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Applied Intelligent Decision Making in Machine Learning
: Data Stream Mining for Big Data
Introduction
Research Issues in Data Stream Mining
Filtering and Counting in a Data Stream
Bloom Filters
Counting the Frequency of Items in a Stream
Count Unique Items in a Data Stream
Sampling from Data Streams
Concept Drift Detection in Data Streams
Causes of Concept Drift
Handling Concept Drift
CUSUM Algorithm
The Higia Algorithm
Dynamic Weighted Majority Algorithm
Discussion
References
Introduction
Literature Review
Materials and Methods
Overall ML Model Development Process
Data Collection
Iris Dataset
Soybean Aphid Dataset
Weed Species Dataset
Shape Features Extraction
Data Cleaning
Feature Selection
Filter Methods
Wrapper Methods
Embedded Methods
Relief Algorithms
Data Splitting
The ML Methods
Linear Discriminant Analysis
k-Nearest Neighbor
Evaluation of ML Methods
Confusion Matrix
Accuracy
Precision
Recall
F-score
Results and Discussion
Results of Evaluated Features from the Dataset
Selected Features from the Dataset
Dataset Test of Normality for Model Selection
Soybean Aphid Identification
Features Ranking
The LDA Model and Evaluation
Weed Species Classification
Features Ranking
The kNN Model and Evaluation
Comparison of Results with the Standard Iris Data
Conclusions
Acknowledgments
References
: A Multi-Stage Hybrid Model for Odia Compound Character Recognition
Introduction
General OCR Stages
Structural Similarity
Projection Profile and Kendall Rank Correlation Coefficient Matching
Local Frequency Descriptor
General Regression Neural Network (GRNN)
Proposed Method
Experiments
Dataset Creation
Experimental Setup
Results and Discussion
Conclusion and Future Scope
References
: Development of Hybrid Computational Approaches for Landslide Susceptibility Mapping Using Remotely Sensed Data in East Sikkim, India
Introduction
Study Materials and Methodology
Area of Research Study
MULTI-COLINEARITY ASSESSMENT (MCT)
Affecting Factors
Landslide Inventory Map (LIM)
Methodology
Hybrid Biogeography-Based Optimization
Hybridization with Differential Evolution
The DE/BBO Algorithm
Local-DE/BBO
Self-Adaptive DE/BBO
Validation of Models
Shortly Structured Methodology
Results and Discussion
Importance of the Conditioning Factors on the Occurrences of Landslides
Application of Hybrid Biogeography-Based Optimization for Landslide Susceptibility Assessment
Conclusion
References
: Domain-Specific Journal Recommendation Using a Feed Forward Neural Network
Introduction
Literature Survey
Content-Based Recommendation System for Domain-Specific Papers
Scraping and Data Integration (Challenges and Solutions for Data Collection)
Limitations on the Size of the Query Results
Fixed Limits
Pagination
Dynamic Contents
Access Limitations
Masked URL Parameters
Robot Recognition and Reverse Turing Tests
Changing the Content of Request Headers
Selecting Appropriate Cookie Settings
Requests and Different Time Intervals
Altering the IP Address
Data Curation
The Complexity of the Integration Operation
Phase 1: Identifying Candidate Journals
Phase 2: Ranking Candidate Journals
Experimental Results and Discussions
Configurations
Result Analysis
Conclusion and Future Work
References
: Forecasting Air Quality in India through an Ensemble Clustering Technique
Introduction
Related Works
Air Quality Prediction
Ensemble Modeling
Variants of Ensemble Models
Ensemble Clustering
Dataset Descriptions
Methodology
Final Cluster Labeling
METIS Function
METIS Algorithm
Phases
Advantages
EXPERIMENTAL RESULTS
Silhouette Coefficient
Calinski-Harabasz Index
Davies-Bouldin Index
Conclusion
References
: An Intelligence-Based Health Biomarker Identification System Using Microarray Analysis
Introduction
Existing Knowledge
Classification Model
Approaches for Feature Selection
Shuffled Frog-Leaping Algorithm and Particle Swarm Optimization (SFLA-PSO)
The Advantage of SFLA
Algorithm for BSFLA-PSO
Experimental Result Analysis
Dataset Considered for This Experiment
Normalization
Details of Classifiers Used in This Experimental Study and Evaluation Metrics
Result Analysis
Performance of Proposed BSFLA-PSO with Prostate Dataset
Performance of Proposed BSFLA-PSO with Leukemia Dataset
Performance of Proposed BSFLA-PSO with ALL/AML Dataset
Performance of Proposed BSFLA-PSO with ADCA Lung Dataset
Performance of Proposed BSFLA-PSO with CNS Dataset
Conclusion
References
: Extraction of Medical Entities Using a Matrix-Based Pattern-Matching Method
Introduction
Background
Methodology
Dataset
Proposed Method
Text Pre-Processing
Trained Matrix Formation
Test Matrix Formation
Pattern Matching
Pruning Non-Medical Concepts
System Evaluation
Results and Discussion
Conclusions and Future Work
Acknowledgments
References
: Supporting Environmental Decision Making Application of Machine Learning Techniques to Australia’s Emissions
Introduction
Data and Methodology
Data
Methodology
Decision Trees
Random Forests
Extreme Gradient Boosting
Support Vector Regression
Data Division and the Experimental Environment
Optimization of Hyperparameters
Parameter Tuning for the DT, RF, and XGBoost Algorithms
Parameter Tuning for the SVR Algorithm
Performance Metrics
Results and Discussion
Development and Validation of the DT Model
Development and Validation of the RF Model
Development and Validation of the XGBoost Model
Development and Validation of the SVR Model
Performance Evaluation of Model
Concluding Remarks
References
: Prediction Analysis of Exchange Rate Forecasting Using Deep Learning-Based Neural Network Models
Introduction
Methodology
Performance Measure
Data Preparation
Results and Simulations
For Sliding Window Size 7
For Sliding Window Size 10
For Sliding Window Size 13
Conclusion
References
: Optimal Selection of Features Using Teaching-Learning-Based Optimization Algorithm for Classification
Introduction
Related Work
Basic Technology
Proposed Model
Result Analysis
Conclusion
References
: An Enhanced Image Dehazing Procedure Using CLAHE and a Guided Filter
Introduction
Literature Survey
White Balance (WB)
CLAHE
GF
Proposed Methodology
Dataset Collection and Analysis
Image Quality Assessment Criteria
Peak Signal-to-Noise Ratio and Mean Squared Error
Entropy
Structural Similarity Index
Contrast Gain
Experimental Results and Discussion
Conclusion and Future Scope
References
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