Table Of Content

Cyber-Physical Systems AI and COVID-19 Cyber-Physical Systems AI and COVID-19 Edited by Ramesh Chandra Poonia Department ofComputer Science,CHRIST(Deemed to be University), Bangalore, India Basant Agarwal Department ofComputer Scienceand Engineering,Indian Instituteof InformationTechnology (IIIT), Kota, India Sandeep Kumar Department ofComputer Scienceand Engineering,CHRIST (Deemed to be University), Bengaluru, India Mohammad S. Khan Network ScienceandAnalysis Lab (NSAL), Departmentof Computing, East Tennessee State University,Johnson City, TN, United States Gonc¸alo Marques Polytechnic ofCoimbra,ESTGOH,Rua General Santos Costa, Oliveira do Hospital, Portugal Janmenjoy Nayak Aditya Instituteof Technologyand Management(AITAM)(An Autonomous Institution),Tekkali, K Kotturu, India AcademicPressisanimprintofElsevier 125LondonWall,LondonEC2Y5AS,UnitedKingdom 525BStreet,Suite1650,SanDiego,CA92101,UnitedStates 50HampshireStreet,5thFloor,Cambridge,MA02139,UnitedStates TheBoulevard,LangfordLane,Kidlington,OxfordOX51GB,UnitedKingdom Copyright©2022ElsevierInc.Allrightsreserved. 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BritishLibraryCataloguing-in-PublicationData AcataloguerecordforthisbookisavailablefromtheBritishLibrary LibraryofCongressCataloging-in-PublicationData AcatalogrecordforthisbookisavailablefromtheLibraryofCongress ISBN:978-0-12-824557-6 ForInformationonallAcademicPresspublications visitourwebsiteathttps://www.elsevier.com/books-and-journals Publisher:MaraConner AcquisitionsEditor:SonniniR.Yura EditorialProjectManager:MarianaL.Kuhl ProductionProjectManager:SwapnaSrinivasan CoverDesigner:MarkRogers TypesetbyMPSLimited,Chennai,India Contents Listofcontributors xiii 1. AI-based implementation of decisive technology for prevention and fight with COVID-19 1 AlokNegiandKrishanKumar 1.1 Introduction 1 1.2 Relatedwork 3 1.3 Proposedwork 5 1.3.1 Facemaskdetection 5 1.3.2 DetectionofCOVIDfromCTimages 7 1.4 Resultsandanalysis 9 1.4.1 Facemaskdetection 9 1.4.2 CTscanimage-basedCOVID-19patientidentification 12 1.5 Conclusion 13 References 13 2. Internet of Things-based smart helmet to detect possible COVID-19 infections 15 ChanchalAhlawatandRajalakshmiKrishnamurthi 2.1 Introduction 15 2.1.1 Epidemiology 17 2.1.2 Treatment 18 2.1.3 Prevention 19 2.1.4 Symptoms 21 2.1.5 StagesofCOVID-19 21 2.1.6 KeymeritsofIoTforCOVID-19pandemic 23 2.1.7 InternetofThingsprocessrequiredforCOVID-19 23 2.1.8 IoTapplicationsforCOVID-19 24 2.2 Relatedwork 24 2.3 IoT-basedsmarthelmettodetecttheinfectionofCOVID-19 27 2.3.1 Objective 27 2.3.2 Methodology 27 2.4 Conclusion 31 References 31 v vi Contents 3. Role of mobile health in the situation of COVID-19 pandemics: pros and cons 37 PriyankaBhaskarandSunitaRao 3.1 Introduction 37 3.2 ImplementationofatrainingmoduleforthemHealthcare worker 39 3.3 Governmentpoliciesforthescale-upofthemHealthservices 40 3.4 PopularmodelsofmHealthservingforpandemicCOVID-19 42 3.5 Ethicalconsideration 42 3.6 SuperiorityofmHealthservicesoverotheravailableservices 46 3.7 Probabilityofconflictofinterestbetweenuserandservice provider 47 3.8 Legalconsideration 48 3.9 Protectionofprivacyofend-users 49 3.10 Conclusion 50 3.11 Futureprospects 51 References 51 