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S P R I N G E R B R I E F S I N A P P L I E D S C I E N C E S A N D T E C H N O LO G Y  FORENSIC AND MEDICAL BIOINFORMATICS P. Venkata Krishna  Sasikumar Gurumoorthy  Mohammad S. Obaidat Internet of Things and Personalized Healthcare Systems SpringerBriefs in Applied Sciences and Technology Forensic and Medical Bioinformatics Series editors Amit Kumar, Hyderabad, India Allam Appa Rao, Hyderabad, India More information about this series at http://www.springer.com/series/11910 P. Venkata Krishna • Sasikumar Gurumoorthy Mohammad S. Obaidat Internet of Things and Personalized Healthcare Systems 123 P. Venkata Krishna Mohammad S. Obaidat Department of Computer Science Department of Computer Sri Padmavati Mahila Visvavidyalayam and Information Science Tirupati, Andhra Pradesh, India Fordham University Bronx, NY, USA Sasikumar Gurumoorthy Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati, Andhra Pradesh, India ISSN 2191-530X ISSN 2191-5318 (electronic) SpringerBriefs in Applied Sciences and Technology ISSN 2196-8845 ISSN 2196-8853 (electronic) SpringerBriefs in Forensic and Medical Bioinformatics ISBN 978-981-13-0865-9 ISBN 978-981-13-0866-6 (eBook) https://doi.org/10.1007/978-981-13-0866-6 Library of Congress Control Number: 2018957056 © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore Contents 1 Sensitivity Analysis of Micro Mass Optical MEMS Sensor for Biomedical IoT Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Mala Serene, Rajasekhara Babu and Zachariah C. Alex 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Modeling and Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Different Shapes of Cantilever . . . . . . . . . . . . . . . . . . . . . . . . 3 1.4 Rectangular-Shaped Micro Mass Optical MEMS Sensor . . . . . 4 1.5 Trapezoidal/Triangular-Shaped Micro Mass Optical MEMS Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.6 Step Profile-Shaped Micro Mass Optical MEMS Sensor . . . . . 7 1.7 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2 Enhancing the Performance of Decision Tree Using NSUM Technique for Diabetes Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Nithya Settu and M. Rajasekhara Babu 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.3 Mutual Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.3.1 Symmetric Uncertainty . . . . . . . . . . . . . . . . . . . . . . . 17 2.3.2 Proposed Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.4 Experimental Result and Discussion . . . . . . . . . . . . . . . . . . . . 18 2.5 Conclusion and Future Scope . . . . . . . . . . . . . . . . . . . . . . . . . 19 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 v vi Contents 3 A Novel Framework for Healthcare Monitoring System Through Cyber-Physical System . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 K. Monisha and M. Rajasekhara Babu 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.2.1 Wireless Body Area Network (WBAN) in Healthcare System . . . . . . . . . . . . . . . . . . . . . . . . 23 3.2.2 Electronic Health Record (EHR) Assisted by Cloud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.2.3 Data Security in Healthcare Application . . . . . . . . . . . 26 3.3 Framework for Healthcare Application Through CPS . . . . . . . 27 3.4 Internet of Medical Things (IoMT) . . . . . . . . . . . . . . . . . . . . . 29 3.5 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.6 Result and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 4 An IoT Model to Improve Cognitive Skills of Student Learning Experience Using Neurosensors . . . . . . . . . . . . . . . . . . . . 37 Abhishek Padhi, M. Rajasekhara Babu, Bhasker Jha and Shrutisha Joshi 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.1.1 Needs or Requirements . . . . . . . . . . . . . . . . . . . . . . . 37 4.1.2 Why This Work? . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.1.3 ThinkGear Measurements (MindSet Pro/TGEM) . . . . . 40 4.2 Existing Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.3 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.4 Result and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 5 AdaBoost with Feature Selection Using IoT to Bring the Paths for Somatic Mutations Evaluation in Cancer . . . . . . . . . 51 Anuradha Chokka and K. Sandhya Rani 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 5.1.1 AdaBoost Technique . . . . . . . . . . . . . . . . . . . . . . . . . 52 5.1.2 Feature Selection Techniques . . . . . . . . . . . . . . . . . . 52 5.1.3 Internet of Things (IoT) . . . . . . . . . . . . . . . . . . . . . . 53 5.1.4 Challenges in Sequencing . . . . . . . . . . . . . . . . . . . . . 53 5.2 Existing Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 5.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 5.3.1 Redundancy and Relevancy Analysis Approach . . . . . 55 5.3.2 Feature Redundancy and Feature Relevancy . . . . . . . . 56 5.3.3 Defining a Framework of AdaBoost Technique with Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . 56 Contents vii 5.3.4 Schematic Representation for the Proposed Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 5.3.5 Algorithm and Analysis . . . . . . . . . . . . . . . . . . . . . . 