Big Data Analytics and Intelligent Techniques for Smart Cities Big Data Analytics and Intelligent Techniques for Smart Cities Edited by Kolla Bhanu Prakash, Janmenjoy Nayak, B T P Madhav, Sanjeevikumar Padmanaban, and Valentina Emilia Balas MATLAB® is a trademark of The MathWorks, Inc and is used with permission The MathWorks does not warrant the accuracy of the text or exercises in this book This book’s use or discussion of MATLAB® software or related products does not constitute endorsement or sponsorship by The MathWorks of a particular pedagogical approach or particular use of the MATLAB® software First edition published 2022 by CRC Press 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742 and by CRC Press Park Square, Milton Park, Abingdon, Oxon, OX14 4RN © 2022 Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, LLC Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint Except as permitted under U.S Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers For permission to photocopy or use material electronically from this work, access www.copyright com or contact the Copyright Clearance Center, Inc (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400 For works that are not available on CCC please contact mpkbookspermissions@ tandf.co.uk Trademark notice: Product or corporate names may be trademarks or registered trademarks and are used only for identification and explanation without intent to infringe Library of Congress Cataloging‑in‑Publication Data Names: Prakash, Kolla Bhanu, editor Title: Big data analytics and intelligent techniques for smart cities / edited by Kolla Bhanu Prakash, Janmenjoy Nayak, B.T.P Madhav, Sanjeevikumar Padmanaban, and Valentina E Balas Description: First edition | Boca Raton, FL: CRC Press, 2021 | Includes bibliographical references and index Identifiers: LCCN 2021019343 (print) | LCCN 2021019344 (ebook) | ISBN 9780367753559 (hbk) | ISBN 9781032034522 (pbk) | ISBN 9781003187356 (ebk) Subjects: LCSH: Smart cities | Big data Classification: LCC TD159.4 B54 2021 (print) | LCC TD159.4 (ebook) | DDC 307.1/16028557—dc23 LC record available at https://lccn.loc.gov/2021019343 LC ebook record available at https://lccn.loc.gov/2021019344 ISBN: 9780367753559 (hbk) ISBN: 9781032034522 (pbk) ISBN: 9781003187356 (ebk) Typeset in Times by codeMantra Dedicated to Parents, family members, students and Almighty Contents Preface .ix Acknowledgments xv Editors xvii Contributors xxi Chapter Big Data for Smart Education Ayesha Naureen, Anil Badarla, and Ahmed A Elngar Chapter Big Data Analytics Using R for Offline Voltage Prediction in an Electric Power System 27 H Prasad and T D Sudhakar Chapter Intelligent Face Recognition Based on Regularized Robust Coding with Deep Learning Process 47 Sandhya Swaminathan and Anitha Perla Chapter Big Data Analysis, Interpretation, and Management for Secured Smart Health Care 73 V Sucharita, P Venkateswara Rao, and Pellakuri Vidyullatha Chapter Big Data Handling for Smart Healthcare System: A Brief Review and Future Directions 93 Arnaja Banerjee, Yashonidhi Srivastava, and Souvik Ganguli Chapter Big Data Analysis for Smart Energy System: An Overview and Future Directions 117 Tanya Srivastava, Abhimanyu Kumar, Swadhin Chakrabarty, and Souvik Ganguli Chapter Optimum Placement of Multiple Distributed Generators in Distribution Systems for Loss Mitigation Considering Load Growth 131 D Kavitha, B Ashok Kumar, R Divya, and S Senthilrani vii viii Contents Chapter Big Data for Smart Energy 149 Hare Ram Sah and Yash Negi Chapter An Intelligent Security Framework for Cyber-Physical Systems in Smart City 167 Dukka Karun Kumar Reddy, H.S Behera, and Bighnaraj Naik Chapter 10 Big Data and Its Application in Smart Education during the COVID-19 Pandemic Situation 187 Saumyadip Hazra and Souvik Ganguli Chapter 11 Role of IoT, Machine Learning, and Big Data in Smart Building 203 K Manimala Chapter 12 Design of Futuristic Trolley System with Comparative Analysis of Previous Models 223 Balla Adi Narayana Raju, Deepika Ghai, and Kirti Rawal Chapter 13 Big Data for Smart Health 249 Chetan S Arage, K V V Satyanarayana, and Nikhil Karande Index 269 Preface In modern days, cities are viewed as the reflection of the face of a nation A smart city is a multifaceted and modernistic urban area that addresses and serves the needs of inhabitants The latest projections indicate that in developing regions by 2030, the world is expected to have 43 megacities with over 10 million inhabitants For any successful