Artificial Intelligence in Industrial Applications: Approaches to Solve the Intrinsic Industrial Optimization Problems PDF
Preview Artificial Intelligence in Industrial Applications: Approaches to Solve the Intrinsic Industrial Optimization Problems
Learning and Analytics in Intelligent Systems 25 Steven Lawrence Fernandes Tarun K. Sharma Editors Artificial Intelligence in Industrial Applications Approaches to Solve the Intrinsic Industrial Optimization Problems Learning and Analytics in Intelligent Systems Volume 25 SeriesEditors GeorgeA.Tsihrintzis,UniversityofPiraeus,Piraeus,Greece MariaVirvou,UniversityofPiraeus,Piraeus,Greece LakhmiC.Jain,KESInternational,Shoreham-by-Sea,UK Themainaimoftheseriesistomakeavailableapublicationofbooksinhardcopy form and soft copy form on all aspects of learning, analytics and advanced intelligentsystemsandrelatedtechnologies.Thementioneddisciplinesarestrongly related and complement one another significantly. Thus, the series encourages cross-fertilization highlighting research and knowledge of common interest. The series allows a unified/integrated approach to themes and topics in these scientific disciplines which will result in significant cross-fertilization and research dissem- ination. To maximize dissemination of research results and knowledge in these disciplines, the series publishes edited books, monographs, handbooks, textbooks andconferenceproceedings. Moreinformationaboutthisseriesathttps://link.springer.com/bookseries/16172 · Steven Lawrence Fernandes Tarun K. Sharma Editors Artificial Intelligence in Industrial Applications Approaches to Solve the Intrinsic Industrial Optimization Problems Editors StevenLawrenceFernandes TarunK.Sharma DepartmentofComputerScience ShobhitUniversity CreightonUniversity Saharanpur,India Omaha,NE,USA ISSN2662-3447 ISSN2662-3455 (electronic) LearningandAnalyticsinIntelligentSystems ISBN978-3-030-85382-2 ISBN978-3-030-85383-9 (eBook) https://doi.org/10.1007/978-3-030-85383-9 ©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicensetoSpringerNature SwitzerlandAG2022 Thisworkissubjecttocopyright.AllrightsaresolelyandexclusivelylicensedbythePublisher,whether thewholeorpartofthematerialisconcerned,specificallytherightsoftranslation,reprinting,reuse ofillustrations,recitation,broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,and transmissionorinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilar ordissimilarmethodologynowknownorhereafterdeveloped. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant protectivelawsandregulationsandthereforefreeforgeneraluse. Thepublisher,theauthorsandtheeditorsaresafetoassumethattheadviceandinformationinthisbook arebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsor theeditorsgiveawarranty,expressedorimplied,withrespecttothematerialcontainedhereinorforany errorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictional claimsinpublishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Preface Thisbookhighlightstheanalyticsandoptimizationissuesintheindustry,proposes newapproaches,andpresentsapplicationsofinnovativeapproachesinrealfacilities. Inthepastfewdecades,therehasbeenanexponentialriseintheapplicationofartifi- cialintelligenceforsolvingcomplexandintricateproblemsarisingintheindustrial domain.Theversatilityofthesetechniqueshasmadethemafavoriteamongscientists andresearchersworkingindiverseareas. Thepresentbookincludesonly12chaptershavingtheapplicationsofintelligent algorithmsintheIndustry. Inchapter“AnEfficientDeepLearningFrameworkforPeopleDetectioninOver- headImages”,Pekeretal.proposeanefficientdeeplearningframeworkfordetecting peoplefromoverheadimages.Theproposedmethodusesamulti-scaleYolov4-Tiny algorithmforpersondetection.Themulti-scalefeatureofthealgorithmenablesitto successfullydetectpeoplewhoappearsmallinanimage.Theproposedmethodnot onlyshowspromisingresults,butcanalsoworkinreal-timeapplicationsduetolow computationalcost. In chapter “Machine Learning Techniques for Grocery Sales Forecasting byAnalyzingHistoricalData”,Yeasminetal.proposedaforecastingmodelusing machinelearningalgorithms,togetaccurateforecastsGrocerySalesForecastingby