Machine Learning Paradigm for Internet of Things Applications

14 Chapter 14Figure 14.1 (a) Pneumonia x-ray image, (b) Healthy x-ray image.Figure 14.2 Xception network architecture.Figure 14.3 (a) Model accuracy, (b) Model loss.Figure 14.4 Confusion matrix.

1 Chapter 2 Table 2.1 Feasibility study summary. Table 2.2 Harvest prediction: Raw data fields. Table 2.3 Paddy harvest prediction - Data set. Table 2.4 Demand predict: Raw data fields. Table 2.5 Rice demand prediction: Data set.Table 2.6 Mutation rate effect.Table 2.7 Mutation probability effect.

2 Chapter 3Table 3.1 Classification accuracy.

3 Chapter 5Table 5.1 Text-based CAPTCHA used in commercial website.Table 5.2 Breaking methodology and success rate of various sources.Table 5.3 Pixel value changed entry.Table 5.4 Look up table entry.

4 Chapter 7Table 7.1 Offline evaluation metrics.Table 7.2 Illustration of confusion matrix.Table 7.3 Overview of additional metrics.Table 7.4 Overview of filtering techniques.Table 7.5 Overview of classifier algorithm.Table 7.6 Overview of the explored dataset.Table 7.7 System generated metric results using MovieLens with Random and SVD wh...Table 7.8 System generated metric results using MovieLens with SVD and SVD++ whe...

5 Chapter 8Table 8.1 Raspberry Pi history with version and configuration.

6 Chapter 9Table 9.1 Phase 2: algorithm for key hub identification.Table 9.2 Phase 3: algorithm for vehicle routing.

7 Chapter 13Table 13.1 Comparison of various routing protocols for different quality paramet...

8 Chapter 14Table 14.1 Dataset description.Table 14.2 Model metric parameters.

1 Cover

2 Table of Contents

3 Title Page

4 Copyright

5 Preface

6 Begin Reading

7 Index

8 End User License Agreement

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Scrivener Publishing100 Cummings Center, Suite 541J Beverly, MA 01915-6106

Next-Generation Computing and Communication Engineering

Series Editors: Dr. G. R. Kanagachidambaresan and Dr. Kolla Bhanu Prakash

Developments in artificial intelligence are made more challenging because the involvement of multi-domain technology creates new problems for researchers. Therefore, in order to help meet the challenge, this book series concentrates on next generation computing and communication methodologies involving smart and ambient environment design. It is an publishing platform for monographs, handbooks, and edited volumes on Industry 4.0, agriculture, smart city development, new computing and communication paradigms. Although the series mainly focuses on design, it also addresses analytics and investigation of industry-related real-time problems.

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Sachin Ahuja

and

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