development of a country, sustainable urbanization is also a key factor Artificial Intelligence (AI) and Machine Learning (ML) approaches have progressively become an integral part of numerous industries They are now finding their way to smart city projects to simplify and advance operational processes at large The smart city analyzes through the lens of mobile crowd-sensing and computing through emphasizing a wider aspect with hybrid sensing, participatory, opportunistic, and mobile social networking with a focus on the integration of machines and human intelligence in this platform as an emerging yet promising solution A synergistic activity between machines and humans is generated by this approach, although machines may process the bulk of raw data and enhance the decision-making process Intelligent techniques such as Deep Learning provide promising future directions in the implementation of several aspects of the smart city including smart transportation, intelligent infrastructures, smart governance, smart urban modeling, sustainability, smart health solutions, smart education, security, and privacy The concept behind a smart city is to use advanced technology and data analytics to efficiently provide services to smart city residents on data collected by sensors Deep Learning plays a vital role in intelligent computer vision for effective decision-making that can be used significantly to obtain data insights, understand data patterns for classification, and/or predict data Smart cities prefer the direction of transfer learning for the distribution of training and testing, transferred from one platform to another Deep Learning approaches with semantic technologies make smart city applications to enable better interaction of smart devices with users The use of Deep Reinforcement learning algorithms combined with virtual objects will help construct virtual representations of physical objects so that the objects would work automatically These techniques derive a future interest for smart cities for the incorporation of speech recognition technologies that allow comprehension of natural language in smart devices The potential area of intelligent learning technologies such as wearables and mobile devices in smart cities allows space for senior citizens and lesser technically savvy users Intelligent learning techniques have transformed the concept of a smart city into existence with the evolution of IoT alongside Big Data analytics The concept behind a smart city is to use advanced technology and data analytics to efficiently provide services to the inhabitants of the smart city on data collected by sensors As we know, smart city-oriented anticipatory platforms collect data from various sources (e.g., sensors) to distinguish the context and apply intelligent approaches to envisage the future outcome The context of Big Data is a recent development study in data analytics for smart cities As Big Data true power comes in the form of data analytics ix 258 Big Data Analytics and Techniques for Smart Cities its cost, while helping to encourage patient concern, advance medical outcomes, and reduce pointless expenses [6] As of now, tremendous knowledge analysis has helped to predict the consequences of doctor decisions, such as whether to continue with the heart operation based on the age, medical health, and well-being perceptions of the patient Basically, after looking into all this, we can infer that the role of tremendous data in the well-being field is to track knowledge indexes associated with medicinal facilities, which are confounding and difficult to supervise using existing facilities, programming, and software Notwithstanding the increasing amount of knowledge on social security, recovery strategies are still evolving [10] In this way, intentional usage and execution-dependent pay have evolved in the social security category as key elements In 2011, public resources associations already generated more than 150 additional bytes of information [11] in order to be useful to the social care framework [12], which would all be productively investigated [12] The requirement to include knowledge related to social security in EHRs exists in a number of systems In the field of bioinformatics, where various terabytes of information are provided by genomic sequencing, an abrupt rise in information connected with medicinal services information processing has also been seen [12] There is an assortment of scientific frameworks required for restorative