Machine learning algorithms and applications / edited by Mettu Srinivas, G. Sucharitha and Anjanna Matta.

Format:

Book

Contributor:

Sucharitha, G., editor.

Srinivas, Mettu., editor.

Matta, Anjanna, editor.

Language:

English

Subjects (All):

Machine learning.

Computer algorithms.

Deep learning (Machine learning).

Physical Description:

1 online resource (305 pages)

Place of Publication:

Hoboken, New Jersey : John Wiley & Sons, [2021]

Summary:

Machine Learning Algorithms is for current and ambitious machine learning specialists looking to implement solutions to real-world machine learning problems. It talks entirely about the various applications of machine and deep learning techniques, with each chapter dealing with a novel approach of machine learning architecture for a specific application, and then compares the results with previous algorithms. The book discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, sentiment analysis, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the user can easily move from the equations in the book to a computer program.

Contents:

Intro

Table of Contents

Title Page

Copyright

Acknowledgments

Preface

Part 1: Machine Learning for Industrial Applications

1 A Learning-Based Visualization Application for Air Quality Evaluation During COVID-19 Pandemic in Open Data Centric Services

1.1 Introduction

1.2 Literature Survey

1.3 Implementation Details

1.4 Results and Discussions

1.5 Conclusion

References

2 Automatic Counting and Classification of Silkworm Eggs Using Deep Learning

2.1 Introduction

2.2 Conventional Silkworm Egg Detection Approaches

2.3 Proposed Method

2.4 Dataset Generation

2.5 Results

2.6 Conclusion

Acknowledgment

3 A Wind Speed Prediction System Using Deep Neural Networks

3.1 Introduction

3.2 Methodology

3.3 Results and Discussions

3.4 Conclusion

4 Res-SE-Net: Boosting Performance of ResNets by Enhancing Bridge Connections

4.1 Introduction

4.2 Related Work

4.3 Preliminaries

4.4 Proposed Model

4.5 Experiments

4.6 Results

4.7 Conclusion

5 Sakshi Aggarwal, Navjot Singh and K.K. Mishra

5.1 Genesis

5.2 The Big Picture: Artificial Neural Network

5.3 Delineating the Cornerstones

5.4 Deep Learning Architectures

5.5 Why is CNN Preferred for Computer Vision Applications?

5.6 Unravel Deep Learning in Medical Diagnostic Systems

5.7 Challenges and Future Expectations

5.8 Conclusion

6 Two-Stage Credit Scoring Model Based on Evolutionary Feature Selection and Ensemble Neural Networks

6.1 Introduction

6.2 Literature Survey

6.3 Proposed Model for Credit Scoring

6.4 Results and Discussion

6.5 Conclusion

7 Enhanced Block-Based Feature Agglomeration Clustering for Video Summarization

7.1 Introduction

7.2 Related Works

7.3 Feature Agglomeration Clustering.

7.4 Proposed Methodology

7.5 Results and Analysis

7.6 Conclusion

Part 2: Machine Learning for Healthcare Systems

8 Cardiac Arrhythmia Detection and Classification From ECG Signals Using XGBoost Classifier

8.1 Introduction

8.2 Materials and Methods

8.3 Results and Discussion

8.4 Conclusion

9 GSA-Based Approach for Gene Selection from Microarray Gene Expression Data

9.1 Introduction

9.2 Related Works

9.3 An Overview of Gravitational Search Algorithm

9.4 Proposed Model

9.5 Simulation Results

9.6 Conclusion

Part 3: Machine Learning for Security Systems

10 On Fusion of NIR and VW Information for Cross-Spectral Iris Matching

10.1 Introduction

10.2 Preliminary Details

10.3 Experiments and Results

10.4 Conclusions

11 Fake Social Media Profile Detection

11.1 Introduction

11.2 Related Work

11.3 Methodology

11.4 Experimental Results

11.5 Conclusion and Future Work

12 Extraction of the Features of Fingerprints Using Conventional Methods and Convolutional Neural Networks

12.1 Introduction

12.2 Related Work

12.3 Methods and Materials

12.4 Results

12.5 Conclusion

Acknowledgements

13 Facial Expression Recognition Using Fusion of Deep Learning and Multiple Features

13.1 Introduction

13.2 Related Work

13.3 Proposed Method

13.4 Experimental Results

13.5 Conclusion

Acknowledgement

Part 4: Machine Learning for Classification and Information Retrieval Systems

14 AnimNet: An Animal Classification Network using Deep Learning

14.1 Introduction

14.2 Related Work

14.3 Proposed Methodology

14.4 Results

14.5 Conclusion

15 A Hybrid Approach for Feature Extraction From Reviews to Perform Sentiment Analysis.

15.1 Introduction

15.2 Related Work

15.3 The Proposed System

15.4 Result Analysis

15.5 Conclusion

16 Spark-Enhanced Deep Neural Network Framework for Medical Phrase Embedding

16.1 Introduction

16.2 Related Work

16.3 Proposed Approach

16.4 Experimental Setup

16.5 Results

16.6 Conclusion

17 Image Anonymization Using Deep Convolutional Generative Adversarial Network

17.1 Introduction

17.2 Background Information

17.3 Image Anonymization to Prevent Model Inversion Attack

17.4 Results and Analysis

17.5 Conclusion

Index

End User License Agreement.

Notes:

Description based on print version record.

Includes bibliographical references and index.

ISBN:

9781119769255

1119769256

9781119769262

1119769264

9781119769248

1119769248

OCLC:

1266222675