57 5.3.6 IoT Wearables to Detect Cancer . . . . . . . . . . . . . . . . 58 5.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 6 A Fuzzy-Based Expert System to Diagnose Alzheimer’s Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 R. M. Mallika, K. UshaRani and K. Hemalatha 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 6.2 Literature Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 6.3 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 6.3.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 6.3.2 Proposed Methodology . . . . . . . . . . . . . . . . . . . . . . . 67 6.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 7 Secured Architecture for Internet of Things-Enabled Personalized Healthcare Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Vikram Neerugatti and A. Rama Mohan Reddy 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 7.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 7.3 Proposed Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 7.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 8 Role of Imaging Modality in Premature Detection of Bosom Irregularity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 Modepalli Kavitha, P. Venkata Krishna and V. Saritha 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 8.2 Mammography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 8.3 Thermography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 8.4 Result Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 8.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 8.6 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 9 Healthcare Application Development in Mobile and Cloud Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 B. Mallikarjuna and D. Arun Kumar Reddy 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 9.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 9.3 Analysis of Health Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . 95 viii Contents 9.4 Proposed Application Overview . . . . . . . . . . . . . . . . . . . . . . . 97 9.5 Experimental Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 9.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 10 A Computational Approach to Predict Diabetic Retinopathy Through Data Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Ashraf Ali Shaik, Ch Prathima and Naresh Babu Muppalaneni 10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 10.1.1 Steps in Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 107 10.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 10.2.1 Description of Dataset . . . . . . . . . . . . . . . . . . . . . . . . 107 10.2.2 Attribute Information . . . . . . . . . . . . . . . . . . . . . . . . 108 10.2.3 Cross-Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 10.2.4 Classification Matrix . . . . . . . . . . . . . . . . . . . . . . . . . 108 10.2.5 Bagging and Boosting . . . . . . . . . . . . . . . . . . . . . . . . 109 10.3 Performance Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 10.3.1 Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 10.3.2 Sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 10.3.3 Specificity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 10.3.4 Classification Matrix . . . . . . . . . . . . . . . . . . . . . . . . . 110 10.4 Tools Used and Results Discussion . . . . . . . . . . . . . . . . . . . . 110 10.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 11 Diagnosis of Chest Diseases Using Artificial Neural Networks . . . . 113 Himaja Gadi, G. Lavanya Devi and N. Ramesh 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 11.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 11.3 Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 11.4 Types of Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 11.5 Back-Propagation Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 116 11.6 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 11.7 Validation Checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 11.8 Results and Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 11.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 12 Study on Efficient and Adaptive Reproducing Management in Hadoop Distributed File System . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 P. Satheesh, B. Srinivas, P. R. S. Naidu and B. Prasanth Kumar 12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 12.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 Contents ix 12.2.1 Distributed Storage . . . . . . . . . . . . . . . . . . . . . . . . . . 123 12.2.2 Information Replication . . . . . . . . . . . . . . . . . . . . . . . 124 12.2.3 Replica Placement . . . . . . . . . . . . . . . . . . . . . . . . . . 125 12.3 Existing System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 12.3.1 Data Locality Problem . . . . . . . . . . . . . . . . . . . . . . . 127 12.4 Proposed System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 12.4.1 System Description . . . . . . . . . . . . . . . . . . . . . . . . . . 127 12.4.2 Replication Management . . . . . . . . . . . . . . . . . . . . . . 129 12.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 12.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

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