Applied intelligent decision making in machine learning / edited by Himansu Das, Jitendra Kumar Rout, Suresh Chandra Moharana, Nilanjan Dey.

Format:

Book

Contributor:

Das, Himansu, editor.

Rout, Jitendra Kumar, editor.

Moharana, Suresh Chandra, editor.

Dey, Nilanjan, 1984- editor.

Series:

Computational intelligence in engineering problem solving

Language:

English

Physical Description:

1 recurso en línea (ix, 251 p.) il.

Edition:

1st ed.

Place of Publication:

Boca Raton : CRC Press, Taylor & Francis Group, 2021.

Summary:

The objective of this edited book is to share the outcomes from various research domains to develop efficient, adaptive, and intelligent models to handle the challenges related to decision making. It incorporates the advances in machine intelligent techniques such as data streaming, classification, clustering, pattern matching, feature selection, and deep learning in the decision-making process for several diversified applications such as agriculture, character recognition, landslide susceptibility, recommendation systems, forecasting air quality, healthcare, exchange rate prediction, and image dehazing. It also provides a premier interdisciplinary platform for scientists, researchers, practitioners, and educators to share their thoughts in the context of recent innovations, trends, developments, practical challenges, and advancements in the field of data mining, machine learning, soft computing, and decision science. It also focuses on the usefulness of applied intelligent techniques in the decision-making process in several aspects. To address these objectives, this edited book includes a dozen chapters contributed by authors from around the globe. The authors attempt to solve these complex problems using several intelligent machine-learning techniques. This allows researchers to understand the mechanism needed to harness the decision-making process using machine-learning techniques for their own respective endeavors.

Contents:

Cover

Half Title

Series Page

Title Page

Copyright Page

Table of Contents

Preface

Notes on the Editors and Contributors

Chapter 1: Data Stream Mining for Big Data

1.1 Introduction

1.2 Research Issues in Data Stream Mining

1.3 Filtering and Counting in a Data Stream

1.3.1 Bloom Filters

1.3.2 Counting the Frequency of Items in a Stream

1.3.3 Count Unique Items in a Data Stream

1.4 Sampling from Data Streams

1.5 Concept Drift Detection in Data Streams

1.5.1 Causes of Concept Drift

1.5.2 Handling Concept Drift

1.5.2.1 CUSUM Algorithm

1.5.2.2 The Higia Algorithm

1.5.2.3 Dynamic Weighted Majority Algorithm

1.6 Discussion

References

Chapter 2: Decoding Common Machine Learning Methods Agricultural Application Case Studies Using Open Source Software

