04264 am 2201045 n 450 9910324027503321201901082-8028-0301-810.4000/books.pusl.9548(CKB)4100000008283988(FrMaCLE)OB-pusl-9548(oapen)https://directory.doabooks.org/handle/20.500.12854/58897(PPN)236711059(EXLCZ)99410000000828398820190528j|||||||| ||| 0freuu||||||m||||txtrdacontentcrdamediacrrdacarrierSavoir, faire, espérer : Les limites de la raison /Henri Van CampBruxelles Presses de l’Université Saint-Louis20191 online resource (832 p.) 2-8028-0005-1 Le titre de cet ouvrage, inspiré de Kant, ouvre un espace immense : celui qu'explore en tous sens, en frayant les trajets multiples de sa recherche, la pensée contemporaine. Réunies dans une collaboration significative, on trouvera ici une quarantaine de contributions, offrant une sorte de coupe ou d'analyse spectrale du tissu vivant de la culture qui est en train de se créer. Elles relèvent pour majeure partie de la philosophie, mais également de la théologie, de la psychanalyse, de la critique littéraire et d'autres sciences humaines. Ceux qui les signent sont tous d'éminents et brillants spécialistes. Le moindre intérêt de ce livre n'est pas de rassembler leurs noms et leurs styles si différents, mais si proches dans leur intelligence et leur bonheur de penser. Un instrument unique de réflexion et d'information est ainsi offert au lecteur qui veut lui aussi penser avec son temps.Savoir, faire, espérer Savoir, faire, espérer Savoir, faire, espérer PhilosophyHumanitiesTheologyespérancepenséeraisonlimitethéologiepsychanalysePhilosophy.Humanities.Theology.190Beaufret Jean154656Bouveresse Jacques160307Breton Stanislas157420Bruaire Claude232081Canguilhem Georges45542Castoriadis-Aulagnier Piera161230Chenu Marie-Dominique156092Congar Yves162014Coppieters de Gibson Daniel1281544Declève Henri142223Deguy Michel155372Delhomme Jeanne169850Descamps Albert-Louis1285631De Keyser Eugénie1281545De Waelhens Alphonse1316317Dubarle Dominique159443Geffré Claude387482Genette Gérard384264Giblet Jean1292047Gouhier Henri155041Granger Gilles-Gaston45159Hersch Jeanne162236Lacroix Jean53449Ladrière Jean346653Levinas Emmanuel143594Marin Louis132855Perrier François1316620Perroux François148753Ramnoux Clémence387517Ricœur Paul127614Rigaux Béda287820Schillebeeckx Edouard1316621Serres Michel50511Starobinski Jean381870Tilliette Xavier162047Todorov Tzvetan142300Touraine Alain50969Trouillard Jean156290Van Camp Henri1316622Van Riet Georges1316623Vergote Antoine460259Wahl François684554Weil Éric141829Van Camp Henri1316622FR-FrMaCLEBOOK9910324027503321Savoir, faire, espérer : Les limites de la raison3032718UNINA04928nam 2201129z- 450 991067404480332120231214133525.0(CKB)5400000000042630(oapen)https://directory.doabooks.org/handle/20.500.12854/76971(EXLCZ)99540000000004263020202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierComputational Intelligence in HealthcareBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 electronic resource (226 p.)3-0365-2377-4 3-0365-2378-2 The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications.Information technology industriesbicsscsEMGdeep learningneural networksgait phaseclassificationeveryday walkingconvolutional neural networkCRISPRleukemia nucleus imagesegmentationsoft covering rough setclusteringmachine learning algorithmsoft computingmultistage support vector machine modelmultiple imputation by chained equationsSVM-based recursive feature eliminationunipolar depressiondiabetic retinopathy (DR)pre-trained deep ConvNetuni-modal deep featuresmulti-modal deep featurestransfer learning1D poolingcross poolingIMUgait analysislong-term monitoringmulti-unitmulti-sensortime synchronizationInternet of Medical Thingsbody area networkMIMUearly detectionsepsisevaluation metricsmachine learningmedical informaticsfeature extractionphysionet challengeelectrocardiogramPremature ventricular contractionsparse autoencoderunsupervised learningSoftmax regressionmedical diagnosisartificial neural networke-healthTri-Fog Health Systemfault data eliminationhealth status predictionhealth status detectionhealth offdiffusion tensor imagingensemble learningdecision support systemshealthcarecomputational intelligenceAlzheimer's diseasefuzzy inference systemsgenetic algorithmsnext-generation sequencingovarian cancerinterpretable modelsInformation technology industriesCastellano Giovannaedt1339017Casalino GabriellaedtCastellano GiovannaothCasalino GabriellaothBOOK9910674044803321Computational Intelligence in Healthcare3059521UNINA