AnalyzingHistoricalData. Inthechapter“MedicationRevelationUtilizingNeuralNetwork”,Kushwahetal. presentareviewoftheapplicationsthatproducepromisingresultsandmethodswill bereviewed.Theuseofvirtualscreeningandencompassingonlineinformationhas alsobeenhighlightedindevelopingleadsynthesispathways. In the chapter “Application of AI in SCM or Supply Chain 4.0”, Singh et al. provide the speculative allusion of application of AI in SCM, reimbursement, and the challenges of AI in the SCM industry. In chapter “Upskilling and Curating thePotentialsofIoTEnabledSmartCities:UseCasesandImplementationStrate- gies”, Sivarethinamohan and Sujatha examine significant aspects of an IoT infras- tructureforsmartcities,outliningtheinnovationsimplementedinthecitiesofIndia asusecasesandImplementationStrategies.Exceptionalattentionisdevotedtothe v vi Preface potentialapplicationsofsmartcities.DattaandMitra,inchapter“IdentifyingImpact ofSanitationandEnvironmentalSafetyinHospitalitySectortoIdentifytheSpread ofCOVID-19UsingPolynomialFunctionSimulation”,detailedandsolvedachal- lengingproblemduetoidentifyingtheimpactofsanitationandenvironmentalsafety inthehospitalitysectortoidentifythespreadofCOVID-19usingPolynomialFunc- tion Simulation. The chapter “The Internet of Things and Advanced Applications in Healthcare”, by Gandhi and Singh, addressed IoT technology, IoT architecture, andIoTadvancedapplicationsinhealthcare.TheyhaveintroducedIoTapplication inhospitalbedsthatwillprovidecompletepatientcareanddecreasetherequirement ofthehospitalcaringstaff,andallactivitiesofthepatientcanbetransmittedtothe doctor’s room or chamber to update them about the patient’s current status/ vital signsinICU.Thechapter“AComprehensiveReviewofRecentAutomaticSpeech Summarization and Keyword Identification Techniques”, by Kumar et al., aims to outlineandexplainsomeofthepopularapproachesinspeechrecognitionsystems at various stages and highlights selected systems’ unique and innovative charac- teristics. In the next chapter “Anomaly Detection in Industrial IoT Applications UsingDeepLearningApproach”,BullaandBirjeproposeananomalydetectionin IndustrialIoTApplicationsusingaDeepLearningapproach.Theproposedanomaly detection scheme uses a multi-step prediction technique and applies an anomaly detection algorithm to detect anomalies. The proposed model increases accuracy over existing approaches, according to the experimental evaluation. In the chapter “NurturingtheRudimentsand UseCasesofOngoingNaturalLanguageGeneration for a Future Profitable Business More Profitable”, Sivarethinamohan and Sujatha presentedastudyofhowNLGenablesmachinesandhumanstocommunicateseam- lessly,simulatinghuman-to-humanconversations,andusingNLGhoworganizations arebuildingnewcustomerexperiences,monetizinginformationassets,introducing new offerings, and streamlining operational costs. Therefore, the coverage of this chapter will answer to the industrialists and new start-ups. What can NLG do for business? And what are the future applications of NLG?. In the chapter “A Study onDeepLearningModelsforMedicalImageSegmentation”,Bhattetal.highlightthe mostpopularalgorithmsbasedonDLarchitecturewithrespecttothesegmentation ofmedicalimages.Bhardwajinthechapter“DeepLearning-basedCyberSecurity SolutionsforSmart-City:ApplicationandReview”discussabouttheConvolutional NeuralNetworks(CNNs),FullyConnectedConvolutionalNetworks(FCNs),Recur- rent.NeuralNetwork(RNN),DeepBeliefNetwork(DBN),BoltzmannMachine,and Auto-encoders. The book is edited to serve a broad readership, including computer scientists, medical professionals, and mathematicians interested in studying tools and tech- niquesforcomputationalintelligenceandapplicationsinhealthcare.Itwillalsobe helpfulforresearchers,graduate,andundergraduatestudentswithaninterestinthe fieldsofArtificialIntelligenceandIndustrialproblems. Preface vii Thisbookwillbeausefulresourceforresearchersandacademiciansaswellas professionalsinterestedinthehighlyinterdisciplinaryfieldofArtificialIntelligence. Omaha,USA StevenLawrenceFernandes Saharanpur,India TarunK.Sharma Contents 1 AnEfficientDeepLearningFrameworkforPeopleDetection inOverheadImages ........................................... 