Description |
1 online resource (263 p.) |
Series |
Computational Intelligence in Engineering Problem Solving Ser |
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Computational Intelligence in Engineering Problem Solving Ser
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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 |
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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 |
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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 |
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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 -- References -- 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 |
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3.4.1 Dataset Creation -- 3.4.2 Experimental Setup -- 3.5 Results and Discussion -- 3.6 Conclusion and Future Scope -- References -- 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 |
Notes |
Description based upon print version of record |
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4.2.5.2.1 The DE/BBO Algorithm |
Subject |
Machine learning.
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Decision making -- Data processing
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Decision making -- Data processing
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Machine learning
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Form |
Electronic book
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Author |
Rout, Jitendra Kumar
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Moharana, Suresh Chandra
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Dey, Nilanjan
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ISBN |
9781000208580 |
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1000208583 |
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9781000208542 |
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1000208540 |
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9781000208566 |
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1000208567 |
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9781003049548 |
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1003049540 |
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