Uncertainty in Computational Intelligence-Based Decision Making / Ali Ahmadian [and three others], editors.

Format:

Book

Contributor:

Ahmadian, Ali, editor.

Series:

Advanced studies in complex systems.

Advanced Studies in Complex Systems Series

Language:

English

Subjects (All):

Computational intelligence.

Decision making--Data processing.

Decision making.

Uncertainty (Information theory).

Physical Description:

1 online resource (340 pages)

Edition:

First edition.

Place of Publication:

London : Academic Press, [2025]

Summary:

Uncertainty in Computational Intelligence-Based Decision-Making focuses on techniques for reasoning and decision-making under uncertainty that are used to solve issues in artificial intelligence (AI).

Contents:

Intro

Uncertainty in Computational Intelligence-Based Decision Making

Copyright

Contents

Contributors

Chapter 1: TOPSIS for the selection of the prediction model in forensic ink analysis

1. Introduction

2. Methodology

2.1. Formulating the problem

2.2. Criteria of alternative

2.2.1. Prediction accuracy

2.2.2. Robustness

2.3. TOPSIS analysis

2.3.1. Construction of decision matrix, D

2.3.2. Construction of normalized decision matrix, V

2.3.3. Calculation of positive ideal solution and negative ideal solution

3. Results and discussions

4. Conclusions

Acknowledgment

References

Chapter 2: Effectiveness of artificial intelligence in determining foot pathologies and designing insoles using plain rad ...

2. Classification of foot types using AI

3. Designing insoles using AI

4. Framework of determining foot pathologies and designing insoles

5. Data collection

5.1. Criteria screening and interview of patients

5.2. Instruments

5.3. Photography and radiography protocol

6. Data preparation and annotation

7. Model training and evaluation

8. Statistical analysis

9. Conclusion

Chapter 3: Outbound logistics business process modeling: Analytic perspective with BPMN 2.0

1. Logistics business process and information system

2. Business process modeling methods

2.1. Review of business process modeling elements and methods

2.1.1. IDEF0

2.1.2. RAD

2.1.3. REAL

2.1.4. EPC

2.1.5. UML

2.1.6. BPMN

2.2. Adopted method for modeling the logistics business process

3. Development of outbound logistics business process modeling

3.1. Identification and classification of BPM requirements for logistics system

3.2. Modeling the outbound logistics system

3.2.1. Order system-Explanatory modeling technique.

3.2.2. Shipping system-Expanded subprocesses

4. Conclusion

Chapter 4: Revolutionizing diabetic foot ulcer treatment prediction: Harnessing the power of artificial intelligence and ...

2. Medical image classification using AI

2.1. Wagner classification with AI

2.2. Normal skin and abnormal skin classification with AI

2.3. Ischemia and infection classification with AI

2.4. Clinical application with AI

3. Framework of DFU treatment prediction

3.1. Data collection

3.1.1. Criteria screening and interview of patients

3.1.2. Instruments

3.1.3. Technique of image photography and standardization

3.2. Data preparation

3.3. Data annotation

3.4. Model training and evaluation

3.5. Treatment prediction

Chapter 5: A systematic review on personalized hybrid diet recommendations

2. Review methodology

2.1. Data analysis

2.2. Appraisal based on region or country

2.3. Evaluation based on journal of publication

2.4. Assessment based on yearly publication and citation

3. Overview of diet recommendations in MCDM

Chapter 6: Impact of number and type of criteria on ranking abnormality in MCDM techniques

2. Major contribution

3. RA

4. HetNet

5. Discussion and findings

6. Conclusion

Chapter 7: Comparison between some methods in fuzzy linear regression

1.1. Comparison of two possibilistic regression approaches and fuzzy error least squares regression

1.2. Goodness-of-fit criteria for evaluating

2. Fuzzy arithmetic

3. Possibilistic regression (Tanaka et al.s method)

3.1. Crisp independent variable and fuzzy response variable

3.2. Disadvantages of the method of Tanaka et al.

4. The method of the least square median

5. Possibilistic regression method of Nasrabadi et al.

5.1. Introducing the method of Nasrabadi et al.

5.2. Estimation of model coefficients

6. The goal programming approach in estimating the coefficients of Hassanpour et al. [20]

6.1. Crisp independent variable and observed responses of triangular fuzzy numbers

6.2. Estimation of model coefficients

6.3. The independent variable and the response variable are both fuzzy

6.4. Estimation of model coefficients

Chapter 8: Artificial intelligence and decision making in climate change studies: A review

