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February 1, 2023

python question Based on three given tables, write a query function: Write a Python function called query_climate_database() which accepts four arguments: country, a string giving

python question Based on three given tables, write a query function: Write a Python function called query_climate_database() which accepts four arguments: country, a string giving the name of a country (e.g. ?South Korea?) for which data should be returned. year_begin and year_end, two integers giving the earliest and latest years […]
February 1, 2023

python and need to help me learn. To create a GUI application in Python using Tkinter, you can follow these steps: Import the Tkinter library: import tkinter as tk Create the main w

python and need to help me learn. To create a GUI application in Python using Tkinter, you can follow these steps: Import the Tkinter library: import tkinter as tk Create the main window (root widget): root = tk.Tk() Define your widgets and their properties, such as buttons, labels, and text […]
February 1, 2023

python exercise Hello, Please write a python code and do LAB A and LAB B as per the instructions NOTE :: Lab B need in word document and wk1code zip file attached below screenshot

python exercise Hello, Please write a python code and do LAB A and LAB B as per the instructions NOTE :: Lab B need in word document and wk1code zip file attached below screenshot output and add in word document. detailed instructions given in PDF Requirements: mentioned in question 1 […]
February 1, 2023

python writing question why you need to learn python, how it actually works Requirements: Python is a high-level, interpreted programming language that was first released in 1991.

python writing question why you need to learn python, how it actually works Requirements: Python is a high-level, interpreted programming language that was first released in 1991. It is known for its simplicity and versatility, making it a popular choice for a wide range of tasks, from web development to […]
February 1, 2023

python case study Python is a lagre non-venomus snake. Requirements: ACNNBasedModelforVenomousandNon-venomousSnakeClassiÞcationNagifaIlmaProgga1(B),NoortazRezoana1(B),MohammadShah

python case study Python is a lagre non-venomus snake. Requirements: ACNNBasedModelforVenomousandNon-venomousSnakeClassiÞcationNagifaIlmaProgga1(B),NoortazRezoana1(B),MohammadShahadatHossain1,RaihanUlIslam2,andKarlAndersson21DepartmentofComputerScienceandEngineering,UniversityofChittagong,Chittagong,Bangladeshhossainms@cu.ac.bd2DepartmentofComputerScience,ElectricalandSpaceEngineering,Lule˚aUniversityofTechnology,Skellefte˚a,Sweden{raihan.ul.islam,karl.andersson}@ltu.seAbstract.Snakesarecurved,limbless,warmbloodedreptilesofthephylumserpents.Anycharacteristics,includingheadform,bodyshape,physicalappearance,textureofskinandeyestructure,mightbeusedtoindividuallyidentifynonvenomousandvenomoussnakes,thatarenotusualamongnon-expertspeoples.Astandardmachinelearningmethod-ologyhasalsobeenusedtocreateanautomatedcategorizationofspeciesofsnakedependentuponthephotograph,inwhichthecharacteristicsmustbemanuallyadjusted.Asaresult,aDeepconvolutionalneuralnetworkhasbeenproposedinthispapertoclassifysnakesintotwocategories:venomousandnon-venomous.Asetofdataof1766snakepicturesisusedtoimplementsevenNeuralnetworkwithourproposedmodel.Theamountofphotographsevenhasbeenincreasedbyutilizingvariousimageenhancementtechniques.Ultimately,thetransferlearn-ingmethodologyisutilizedtoboosttheidentificationprocessaccuracyevenmore.Five-foldcross-validatingforSGDoptimizershowsthattheproposedmodeliscapableofclassifyingthesnakeimageswithahighaccuracyof91.30%.WithoutCrossvalidationthemodelshows90.50%accuracy.Keywords:Snake?CNN?Dataaugmentation?Deeplearning?Transferlearning?Crossvalidation1IntroductionSnakesareectothermic,amnioticreptiles,surroundedinsepals,justlikeothersquamates.Severalsnakespecieshaveskullswithaslewofjointsthantheirreptileancestral,allowingittoswallowpredatorswiththeirextremelymaneu-verablejawsrelativelylargeunlikethereownheads.Therearetwotypesofsnakessuchasnon-venomous(non-poisonoussnake)andvenomous(poisonousN.Rezoana?equalcontribution.cSpringerNatureSwitzerlandAG2021M.Mahmudetal.(Eds.):AII2021,CCIS1435,pp.216?231,2021.https://doi.org/10.1007/978-3-030-82269-9_17 SnakeClassification217snake).VenomoussnakesaremembersofthesuborderSerpentsandareabletodevelopvenomthattheyusetoattackprey,defendthemselvesandhelpdigesttheirprey.Utilizingholloworgroovedfangs,thevenomisusuallyreleasedbyinjection,whileothervenomoussnakeslackwell-developedfangs.Non-venomoussnakes,exceptformassiveconstrictorsnakessuchastheGreenAnacondaortheBurmesePython,aregenerallybenigntohumans.Likevenomoussnakes,non-venomoussnakeshaveteeth.Snakeenvenomingisamajor,worldwidecommonhealthissuewiththegreatestprevalenceinSoutheastAsia.Fig.1.Top5countrieswiththehighestrateofsnakebitedeathsper100,000peopleAanalysisfocusedon60articleshasreportedthat363victimswithsnakebites,bothvenomous(88%)andnonvenomous(12%)werediagnosedandtreatedifnecrosisexists(15.2%)[30].Noinfectionsweredetectedinpatientsalthoughtheantibioticswerenotused.Thus,basedontheanalysis,itcanbeimpliedthatantibioticsarepresentinthesnake,consideringthefactthatverylittlerawdataisgiven.Inabilitytoidentifysnakefromthevisiblecharacteristicsisanimportantcauseofmortalityduetosnakebite.Forcenturies,snakevenom,particularlyinChineseculture,havealsobeenusedasmedicinetools.Anyoftheleadingdrugsforhighbloodpressure,cardiovasculardisease,andheartattacksusedsnakevenomasablueprint.Asaconsequence,snakevenomisoftenknownasamini-drugrepository,witheachmedicinebeingclinicallye?ective.Forexample,theFDAhaslicensedmedicinesrelyingonsnakevenom,suchasCaptoprilR(Enalapril),IntegrilinR(Eptifibatide),andAggrastatR(Tirofiban)[24].Asidefromtheseapprovedmedicines,variousothersnakevenommaterialsfornumerousthera-peuticapplicationsarepresentlyinpre-clinicalorclinicaltrials.Snakebiteenvenomingisawell-knowntropicaldiseaseexacerbatedbyaven-omoussnakeaccidentallyinjectinganextremelymodifiedpoisonintohumans.Thesnake?sfangs,thatareremodeledteethattachedtoapoisonglandsviaainletareusedtoextractthepotion.Nearly2millionresidentsinAsiaare 218N.I.Proggaetal.poisonedbysnakeseveryyear,althoughthereareapproximately435,000to580,000snakebitesannuallyinAfricathatrequiremedication.Women,childrenandfarmersinvulnerableremoteregionsinlowandmiddle-incomecountriesarea?ectedbyenvenoming.Thegreatestriskexistsinplaceswherehealthcareservicesareinadequateandmedicalfacilitiesareinsufficient.Thedetectionofsnaketypesisakeyaspectofthediagnosisprocess.Classi-ficationanddetectionofvenomousandnon-venomoussnakehasmanydynamicapplicationssuchas:Itcanhelpthehealthprofessionalindeterminingthebestanti-venomtouse.Snakevenomisoftenknownasamini-drugrepository,sobyclassifyingthevenomoussnakefromthenonvenomoussnakethevenomcanbeusedtoproducemoredrugsformankind.2RelatedWorkTheDeepConvolutionNeuralNetwork(CNN)hascarriedoutinpreviousdecadesanumberofgroundbreakingfindingsonimagerecognition,objectiden-tification,semanticclassificationetc.