deciphering, which could then be used for patient consideration [13] The social insurance informatics network is testing the diverse origins and forms of tremendous details to establish information processing strategies There is a huge interest in a process that incorporates diverse sources of information [7] It is necessary to use multiple logical methodologies to perceive inconsistencies in enormous calculations of data from different data sets (Figure 13.4) There is currently no system that holds all cloud records relating to medicine or healthcare, such as diagnostic testing, scans, or prescriptions from a patient between appointments that can be viewed with a safe device from anywhere Many medical-related agencies today use computer devices and software to store data on the machine and implement a manual method that eliminates the effort a human has FIGURE 13.4 Contributions of big data in healthcare system Big Data for Smart Health 259 to put to access control, and decreases the effort and time to access those control But it also takes more time for consumers who are unable to view data electronically from their own location to enter the location manually This can be a daunting challenge as there is no one source where complete data store relevant to medical/health the consumer can access from any point of location so that customer commitment and time can be minimized Interoperability and multiple data specifications or formats are a big obstacle for data fusion activities Many researchers have been working on the system for many years to store all data which are medical-related stores dispersed or consolidated and capable of being access them from any place, but they are unable to enforce them due to security issues Furthermore, multiple cloud data storage is feasible, but it increases the sharing of records and creates uncertainty and time These needless and risky examinations, though placing the patient at risk at the same time, are not used A similar challenge is also posed by drug theft From this point on, another daunting challenge is to keep data protected from unauthorized persons Many organizations have been focusing on data protection over the last few years so that data access on the server can be shielded from unwanted persons As a result, providers of health care are searching for other reliable ways to protect accessible data records that are personally stored on the device or network The aim of forecasting a known output or goal begins with supervised learning In Machine Learning contests, where human competitors are judged on their results on standard data sets, repetitive supervised learning challenges include handwriting identification (such as identifying handwritten digits), classifying photographs of items (e.g., is this a cat or a dog?), and text labelling (e.g., is this a clinical experiment for a financial report or a cardiac disease) Notably, all these are tasks that could be performed best by a skilled individual, and so the program also tries to imitate human output Where there are no results to predict, as with unsupervised learning Instead, in the results, we attempt to identify naturally occurring trends or groupings This is necessarily a more complex challenge to judge and also its success in subsequent supervised learning activities tests the importance of certain classes gained by unsupervised learning Generally, there are various basic barriers for dealing with them in the area of the healthcare sector Some of the main problems involved in handling EHRs are as follows: It is very difficult to exchange medical or treatment data with adequately covered consumers Patients have restricted access, for safety reasons, to their own data and to their administrators Any data not accessible on any decentralized authentication scheme Not allowed to receive health-related alerts from time to time 13.4.1 The Frameworks Available for the Analysis of Healthcare Data A systematic methodology composed of six key skills that companies need to consider when building up a Big Data enterprise is the architecture of Big Data The diagram below illustrates the Big Data Framework (Figure 13.5) 260 Big Data Analytics and Techniques for Smart Cities FIGURE 13.5 Big data process 13.4.2 The Six Key Features of the Architecture 13.4.2.1 Strategy of Big Data For a number of organizations, data has become a competitive tool The ability to analyses vast data sets and detect trends in the data will be a strategic edge for organizations