| Vertiv | 94.6 | 46 | 46 |
| IBM | 74.0 | 36 | 8 |
| Cloudflare | 60.0 | 68 | 0 |
| Hetzner | 51.2 | 9 | 5 |
| Tesla | 45.6 | 24 | 0 |
| Palantir Technologies | 45.4 | 10 | 2 |
| OVHcloud | 42.0 | 6 | 3 |
| Microsoft | 40.1 | 16 | 0 |
| Data Analytics for Machine Learning | 39.8 | 5 | 3 |
| PyTorch | 39.5 | 4 | 4 |
| European High-Performance Computing Joint Undertaking | 37.2 | 4 | 3 |
| Microsoft Docs | 36.4 | 3 | 4 |
| PricewaterhouseCoopers | 34.0 | 3 | 3 |
| SAP ERP | 32.6 | 5 | 1 |
| Slurm | 31.1 | 3 | 2 |
| CustomGPT.ai | 31.1 | 3 | 2 |
| Azure DevOps Server | 29.5 | 7 | 0 |
| Q18698690 | 29.5 | 7 | 0 |
| Microsoft SQL Server | 27.6 | 6 | 0 |
| Oracle CRM | 27.6 | 6 | 0 |
| Oracle Fusion Applications | 27.6 | 6 | 0 |
| Tesla Cybertruck | 27.6 | 6 | 0 |
| Apple Intelligence | 27.4 | 0 | 13 |
| Azure | 27.0 | 2 | 2 |
| Equinix | 27.0 | 2 | 2 |
| Jones Lang LaSalle | 27.0 | 2 | 2 |
| IBM Power Systems | 27.0 | 2 | 2 |
| DigitalOcean | 27.0 | 2 | 2 |
| Oracle Cloud | 27.0 | 2 | 2 |
| Q64008087 | 27.0 | 2 | 2 |
| deep learning super sampling | 27.0 | 2 | 2 |
| Group 42 | 27.0 | 2 | 2 |
| Flexcompute, inc. | 27.0 | 2 | 2 |
| Salesforce | 27.0 | 2 | 2 |
| Claude | 27.0 | 2 | 2 |
| Fooocus | 27.0 | 2 | 2 |
| A Critical Analysis of the Largest Source for Generative AI Training Data: Common Crawl | 27.0 | 2 | 2 |
| NVIDIA Project DIGITS | 27.0 | 2 | 2 |
| Super Renders Farm | 27.0 | 2 | 2 |
| Center for High Performance Computing | 27.0 | 2 | 2 |
| Q215273 | 26.8 | 3 | 1 |
| Rackspace Technology | 26.8 | 3 | 1 |
| IBM Bluemix | 26.8 | 3 | 1 |
| Oracle Cloud Platform | 26.8 | 3 | 1 |
| Hugging Face | 26.8 | 3 | 1 |
| European Capital of Culture | 25.4 | 5 | 0 |
| Oracle Database | 25.4 | 5 | 0 |
| Media Creation Tool | 25.4 | 5 | 0 |
| General Electric | 22.8 | 4 | 0 |
| Q80689 | 22.8 | 4 | 0 |
| Atlassian | 22.8 | 4 | 0 |
| Oracle E-Business Suite | 22.8 | 4 | 0 |
| Disease Ontology | 22.8 | 4 | 0 |
| Tesla Model 3 | 22.8 | 4 | 0 |
| Microsoft Lumia 640 XL | 22.8 | 4 | 0 |
| Tesla Model Y | 22.8 | 4 | 0 |
| Libertinus | 22.8 | 4 | 0 |
| Legion | 22.8 | 4 | 0 |
| Jordan/Zalaznick Advisers Inc. | 22.8 | 4 | 0 |
| Red Hat | 22.8 | 2 | 1 |
| OpenShift | 22.8 | 2 | 1 |
| Scaleway | 22.8 | 2 | 1 |
| Microsoft Partner Network | 22.8 | 2 | 1 |
| Verari Technologies | 22.8 | 2 | 1 |
| OpenAI | 22.8 | 2 | 1 |
| bidirectional encoder representations from transformers | 22.8 | 2 | 1 |
| Microsoft Academic Graph | 22.8 | 2 | 1 |
| IBM Cloud | 22.8 | 2 | 1 |
| Seagate Exos X | 22.8 | 2 | 1 |
| OpenAI OpCo | 22.8 | 2 | 1 |
| Anyscale, Inc. | 22.8 | 2 | 1 |
| Dolly | 22.8 | 2 | 1 |
| Efficient Memory Management for Large Language Model Serving with PagedAttention | 22.8 | 2 | 1 |
| SambaNova Systems | 22.8 | 2 | 1 |
| Hugging Face Hub | 22.8 | 2 | 1 |
