule based methods.

scholarly journals Research on the model for tobacco disease prevention and control based on case-based reasoning and knowledge graph

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1947-1952 ◽  
Author(s):  
Lichuan Gu ◽  
Yingchun Xia ◽  
Xiaohui Yuan ◽  
Chao Wang ◽  
Jun Jiao
Keyword(s):  
Disease Prevention ◽  
Prevention And Control ◽  
Principal Component ◽  
Support Vector ◽  
Knowledge Graph ◽  
Case Based Reasoning ◽  
Long Time ◽  
And Control ◽  
Tobacco Disease ◽  
Case Based

Tobacco is one of the most important economic crops in China. The yield and quality of tobacco reduce severely because of long-time disease invasion. Currently, the main focus of researches on tobacco disease prevention and control is the diagnosis of disease that has occurred, which ignores to predict disease before it outbreaks. Therefore, in this paper, we follow the idea that prediction is used before disease prevention and control and study the model for tobacco disease prevention and control by using knowledge graph and case-based reasoning (CBR). In order to implement the model, we choose tobacco mosaic virus (TMV) as research object and follow the following methods to prevent occurrence of that. At first, a method to predicting environmental factors by using principal component analysis (PCA) and support vector machine (SVM) is proposed. According to the prediction result, knowledge graph and CBR are used to retrieve the most similarity case and finally determine the best solution. Experimental results demonstrate that our model can achieve high accuracy and give the most appropriate scheme for disease prevention and control.

scholarly journals Fault Detection in the MSW Incineration Process Using Stochastic Configuration Networks and Case-Based Reasoning

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7356
Author(s):  
Chenxi Ding ◽  
Aijun Yan
Keyword(s):  
Fault Detection ◽  
Back Propagation ◽  
Waste Incineration ◽  
Training Sample ◽  
Support Vector ◽  
Case Based Reasoning ◽  
Detection Model ◽  
Msw Incineration ◽  
Incineration Process ◽  
Case Based

Fault detection in the waste incineration process depends on high-temperature image observation and the experience of field maintenance personnel, which is inefficient and can easily cause misjudgment of the fault. In this paper, a fault detection method is proposed by combining stochastic configuration networks (SCNs) and case-based reasoning (CBR). First, a learning pseudo metric method based on SCNs (SCN-LPM) is proposed by training SCN learning models using a training sample set and defined pseudo-metric criteria. Then, the SCN-LPM method is used for the case retrieval stage in CBR to construct the fault detection model based on SCN-CBR, and the structure, algorithmic implementation, and algorithmic steps are given. Finally, the performance is tested using historical data of the MSW incineration process, and the proposed method is compared with typical classification methods, such as a Back Propagation (BP) neural network, a support vector machine, and so on. The results show that this method can effectively improve the accuracy of fault detection and reduce the time complexity of the task and maintain a certain application value.

Development of an energy prediction tool for commercial buildings using case-based reasoning

2014 ◽  
Vol 81 ◽  
pp. 152-160 ◽  
Author(s):  
Danielle Monfet ◽  
Maria Corsi ◽  
Daniel Choinière ◽  
Elena Arkhipova
Keyword(s):  
Commercial Buildings ◽  
Case Based Reasoning ◽  
Prediction Tool ◽  
Energy Prediction ◽  
Case Based

scholarly journals Safety Early Warning Research for Highway Construction Based on Case-Based Reasoning and Variable Fuzzy Sets

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Yan Liu ◽  
Ting-Hua Yi ◽  
Zhen-Jun Xu
Keyword(s):  
Early Warning ◽  
Construction Projects ◽  
Highway Construction ◽  
Early Warning Systems ◽  
Construction Safety ◽  
Support Vector ◽  
Case Based Reasoning ◽  
Ability To Act ◽  
Case Based ◽  
Early Warnings

As a high-risk subindustry involved in construction projects, highway construction safety has experienced major developments in the past 20 years, mainly due to the lack of safe early warnings in Chinese construction projects. By combining the current state of early warning technology with the requirements of the State Administration of Work Safety and using case-based reasoning (CBR), this paper expounds on the concept and flow of highway construction safety early warnings based on CBR. The present study provides solutions to three key issues, index selection, accident cause association analysis, and warning degree forecasting implementation, through the use of association rule mining, support vector machine classifiers, and variable fuzzy qualitative and quantitative change criterion modes, which fully cover the needs of safe early warning systems. Using a detailed description of the principles and advantages of each method and by proving the methods’ effectiveness and ability to act together in safe early warning applications, effective means and intelligent technology for a safe highway construction early warning system are established.

scholarly journals Aircraft Troubleshooting Optimization Using Case-based Reasoning and Decision Analysis

2020 ◽  
Vol 12 (1) ◽  
pp. 8
Author(s):  
Gustavo Borba Evangelista ◽  
Guilherme Conceição Rocha ◽  
Wlamir Olivares Loesch Vianna
Keyword(s):  
Decision Analysis ◽  
Field Experience ◽  
Nearest Neighbor ◽  
Fault Isolation ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Support Vector ◽  
Case Based Reasoning ◽  
K Nearest Neighbor ◽  
Case Based

The Fault Isolation Manual (FIM) can be seen as a specialist system that carries the expectations and expertise of engineers and technical team concerning the aircraft components and systems operation. It is basically a manual that supports the maintainers regarding the actions to perform in determined situations to properly isolate a fault. Although the FIM is the most common tool that assists maintainer on the troubleshooting process today, it does not adequately consider field experience and it does not explore situations where the maintenance operator has limited resources, such as a lack of tools and equipment. These drawbacks are essentially caused by the lack of flexibility or adaptability of this method since it is a static manual. There are several dynamic methods studied in the field of system troubleshooting and aircraft maintenance such as Artificial Neural Networks, Support Vector Machine, K Nearest Neighbor and many other machine learning algorithms. These techniques are considered very powerful and useful; however, the training process of the data-driven strategies requires a large amount of data to provide a reliable result. In this context, the present work proposes a combination of data-driven with legacy knowledge-based approaches. The following techniques are employed to integrate the concepts mentioned: decision trees that explore the legacy knowledge with its topology based on the FIM, truth tables and decision analysis that explores Bayes’ rule to assist the decision- making process and case-based reasoning, technique that enables the learning from the field experience.

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