LEADER 03498nam 2201153z- 450 001 9910595066803321 005 20240301163704.0 035 $a(CKB)5680000000080865 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/92125 035 $a(EXLCZ)995680000000080865 100 $a20202209d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial Intelligence in Oral Health 210 $aBasel$cMDPI Books$d2022 215 $a1 electronic resource (190 p.) 311 $a3-0365-5143-3 311 $a3-0365-5144-1 330 $aThis Special Issue is intended to lay the foundation of AI applications focusing on oral health, including general dentistry, periodontology, implantology, oral surgery, oral radiology, orthodontics, and prosthodontics, among others. 606 $aMedicine$2bicssc 610 $amachine learning 610 $aartificial intelligence 610 $amalocclusion 610 $adiagnostic imaging 610 $aactive learning 610 $amaxillary sinusitis 610 $aconvolutional neural network 610 $adeep learning 610 $asegmentation 610 $aoral microbiota 610 $aLEfSe 610 $aPCoA 610 $aalloprevotella 610 $aprevotella 610 $acore microbiota 610 $aartificial neural networks 610 $aoral cancer diagnosis 610 $aoral cancer prediction 610 $apit and fissure sealants 610 $acaries assessment 610 $avisual examination 610 $aclinical evaluation 610 $aconvolutional neural networks 610 $atransfer learning 610 $adeep learning network 610 $aYOLOv4 610 $amandibular third molar 610 $ainferior alveolar nerve 610 $acontact relationship 610 $apanoramic radiograph 610 $adeep learning methods 610 $acaries diagnosis 610 $adental panoramic images 610 $aradiography 610 $aFourier transform infrared spectroscopy 610 $aFTIR imaging 610 $aspectral biomarker 610 $amultivariate analysis 610 $adiscriminant model 610 $aoral squamous cell carcinoma 610 $aoral epithelial dysplasia 610 $aoral potentially malignant disorder 610 $arisk stratification 610 $aearly oral cancer detection 610 $adentigerous cysts 610 $ahistopathology images 610 $aimage classification 610 $aodontogenic keratocysts 610 $aradicular cysts 610 $aAI 610 $ascreening 610 $adiagnosis 610 $adentistry 610 $aultrasonography 610 $atongue 610 $aalgorithm 610 $adysphagia 610 $aimpacted 610 $atooth 610 $adetection 610 $aneural networks 610 $aproximal caries 610 $atraining strategy 610 $asmall dataset 610 $aperiapical radiograph 610 $aX-ray 610 $atooth extraction 610 $aoroantral fistula 610 $aoperative planning 615 7$aMedicine 700 $aLee$b Jae-Hong$4edt$0275565 702 $aLee$b Jae-Hong$4oth 906 $aBOOK 912 $a9910595066803321 996 $aArtificial Intelligence in Oral Health$93035538 997 $aUNINA LEADER 03366nam 2200937z- 450 001 9910557363803321 005 20220111 035 $a(CKB)5400000000042254 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/77118 035 $a(oapen)doab77118 035 $a(EXLCZ)995400000000042254 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aAdvances in Cereal Crops Breeding 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (196 p.) 311 08$a3-0365-2650-1 311 08$a3-0365-2651-X 330 $aThis Special Issue on 'Advances in Cereal Crops Breeding' comprises 10 papers covering a wide range of subjects, including the expression-level investigation of genes in terms of salinity stress adaptations and their relationships with proteomics in rice, the use of genetic analysis to assess the general combining ability (GCA) and specific combining ability (SCA) in promising hybrids of maize, the use of DNA markers based on PCR in rice, the identification of quantitative trait loci (QTLs) in wheat and simple sequence repeats (SSR) in rice, the use of single-nucleotide polymorphisms (SNP) in a genome-wide association study (GWAS) in cereals, and Nanopore direct RNA sequencing of related with LTR RNA retrotransposon in triticale prior to the genomic selection of heterotic maize hybrids. 606 $aResearch & information: general$2bicssc 610 $aabiotic stress 610 $aauxin 610 $abarley 610 $ablast 610 $abreeding 610 $acombining ability 610 $adendrogram 610 $adensity tolerance 610 $adrought stress 610 $agene effects 610 $agenes 610 $agenetic diversity 610 $agenetic resources 610 $agenome editing 610 $agrain yield 610 $aGWAS 610 $ahealth benefits 610 $along non-coding RNAs 610 $amachine learning algorithms 610 $amaize 610 $amarker-assisted selection 610 $amass spectrometry 610 $ametabolomics 610 $amixed linear and Bayesian models 610 $an/a 610 $aNanopore sequencing 610 $anitrogen use efficiency 610 $aoat 610 $aparametric and nonparametric models 610 $aPCR analysis 610 $aPIN 610 $aprediction accuracy 610 $aproteomics 610 $aQMrl-7B 610 $aQTL 610 $aretrotransposons 610 $arice 610 $aroot 610 $aroot traits 610 $asalinity 610 $asalinity tolerance 610 $aseed development 610 $aSSR markers 610 $asubmergence tolerance 610 $atraining set size and composition 610 $atriticale 610 $aTriticum aestivum L. 610 $aVietnamese landraces 610 $awheat 610 $aYUCCA 615 7$aResearch & information: general 700 $aLoskutov$b Igor G$4edt$01291791 702 $aLoskutov$b Igor G$4oth 906 $aBOOK 912 $a9910557363803321 996 $aAdvances in Cereal Crops Breeding$93021926 997 $aUNINA