scholarly journals ECO-Case Based Reasoning Tool for Environmental Product Design

2019 ◽  
Vol 1 (2) ◽  
Author(s):  
Marziah Abdul Wahab ◽  
Nurul Aini Othman ◽  
Awanis Romli
Keyword(s):  
Environmental Impact ◽  
Product Design ◽  
Early Stage ◽  
Forecast Error ◽  
Forecast Accuracy ◽  
Total Carbon ◽  
Product Development Process ◽  
Case Based Reasoning ◽  
Case Based

Case Based Reasoning (CBR) is one of the artificial intelligent methodologies that is widely used in problem solving by reusing the most similar previous experiences stored in the library. A framework of ECO-CBR for Life Cycle Assessment data collection has been used and the process was carried out using SolidWorks program. The practicality of the tool has been validated using case study, which then provides solution. The output enable researchers to determine forecast error and forecast accuracy, by valuing the calculation from Total Carbon Footprint, Energy Consumption, Air Acidification, and Water Eutrophication. ECO-CBR is able to assist designers in product design. Due to the limitation of environmental impact consideration in product sustainability, there is a demand to propose a tool that can assist designers to reduce environmental impact of product design at early stage. The model works as an essential guideline to lessen repeated mistakes in the product development process and help designers measure the risks before concluding ideal decisions. Minor errors that occur through the study showed that ECO-CBR is reliable to be implemented in order to find a better solution.

Web Effort Estimation Using Case-Based Reasoning

2008 ◽  
pp. 158-202
Author(s):  
Emilia Mendes
Keyword(s):  
Prediction Accuracy ◽  
Early Stage ◽  
Real Data ◽  
Project Managers ◽  
Case Based Reasoning ◽  
Effort Estimation ◽  
Software Projects ◽  
Software Practitioners ◽  
Case Based

Software practitioners recognise the importance of realistic effort estimates to the successful management of software projects, the Web being no exception. Having realistic estimates at an early stage in a project’s life cycle allow project managers and development organisations to manage resources effectively. Several techniques have been proposed to date to help organisations estimate effort for new projects. One of these is a machine-learning technique called case-based reasoning. This chapter presents a case study that details step by step, using real data from completed industrial Web projects, how to obtain effort estimates using case-based reasoning, and how to assess the prediction accuracy of this technique. The reason to describe the use of case-based reasoning for effort estimation is motivated by its previous use with promising results in Web effort estimation studies.

Identification of Coronary Artery Disease using Artificial Neural Network and Case Based Reasoning

2020 ◽  
Vol 13 ◽  
Author(s):  
Varun Sapra ◽  
M.L Saini ◽  
Luxmi Verma
Keyword(s):  
Neural Network ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Early Stage ◽  
Second Phase ◽  
Case Based Reasoning ◽  
Data Set ◽  
Artery Disease ◽  
Severity Of The Disease ◽  
Case Based

Background: Cardiovascular diseases are increasing at an alarming rate with very high rate of mortality. Coronary artery disease is one of the type of cardiovascular disease, which is not easily diagnosed in its early stage. Prevention of Coronary Artery Disease is possible only if it is diagnosed, at early stage and proper medication is done. Objective: An effective diagnosis model is important not only for the early diagnosis but also to check the severity of the disease. Method: In this paper, a hybrid approach is followed, with the integration of deep learning (multi-layer perceptron) with Case based reasoning to design analytical framework. This paper suggests two phases of the study, one in which the patient is diagnosed for Coronary artery disease and in second phase, if the patient is suffering from the disease then employing Case based reasoning to diagnose the severity of the disease. In the first phase, multilayer perceptron is implemented on reduced dataset and with time-based learning for stochastic gradient descent respectively. Results: The classification accuracy is increase by 4.18 % with reduced data set using deep neural network with time based learning. In second phase, if the patient is diagnosed as positive for Coronary artery disease, then it triggers the Case based reasoning system to retrieve from the case base, the most similar case to predict the severity for that patient. The CBR model achieved 97.3% accuracy. Conclusion: The model can be very useful for medical practitioners as a supporting decision system and thus can save the patients from unnecessary medical expenses on costly tests and can improve the quality and effectiveness of medical treatment.