1. Climate change: Impacts, risks

2. Decision making

3. AI

Chapter 9: Computational decision intelligence approaches for drought prediction: A review

2. Drought: Types and indices

3. Computation intelligence approaches

4. The role of artificial neural networks

5. Conclusions

Chapter 10: A review of the applications of computational decision intelligence approaches in agrometeorology

2. Agrometeorology

3. Fuzzy logic in agrometeorology

4. Machine learning in agrometeorology

5. Mathematical decision making

6. Conclusions

Chapter 11: A fuzzy logic design for self-driving vehicle to avoid obstacles

2. Objective

3. Related works

3.1. Conventional obstacles avoidance algorithm

3.1.1. Bug algorithm example

3.1.2. Potential field algorithm

3.2. Intelligent obstacles avoidance algorithm

3.2.1. Genetic algorithm

3.2.2. Artificial neural network algorithm

3.2.3. Fuzzy logic based algorithm

4. Architecture

5. Self-driving vehicle sensors

5.1. Detection sensor used on self-driving vehicle

5.2. LiDAR-based detection method.

5.3. Stereo vision-based detection method

5.4. Miscellaneous sensor fusion detection method

6. Deep neural network

6.1. Artificial neural network

6.2. Convolutional neural networks

6.2.1. Resizing of captured imaged

6.2.2. Features extraction and combining

6.2.3. Convolutional neural networks architecture

7. Fuzzy logic controller

7.1. Basics of fuzzy logic

7.2. Fuzzy logic controller

7.2.1. Fuzzification unit

7.2.2. Inference system (reasoning unit)

7.2.3. Defuzzification unit

8. Fuzzy logic controller for stationary obstacle

9. Fuzzy logic controller for moving obstacle

10. Development of environment

10.1. Simulation result in case of stationary obstacles

10.2. Simulation result in case of moving obstacles

11. Conclusion and future work

Chapter 12: K-means clustering over distributed environment: A review

1.1. Hadoop

2. K-means clustering

3. Comparison

4. Experimental result and discussion

4.1. Comparison of K-means (IEC) results with simple K-means results

5. Conclusion and future research scope

Chapter 13: Advanced frequent itemset mining algorithm (AFIM)

2. Related work

3. Methodology

3.1. Algorithm

3.1.1. Algorithmic steps for single datasets

3.1.2. Algorithmic steps for two different datasets

3.2. Highlights of AFIM

3.2.1. Improved statistical information

3.2.1.1. Types of counters

3.2.2. Improved data structure

3.2.3. In-built flexibility

4. Experiment

4.1. Datasets

4.2. Experimental result

5. Conclusion and future extensions

Chapter 14: TEAM: Trust evaluation and analysis of misbehavior in WSNs

3. Proposed lightweight trust model

3.1. CM-to-CM trust evaluation scheme.

3.2. CM-to-CM peer recommendation trust estimation (FTx,y(Deltat))

3.3. CH-to-CH direct trust estimation

3.4. BS to CH feedback trust calculation

4. Simulation and results discussion

5. Conclusion

Conflicts of interest

Acknowledgments

Chapter 15: Computational intelligence in decision support: Scope and techniques

1.1. Background

1.2. Purpose and scope

2. Computational decision science: An overview

2.1. Need for decision processes in the era of big data

2.2. Role of computational decision science

3. Techniques in computational decision making

3.1. Algorithm for data analysis using computational decision-making techniques

3.2. Machine learning techniques

3.3. Optimization techniques

4. Computational intelligence for decision support

4.1. Enhancing decision support intelligence

4.2. Applications in economics, finance, and investment management

4.2.1. Economics

4.2.2. Finance

4.2.3. Investment management

4.3. Integration of database management systems and artificial intelligence

4.4. Merging computational intelligence and DBMS for improved decision making

5. Challenges in computational decision making

5.1. Incorrect assumptions and statistical biases

5.1.1. Mitigation strategies

5.2. Curse of dimensionality

5.3. Impact of model complexity on performance

5.4. Constraints on decisions in computational techniques

5.4.1. Addressing restriction

6. Comparative analysis of computational techniques

6.1. Role of intelligent decision making

6.2. Neural networks vs decision trees

6.2.1. Comparative analysis

6.3. Artificial intelligence in decision support

6.3.1. Enhanced decision making across domains

6.4. Evaluating computational techniques for decision making

7. Conclusion

7.1. Summary of findings.

7.2. Recommendations for future research.

Notes:

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

0-443-21476-X

OCLC:

1456759423