LEADER 03213nam 2200757 450 001 9910451838903321 005 20211005095654.0 010 $a1-280-36844-6 010 $a9786610368440 010 $a0-7136-7867-4 010 $a1-4081-3406-3 010 $a1-4081-0159-9 035 $a(CKB)1000000000483019 035 $a(EBL)655415 035 $a(OCoLC)703137701 035 $a(SSID)ssj0000273906 035 $a(PQKBManifestationID)11219545 035 $a(PQKBTitleCode)TC0000273906 035 $a(PQKBWorkID)10314955 035 $a(PQKB)11696716 035 $a(MiAaPQ)EBC5114546 035 $a(MiAaPQ)EBC655415 035 $a(Au-PeEL)EBL5114546 035 $a(CaPaEBR)ebr11467010 035 $a(OCoLC)243597808 035 $a(MiAaPQ)EBC5268843 035 $a(Au-PeEL)EBL655415 035 $a(CaONFJC)MIL36844 035 $a(Au-PeEL)EBL5268843 035 $a(OCoLC)1099319925 035 $a(EXLCZ)991000000000483019 100 $a20171206h20052005 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aWork well from home $ehow to run a successful home office 205 $a1st ed. 210 1$aLondon, [England] :$cBloomsbury,$d2005. 210 4$dİ2005 215 $a1 online resource (97 p.) 225 1 $aSteps to Success 300 $aDescription based upon print version of record. 311 $a0-7475-7737-4 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aCover; Titlepage; Copyright; Contents; Could you work well from home?; 1 Deciding whether working from home would work for you; 2 Setting up your home office; 3 Getting used to working from home; 4 Learning to prioritise tasks; 5 Maintaining your relationships with the office and key contacts; 6 Working as part of a virtual team; 7 Coping with feelings of isolation; 8 Setting up as a free agent; Where to find more help; Index 330 $aAn increasing number of people are deciding to work from home. Whether they are setting up their own business or trying to cut down on the amount of time they spend commuting , the idea of turning a space at home into an office is an appealing one. Work well from home helps you make that idea a reality . Filled with help on making working from home work for you, this book covers a range of essential issues including setting up your office, working as part of a virtual team, managing professional relationships, and dealing with feelings of isolation. It contains: a quiz to assess strengths and 410 0$aSteps to success (London, England) 606 $aTelecommuting 606 $aEmployees$xEffect of automation on 606 $aTelecommuting$xSocial aspects 608 $aElectronic books. 615 0$aTelecommuting. 615 0$aEmployees$xEffect of automation on. 615 0$aTelecommuting$xSocial aspects. 676 $a331.25 712 02$aAdam and Charles Black (Firm), 712 02$aBloomsbury (Firm) 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910451838903321 996 $aWork well from home$92453295 997 $aUNINA LEADER 03813nam 2200949z- 450 001 9910566475403321 005 20220506 035 $a(CKB)5680000000037625 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/81101 035 $a(oapen)doab81101 035 $a(EXLCZ)995680000000037625 100 $a20202205d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aMachine Learning for Biomedical Application 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 online resource (198 p.) 311 08$a3-0365-3445-8 311 08$a3-0365-3446-6 330 $aBiomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, this field of science has developed rapidly. This means that a large amount of data has been generated, due to (among other reasons) the processing, analysis, and recognition of a wide range of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning. It is a summary of the Special Issue "Machine Learning for Biomedical Application", briefly outlining selected applications of machine learning in the processing, analysis, and recognition of biomedical data, mostly regarding biosignals and medical images. 606 $aResearch & information: general$2bicssc 610 $aall convolutional network (ACN) 610 $aAmyotrophic Lateral Sclerosis (ALS) 610 $abatch normalization (BN) 610 $ablindness 610 $acephalometric landmark 610 $aCNN 610 $acomputed tomography 610 $acomputer vision 610 $acomputer-aided diagnosis 610 $aCT images 610 $adeep learning 610 $adepthwise separable convolution (DSC) 610 $adisease prediction 610 $adynamic contrast-enhanced MRI 610 $aECG 610 $aEEG 610 $aelectrocardiogram (ECG) 610 $aelectronic human-machine interface 610 $aElectronic Medical Record (EMR) 610 $aEMG 610 $aensemble convolutional neural network (ECNN) 610 $agesture recognition 610 $aglomerular filtration rate 610 $aHRV signals 610 $aIMU 610 $ainertial sensors 610 $ainstance segmentation 610 $aintracranial hemorrhage 610 $akidney perfusion 610 $alung cancer 610 $aMIT-BIH database 610 $amulti-layer perceptron 610 $an/a 610 $aobstructive sleep disorder 610 $aovernight polysomnogram 610 $aparameter estimation 610 $apharmacokinetic modeling 610 $apulmonary fibrosis 610 $aradiotherapy 610 $arandom forest 610 $aregistration 610 $aresidual learning 610 $aResNet 610 $aretinal blood vessel image 610 $asemantic gap 610 $asleep disorder 610 $aU-shaped neural network 610 $aweighted Jaccard index (WJI) 610 $aX-ray 615 7$aResearch & information: general 700 $aStrzelecki$b Micha?$4edt$01319558 702 $aBadura$b Pawel$4edt 702 $aStrzelecki$b Micha?$4oth 702 $aBadura$b Pawel$4oth 906 $aBOOK 912 $a9910566475403321 996 $aMachine Learning for Biomedical Application$93033964 997 $aUNINA