scholarly journals Research on the Optimization Model of the Abrasive Blocks Using Weighted Case-Based Reasoning

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Wei Gao ◽  
Shengqiang Yang ◽  
Jianyan Tian ◽  
Yan Yang ◽  
Xiaojian Fan ◽  
...  
Keyword(s):  
Production Efficiency ◽  
Rapid Development ◽  
Case Based Reasoning ◽  
Universal Method ◽  
Extensive Experience ◽  
Variable Weights ◽  
Finishing Process ◽  
Depth Analysis ◽  
Barrel Finishing ◽  
Case Based

Barrel finishing process is a universal method to improve the surface quality of parts. It is widely used in high-performance parts of high-end equipment. As a necessary tool consumable for barrel finishing process, the characteristic parameters of the abrasive blocks affect the processing quality and production efficiency. However, the current method for selecting the abrasive blocks requires large number of experiments based on the operator’s extensive experience, which does not meet the rapid development needs of the barrel finishing process. Therefore, this paper proposes a case-based reasoning model with variable weights to achieve the intelligent optimization of the abrasive blocks. Based on the in-depth analysis of the characteristics of the barrel finishing process, a reasonable case base is established firstly, which is to determine the comprehensive case features and the solution of the case. AHP (analytic hierarchy process) is proposed to determine the weight of case features and to dynamically adjust the weight of case features according to the characteristics of the parts to be processed and users’ processing requirements. The results show that the proposed case-based reasoning model with variable weights can quickly, accurately, and reasonably select the abrasive blocks during the process of making processing technique of the barrel finishing, which will lay a necessary foundation for the effective implementation of the barrel finishing process and contribute significantly to the improvement of its efficiency.

Case-based reasoning for safety assessment of critical software

2020 ◽  
pp. 1-17
Author(s):  
Habib Hadj-Mabrouk
Keyword(s):  
Machine Learning ◽  
Rail Transport ◽  
Machine Learning Techniques ◽  
Transport Systems ◽  
Risk Level ◽  
Case Based Reasoning ◽  
Learning Techniques ◽  
Future System ◽  
Depth Analysis ◽  
Case Based

The commissioning of a new guided or automated rail transport system requires an in-depth analysis of all the methods, techniques, procedures, regulations and safety standards to ensure that the risk level of the future system does not present any danger likely to jeopardize the safety of travelers. Among these numerous safety methods implemented to guarantee safety at the system, automation, hardware and software level, there is a method called “Software Errors and Effects Analysis (SEEA)” whose objective is to determine the nature and the severity of the consequences of software failures, to propose measures to detect errors and finally to improve the robustness of the software. In order to strengthen and rationalize this SEEA method, we have agreed to use machine learning techniques and in particular Case-Based Reasoning (CBR) in order to assist the certification experts in their difficult task of assessing completeness and the consistency of safety of critical software equipment. The main objective consists, from a set of data in the form of accident scenarios or incidents experienced on rail transport systems (experience feedback), to exploit by automatic learning this mass of data to stimulate the imagination of certification experts and assist them in their crucial task of researching scenarios of potential accidents not taken into account during the design phase of new critical software. The originality of the tool developed lies not only in its ability to model, capitalize, sustain and disseminate SEEA expertise, but it represents the first research on the application of CBR to SEEA. In fact, in the field of rail transport, there are currently no software tools for assisting SEEAs based on machine learning techniques and in particular based on CBR.

scholarly journals Comparative Analysis of Exemplar-Based Approaches for Students’ Learning Style Diagnosis Purposes

2021 ◽  
Vol 11 (15) ◽  
pp. 7083
Author(s):  
Daiva Goštautaitė ◽  
Jevgenij Kurilov
Keyword(s):  
Comparative Analysis ◽  
Learning Styles ◽  
Student Learning ◽  
Learning Style ◽  
Computational Models ◽  
Driving Forces ◽  
Rapid Development ◽  
Case Based Reasoning ◽  
Case Based ◽  
Student Learning Styles

A lot of computational models recently are undergoing rapid development. However, there is a conceptual and analytical gap in understanding the driving forces behind them. This paper focuses on the integration between computer science and social science (namely, education) for strengthening the visibility, recognition, and understanding the problems of simulation and modelling in social (educational) decision processes. The objective of the paper covers topics and streams on social-behavioural modelling and computational intelligence applications in education. To obtain the benefits of real, factual data for modeling student learning styles, this paper investigates exemplar-based approaches and possibilities to combine them with case-based reasoning methods for automatically predicting student learning styles in virtual learning environments. A comparative analysis of approaches combining exemplar-based modelling and case-based reasoning leads to the choice of the Bayesian Case model for diagnosing a student’s learning style based on the data about the student’s behavioral activities performed in an e-learning environment.