2.1 Introduction

2.2 Literature Review

2.3 Materials and Methods

2.3.1 Overall ML Model Development Process

2.3.2 Data Collection

2.3.2.1 Iris Dataset

2.3.2.2 Soybean Aphid Dataset

2.3.2.3 Weed Species Dataset

2.3.3 Shape Features Extraction

2.3.4 Data Cleaning

2.3.5 Feature Selection

2.3.5.1 Filter Methods

2.3.5.2 Wrapper Methods

2.3.5.3 Embedded Methods

2.3.5.4 Relief Algorithms

2.3.6 Data Splitting

2.3.7 The ML Methods

2.3.7.1 Linear Discriminant Analysis

2.3.7.2 k-Nearest Neighbor

2.3.8 Evaluation of ML Methods

2.3.8.1 Confusion Matrix

2.3.8.2 Accuracy

2.3.8.3 Precision

2.3.8.4 Recall

2.3.8.5 F-score

2.4 Results and Discussion

2.4.1 Results of Evaluated Features from the Dataset

2.4.2 Selected Features from the Dataset

2.4.3 Dataset Test of Normality for Model Selection

2.4.4 Soybean Aphid Identification

2.4.4.1 Features Ranking

2.4.4.2 The LDA Model and Evaluation

2.4.5 Weed Species Classification

2.4.5.1 Features Ranking.

2.4.5.2 The kNN Model and Evaluation

2.4.6 Comparison of Results with the Standard Iris Data

2.5 Conclusions

Acknowledgments

Chapter 3: A Multi-Stage Hybrid Model for Odia Compound Character Recognition

3.1 Introduction

3.2 Background

3.2.1 General OCR Stages

3.2.2 Structural Similarity

3.2.3 Projection Profile and Kendall Rank Correlation Coefficient Matching

3.2.4 Local Frequency Descriptor

3.2.5 General Regression Neural Network (GRNN)

3.3 Proposed Method

3.4 Experiments

3.4.1 Dataset Creation

3.4.2 Experimental Setup

3.5 Results and Discussion

3.6 Conclusion and Future Scope

Chapter 4: Development of Hybrid Computational Approaches for Landslide Susceptibility Mapping Using Remotely Sensed Data in East Sikkim, India

4.1 Introduction

4.2 Study Materials and Methodology

4.2.1 Area of Research Study

4.2.2 Multi-colinearity Assessment (MCT)

4.2.3 Affecting Factors

4.2.4 Landslide Inventory Map (LIM)

4.2.5 Methodology

4.2.5.1 Hybrid Biogeography-Based Optimization

4.2.5.2 Hybridization with Differential Evolution

4.2.5.2.1 The DE/BBO Algorithm

4.2.5.2.2 Local-DE/BBO

4.2.5.2.3 Self-Adaptive DE/BBO

4.2.6 Validation of Models

4.2.7 Shortly Structured Methodology

4.3 Results and Discussion

4.3.1 Importance of the Conditioning Factors on the Occurrences of Landslides

4.3.2 Application of Hybrid Biogeography-Based Optimization for Landslide Susceptibility Assessment

4.4 Conclusion

Chapter 5: Domain-Specific Journal Recommendation Using a Feed Forward Neural Network

5.1 Introduction

5.2 Literature Survey

5.3 Content-Based Recommendation System for Domain-Specific Papers

5.3.1 Scraping and Data Integration (Challenges and Solutions for Data Collection).

5.3.1.1 Limitations on the Size of the Query Results

5.3.1.1.1 Fixed Limits

5.3.1.1.2 Pagination

5.3.1.2 Dynamic Contents

5.3.1.3 Access Limitations

5.3.1.3.1 Masked URL Parameters

5.3.1.3.2 Robot Recognition and Reverse Turing Tests

5.3.1.3.3 Changing the Content of Request Headers

5.3.1.3.4 Selecting Appropriate Cookie Settings

5.3.1.3.5 Requests and Different Time Intervals

5.3.1.3.6 Altering the IP Address

5.3.2 Data Curation

5.3.2.1 The Complexity of the Integration Operation

5.3.3 Phase 1: Identifying Candidate Journals

5.3.4 Phase 2: Ranking Candidate Journals

5.4 Experimental Results and Discussions

5.4.1 Configurations

5.4.2 Result Analysis

5.5 Conclusion and Future Work

Chapter 6: Forecasting Air Quality in India through an Ensemble Clustering Technique

6.1 Introduction

6.2 Related Works

6.2.1 Air Quality Prediction

6.2.2 Ensemble Modeling

6.2.2.1 Variants of Ensemble Models

6.2.3 Ensemble Clustering

6.3 Dataset Descriptions

6.4 Methodology

6.4.1 Final Cluster Labeling

6.4.2 METIS Function

6.4.2.1 METIS Algorithm

6.4.3 Phases

6.4.4 Advantages

6.5 Experimental Results

6.5.1 Silhouette Coefficient

6.5.2 Calinski-Harabasz Index

6.5.3 Davies-Bouldin Index

6.6 Conclusion

Chapter 7: An Intelligence-Based Health Biomarker Identification System Using Microarray Analysis