1 MusaPeker,Bilge˙Inci,ElnuraMusaog˘lu,HüseyinÇobanog˘lu, NadirKocakır,andÖnderKarademir 1.1 Introduction ............................................. 1 1.1.1 LiteratureSurvey ................................. 2 1.1.2 ContributionSummary ............................ 3 1.2 Dataset ................................................. 3 1.3 Methods ................................................ 5 1.3.1 ObjectDetectionAlgorithms ....................... 5 1.3.2 The Detection Approach of Yolo-Based Algorithms ...................................... 6 1.3.3 ProposedMulti-ScaleYolov4-TinyAlgorithm ........ 8 1.4 ExperimentalSetup ....................................... 11 1.4.1 TrainingStage .................................... 11 1.4.2 ModelEvaluationMetrics .......................... 13 1.5 ExperimentalResultsandDiscussion ........................ 13 1.5.1 ImpactoftheDifferentNMSAlgorithms ............. 15 1.5.2 ImpactofTrainingDatasetSize ..................... 18 1.6 Conclusions ............................................. 18 References .................................................... 19 2 MachineLearningTechniquesforGrocerySalesForecasting byAnalyzingHistoricalData ................................... 21 NilufaYeasmin, SamanHassanzadehAmin, andBabakMohamadpourTosarkani 2.1 Introduction ............................................. 21 2.2 LiteratureReview ........................................ 22 2.3 ExploratoryDataAnalysis ................................. 23 2.3.1 DataSourceandDataFiles ......................... 25 2.3.2 DataAnalysis .................................... 26 ix x Contents 2.4 Methodology ............................................ 30 2.4.1 LinearRegression ................................. 30 2.4.2 DecisionTrees ................................... 31 2.4.3 RandomForest ................................... 31 2.4.4 ArtificialNeuralNetworks ......................... 32 2.5 ExperimentalResultsandAnalysis ......................... 33 2.6 ConclusionsandFutureWork .............................. 35 References .................................................... 35 3 MedicationRevelationUtilizingNeuralNetwork ................. 37 VirendraSinghKushwah, AshishSolanki, BhavyaManojVotavat,andAmanJain 3.1 Introduction ............................................. 37 3.2 MaterialandMethods ..................................... 39 3.2.1 MLAlgorithmsusedinDrugDiscovery .............. 39 3.2.2 Dataset .......................................... 40 3.2.3 Structure Based Deep Convolutional Neural Network ......................................... 41 3.3 ResultsandAnalysis ...................................... 42 3.4 Conclusion .............................................. 48 References .................................................... 48 4 ApplicationofAIinSCMorSupplyChain4.0 ................... 51 S.P.Singh,J.Rawat,M.Mittal,I.Kumar,andC.Bhatt 4.1 Introduction ............................................. 51 4.2 LiteratureSurvey ......................................... 54 4.3 AdvantagesofAIiNSCM ................................. 56 4.3.1 AutomatedDecision-Making ....................... 56 4.3.2 AccurateInventoryManagement .................... 57 4.3.3 WarehouseEfficiency ............................. 57 4.3.4 EnhancedSafety .................................. 58 4.3.5 ReducedOperationsCosts ......................... 58 4.3.6 On-TimeDelivery ................................ 58 4.4 ReimbursementofAI-BasedSCM .......................... 59 4.4.1 BolsteringPlanningandSchedulingActivities ........ 59 4.4.2 IntelligentDecision-Making ........................ 60 4.4.3 End-To-EndVisibility ............................. 61 4.4.4 ActionableAnalyticalInsights ...................... 61 4.4.5 InventoryandDemandManagement ................. 61 4.4.6 BoostingOperationalEfficiencies ................... 62 4.4.7 UnlockingFleetManagementEfficiencies ............ 62 4.4.8 StreamliningEnterpriseResourcePlanning(ERP) ..... 62 4.5 ChallengesofAIiNSCM ................................. 63 4.5.1 SystemComplexities .............................. 63 4.5.2 TheScalabilityFactor ............................. 63 4.5.3 TheCostofTraining .............................. 63
The list of books you might like