[21].Nowithasbecomeprominentinresearchforsomedaybecauseitcanhandleanimmensenumberofdata[4].CNNoutperformsthemachinelearningapproachintermsofefficiency.Classifi-cationandidentificationofimageswasaniconicmachinelearningissue,whichwasovercomethroughdeeplearning.Inparticular,technologiesdealingwiththevisualinformation,includingthebiggestimageclassificationdataset(ImageNet),computervisionandNLPanalysiswerequiteimpressiveandtheout-comeshavebeenobtainedisextraordinary.ThatiswhywechooseCNNforoutstudy.Thealgorithmsofdeeplearningaredesignedtoimitatetheroleofthehumanbrain[8].Inbiologicaldata[22,23]deeplearninghasbeenused.AlsoinobjectdetectionCNNhasshownoutstandingperformance[11,27,37].CNNhasalreadyshownremarkablee?ectivenessindiagnosticimagerecognition,suchasthedetectionoflungcancer[14],skincancer[28],oralcancer[18],braintumors[10],andotherdiseases.ByUsingobjectdetectionandamachinelearningmethodfocusedonMaskRegion-basedConvolutionalNeuralNetwork(MaskR-CNN)hasbeenimple-mentedforsnakespeciesclassification[7].Todiscernreptilespeciesfromimages,deeplearning,specificallyCNN,hasbeenused[5].Snakestingmarksimageswerealsousedtoidentifypoisonousandnon-poisonoussnakesusingtheCNNmethod[20].Jamesetal.[16]presentedasemiautomaticmethodinordertodi?erentiatesixdistinctorganismsbyeliminatingtaxonomicalcharacteristicsfromthepho-tos.Therewere1,299photographsinthedatasetand88photosinthelowestfrequentlevel.Theouteredgetaxonomicalcharacteristicsareleastessentialfororganismrecognitionthantheintofrontandside-viewcharacteristics,accordingtovariousfeaturedetectiontechniques.Inobjectdetectionandimageprocessing,variousmodelarchitectureshavebeenevaluated.Todiscern5specificsnakevarietiesAbdurrazaqetal.[2]used3distinctConvolutionalNeuralNetwork(CNN)frameworks.Inthisstudythey SnakeClassification219workedwithasetof415pictures.Therewere72photosrequiredforthelesscommonsnaketype.Themaximumoutcomeswereachievedbyamedium-sizedclassificationframework.TheSiamesenetworkwasusedbyAbeysingheetal.[3]tocategorizeacom-parativelylimiteddatasetthatcontains200photographsof84organismsfromtheWHO.Themethoddiscussedintheirpaperfocusesonone-shootlearning,as3to16photosperhabitatwereprovidedinthecollectionofdata.Theoutcomescollectedbytheautomaticcategorizationprocessislowerthantheexactnessofhumanidentificationaccuracy.Snakebitepoisoningisanoverlookedenvironmentaldiseaseswhichmurdersover100,000inhabitantsandslaughtersover400,000peryear[36].Snakebiteisafrequentworkplacehazardforcitizenswhomaketheirlivingincultivation,includingthoseinSouth-EastCommunitiesinasia.Anautomatedidentifica-tionofaspeciesofsnakedependingonitsphotographhasalreadybeencon-structed,aspreviouslymentioned.Asaclassifier,aseveralsupervisedmachinelearningtechniquehasbeenimplementedlikeNaiveBayes,DecisionTreeJ48,k-NearestNeighbors,orBack-PropagationNeuralNetwork.Therequirementsinthefeaturesextractionprocess,ontheotherhand,arenoteasytotraininconventionalmachinelearningtechniquesandmustbemanuallycalibrated.Asaresult,throughoutthisstudy,importantcontributionshasmadeinthefollowingareas:?ACNNbasedmodelhasbeenproposedtoclassifyvenomousandnon-venomoussnake.?Variousarchitectureshavebeencomparedintermsofperformance.?K-foldcrossvalidationhasbeenappliedonourproposedmodelwiththreedi?erentoptimizertoimprovesgeneralizationcapacity.?Thesystemcandetectbothvenomousandnon-venomoussnakesinrealtime,allowingnon-expertstorecognizesnakespecieswithgreateraccuracythanpreviouslymentionedapproaches.3DataPre-processingAtfirst,wewilldiscussaboutthedataset.Toinitiate,thedatasetandmodelcreationplanwillbediscussedindepthtoaidintheplanningoftheproposedmodel.Followingthat,we?llgothroughtheproposedmodelarchitecturesim-ulationmethodindetail,aswellasthetrainingmethodologyfordeterminingthebestparametermodifications.Ultimately,we?llusemodelingapproachestodemonstratecriticalflawsinvisualindicatorsinadditiontocreatingareportedsnakemoreidentifiable.3.1AboutDatasetThesetofdatacomesfromkaggle.comandcontainsabout1766snakeimages.Perphotowasassignedtoacategoryandwasdividedintogroupsbytherespec-tiveclasslabelssuchasnon-venomousandvenomous.Sincereformattingis 220N.I.Proggaetal.amongthemostimportantphasesofdatapreprocessing,allimagesarerefor-mattedto224×224pixels.Figure3andFig.4depictsseveralphotographsfromthebenchmarkdataset.Figures3and4demonstratethattherearenumerousdi?erencesbetweenvenomousandnon-venomoussnakesintermsofphysicalappearancessuchasheadstructure,eyeshape,skincolour,andsoon.Themen-tionedfeatureswillaidourproposedmodelinlearningthedistinctionsbetweenpoisonousandnon-poisonoussnakes.Thesetofdatahasbeensplitintotrain,validation,andtestsegmentsinanappropriateproportionFig.2.Non-venomoussnakeimageFig.3.Venomoussnakeimage3.2DataAugmentationItiswellestablishedthatevenamassivequantityofdatainthedatasetsisneededtoachieveabetterresultforaCNNmodel.ThedataaugmentationtechniquesisnecessaryforcorrectlyimplementingaCNNarchitecture.Thismethodologypreventsdatamanipulationandmaintainstheinitialreliability.Thistechniqueisoftenusedduringthetrainingprocesstoincreasetheefficiencyofthearchitecturebyfixingoverfittingproblems.Ifthedatasetislargeenough,severalfeaturescanbeextractedfromitandcomparedtounidentifieddata.However,ifthereisinsufficientdata,dataaugmentationmaybeimplementedtoboostthemodel?saccuracy[9,15,26,33].Throughapplyingaugmentationoper-ationstotrainingimages,suchasrandomrotation,shift,zoom,noise,flips,andsoon,dataaugmentationwillgeneratemultiplepictures[33].Everyparame-terhastheabilitytorepresentphotosinanumberofaspectsandcomeup SnakeClassification221withparticularfeaturesduringthetrainingphase,increasingtheframework?se?ectiveness.Sinceourdatasetissmallerinsize,weimplementedavarietyofaugmentationfunctionsonit.Fortheaugmentation,ImageDataGeneratorwasutilized.Figure5representstheprimarypictureaswellastheaugmentedphotoscreatedfromthat.Weimplementedthemodelwith80%oftheoverallofthepicturesandusedtheremaining20%tovalidatethesystemthroughoutevalua-tion.ThesettingsforimageaugmentationusedinourexperimentcanbeseeninTable1.Fig.4.DataaugmentationTable1.ImagesaugmentationsettingsAugmentationsettingRangeRotation0.2Zoom0.1Contast0.1HorizontalflipTrue4MethodologyConvolutionalneuralnetworksisinspiredbyneurologicalmechanisms.Aconvo-lutionalneuralsystemismadeupofmanylayers,includingconvolutionlayers,poolinglayers,andfullyconnectedlayers,anditusesaback-propagationalgo-rithmtoobtainfeaturestotrainthemodelproperly.Figure6depictstheoverallresearch?ssystemflowchart.4.1ModelConstructionInthisstudy,theframeworkwasimplementedusingaConvolutionalNeuralNet-work(CNN)anddataaugmentation.First,thearchitectureusesthedatasettotakethepictures.Thenpreprocessingbegins.Thensomeaugmentationparam-etersareusedtoenlargethedataset.Ultimately,theenlargedsetofdatais 