LEADER 01449nam 2200385 450 001 9910702755403321 005 20150107162905.0 035 $a(CKB)5470000002430645 035 $a(OCoLC)899286925 035 $a(EXLCZ)995470000002430645 100 $a20150107d1960 ua 0 101 0 $aeng 135 $aurbn||||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 14$aThe Virginia pine sawfly outbreak 1955-1959 /$fThomas McIntyre 210 1$aUpper Darby, PA. :$cForest Service, U.S. Dept. of Agriculture, Northeastern Forest Experiment Station,$d1960. 215 $a1 online resource (4 pages) $cillustration, maps 225 1 $aForest research notes / Northeastern Forest Experiment Station ;$vno. 99 300 $aTitle from title screen (viewed Dec. 31, 2014). 300 $aPublication pre-dates Federal Depository Library Program (FDLP) item numbers. No FDLP item number has been assigned. 320 $aIncludes bibliographical references. 606 $aSawflies 606 $aScrub pine$xDiseases and pests$xHistory 615 0$aSawflies. 615 0$aScrub pine$xDiseases and pests$xHistory. 700 $aMcIntyre$b Thomas$01396438 712 02$aNortheastern Forest Experiment Station (Radnor, Pa.), 801 0$bGPO 801 1$bGPO 906 $aBOOK 912 $a9910702755403321 996 $aThe Virginia pine sawfly outbreak 1955-1959$93456550 997 $aUNINA LEADER 03214nam 22005773 450 001 9911019110303321 005 20250203110102.0 010 $a9781394272242 010 $a1394272243 010 $a9781394272266 010 $a139427226X 010 $a9781394272259 010 $a1394272251 035 $a(MiAaPQ)EBC31825527 035 $a(Au-PeEL)EBL31825527 035 $a(CKB)36973256900041 035 $a(OCoLC)1478704055 035 $a(CaSebORM)9781394272235 035 $a(OCoLC)1481589419 035 $a(OCoLC-P)1481589419 035 $a(Perlego)4728826 035 $a(EXLCZ)9936973256900041 100 $a20241214d2025 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aInternet of Medicine for Smart Healthcare 205 $a1st ed. 210 1$aNewark :$cJohn Wiley & Sons, Incorporated,$d2025. 210 4$dİ2025. 215 $a1 online resource (567 pages) 311 08$a9781394272235 311 08$a1394272235 330 $aThis book provides in-depth explanations and discussions of the latest applications of Artificial Intelligence (AI), machine learning, and the Internet of Medicine, offering readers the cutting edge on this rapidly growing technology that has the potential to transform healthcare and improve patient outcomes. Over the past five years, there have been significant advances in healthcare through the use of artificial intelligence (AI) and machine learning (ML) technologies. AI and machine learning in medical imaging has significantly improved the accuracy and speed of medical imaging analysis, accelerated the drug discovery process by identifying potential drug targets and predicting the efficacy and safety of new drugs, and enabled personalized medicine by analyzing large amounts of patient data to identify individualized treatment plans based on a patient's genetic makeup and medical history. Internet of Medicine (IoM) refers to the integration of the Internet of Things (IoT) and connected medical devices with healthcare systems and processes to enable remote monitoring, diagnosis, and treatment of patients. IoM is a subset of the larger Internet of Things concept, which involves the connection of everyday devices and appliances to the internet for various purposes. IoM has the potential to revolutionize healthcare by improving patient outcomes, reducing costs, and increasing efficiency. Some of the specific applications of IoM include remote patient monitoring, real-time data analysis, predictive analytics, smart hospitals, and personalized medicine. 606 $aArtificial intelligence$xMedical applications 615 0$aArtificial intelligence$xMedical applications. 676 $a610.285/63 700 $aKumar$b Abhishek$0977677 701 $aVyas$b Narayan$01837560 701 $aSingh Rathore$b Pramod$01837561 701 $aAnand$b Abhineet$01837562 701 $aDixit$b Pooja$01837563 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911019110303321 996 $aInternet of Medicine for Smart Healthcare$94416308 997 $aUNINA