02311nam2 2200445 i 450 VAN012684320230626021607.418N978303025827620200217d2019 |0itac50 baengCH|||| |||||ˆ3: ‰Neural Networks and ExtensionsMichel Denuit, Donatien Hainaut, Julien TrufinChamSpringer2019xiii, 250 p.ill.24 cm001VAN01243832001 Springer Actuarial Lecture notes210 Cham [etc.]Springer001VAN01268442001 Effective Statistical Learning Methods for ActuariesMichel Denuit, Donatien Hainaut, Julien Trufin210 ChamSpringer2019-2020215 volumiill.24 cm3VAN0236744Effective Statistical Learning Methods for Actuaries. 3, Neural Networks and Extensions298330368-XXComputer science [MSC 2020]VANC019670MF62-XXStatistics [MSC 2020]VANC022998MF62M45Neural nets and related approaches to inference from stochastic processes [MSC 2020]VANC026513MF62P05Applications of statistics to actuarial sciences and financial mathematics [MSC 2020]VANC030682MFActuarial modelingKW:KDeep learing for insuranceKW:KInsurance risk classificationKW:KMachine learningKW:KNeural networksKW:KCHChamVANL001889DenuitMichelVANV098250781288HainautDonatienVANV098251781289TrufinJulienVANV098252781290Springer <editore>VANV108073650ITSOL20240614RICAhttp://doi.org/10.1007/978-3-030-25827-6E-book – Accesso al full-text attraverso riconoscimento IP di Ateneo, proxy e/o ShibbolethBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICAIT-CE0120VAN08NVAN0126843BIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA08CONS e-book 1565 08eMF1565 20200217 Effective Statistical Learning Methods for Actuaries. 3, Neural Networks and Extensions2983303UNICAMPANIA02025nam 2200457z- 450 9910346938303321202102111000007047(CKB)4920000000101139(oapen)https://directory.doabooks.org/handle/20.500.12854/45946(oapen)doab45946(EXLCZ)99492000000010113920202102d2007 |y 0gerurmn|---annantxtrdacontentcrdamediacrrdacarrierEin Beitrag zur Verbesserung und Erweiterung der Lidar-Signalverarbeitung für FahrzeugeKIT Scientific Publishing20071 online resource (XII, 126 p. p.)Schriftenreihe / Institut für Mess- und Regelungstechnik, Universität Karlsruhe (TH)3-86644-174-6 Laserscanner werden in Fahrzeugen zur Umfelderfassung eingesetzt. Die vorliegende Arbeit untersucht, wie aus den Messungen des Laserscanners der Abstand und die Bewegung anderer Fahrzeuge bestimmt werden können. Verschiedene Ansätze zur Gruppierung der Messungen werden verglichen und hinsichtlich ihrer Eignung für Fahrzeuge bewertet. Darauf aufbauend wird analysiert, wie der Einfluss stochastischer und deterministischer Fehler auf die Bestimmung von Abstand und Bewegung minimiert werden kann.Technology: general issuesbicsscAbstandsmessungBewegungsmessungFahrerassistenzsystemLaserscannerrecursive state estimationSchätzunglidar scannerSignalverarbeitungstatistical procedureStatistikZustandsschätzungTechnology: general issuesKapp Andreasauth1307602BOOK9910346938303321Ein Beitrag zur Verbesserung und Erweiterung der Lidar-Signalverarbeitung für Fahrzeuge3028841UNINA04942nam 2201345z- 450 991055766640332120220111(CKB)5400000000044842(oapen)https://directory.doabooks.org/handle/20.500.12854/76871(oapen)doab76871(EXLCZ)99540000000004484220202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierDrug-Drug InteractionsBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online resource (242 p.)3-0365-2035-X 3-0365-2036-8 Drug-drug interactions (DDIs) cause a drug to affect other drugs, leading to reduced drug efficacy or increased toxicity of the affected drug. Some well-known interactions are known to be the cause of adverse drug reactions (ADRs) that are life threatening to the patient. Traditionally, DDI have been evaluated around the selective action of drugs on specific CYP enzymes. The interaction of drugs with CYP remains very important in drug interactions but, recently, other important mechanisms have also been studied as contributing to drug interaction including transport- or UDP-glucuronyltransferase as a Phase II reaction-mediated DDI. In addition, novel mechanisms of regulating DDIs can also be suggested. In the case of the substance targeted for interaction, not only the DDIs but also the herb-drug or food-drug interactions have been reported to be clinically relevant in terms of adverse side effects. Reporting examples of drug interactions on a marketed drug or studies on new mechanisms will be very helpful for preventing the side effects of the patient taking these drugs. This Special Issue aims to highlight current progress in understanding both the clinical and nonclinical interactions of commercial drugs and the elucidation of the mechanisms of drug interactions.Biology, life sciencesbicsscResearch & information: generalbicssc(‒)-sophoranone1A22B62C192C82C92D63A4ADMEadverse drug reactionsbiflavonoidchronic kidney diseasecompetitive inhibitioncomputational predictionCYPCYP1A1CYP1A2CYP2C9CYP2D6CYP3ACYP3A4cytochrome P450cytochromes P450DDIDexamethasonedrug interactiondrug interactionsdrug metabolismdrug transporterdrug-drug interactiondrug-drug interactiondrug-drug interactionsdrug-drug interactionsexpressionfexofenadinegepantshigh plasma protein bindingin silicoin vitroin vivoinhibitorixazomibKetoconazolelasmiditanLexicomplow permeabilityLoxoprofenmechanism-based inhibitionmetabolic DDImetabolismmigrainemonoclonal antibodiesnon-competitive inhibitionO-desmethyltramadolOATP1B1OATP1B3organic anion transporting polypeptide 1A2 (OATP1A2)P-glycoprotein (P-gp)P450pharmacokineticsphysiologically-based pharmacokineticsplasma concentrationpolypharmacypotent inhibitionQSARregulationRumex acetosaselamariscina Asignal detection algorithmsspontaneous reporting systemssubset analysissubstratesystemic exposuretadalafilticagrelortissue-specifictramadoltyrosine kinase inhibitorsubiquitinationuridine 5'-diphosphoglucuronosyl transferaseBiology, life sciencesResearch & information: generalKim Dong Hyunedt1304704Lee SangkyuedtKim Dong HyunothLee SangkyuothBOOK9910557666403321Drug-Drug Interactions3027633UNINA