For example, in choosing what movies or series to make, Netflix looks at consumer behavior By defining which providers to loan money and advice on their site, the Chinese sourcing site Alibaba has become one of the global giants Large Data is Big Business today Enterprise companies need a sound Big Data plan to generate positive benefits from investments in Digital Data How can investment gains be obtained, and where can Big Data research and analytics target efforts? There are practically infinite options for research, and companies may potentially get lost in the data bytes of zetta The first phase to the sustainability of Big Data is a solid and organized Big Data approach 13.4.2.2 Big Data Architecture Organizations must have the caliber to store and handle vast volumes of data in order to deal with huge data sets In order to achieve this, to facilitate Big Data, the enterprise should have the underlying IT infrastructure Therefore, organizations should provide a robust infrastructure of Big Data to enable the study of Big Data To promote Big Data, how businesses plan and set up their architecture? And, from a storage and transmission standpoint, what are the requirements? The technological capabilities of Big Data ecosystems are called by the Big Data Design aspect of the Big Data Platform It addresses the different positions within a Big Data Framework that are present and looks at the best architectural practices This section would suggest the reference Big Data architecture of the National Institute Big Data for Smart Health 261 of Standards and Technology (NIST) in keeping with the v endor-independent nature of the system 13.4.2.3 Big Data Algorithms To have a detailed knowledge of statistics and algorithms is a profound capacity to deal with results Therefore, to deduct insights from evidence, Big Data experts need to have a strong background in analytics and algorithms Algorithms are simple descriptions of how a class of problems should be solved Calculations, data analysis, and automatic logic functions may be done by algorithms Valuable knowledge and observations can be gained by applying algorithms to vast quantities of data The system portion of Big Data algorithms relies on the skills of someone who has aspirations to work with Big Data It seeks to create a stable base including fundamental operations of mathematics and offers an introduction to various algorithm groups 13.4.2.4 Big Data Processes It is a profound skill to work with outcomes and provide a clear understanding of algorithms and statistics Therefore, Big Data experts are expected to have a deep background in algorithms and analytics to deduct lessons from evidence Simple examples of ways to address a group of issues are known as Algorithms Algorithms can perform data analysis, automatic logic functions, and calculations Through application of algorithms to large data amounts, useful insights and information can be obtained Algorithms of Big Data rely on the expertise of someone who has an aspiration to work with Big Data in the machine section It aims to establish a consistent basis that requires operations that are simple and mathematical and provides an introduction to algorithms of different classes 13.4.2.5 Big Data Functions The functions of Big Data involve the functional aspects of the application of Big Data in companies This part of the Big Data system explores how organizations should coordinate themselves to define positions in Big Data and address duties and obligations in Big Data organizations The effectiveness of Big Data programs is profoundly influenced by corporate culture, organizational frameworks, and work functions Therefore, we will study some “best practices” in setting up Big Data firms The nontechnical elements of Big Data are addressed in the Big Data Roles portion of the Big Data System You’ll learn how to set up a Center of Excellence for Big Data (BDCoE) In addition, it also discusses crucial performance drivers for beginning the organization’s Big Data mission 13.4.2.6 Artificial Intelligence Artificial Intelligence (AI) tackles the last part of the Big Data System AI is one of today’s big fields of concern and promises a whole world of opportunity We discuss the relationship between Big Data and Artificial Intelligence in this section and describe core AI characteristics 262 Big Data Analytics and Techniques for Smart Cities Many companies are willing to launch ventures in the area of AI, but others are not sure where to commence