4. Combating COVID-19 using object detection techniques for next-generation autonomous systems 55 HrishikeshShenai,JayGala,KaustubhKekre,PranjalChitaleand RuhinaKarani 4.1 Introduction 55 4.2 Needforobjectdetection 56 4.3 Objectdetectiontechniques 56 4.3.1 R-CNNfamily 57 4.3.2 YOLOfamily 62 4.4 ApplicationsofobjectiondetectionduringCOVID-19crisis 66 4.4.1 Moduleforautonomoussystems(potholedetection) 66 4.4.2 Socialdistancingdetector 67 4.4.3 COVID-19detectorbasedonX-rays 69 4.4.4 Facemaskdetector 70 4.5 Conclusion 71 References 72 5. Non-contact measurement system for COVID-19 vital signs to aid mass screening—An alternate approach 75 VijayJeyakumar,K.NirmalaandSachinG.Sarate 5.1 Introduction 75 5.2 COVID-19globalscenarios 76 5.2.1 Infections,recoveryandmortalityrate 76 5.2.2 Economyandenvironmentalimpacts 77 Contents vii 5.3 MeasurementandtestingprotocolsofCOVID-19 78 5.3.1 Measurementmethods 79 5.3.2 COVID-19innovations 80 5.4 Non-contactapproachestophysiologicalmeasurement 81 5.4.1 Needfornon-contactmeasurement 82 5.4.2 Stateofthearttopriorwork 83 5.4.3 Proposedapproach 84 5.4.4 Methodology 85 5.4.5 Preliminaryexperimentalresults 85 5.5 Conclusion 89 Acknowledgment 90 References 91 6. Evolving uncertainty in healthcare service interactions during COVID-19: Artificial Intelligence - a threat or support to value cocreation? 93 SumitSaxenaandAmritesh 6.1 Introduction 93 6.2 Servicedominantlogicinmarketing 96 6.3 Serviceinteractionsandcocreatedwellbeing 97 6.4 Uncertaintyduetopandemic 98 6.5 Uncertaintyinhealthcare 98 6.5.1 Impactofpandemic-leduncertaintyonapatient’smind 102 6.5.2 Impactofpandemic-leduncertaintyonservice interactions 102 6.6 TheemergingroleofArtificialIntelligence 103 6.7 AIcombatinguncertaintyandsupportingvaluecocreationin healthcareinteractions 104 6.8 Thespill-overeffectofArtificialIntelligence 107 6.9 Conclusionandfuturework 109 References 110 7. The COVID-19 outbreak: social media sentiment analysis of public reactions with a multidimensional perspective 117 BasantAgarwal,VaishnaviSharma,PriyankaHarjule,VinitaTiwariand AshishSharma 7.1 Introduction 117 7.2 Datacollection 119 7.3 Sentimentanalysisofthetweetscollectedworldwide 120 7.4 SentimentanalysisofTweetsforIndia 123 7.4.1 COVID-19analysisforindividualcityofIndia—Mumbai 127 7.5 Analysisoffewmosttrendinghashtags 130 viii Contents 7.5.1 Opinionanalysisforthehashtag#WorkFromHome 131 7.5.2 Sentimentanalysisof#MigrantWorkers 135 7.6 Conclusion 137 References 138 8. A new approach to predict COVID-19 using artificial neural networks 139 SohamGuhathakurata,SayakSaha,SouvikKundu, ArpitaChakrabortyandJyotiSekharBanerjee 8.1 Introduction 139 8.2 Relatedstudies 140 8.3 Fundamentalsymptomsandconditionsresponsible forCOVID-19infection 141 8.4 ProposedCOVID-19detectionmethodology 142 8.5 Briefdescriptionofartificialneuralnetworks 145 8.5.1 Principlesofartificialneuralnetwork 145 8.6 ParametersettingsfortheproposedANNmodel 149 8.7 Experimentalresultsanddiscussion 151 8.8 PerformancecomparisonbetweenANNandother classificationalgorithms 153 8.9 Conclusion 155 Appendix 156 References 157 9. Rapid medical guideline systems for COVID-19 using database-centric modeling and validation of cyber-physical systems 161 ManiPadmanabhan 