Web Development Effort Estimation

2011 ◽  
pp. 26-56 ◽  
Author(s):  
Emilia Mendes ◽  
Silvia Abrahão
Keyword(s):  
Multivariate Regression ◽  
Project Managers ◽  
Case Based Reasoning ◽  
Development Effort ◽  
Web Development ◽  
Effort Estimation ◽  
Estimation Techniques ◽  
Classification And Regression ◽  
Case Based

Effort models and effort estimates help project managers allocate resources, control costs and schedule, and improve current practices, leading to projects that are finished on time and within budget. In the context of Web development and maintenance, these issues are also crucial, and very challenging, given that Web projects have short schedules and a highly fluidic scope. Therefore, the objective of this chapter is to introduce the concepts related to Web effort estimation and effort estimation techniques. In addition, this chapter also details and compares, by means of a case study, three effort estimation techniques, chosen for this chapter because they have been to date the ones mostly used for Web effort estimation: Multivariate regression, Case-based reasoning, and Classification and Regression Trees. The case study uses data on industrial Web projects from Spanish Web companies.

A CBR-based hybrid model for predicting a construction duration and cost based on project characteristics in multi-family housing projects

2010 ◽  
Vol 37 (5) ◽  
pp. 739-752 ◽  
Author(s):  
ChoongWan Koo ◽  
TaeHoon Hong ◽  
ChangTaek Hyun ◽  
KyoJin Koo
Keyword(s):  
Hybrid Model ◽  
Early Stage ◽  
Case Based Reasoning ◽  
Uncertain Information ◽  
Public And Private ◽  
Housing Projects ◽  
Analysis Process ◽  
Complex Correlation ◽  
Family Housing ◽  
Case Based

Decision-making in the early stage of a project has a significant impact on the project. However, limited and uncertain information on the project and a complex correlation among various factors that affect the project’s construction duration and cost, make it difficult to predict and manage the project. Therefore, this study developed a case-based reasoning (CBR)-based hybrid model with which to predict the construction duration and cost of a project in its early stage. One hundred and one cases among multi-family housing projects that were completed between 2000 and 2005 were used. The CBR-based hybrid model developed in this study is the result of integrating the advantages of (i) prediction methodologies, such as case-based reasoning, multiple regression analysis, and artificial neural networks, (ii) the optimization process using a genetic algorithm, and (iii) the probability distribution and the analysis process of outlier using Monte-Carlo simulation. The results of this study are expected to support the owners and managers who are in charge of estimating budget and construction duration in both public and private sectors, in predicting accurately the construction duration and cost at the business planning or early stage of a project.

scholarly journals HYBRID FORECASTING SYSTEM BASED ON CASE-BASED REASONING AND ANALYTIC HIERARCHY PROCESS FOR COST ESTIMATION

2013 ◽  
Vol 19 (1) ◽  
pp. 86-96 ◽  
Author(s):  
Sangyong Kim
Keyword(s):  
Analytic Hierarchy Process ◽  
Cost Estimation ◽  
Prediction Models ◽  
Early Stage ◽  
Case Based Reasoning ◽  
Cost Estimating ◽  
Analytic Hierarchy ◽  
Highway Projects ◽  
Hierarchy Process ◽  
Case Based

Cost estimating of highway projects with high accuracy at the early stage of project development is crucial for planning and feasibility studies. Various research have been attempted to develop cost prediction models in the early stage of a construction life cycle. This study uses the hybrid estimating tool to provide an effective cost data management for highway projects and accordingly develops a realistic cost estimating system. This study focused on the development of a more accurate estimate technique for highway projects in South Korea at the early stage using hybrid analytic hierarchy process (AHP) and case-based reasoning (CBR). Real case studies are used to demonstrate and validate the benefits of the proposed approach. It is expected that the developed CBR system is to provide decision-makers with accurate cost information to asses and compare multiple alternatives for obtaining the optimal solution and controlling cost.

scholarly journals Improving Similarity-Based Retrieval Efficiency by Using Graphic Processing Units in Case-Based Reasoning

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Lukas Malburg ◽  
Maximilian Hoffmann ◽  
Simon Trumm ◽  
Ralph Bergmann
Keyword(s):  
Early Stage ◽  
Similarity Measures ◽  
Case Based Reasoning ◽  
Graphic Processing Units ◽  
Data Intensive ◽  
Source Case ◽  
Accelerated Growth ◽  
Performance Gains ◽  
Case Based ◽  
Graphic Processing

The accelerated growth of available data causes case bases of increasing sizes and thus lowers efficiency during the case retrieval phase in Case-Based Reasoning (CBR) systems. Even though, many complex and data-intensive tasks are solved by using Graphic Processing Units (GPUs), its application in CBR research has yet to advance past the early stage phase. In this paper, we present an approach to use CUDA-compatible GPUs for similarity assessment of structural, feature vector based cases. Our approach supports several syntactic and semantic similarity measures and is implemented in the open-source case-based reasoning framework ProCAKE. When comparing to current retrieval techniques that calculate similarities on the CPU, our GPU-based approach outperforms them by a factor of up to 37. In addition, our evaluation indicates that the performance gains increase with higher case complexity.