their journey In the sense of delivering market opportunities to corporate organizations, the Big Data Architecture takes a practical overview of AI Therefore, the last segment of the framework illustrates how AI is a sensible next move for companies who have set up the Big Data Platform’s other capabilities The last feature of the Big Data System has been interpreted for purposes as a lifecycle In order to have long-term benefit, AI should continue to learn constantly from the Big Data in the enterprise (Figures 13.6 and 13.7) FIGURE 13.6 Generalized workflow of big data FIGURE 13.7 Big data process for data utilization Big Data for Smart Health 263 13.5 IMPACT OF BIG DATA ON THE HEALTHCARE SYSTEM In relation to the most suitable or accurate patient assessment and the quality evidence used in the w ell-being informatics framework [14], the capacity of tremendous knowledge could affect outcomes In this regard, the review of enormous data measures would have a significant effect on the framework of clinical administrations in five respects, or “pathways.” Improving conditions for patients on this path, as seen below, will be the focus of the system for medicinal treatment which will have a direct effect on the patient Correct living: Correct living alludes to a greater and more beneficial life for the patient [14] By careful living, patients might supervise themselves by making the right choices for themselves, making wise decisions, and enhancing their prosperity in the light of the use of data drilling Patients should expect a working job of recognizing a balanced life by selecting the right way for their day-to-day well-being, with regard to their dietary schedule, preventive thought, fitness, and multiple real life exercises [15] Right care: This path means that patients have access to the most acceptable research and that all vendors receive comparable knowledge and provide common priorities to prevent unnecessary structure and effort In the time of tremendous knowledge, the angle has turned out to be increasingly rational Right provider: In this manner, healthcare professionals will obtain a broad outlook on their patients by consolidating data from multiple outlets, such as clinical hardware, broad w ell-being perspectives, and financial information [14] The existence of this data allows individual expert cooperatives to perform assessments and improve the skills to recognize and provide patients with stronger diagnostic alternatives Correct innovation: This direction perceives the latest epidemic conditions, innovative drugs, and experimental medicinal therapies that will continue to advance [14] Similarly, improvement in patient care structures, such as the revision of drugs and the success of creative work efforts, would empower stronger approaches to promoting development and patient w ell-being through the national social security framework For stakeholders, the usability of previously tentative information is critical This data will be used to examine h igh-potential targets and identify tactics for improving traditional methods of medical research Right value: Suppliers must give their patients careful and continuing attention to enhance the ell-being-related administration The highest useful efficiency and evaluation of w results accepted by their social security framework must be reached by patients For example, separating and wrecking data deception, monitors, and squandering, and enhancing resources are allocations that should be taken to ensure the savvy use of information (Figure 13.8) 13.5.1 Data Science of Healthcare Data Analytics In the world of healthcare, there has been a data boom in Big Data It took more than a decade for conventional methods implemented earlier to study genomics, DNA, and cancer with testing and techniques through the Human Genome Project to understand and evaluate the structure of DNA and data patterns In order to evaluate chronic illnesses for treatment and recovery, Big Data Analytics has implemented groundbreaking instruments and techniques In order to clarify the possible root 264 FIGURE 13.8 Big Data Analytics and Techniques for Smart Cities Healthcare system using IoT and concept of big data causes of tumor growth causing disease, gene sequencing has been used From terabytes to exabytes, data has evolved exponentially X-Ray, CT scan, and MRI healthcare results have risen by leaps and bounds in terms of the amount of results Via Big Data analytics, advanced medical technology allowed