9.1 Introduction 161 9.2 GlobalpandemicofCOVID-19 162 9.3 Database-centriccyber-physicalsystemsforCOVID-19 164 9.3.1 Cyber-physicalsystems 164 9.3.2 Flowofrapiddatabase-centriccyber-physicalsystem 165 9.4 Modelingandvalidationofrapidmedicalguidelinesystems 166 9.5 Conclusion 168 References 168 10. Machine learning and security in Cyber Physical Systems 171 NehaV.Sharma,NarendraSinghYadavandSaurabhSharma 10.1 Introduction 171 10.2 Relatedwork 174 10.2.1 Phishing 174 Contents ix 10.2.2 Intrusiondetectionfornetworks 175 10.2.3 Keystrokeelementsvalidation 175 10.2.4 Breakinghumancollaborationproofs(CAPTHAs) 175 10.2.5 Cryptography 175 10.2.6 Spamdetectionforsocialnetworking 176 10.3 Motivation 176 10.4 Importanceofcybersecurityandmachinelearning 177 10.5 MachinelearningforCPSapplications 178 10.6 FutureforCPStechnology 179 10.6.1 Cyberphysicalsystemsandhuman 181 10.6.2 CPSandartificialintelligence 181 10.6.3 Trustworthy 181 10.6.4 Cyberphysicalsystemsofsystems 182 10.7 ChallengesandopportunitiesinCPS 182 10.8 Conclusion 185 References 185 11. Impact analysis of COVID-19 news headlines on global economy 189 AnanyaMalik,YashTejasJaveri,ManavShahandRamchandra Mangrulkar 11.1 Introduction 189 11.2 Relatedwork 190 11.3 Proposedmethodology 194 11.3.1 Dataanddatapreprocessing 194 11.3.2 Sentimentanalysis 197 11.3.3 PredictionofNiftyscore 199 11.4 Resultsandexperimentalframework 201 11.4.1 Linearregression 201 11.4.2 Polynomialregressionwithdegree3 202 11.4.3 Randomforestregression 202 11.4.4 Gradientboostregressor 203 11.5 Conclusion 205 References 205 Furtherreading 206 12. Impact of COVID-19: a particular focus on Indian education system 207 PushpaGothwal,BoskyDharmendraSharma,NanditaChaubeand NadeemLuqman 12.1 Introduction 207 12.2 ImpactofCOVID-19oneducation 208 12.2.1 Effectofhomeconfinementonchildrenandteachers 211 12.2.2 Amultidimensionalimpactofuncertainty 214 x Contents 12.3 SustainingtheeducationindustryduringCOVID-19 214 12.4 Conclusion 216 References 216 13. Designing of Latent Dirichlet Allocation Based Prediction Model to Detect Midlife Crisis of Losing Jobs due to Prolonged Lockdown for COVID-19 219 BasabdattaDas,BarshanDas,AvikChatterjeeandAbhijitDas 13.1 Introduction 219 13.2 Literaturesurvey 220 13.3 Methodology 221 13.3.1 Distinguishingmidlifecrisissymptoms 222 13.3.2 Designingofthepredictionmodel 223 13.3.3 ApplicationofLDAandstatisticalcomparison 223 13.4 Resultanddiscussion 225 13.5 Conclusionandfuturescope 227 References 228 14. Autonomous robotic system for ultraviolet disinfection 231 RikiPatel,HarshalSanghviandAbhijitS.Pandya 14.1 Introduction 231 14.2 Background 232 14.2.1 Ultravioletlightfordisinfection 232 14.2.2 Exposuretimefordeactivationofthebacteria 233 14.2.3 FlowchartofUVbotcontrollogic 233 14.2.4 Calculationsrelatedtothetimefordisinfection 233 14.3 Implementation 234 14.4 Modeltopology 236 14.4.1 UV-Clightroboticvehicle 237 14.5 Conclusion 239 References 240 15. Emerging health start-ups for economic feasibility: opportunities during COVID-19 241 ShwetaNanda 15.1 Introduction 241 15.2 Health-techverticalsforstart-ups 243 15.3 Researchgap 244 15.4 Aimofthestudy 244 15.5 Researchmethodology 244 15.5.1 Problemstatement 244 15.5.2 Typeofresearch 245