融合CNN与BiLSTM的刑事案件决策研究-豆柴文库

您所在位置：网站首页 / 融合CNN与BiLSTM的刑事案件决策研究.docx / 文档详情

在线预览结束，喜欢就下载吧，查找使用更方便

5 金币

下载文档

/ 2

下载提示

如果您无法下载资料，请参考说明：

1、部分资料下载需要金币，请确保您的账户上有足够的金币

2、已购买过的文档，再次下载不重复扣费

3、资料包下载后请先用软件解压，在使用对应软件打开

文本预览

融合CNN与BiLSTM的刑事案件决策研究
Title:IntegrationofCNNandBiLSTMforCriminalCaseDecisionMaking:AResearchStudy
Abstract:
TheintegrationofConvolutionalNeuralNetwork(CNN)andBidirectionalLongShort-TermMemory(BiLSTM)modelsholdssignificantpromisetoenhancetheaccuracyandeffectivenessofcriminalcasedecision-makingsystems.Inthisresearchpaper,weexplorethepotentialapplicationofthishybridapproachincrimeanalysis,aimingtofacilitatebetterdecisionsandimprovetheefficiencyofcriminaljusticesystems.WediscussthetheoreticalfoundationsofCNNandBiLSTM,outlinetheirindividualfunctionalities,andillustratehowtheirintegrationcouldleadtoimprovedcasedecision-makingoutcomes.Furthermore,weprovideinsightsintopotentialchallengesandfutureresearchdirections.
Introduction:
Criminalcasedecision-makingisacriticaltaskthatrequirescomprehensiveanalysisofvarioustypesofevidenceandcontextualinformation.Traditionalapproachesoftenrelyonhumanexpertiseandareoftenlimitedbybiasesanddecision-makinginconsistencies.Theemergenceofdeeplearningtechniques,suchasCNNandBiLSTM,hasshowngreatpotentialforenhancingtheaccuracyandefficiencyofcriminalcaseanalytics.ThispaperinvestigatestheintegrationofCNNandBiLSTMmodelstoimprovedecision-makingincriminalcases.
1.ConvolutionalNeuralNetwork(CNN):
1.1BackgroundandArchitecture:
CNNisadeeplearningmodelwidelyusedincomputervisiontasks.Itisparticularlyeffectiveindetectingandextractingfeaturesfromimages.CNNconsistsofmultiplelayers,includingconvolutionallayers,poolinglayers,andfullyconnectedlayers.Theselayersallowthemodeltoextractfeatureshierarchically,enablingittolearncomplexpatternsintheinputdata.
2.BidirectionalLongShort-TermMemory(BiLSTM):
2.1BackgroundandArchitecture:
BiLSTMisatypeofrecurrentneuralnetwork(RNN)thatcananalyzesequentialdatainbothforwardandbackwarddirections.UnliketraditionalRNNmodels,BiLSTMovercomesthelimitationsofvanishinggradientsbyutilizingmemorycellsthathelpcapturelong-termdependenciesandanalyzetemporalpatternsintheinputdata.
3.IntegrationofCNNandBiLSTMforCriminalCaseDecisionMaking:
Crim