the diagnosis of the patient’s records and a comparison of this with the global population to isolate the noises from the signal to recognize the dynamics of tumor growth that were not historically available to speed up the diagnosis and care Though there are many hypotheses and methods that can be implemented for the diagnosis of the illnesses, this chapter briefly examines some of the main techniques (Figure 13.9) It gets train data in less time span, one of the most relevant ones to merge healthcare with computer learning or data analysis Unmonitored learning is the primary purpose of learning from trained data, since unmonitored learning does not require training data The knowledge used for unsupervised learning is not organized and processed correctly somewhere in the system We are able to operate on health data and provide protection by using the various approaches Mostly, by creating the cluster of data and correctly evaluating those data, we work on captured data These details are then saved on the cloud so that we can view the information at any time and place Many of the targets that can be accomplished by focusing on health data are as follows: • Improve interoperability through the processing and preservation of distributed cloud data • Design and eliminate effective data validation methods from the possibility of hacking • Save patients time and money Creating an effective system for maintaining access to cloud-based electronic health information Big Data for Smart Health FIGURE 13.9 265 How healthcare-related data processed • Providing home health care facilities that encourage the importance of life for patients: • Minimizing sickness, impairment, and disorders in patients • Maximizing the possible degree of freedom for patients • To maximize efficiency, security, results, and transparency 13.6 APPLICATIONS OF IOT IN HEALTHCARE WITH BIG DATA Due to its different variety of usage in various industries, the growth of the IoT is thrilling for everybody It has many uses in healthcare Healthcare IoT assists with (Figure 13.10): • • • • Reducing wait time in emergency departments Patient, employees, and product monitoring Strengthening drug treatment Ensuring vital hardware availability IoT has also unveiled many wearables and accessories that have made patients’ lives easy 13.7 CONCLUSION In view of the ever-growing number of computerized business processes and the vast volume of data available in the healthcare sector to be processed in parallel, it 266 FIGURE 13.10 Big Data Analytics and Techniques for Smart Cities Healthcare monitoring system is now unavoidable to accept and use IoT Through applying various techniques of Big Data in the healthcare system and data made accessible to patients by IoT, it is possible to make reliable forecasts or projections about potential outcomes In different grouping processes, this analysis used a community of people who are in the phase Since the use of Big Data and IoT techniques in classification studies results in detailed results followed by substantial time and cost reductions, it is widely recommended that these techniques be used in data processing This research is considered to be beneficial for healthcare organizations and individuals working in all fields of employment that have adapted to the computerization of their business processes and use large-scale data REFERENCES Wenjin Yu, Tharam Dillon, Life Fellow, IEEE, Fahed Mostafa, Wenny Rahayu, Member, IEEE, and Yuehua Liu, “A global manufacturing big data ecosystem for fault detection in predictive maintenance”, IEEE Transactions on Industrial Informatics, Vol 16, No 1, January 2020 Pau Suan Mung, and Sabai Phyu,” Effective analytics on healthcare big data using ensemble learning” 2020 IEEE Conference on Computer Applications ( ICCA), Yangon, pp. 1–4, 2020 doi: 10.1109/ICCA49400.2020.9022853 Stefano Proto, Evelina Di Corso, Daniele Apiletti, Luca Cagliero, Tania Cerquitelli, Giovanni Malnati, and Davide Mazzucchi, “R EDTag: A predictive maintenance framework for parcel delivery services”, IEEE, January 6, 2020 Aras Can Onal, Omer Berat Sezer, Murat Ozbayoglu, and Erdogan Dogdu, “Weather data analysis and sensor fault detection using an extended IoT framework with semantics, big data, and machine learning”, 2017 IEEE International Conference on Big Data (BIGDATA) Boston, MA, 11–14 Dec 2017 Big Data for Smart Health 267 Sunder Ali Khowaja, Aria Ghora Prabono, Feri Setiawan, Bernardo Nugroho Yahya, and Seok-Lyong Lee, “Contextual