222N.I.Proggaetal.Fig.5.SystemflowchartusedtoforecasttheclassbytheCNNarchitecture.Thepictureswerestan-dardisedtosomeextenttobeproperlycategorized.CNNitselfperformedthecharacteristicretrievalofthepictures.We?lldescribeourrecommendedmodelarchitectureindetailinthissection.Theproposedmethodincludesthreebasiccomponents:featureextraction,identification,andclassification.Inthebegin-ning,weincludesynchronizedlayersofconvolution,activation,andmax-poolinginoursystemdesign.Thefeaturesarethenpassedintoaflattenedlayer.Theflattenedattributesorfeaturesarethentransferredintodenselayers.Inthedenselayerdropouthasbeenusedtoavoidoverfitting.Theclassificationpro-cesswasthencompletedbytheultimatelayer,whichstatedthesoftmaxlayer.Thephotoswereprovidedtothemodelforthetrainingprocessafterthedataaugmentation.ThereisaCBr=iconvblockinthesystembuilt,accompaniedviaamaxpoolingstratumthatisthreetimesinconsonanceinsequentialformat.Maxpoolingisanefficientwayofdownsizingthetightly-scalephotos,requiringmaximalratesforeachstratum,sincemostofthefeaturesgeneratedareignoredbyutilizinga3×3filtersize.Asdiscussedduringtheprecedinganalysissuperim-posedmaxpoolingframesdonotsignificantlyincreaseoverthenonoverlappingwindows,sothemaxpoolinglayersusedduringourexperimentwas2×2withstride2.Itconsistsof3convolutionarylayers,with16filtersof3×3inthefirstconvolutionallayer,32filtersinthesecondconvolutionallayersandthethirdlayeris64layers3×3indimension.Thescaleofthekernelis2.Theproducedfeaturesarereusedbytheattachedactivationfunctionintheearlystagestoconstructanuniquefeaturemapasoutputjustaftertheconvolutionlayer.Inequation,convolutionoveranimageq(x,y)isdefinedusingafilterp(x,y).p(x,y)??q(x,y)=jy=??jky=??kw(m,n)f(x??m,y??n)(1)Togeneratethelow-levelfeaturesalllayersofconvolutionwasaccompaniedbytheReLUactivationfunction.Followingthepreviousconvolutionlayers,theReLUactivationfunctionwasusedinthehiddenlayeraswell.ReLUhasanumberofadvantages,themostnotableofwhichisitsabilitytoquicklydis-tributegradients.Asaresult,calculatingtheessentialcharacteristicsofCNNintheprovisionalmass,reducesthechancesofgradientextinction.Theactivat-ingmechanismtendstoperformcomponent-by-componentoperationsonthisgiveninputfeaturemap,sincetheresulttendstobetheidenticaldimensionas SnakeClassification223Fig.6.Modelarchitecturetheorigin.Asaresult,amongthemostcommonactivationfunctions,ReLUisbeingusedinthealllayers.ReLU[12]hasbeenincludedtodemonstratethatthearchitecturedoesnotexperiencelinearity,asshowninthecorrespondingformula:R(x)=max(0,x)(2)Thearchitectureisthenexposedintheflattenedlayerstoconvertthefeaturemap.Fromfeaturemapgeneratedbythepreviousconvolutionlayerstocompletethecategorizationtask,asingledimensionalfeaturevectorhasbeenconstructedbyflattenedlayer.Adrop-outlayerislinkedfollowingtheFullyconnectedorhiddenlayerandthesizeofthefullyconnectedlayeris128.ThisdroppinglayerwilldumptheweightsoftheFullyconnectedlayeratrandomduringtrainingtomitigateunnecessaryweightandoverfitting.ForourCNNmodel,wechoose0.2asthedropoutrangeforthedropoutlayer.Thedropoutlayer?sjobistodiscard20%ofthenodesineachFClayerinordertopreventoverfitting[29].Thehyperparameterswerefine-tunedbylayeringagainuntilfaultdidnotradicallyalter.Inaddition,therangeofthedropoutfortheproposedmodelwasdeterminedbyexperimentingforseveralconstants.Thedrop-outsrangeof0.2wasconsideredasthebestamountofthedropoutlayerbecauseittendedtopreventoverfittingratherthanothersvalue.Inordertomaximizetheoutcometheparametersarebeingadapted.Ultimately,sincetherearetwocategoriestheoutputlayercontainstwonodes.Thesoftmax[32]activationfunctionwasusedjustaftertheFClayer,asshowninthefollowingequation,toclassifysnakeimagesintonon-venomousandvenomouscategories.Figure8portraystherepresentationsofthesuggestedmodel?slayers. 224N.I.Proggaetal.Softmax(x)=eiii=0ei(3)Fig.7.Theproposedmodel?sdetailedlayerrepresentation4.2TheImplementationProcedureThecodingfortheframeworkwasproducedandimplementedinGoogleColab[6]usingthepythonprogramminglanguage.Keras[13],Tensorflow[1],NumPy[35],andMatplotlib[31]werethelibrariesutilizedinthewholestudy.Theback-endoftheframeworkwaschosenasTensorflow,andkerashasbeenutilizedtoo?eradditionalbuilt-infunctionalitysuchasactivationfunctions,optimizers,layers,andsoon.KerasAPIwasusedtoenhancethedataset.NumPyisaPythonlibraryformathematicalevaluation.Confusionmatrix,splittrainandtestfiles,modelcheckpoint,callbackmechanism,aswellasotherschematicrep-resentationlikeconfusionmatrix,lossagainstepochsgraphs,accuracyagainstepochscurves,andmanymore,areallgeneratedusingSklearn.Thematplotliblibraryisalsoneededtocreatevisualrepresentationsofthepreviouslymentioneddiagrams,suchastheconfusionmatrix.5ExperimentalEvaluationTheperformanceoftheimplementedmodeltoclassifyphotosofsnakesspecif-icallygroupedintotwogroups,non-venomousandvenomous,willbecoveredinthissection.Inaddition,wewillcompareourproposedmodeltoothertra-ditionalmodelssuchasInceptionNet,Resnet50,VGG16,VGG19,Xception,MobileNetv2andInceptionResnetV2.Theoutcomesofourmodel?skfoldcrossvalidationfordi?erentoptimizerswillalsobearticulated. SnakeClassification2255.1TuningoftheHyper-parametersSincefine-tunedhyper-parametersalsohavemajorimpactontheCNNarchi-tecture?sperformanceandtheyareareessentialbecausetheystronglyimpactthemodel?sattitude.TheAdam,Adamax,andSGDoptimizerswereutilizedtotrainIing100epochsfortheproposedmodel,withalearningrateof0.0001withthebatchsizeof32.Kfoldcrossvalidationhasalsobeenperformedforthevariousoptimizers.Asthelossfunctioncategoricalcrossentropywasbeingused,thelossofclassprobabilitycausedbythesoftmaxfunctionwasalsodeterminedbythislossfunction.Finally,calculateeachcategory?sprobabilityofoccurrence.ModelCheckpointhasbeenaddedasacallbackfunctionaswell.5.2K-FoldCrossValidationTotestourmodel,weattempt5foldcross-validation.Duetothegeneralopera-tionaloverhead,thisphaseisnormallyavoidedinCNNs.ThedatasetisdividedintothreesectionsusingK-foldscross-validation.Fold1iscomposedofpart1asatrainingset,part2asavalidationset,andpart3astestingset,whilefold2isconsistsofpart2,part1andpart3asatraining,validationandtestingsetrespectively.Thus,thefoldcontinuesuntilitreaches5folds,witheachfoldcontaininguniquetraining,validation,andtestingdatasets.TheK-foldcrossvalidationapproachaccountsfortheutilizationofvarioustrainingandtest-ingdata,whichreducesoverfittingandimprovesgeneralizationcapacity.Asaconsequence,wemaygeneralizeouroutcomesoverthedataset.5.3ResultFigure8depictstheaccuracyoftheproposedmodelascomparedtootherstan-dardConvolutionneuralnetworkslikeInceptionNet,VGG19,Resnet50,Xcep-tionNet,MobileNetv2,