LEADER 00949nam0 2200301 450 001 9910509508003321 005 20211210080836.0 010 $a9788820345730$bvol. 1 100 $a20211210d2011----km y0itay50 ba 101 1 $aita$cger 102 $aIT 105 $aa ac 001yy 200 1 $aAtlante di architettura$etavole e testi$f[di Werner Müller e Gunther Vogel] 210 $aMilano$cEditore Ulrico Hoepli$d2011 215 $a2 v.$cill.$d19 cm 307 $a1.: IX. 260, [17] p. : ill. 327 1 $a1.: Dalle origini all'era cristiana 454 0$12001$aDTV - Atlas zur Baukunst$953713 610 0 $aArchitettura$aOrigini$aSec. 20.$aAtlanti 700 1$aMüller,$bWerner$0397576 701 1$aVogel,$bGunther$0344081 801 0$aIT$bUNINA$gREICAT$2UNIMARC 901 $aBK 912 $a9910509508003321 952 $aARCH A 409/1$b793/2021$fFARBC 959 $aFARBC 996 $aDTV - Atlas zur baukunst$953713 997 $aUNINA LEADER 04139nam 2201153z- 450 001 9910595066903321 005 20231214133325.0 035 $a(CKB)5680000000080864 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/92138 035 $a(EXLCZ)995680000000080864 100 $a20202209d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputational Methods for Medical and Cyber Security 210 $aBasel$cMDPI Books$d2022 215 $a1 electronic resource (228 p.) 311 $a3-0365-5115-8 311 $a3-0365-5116-6 330 $aOver the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields. 610 $afintech 610 $afinancial technology 610 $ablockchain 610 $adeep learning 610 $aregtech 610 $aenvironment 610 $asocial sciences 610 $amachine learning 610 $alearning analytics 610 $astudent field forecasting 610 $aimbalanced datasets 610 $aexplainable machine learning 610 $aintelligent tutoring system 610 $aadversarial machine learning 610 $atransfer learning 610 $acognitive bias 610 $astock market 610 $abehavioural finance 610 $ainvestor?s profile 610 $aTeheran Stock Exchange 610 $aunsupervised learning 610 $aclustering 610 $abig data frameworks 610 $afault tolerance 610 $astream processing systems 610 $adistributed frameworks 610 $aSpark 610 $aHadoop 610 $aStorm 610 $aSamza 610 $aFlink 610 $acomparative analysis 610 $aa survey 610 $adata science 610 $aeducational data mining 610 $asupervised learning 610 $asecondary education 610 $aacademic performance 610 $atext-to-SQL 610 $anatural language processing 610 $adatabase 610 $amachine translation 610 $amedical image segmentation 610 $aconvolutional neural networks 610 $aSE block 610 $aU-net 610 $aDeepLabV3plus 610 $acyber-security 610 $amedical services 610 $acyber-attacks 610 $adata communication 610 $adistributed ledger 610 $aidentity management 610 $aRAFT 610 $aHL7 610 $aelectronic health record 610 $aHyperledger Composer 610 $acybersecurity 610 $apassword security 610 $abrowser security 610 $asocial media 610 $aANOVA 610 $aSPSS 610 $ainternet of things 610 $acloud computing 610 $acomputational models 610 $ametaheuristics 610 $aphishing detection 610 $awebsite phishing 700 $aLuo$b Suhuai$4edt$01322438 702 $aShaukat$b Kamran$4edt 702 $aLuo$b Suhuai$4oth 702 $aShaukat$b Kamran$4oth 906 $aBOOK 912 $a9910595066903321 996 $aComputational Methods for Medical and Cyber Security$93035003 997 $aUNINA LEADER 04554nam 22008295 450 001 9910484711503321 005 20251226202947.0 010 $a3-642-14684-8 024 7 $a10.1007/978-3-642-14684-8 035 $a(CKB)2670000000036347 035 $a(SSID)ssj0000446468 035 $a(PQKBManifestationID)11281720 035 $a(PQKBTitleCode)TC0000446468 035 $a(PQKBWorkID)10496048 035 $a(PQKB)10602187 035 $a(DE-He213)978-3-642-14684-8 035 $a(MiAaPQ)EBC3065575 035 $a(PPN)149018290 035 $a(BIP)37160853 035 $a(BIP)31787118 035 $a(EXLCZ)992670000000036347 100 $a20100724d2010 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aFinite-State Methods and Natural Language Processing $e8th International Workshop, FSMNLP 2009, Pretoria, South Africa, July 21-24, 2009, Revised Selected Papers /$fedited by Anssi Yli-Jyrä, Andras Kornai, Jacques Sakarovitch, Bruce Watson 205 $a1st ed. 2010. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2010. 215 $a1 online resource (X, 147 p. 44 illus.) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v6062 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a3-642-14683-X 320 $aIncludes bibliographical references and index. 327 $aTutorials -- Learning Finite State Machines -- Special Theme Tutorials -- Developing Computational Morphology for Low- and Middle-Density Languages -- Invited Papers -- fsm2 ? A Scripting Language Interpreter for Manipulating Weighted Finite-State Automata -- Selected Operations and Applications of n-Tape Weighted Finite-State Machines -- OpenFst -- Special Theme Invited Talks -- Morphological Analysis of Tone Marked Kinyarwanda Text -- Regular Papers -- Minimizing Weighted Tree Grammars Using Simulation -- Compositions of Top-Down Tree Transducers with ?-Rules -- Reducing Nondeterministic Finite Automata with SAT Solvers -- Joining Composition and Trimming of Finite-State Transducers -- Special Theme Extended Abstracts -- Porting Basque Morphological Grammars to foma, an Open-Source Tool -- Describing Georgian Morphology with a Finite-State System -- Finite State Morphology of the Nguni Language Cluster: Modelling and Implementation Issues -- A Finite State Approach to Setswana Verb Morphology -- Competition Announcements -- Zulu: An Interactive Learning Competition. 330 $aThis book constitutes the refereed proceedings of the 8th International Workshop on the Finite-State-Methods and Natural Language Processing, FSMNLP 2009. The workshop was held at the University of Pretoria, South Africa on July 2009. In total 21 papers were submitted and of those papers 13 were accepted as regular papers and a further 6 as extended abstracts. The papers are devoted to computational morphology, natural language processing, finite-state methods, automata, and related formal language theory. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v6062 606 $aNatural language processing (Computer science) 606 $aComputer programming 606 $aCompilers (Computer programs) 606 $aArtificial intelligence 606 $aMachine theory 606 $aComputer science 606 $aNatural Language Processing (NLP) 606 $aProgramming Techniques 606 $aCompilers and Interpreters 606 $aArtificial Intelligence 606 $aFormal Languages and Automata Theory 606 $aComputer Science Logic and Foundations of Programming 615 0$aNatural language processing (Computer science). 615 0$aComputer programming. 615 0$aCompilers (Computer programs). 615 0$aArtificial intelligence. 615 0$aMachine theory. 615 0$aComputer science. 615 14$aNatural Language Processing (NLP). 615 24$aProgramming Techniques. 615 24$aCompilers and Interpreters. 615 24$aArtificial Intelligence. 615 24$aFormal Languages and Automata Theory. 615 24$aComputer Science Logic and Foundations of Programming. 676 $a006.3/5 701 $aYli-Jyra$b Anssi$01623135 712 12$aFSMNLP 2009 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484711503321 996 $aFinite-state methods and natural language processing$94197004 997 $aUNINA