Activity Based Healthcare Internet of Things, Services, and People (HIoTSP): An Architectural Framework for Healthcare Monitoring Using Wearable Sensors”, 1389-1286/© 2018 Elsevier B.V All rights reserved Lei Wang, Chen Yibo, Peng Li, and Lingxiao Zhao, “Multimode Data Fusion Based Remote Healthcare Framework”, BDET 2018, August 25–27, 2018, Chengdu, China © 2018 Association for Computing Machinery Bikash Kanti Sarkar, “Big Data for Secure Healthcare System: A Conceptual Design”, Received: October 2016 / Accepted: March 2017 © The Author(s) 2017 Yueyao Wang, Qinmin Hu, Yang Song, and Liang He, “Potentiality of Healthcare Big data: Improving Search by Automatic Query Reformulation”, 978-1-5386-2715-0/17/$31.00 ©2017 IEEE Ali Al-Badia, Ali Tarhinia, and Asharul Islam Kha, “Exploring Big Data Governance Frameworks”, 1877-0509 © 2018 The Authors Published by Elsevier Ltd 10 Shiva Raj Pokhrel, Keshav Sood, Shui Yu, and Mohammad Reza Nosouhi, Policy-based Bigdata Security and QoS Framework for SDN/ IoT: An Analytic “ Approach”, 978-1-7281-1878-9/19/$31.00 ©2019 IEEE 11 Qing Wang, Xiaodong Wang, and Ye Tao, “A User Profile Analysis Framework Driven by Distributed Ma-chine Learning for Big Data”, AICS 2019, July 1 2–13, 2019, Wuhan, Hubei, China © 2019 Association for Computing Machinery 12 Ahmed E Youssef, “A framework for secure healthcare systems based on big data analytics in mobile cloud computing environments”, International Journal of Ambient Systems and Applications (IJASA) Vol.2, No.2, 1–11, June 2014 13 G Shwetha, P.R Visali Lakshmi, and N Sri Madhava Raja, “Analysis of Medical Image and Health Informatics Using Bigdata”, 978-1-5090-4855-7/17/$31.00 ©2017 IEEE 14 Haiping Huang, Tianhe Gong, Ning Ye, Ruchuan Wang, and Yi Dou, “Private and Secured Medical Data Transmission and Analysis for Wireless Sensing Healthcare System” 15 Hai Tao, Md Zakirul Alam Bhuiyan, Ahmed N Abdalla, Mohammad Mehedi Hassan, Jasni Mohamad Zain, and Thaier Hayajneh, “Secured Data Collection with Hardwarebased Ciphers for IoT-based Healthcare”, 2327–4662 (c) 2018 IEEE Index access control 86 access node 174 acquisition layer 207 actuators 207 AdaBoost algorithm 172 adaptive boosting 172 algorithm regularized 56 algorithm SFLA 135 Amazon Kinesis 215 analysis MOA 215 analysis PCA 59 ANN artificial 28 ANN based classifier 214 ANN based ML 214 ANN clustering 212 ANN decision 206 ANN model 214 ANN prediction 214 ANNs Bayesian 214 Apache Flume 215 Apache Hadoop 161 Apache Kafka 215 Apache Mahout 104 Apache SAMOA 215 Apache Spark 122, 215 Apache Storm 215 Arduino based intelligent 230 ARIMA 121 ARIMA model 121 ARIMA predictions 121 artificial intelligence 98, 223, 261 attack classifier 179 attack comparative 185 attack FPR 179 attack malicious 179 Azure 215 Bayesian algorithm 214 Bayesian classifier 212 Bayesian estimation 52 Bayesian formula 52 Bayesian inference 124 Bayesian network 96, 121, 212 Blockchain 104 Business intelligence 150 cart boosting 211 CC amalgamation 106 classification module 244 cloud computing 149 cloud storage 107 CMU MultiPIE 60 CNN bagging 121 coding minimization 33 computing MCC 256 computing telemedicine 107 confusion matrix 182 congestion control 80 consequent analytics 23 continuous timestamp discrete normality 175 control devices 207 convergence 57 COVID education 196 COVID pandemic 196 CPSs vary 170 crystal display 227 currently associations 252 currently data 74 cyber physical systems 94 cyphers graph 121 data privacy 82 Data Anomaly (DoS) 170 decision tree 173 deep learning 47–71 discrete based personalized 12 discrete normality nominal 175 disease diagnosis 255 diseases hypoxia 100 disguise method test 50 disguise occlusion 67 disguise subset 65 disposition algorithm 171 distinguished teaching 12 distribution anomaly 176 distribution DSOS 175 doctors track 253 Dogdu Weather 266 DoS attack 171 DoS denial 132 DoS passive 171 drugs equipment 255 Dudley Artificial 113 due omnipresence 16 dynamic monitoring 152 ECG EDR 99 EHR 97 EHRs PACS 75 269 270 EMR 107 engineering optimization 146 epitomize learning error function 33 error reduction 255 error values 44 essential components 169 essential skills establish innovation 152 estimate function 135 estimated weight 62, 64 estimation agents 207 ethernet bluetooth 207 ethical theory 113 Euclidean based distances 210 evolutionary algorithms 29 evolutionary strategies 120 exploratory data 174 extended Yale 58, 59 face disguise 65, 70 face recognition 47 feature distribution 175 feature extraction 203 