InceptionResnetV2andVGG16.Despiteprovidingadatasetwithalimitednumberofphotographs,thedesignedmodelproducesahighcategorizationaccuracycpmparedtootherCNNmodels.AsshownintheFig.9InceptionNet,VGG19,Resnet50,XceptionNet,MobileNetv2,InceptionResnetV2andVGG16has82.38%,43.75%,81.81%,80.94%,82.35%,89.62%and62.50%accuracyrespectively.Ourmodeloutperformedthenthesemodelswith90.50%accuracy.Thebestresultcorrespondstothemodelweintroduced,basedonthecurrentInceptionNet,VGG19,Resnet50,XceptionNet,MobileNetv2,InceptionResnetV2,andVGG16models.Asaresult,itcanbeconcludedthatthedevelopedframeworkoutperformstheotherones.Theperformanceofallevaluationmodels,asseeninFig.8,indicatethatthemethodissuperiortoothers.Figure9comparestheproposedmodel?saccuracyandlosscurvetothoseofothertraditionalCNNmodels.Fromtheshapeofthecurvesinthismentionedfigure,wecanseethatotherconvolutionalmodelshaveapropensitytooverfitorunderfit,whileourproposedmodelseemstohaveagoodfit.Ourproposedmodel?shighaccuracyandlowlosshavealreadybeenconsideredthebestcasescenarioforanyCNNmodel.Figure10showsthedetectionperformanceofthe 226N.I.Proggaetal.Fig.8.Accuracyofproposedmodel&othertraditionalmodelsproposedmodelandshowsthedetectionresultofthetestdataset?sintwoclasses:venomousandnonvenomous.IncomparisontothepreviouslylistedotherCNNmodels,40imageswerearbitrarilycheckedand9ofthosebeingseeninFig.6.Theidentificationperformanceforthedevelopedmethodisreallyexceedinglydecent.TheconfusionmatricesoftheInceptionNet,VGG19,Resnet50,Xcep-tionNet,MobileNetv2,InceptionResnetV2,andVGG16models,aswellasthesuggestedsystem,areshowninFig.11.ForouradaptedCNNmodel,thediagonalmagnitudeoftheconfusionmatricesinbothclassesisgreaterthantheothermodels.Thatis,theproposedmodelwillpreciselydistinguishthesamenumberoftestsamplesfromourtestdatasetasthecurrentNeuralnetworkmodel.Asaresult,ourmodelsuccessfullyoutperformedtheotherconventionalNeuralnetworkmodelinthisareaaswell.Finally,Fig.12demonstratestheK-foldcrossvalidationresultoftheproposedmodelutilizingthreeoptimizers:Adamax,Adam,andSGD.ByusingadamaxoptimizerforKfoldcrossvali-dationthevalidationaccuracyandthetestingaccuracyforfold-1is83.58%&86.87%,forfold-2itis81.83%&82.52%,forfold-3itis77.44%&80.07%,forfold-4itis84.24%&84.96&andforfold-5itis86.87%&83.20%respectively.Foradamoptimizerthevalidationaccuracyandthetestingaccuracyforfold-1is86.56%&89.22%,forfold-2itis87.57%&88.04%,forfold-3itis83.35%&84.42%,forfold-4itis87.78%&90.48%andforfold-5itis84.36%&88.43%respectively.ForSGDoptimizerthevalidationaccuracyandthetestingaccu-racyforfold-1is84.85%&85.78%,forfold-2itis88.30%&89.67%,forfold-3itis84.78%&85.61%,forfold-4itis83.77%&88.04%,andforfold-5itis87.73%&91.30respectively.TheSGDoptimizerinfold-5producesbetteroutcomesfortheimplementedmodel,aswellastheotherresultsarealsoquitesatisfactory.Alsoitdemonstratesthattheaccuracyoftheclassificationmodelisuna?ectedbythetrainingdata. SnakeClassification227(a)InceptionNet(b)VGG19(c)Resnet50(d)Xception(e)Mobilenetv2(f)Inception-Resnet-v2(g)VGG16(h)ProposedModelFig.9.AccuracyandlosscurveFig.10.Resultofdetectionproducedbytheproposedmodel 228N.I.Proggaetal.