Combining Case Based Reasoning and Shape Matching Based on Clearance Analyzes to Support the Reuse of Components

2012 ◽  
Author(s):  
Joel Johansson
Keyword(s):  
Process Design ◽  
Lead Time ◽  
Shape Matching ◽  
Product Development Process ◽  
Prototype System ◽  
Case Based Reasoning ◽  
Manufacturing Companies ◽  
Key Factor ◽  
Product Specifications ◽  
Case Based

For manufacturing companies it is important to develop and produce products that meet requirements from customers and investors. One key factor in meeting these requirements is the efficiency of the product development process. Design automation is a powerful tool to increase efficiency in that process resulting in shortened lead-time, improved product performance, and ultimately decreased cost. Further, automation is beneficial as it increases the ability to adapt products to new product specifications, which is critical to some categories of products. In this paper the retrieval and evaluation processes of the Case Based Reasoning (CBR) method are extended to include shape matching. This enhanced CBR method supports the reuse of existing components when introducing new variants of variant-rich products. The matching method is based on clearance analyzes and is performed during the retrieval of cases and supports the evaluation of suggestions. The method is described along with a prototype-system where the process of selecting components for roof racks for cars is targeted for automation. One specific component of the roof rack is targeted, namely a rubber pad used in the interface between the car roof and the rack.

Improved similarity measure in case-based reasoning: a case study of construction cost estimation

2019 ◽  
Vol 27 (2) ◽  
pp. 561-578 ◽  
Author(s):  
Won-Gil Hyung ◽  
Sangyong Kim ◽  
Jung-Kyu Jo
Keyword(s):  
Cost Estimation ◽  
Hybrid Approach ◽  
Optimal Solution ◽  
Case Based Reasoning ◽  
Optimization Approach ◽  
Cost Information ◽  
Planning Stage ◽  
Content Type ◽  
Case Based

Purpose Applied a hybrid approach using genetic algorithms (GAs) for a case-based retrieval process in order to increase the overall improved cost accuracy for a case-based library. The paper aims to discuss this issue. Design/methodology/approach A weight optimization approach using case-based reasoning (CBR) with proposed GAs for developing the CBR model. GAs are used to investigate optimized weight generation with an application to real project cases. Findings The proposed CBR model can reduce errors consistently, and be potentially useful in the early financial planning stage. The authors suggest the developed CBR model can provide decision-makers with accurate cost information for assessing and comparing multiple alternatives in order to obtain the optimal solution while controlling cost. Originality/value The system can operate with more accuracy or less cost, and CBR can be used to better understand the effects of factor interaction and variation during the developed system’s process.

A Case-Based-Reasoning System for Feature Selection and Diagnosing Asthma

2017 ◽  
pp. 444-459
Author(s):  
Somayeh Akhavan Darabi ◽  
Babak Teimourpour
Keyword(s):  
Risk Factors ◽  
Feature Selection ◽  
Chronic Disease ◽  
Early Stage ◽  
Case Based Reasoning ◽  
Asthma Diagnosis ◽  
Stage Of Disease ◽  
Reasoning System ◽  
Therapeutic Measures ◽  
Case Based

Asthma is a chronic disease of the airways in the lungs. The differentiation between asthma, COPD and bronchiectasis in the early stage of disease is very important for the adoption of appropriate therapeutic measures. In this research, a case-based-reasoning (CBR) model is proposed to assist a physician to therapy. First of all, features and symptoms are determined and patients' data is gathered with a questionnaire, then CBR algorithm is run on the data which leads to the asthma diagnosis. The system was tested on 325 asthmatic and non-asthmatic adult cases and the accuracy was eighty percent. The consequences were promising. This study was performed in order to determine risk factors for asthma in a specific society and the results of research showed that the most important variables of asthma disease are symptoms hyper-responsive, frequency of cough and cough.

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