framework smart 10 futuristic trolley 223, 225, 227, 229 Gabor features 65 gateway 105, 155, 166 Gaussian distribution 50, 52 Gaussian fidelity 54 Gaussian function 54 Gaussian kernel 54 Gaussian Laplacian 49, 50, 52, 53 Gaussian models 50 generation MW Max 39 Genome 263 Google meet 187, 197 Google processes 149 grid applications 149 ground vehicle 245 group based collaborative 12 Hadoop allows 42 Hadoop clusters 53 Hadoop DataWrapper 29 Hadoop framework 111 Hadoop SMASH 125 healthcare monitoring 267 healthcare systems 115 heating 204 heating ventilation 204 heavy 225 heavy loads 225 hidden Markov 214 human Genome 263 Index HybrEx 87, 88 hybrid cloud 159 hypertension frequency 81 IaaS 159 IBDA framework 124 IBM Watson 30 identification RFID 225 image classification 242 image detection 239 image identification 242 images dimensions 65 images labeled 235 improve interoperability 264 improve learning 22 informatics 257 integer linear 256 integer nonlinear 133 IoT based sensors 203 IoT BDA 124 IoT framework 266 IoT gadgets 253 IoT gateway 149, 155 IoT healthcare 264 IoT hospitals 253 IoT LoWPAN 172 IoT RFID 239 IoT sensors 160 IoT transforming 252 IoT wearables 101 IoT Zigbee 102 IR rays 230, 238 item classification 244 items detection 242 Jacobian matrix 29 Kerberos Hashing 86 Laplacian distribution 50 Laplacian Gaussian 50 layer authenticating 86 leap work 139 leaping algorithm 133 learning algorithms 203 learning analytics 14 learning cell learning content 15 learning contests 259 learning distinguishing 71 learning false positive 69 learning generality 53 learning reinforcement 203 learning SVM 59 linear programming 256 linear regression 27 271 Index LineMax Apparent 39 LMS Virtual 187 location awareness 16 location destination 174 logistic regressions 104 long range communication 102 LoRaWAN 102 machine learning 78 MATLAB® 31 MATLAB® Minitab 31 MCC work 256 METHOD ensemble 172 ML classifiers 170, 179, 181, 185 MLP Bayesian 212 model prediction tuning parameters data 213 MongoDB Cassandra 29 MongoDB multistage 19 MWMax Reactive 39 MWMin Real 39 MWPower Factor 140, 141 MWReactive Power 38 nodes physiological conditions 98 neural network 28 neurons 214 occupancy estimation 207 parallel computing 163 perceptron MLP 179 performance computing 200 personalized expertise personalized learning 12 pertaining generator 39 pervasive 14 planning ERP 235 positioning system 103 postevent analysis 152 power capability 140, 141 power engineering 121 power flow 39 power generation 39 predict learner 14 preprocessing feature 203 PSO 133 PTC sensors 100 public awareness 226 public clouds 159 pulse oximeter 100 pulse sensors 99 pulse width 225 PV generator 37 Q-learning algorithm 215 Q-learning obstacle 232 Q-learning reinforcement 215 QoE Quality 111 QoS future 111 QR code 242 regression analysis 96 reinforcement learning 203 renewable energy 151 renewable sustainable 128 RF kNearest 179 RF kNN 212 RF Naïve 172 RFID based billing 228 RFID based object 245 RFID circular polarized 241 RFID rechargeable 227 RFID tag 227, 229 R linear regression 33 robust coding 49 R studio analytics 27 SCADA energy 152 scan anomaly malicious 176 sensor data 210 sensor networks 227 sensors calculate 240 sensors controllers 156 server communication 228 SFLA algorithm 135, 140 SFLA approaches 139 SFLA PSO 133 sharing framework 112 shortrange communication 101 similarly improvement 263 similarly iterations 143 similarly precision recall 183 smart computing smart pedagogy 1, 11 smart short 151 spying equivalent 183 spying predicted 179 strategies GES 120 SVM based 212 SVM BPNN 212 SVM classifier 120 SVM decision 212 SVM kNN 212 SVM LLC 61 SVM LRC 60, 61 SVM NFL 59 SVMs hidden 212 SVM SRC 59 SVR method 121 SVR worked 120 UHF RFID 233, 240 272 wearable technology XBee wireless 233, 240 XRay CT 264 Index ZigBee adapter 233, 240 ZigBee bluetooth 101 ZigBee Kiosk 239 ZigBee microcontrollers 228 .. .Big Data Analytics and Intelligent Techniques for Smart Cities Big Data Analytics and Intelligent Techniques for Smart Cities Edited by Kolla Bhanu Prakash, Janmenjoy Nayak, B T P Madhav,... Cataloging‑in‑Publication Data Names: Prakash, Kolla Bhanu, editor Title: Big data analytics and intelligent techniques for smart cities / edited by Kolla Bhanu Prakash, Janmenjoy Nayak, B.T.P Madhav, Sanjeevikumar... Cyber-Physical Systems in Smart City 167 Dukka Karun Kumar Reddy, H.S Behera, and Bighnaraj Naik Chapter 10 Big Data and Its Application in Smart Education during the COVID-19 Pandemic Situation