(a)InceptionNet(b)VGG19(c)Resnet50(d)Xception(e)Mobilenetv2(f)InceptionResnetv2(g)VGG16(h)ProposedModelFig.11.Confusionmatrixofthemodels(a)adamaxOptimizer(b)AdamOptimizer(c)SGDOptimizerFig.12.K-Foldcrossvalidationforproposedmodelusingdi?erentoptimizer6EpilogueandFutureWorkAnewconvolutionalneuralnetwork-basedarchitecturefordetectingandclassi-fyingvenomousandnon-venomoussnakeswassuggestedduringthiswholestudy.Theframework?sabilitytoacquiresnakefeaturesusingneuralnetworkblocksisclearlydemonstrated.IncomparisontothevariouspossiblythebestConvolu-tionalneuralnetworkframeworksInceptionNet,VGG19,Resnet50,XceptionNet,MobileNetv2,InceptionResnetV2,andVGG16,thearchitecturehasremarkablecategorizationaccuracy.Thisstudylooksathowtodevelopandcreateavenomousandnonvenomoussnakeclassificationmodelthatcouldhelpmankind.Snakevenomouscanbeusedasmedicinaltoolsbydistinguishingbetweenvenomousandnon-venomoussnakes.Snakebitediseasemaybeminimizedbyidentifyingthespeciesofsnakeandadministeringappropriatetreatment. SnakeClassification229Thesuggestedsolutiongreatlyoutshinesstate-of-the-artframeworks,withadramaticincreaseinaccuracyof90.50%,accordingtotheexperimentalresearchreview.Inaddition,themodelperformsadmirablyintermsofKfoldcrossval-idationoutcomes.Itwouldhavebeenmoreusefulinupcominganalysisifweperformagridhuntonthehyper-parametersandfindthemostsuitablenum-berofparametervaluesforKfoldcrossvalidation.Finally,webelievethatthecurrentfindingswouldhopefullyresolveuniquestimulationinrecognizingaddi-tionalsnakeimagesandcategorizingtheminArtificialintelligencebasedenvi-ronments,particularlyinmedicalapplication.Tocreateoursuggestedstructuremorereliableandauthenticated,moreanalysisshouldbedonetoevaluateandgatheradiversedatasetwithsignificantquantitiesofinformationaboutsnakephotographs.Furthermore,manyadditionalNeuralnetworks,suchasDenseNet,EfficientNet,andothers,canbeusedtoenhanceinformationhorizons,andthemodel?soutputcanbecomparedtothatofothermainstreammachinelearningtechniques.Wewilltrytoincludemorepicturesofsnakeinfuturecapturedindi?erentenvironmentandlighteningtoanalyzethemodelperformanceonthoseimages.Alsoanintegrationofdata-driven(CNN)andknowledge-driven(BRBES)approachcanbeproposedtoanalyzeriskassessmentofasnakebiteinthehumanbody[17,19,25,34].References1.Abadi,M.,etal.:Tensorflow:asystemforlarge-scalemachinelearning.In:12th{USENIX}SymposiumonOperatingSystemsDesignandImplementation({OSDI}2016),pp.265?283(2016)2.Abdurrazaq,I.S.,Suyanto,S.,Utama,D.Q.:Image-basedclassificationofsnakespeciesusingconvolutionalneuralnetwork.In:2019InternationalSeminaronResearchofInformationTechnologyandIntelligentSystems(ISRITI),pp.97?102.IEEE(2019)3.Abeysinghe,C.,Welivita,A.,Perera,I.:Snakeimageclassificationusingsiamesenetworks.In:Proceedingsofthe20193rdInternationalConferenceonGraphicsandSignalProcessing,pp.8?12(2019)4.Albawi,S.,Mohammed,T.A.,Al-Zawi,S.:Understandingofaconvolutionalneu-ralnetwork.In:2017InternationalConferenceonEngineeringandTechnology(ICET),pp.1?6.IEEE(2017)5.Annesa,O.D.,Kartiko,C.,Prasetiadi,A.,etal.:Identificationofreptilespeciesusingconvolutionalneuralnetworks(CNN).JurnalRESTI(RekayasaSistemDanTeknologiInformasi)4(5),899?906(2020)6.Bisong,E.:Googlecolaboratory.In:BuildingMachineLearningandDeepLearningModelsonGoogleCloudPlatform,pp.59?64.Springer(2019)7.Bloch,L.,etal.:Combinationofimageandlocationinformationforsnakespeciesidentificationusingobjectdetectionandefficientnets.CLEFworkingnotes(2020)8.Chauhan,R.,Ghanshala,K.K.,Joshi,R.:Convolutionalneuralnetwork(CNN)forimagedetectionandrecognition.In:2018FirstInternationalConferenceonSecureCyberComputingandCommunication(ICSCCC),pp.278?282.IEEE(2018) 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