The Internet of Medical Things (IoMT)

12 Chapter 15Figure 15.1 Evolution of EHR.

1 Chapter 1Table 1.1 Physiochemical characters of EGFR, K-ras, and TP53 proteins as determi...Table 1.2 The number disulfide bonds were quantitated by Cys_Rec prediction prog...Table 1.3 Secondary structure of the EGFR, K-ras oncogene protein, and TP53.Table 1.4 Composition of α-helix EGFR, K-ras oncogene protein, and TP53.Table 1.5 Validation of the EGFR, K-ras oncogene protein, and TP53.Table 1.6 Predicted active sites of the EGFR, K-ras oncogene protein, and TP53.Table 1.7 Docking result of the EGFR, K-ras oncogene protein, and TP53.

2 Chapter 3Table 3.1 Type 1 VMM Approach.Table 3.2 Type 2 VMM Approach.

3 Chapter 4Table 4.1 Various symptoms of brain tumors.Table 4.2 Sample images used for classification purpose.Table 4.3 Performance of AlexNet pre-trained network.Table 4.4 Performance of GoogleNet pre-trained network.Table 4.5 Performance of ResNet101 pre-trained network.Table 4.6 Comparison of performance metrics between AlexNet, GoogleNet, and ResN...Table 4.7 Evaluation of accuracy and processing time of pre-trained network s .

4 Chapter 6Table 6.1 Applications of deep learning networks.

5 Chapter 10Table 10.1 Blood glucose classification.Table 10.2 Blood pressure classification.Table 10.3 Symptoms and signs of diabetic types.Table 10.4 DE parameter settings.

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

Advances in Learning Analytics for Intelligent Cloud-IoT Systems

Series Editors: Dr. Souvik Pal and Dr. Dac-Nhuong Le

Scope: The role of adaptation, learning analytics, computational Intelligence, and data analytics in the field of Cloud-IoT Systems is becoming increasingly essential and intertwined. The capability of an intelligent system depends on various self-decision making algorithms in IoT Devices. IoT based smart systems generate a large amount of data (big data) that cannot be processed by traditional data processing algorithms and applications. Hence, this book series involves different computational methods incorporated within the system with the help of Analytics Reasoning and Sense-making in Big Data, which is centered in the Cloud and IoT-enabled environments.

The series seeks volumes that are empirical studies, theoretical and numerical analysis, and novel research findings. The series encourages cross-fertilization of highlighting research and knowledge of Data Analytics, Machine Learning, Data Science, and IoT sustainable developments.

Please send proposals to:

Dr. Souvik Pal

Department of Computer Science and Engineering

Global Institute of Management and Technology

Krishna Nagar

West Bengal, India

souvikpal22@gmail.com

Dr. Dac-Nhuong Le

Faculty of Information Technology, Haiphong University, Haiphong, Vietnam

huongld@hus.edu.vn

Publishers at Scrivener

Martin Scrivener ( martin@scrivenerpublishing.com) Phillip Carmical ( pcarmical@scrivenerpublishing.com)

Edited by

R.J. Hemalatha

D. Akila

D. Balaganesh

and

Anand Paul

This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA © 2022 Scrivener Publishing LLC

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