scholarly journals Adaptable Web Prediction Framework for Disease Prediction Based on the Hybrid Case Based Reasoning Model

2016 ◽  
Vol 6 (6) ◽  
pp. 1212-1216
Author(s):  
B. Trstenjak ◽  
D. Donko ◽  
Z. Avdagic
Keyword(s):  
Prediction Models ◽  
Rapid Development ◽  
Object Oriented ◽  
Case Based Reasoning ◽  
Disease Prediction ◽  
Machine Learning Methods ◽  
Diagnosis Accuracy ◽  
Online Prediction ◽  
Public Repositories ◽  
Case Based

Nowadays, we are witnessing the rapid development of medicine and various methods that are used for early detection of diseases. In order to make quality decisions in diagnosis and prevention of disease, various decision support systems based on machine learning methods have been introduced in the medical domain. Such systems play an increasingly important role in medical practice. This paper presents a new web framework concept for disease prediction. The proposed framework is object-oriented and enables online prediction of various diseases. The framework enables online creation of different autonomous prediction models depending on the characteristics of diseases. Prediction process in the framework is based on a hybrid Case Based Reasoning classifier. The framework was evaluated on disease datasets from public repositories. Experimental evaluation shows that the proposed framework achieved high diagnosis accuracy.

Research on Emergency Aid Decision-Making Model for Environmental Emergency Based on Case-Based Reasoning

2014 ◽  
Vol 675-677 ◽  
pp. 206-212
Author(s):  
Ying Ju Zhang
Keyword(s):  
Decision Making ◽  
Nearest Neighbor ◽  
Layer Structure ◽  
Prototype System ◽  
Case Based Reasoning ◽  
Universal Method ◽  
Emergency Decision ◽  
Aid Decision ◽  
Decision Making Model ◽  
Case Based

The paper applied method of case-based reasoning to environmental emergency aid decision-making, and provided a decision-making method based on experience of historical cases to emergency decision-makers. Firstly, a universal method for describing and organizing environmental emergency cases based on three-tier architecture was proposed based on feature analysis of environmental emergency cases. Then a kind of two-layer structure similarity algorithm was designed based on attribute features of environmental emergency cases, which can effectively avoid the defect of traditional Nearest-Neighbor Algorithm. Finally, a CBR prototype system of emergency aid decision-making model was developed and an example of environmental mass incident case was used to testify the practicability of the model. The case example show that the emergency aid decision-making model for environmental emergency based on CBR is applicable in real work.

Solving the Message Classification Problem in Voice Interaction Systems

Vestnik MEI ◽  
2020 ◽  
Vol 5 (5) ◽  
pp. 132-139
Author(s):  
Ivan E. Kurilenko ◽  
◽  
Igor E. Nikonov ◽  
Keyword(s):  
Artificial Intelligence ◽  
Classification Problem ◽  
Subject Area ◽  
Text Messages ◽  
Case Based Reasoning ◽  
Short Text ◽  
Proposed Modification ◽  
Voice Interaction ◽  
Text Content ◽  
Case Based

A method for solving the problem of classifying short-text messages in the form of sentences of customers uttered in talking via the telephone line of organizations is considered. To solve this problem, a classifier was developed, which is based on using a combination of two methods: a description of the subject area in the form of a hierarchy of entities and plausible reasoning based on the case-based reasoning approach, which is actively used in artificial intelligence systems. In solving various problems of artificial intelligence-based analysis of data, these methods have shown a high degree of efficiency, scalability, and independence from data structure. As part of using the case-based reasoning approach in the classifier, it is proposed to modify the TF-IDF (Term Frequency - Inverse Document Frequency) measure of assessing the text content taking into account known information about the distribution of documents by topics. The proposed modification makes it possible to improve the classification quality in comparison with classical measures, since it takes into account the information about the distribution of words not only in a separate document or topic, but in the entire database of cases. Experimental results are presented that confirm the effectiveness of the proposed metric and the developed classifier as applied to classification of customer sentences and providing them with the necessary information depending on the classification result. The developed text classification service prototype is used as part of the voice interaction module with the user in the objective of robotizing the telephone call routing system and making a shift from interaction between the user and system by means of buttons to their interaction through voice.

Feature level intentions based product recommendations with case-based reasoning

2018 ◽  
Vol 6 (1) ◽  
pp. 266-274
Author(s):  
D. Teja Santosh ◽  
◽  
K.C. Ravi Kumar ◽  
P. Chiranjeevi ◽  
◽  
...  
Keyword(s):  
Case Based Reasoning ◽  
Product Recommendations ◽  
Case Based
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