7.1 Introduction

7.2 Existing Knowledge

7.3 Classification Model

7.4 Approaches for Feature Selection

7.4.1 Shuffled Frog-Leaping Algorithm and Particle Swarm Optimization (SFLA-PSO)

7.4.2 The Advantage of SFLA

7.4.3 Algorithm for BSFLA-PSO

7.5 Experimental Result Analysis

7.5.1 Dataset Considered for This Experiment

7.5.2 Normalization.

7.5.3 Details of Classifiers Used in This Experimental Study and Evaluation Metrics

7.5.4 Result Analysis

7.5.4.1 Performance of Proposed BSFLA-PSO with Prostate Dataset

7.5.4.2 Performance of Proposed BSFLA-PSO with Leukemia Dataset

7.5.4.3 Performance of Proposed BSFLA-PSO with ALL/AML Dataset

7.5.4.4 Performance of Proposed BSFLA-PSO with ADCA Lung Dataset

7.5.4.5 Performance of Proposed BSFLA-PSO with CNS Dataset

7.6 Conclusion

Chapter 8: Extraction of Medical Entities Using a Matrix-Based Pattern-Matching Method

8.1 Introduction

8.2 Background

8.3 Methodology

8.3.1 Dataset

8.3.2 Proposed Method

8.3.2.1 Text Pre-Processing

8.3.2.2 Trained Matrix Formation

8.3.2.3 Test Matrix Formation

8.3.2.4 Pattern Matching

8.3.2.5 Pruning Non-Medical Concepts

8.4 System Evaluation

8.5 Results and Discussion

8.6 Conclusions and Future Work

Chapter 9: Supporting Environmental Decision Making Application of Machine Learning Techniques to Australia's Emissions

9.1 Introduction

9.2 Data and Methodology

9.2.1 Data

9.2.2 Methodology

9.2.2.1 Decision Trees

9.2.2.2 Random Forests

9.2.2.3 Extreme Gradient Boosting

9.2.2.4 Support Vector Regression

9.2.3 Data Division and the Experimental Environment

9.2.4 Optimization of Hyperparameters

9.2.4.1 Parameter Tuning for the DT, RF, and XGBoost Algorithms

9.2.4.2 Parameter Tuning for the SVR Algorithm

9.2.5 Performance Metrics

9.3 Results and Discussion

9.3.1 Development and Validation of the DT Model

9.3.2 Development and Validation of the RF Model

9.3.3 Development and Validation of the XGBoost Model

9.3.4 Development and Validation of the SVR Model

9.3.5 Performance Evaluation of Model

9.4 Concluding Remarks

References.

Chapter 10: Prediction Analysis of Exchange Rate Forecasting Using Deep Learning-Based Neural Network Models

10.1 Introduction

10.2 Methodology

10.2.1 Performance Measure

10.2.2 Data Preparation

10.3 Results and Simulations

10.3.1 For Sliding Window Size 7

10.3.2 For Sliding Window Size 10

10.3.3 For Sliding Window Size 13

10.4 Conclusion

Chapter 11: Optimal Selection of Features Using Teaching-Learning-Based Optimization Algorithm for Classification

11.1 Introduction

11.2 Related Work

11.3 Basic Technology

11.4 Proposed Model

11.5 Result Analysis

11.6 Conclusion

Chapter 12: An Enhanced Image Dehazing Procedure Using CLAHE and a Guided Filter

12.1 Introduction

12.2 Literature Survey

12.3 Background Study

12.3.1 White Balance (WB)

12.3.2 CLAHE

12.3.3 GF

12.4 Proposed Methodology

12.5 Dataset Collection and Analysis

12.6 Image Quality Assessment Criteria

12.6.1 Peak Signal-to-Noise Ratio and Mean Squared Error

12.6.2 Entropy

12.6.3 Structural Similarity Index

12.6.4 Contrast Gain

12.7 Experimental Results and Discussion

12.8 Conclusion and Future Scope

Index.

Notes:

Incluye índice.

OCLC-licensed vendor bibliographic record.

ISBN:

1-00-304954-0

1-003-04954-0

1-000-20854-0

1-000-20858-3

9781003049548

OCLC:

1224544608