| Microsoft Security Copilot | 22.8 | 2 | 1 |
| krish567366 / Federated-AI-Network | 22.8 | 2 | 1 |
| Transformation Operating Framework | 22.8 | 2 | 1 |
| Adobe Premiere Pro | 21.6 | 0 | 7 |
| Microsoft Intune | 21.6 | 0 | 7 |
| Alibaba Cloud | 21.2 | 1 | 2 |
| J. Nathan Kutz | 21.2 | 1 | 2 |
| Vercel Inc. | 21.2 | 1 | 2 |
| TensorFlow.js | 21.2 | 1 | 2 |
| Contentsquare | 20.2 | 0 | 6 |
| Microsoft Windows | 19.6 | 3 | 0 |
| JDeveloper | 19.6 | 3 | 0 |
| University College London | 19.6 | 3 | 0 |
| Jakarta EE | 19.6 | 3 | 0 |
| Eurostat | 19.6 | 3 | 0 |
| Arm Holdings | 19.6 | 3 | 0 |
| Oracle SQL Developer | 19.6 | 3 | 0 |
| SAP NetWeaver Business Intelligence | 19.6 | 3 | 0 |
| LenovoEMC | 19.6 | 3 | 0 |
| Joint Research Centre | 19.6 | 3 | 0 |
| Debian GNU/Hurd | 19.6 | 3 | 0 |
| Debian Live | 19.6 | 3 | 0 |
| Ledger | 19.6 | 3 | 0 |
| Datalogix | 19.6 | 3 | 0 |
| Microsoft Lumia 640 | 19.6 | 3 | 0 |
| MiTAC Holdings | 19.6 | 3 | 0 |
| G2 | 19.6 | 3 | 0 |
| Dataiku | 19.6 | 3 | 0 |
| Nokia 6 | 19.6 | 3 | 0 |
| InfraKit | 19.6 | 3 | 0 |
| Japan–European Union Comprehensive Economic Partnership Agreement | 19.6 | 3 | 0 |
| Oracle ERP Cloud | 19.6 | 3 | 0 |
| Oracle HCM Cloud | 19.6 | 3 | 0 |
| RDNA | 19.6 | 3 | 0 |
| ACM SIGCHI Bulletin | 19.6 | 3 | 0 |
| emacs-gnuplot | 19.6 | 3 | 0 |
| Microsoft Learn | 19.6 | 3 | 0 |
| Microsoft Typography | 19.6 | 3 | 0 |
| Q136411263 | 19.6 | 3 | 0 |
| Q137719988 | 19.6 | 3 | 0 |
| Amazon | 18.6 | 0 | 5 |
| Pixar | 17.0 | 1 | 1 |
| System Center Operations Manager | 17.0 | 1 | 1 |
| Microsoft 365 | 17.0 | 1 | 1 |
| OpenStack | 17.0 | 1 | 1 |
| IBM General Parallel File System | 17.0 | 1 | 1 |
| Windows HPC Server 2008 | 17.0 | 1 | 1 |
| Agile Software Corporation | 17.0 | 1 | 1 |
| Gateway Load Balancing Protocol | 17.0 | 1 | 1 |
| OptiX | 17.0 | 1 | 1 |
| Autodesk Toxik | 17.0 | 1 | 1 |
| Coolfluid | 17.0 | 1 | 1 |
| CoreSite | 17.0 | 1 | 1 |
| Digital Realty Trust | 17.0 | 1 | 1 |
| Linode | 17.0 | 1 | 1 |
| Q6714093 | 17.0 | 1 | 1 |
| Oracle Enterprise Manager Ops Center | 17.0 | 1 | 1 |
| River Trail | 17.0 | 1 | 1 |
| Penguin Computing | 17.0 | 1 | 1 |
| VMware vSphere | 17.0 | 1 | 1 |
| Virtustream | 17.0 | 1 | 1 |
| Stratosphere | 17.0 | 1 | 1 |
| Arnold | 17.0 | 1 | 1 |
| Lunavi | 17.0 | 1 | 1 |
| IBM cloud computing | 17.0 | 1 | 1 |
| Bitbucket Data Center | 17.0 | 1 | 1 |
| Mirantis | 17.0 | 1 | 1 |
| Daniela Witten | 17.0 | 1 | 1 |
| Veeam Backup & Replication | 17.0 | 1 | 1 |
| Nvidia DGX | 17.0 | 1 | 1 |
| RIKEN Center for Computational Science | 17.0 | 1 | 1 |
| Systems Applications & Products in Data Processing (Canada) | 17.0 | 1 | 1 |
| SAP Converged Cloud | 17.0 | 1 | 1 |
| Seeing AI | 17.0 | 1 | 1 |
| Netlify | 17.0 | 1 | 1 |
| Ryota Tomioka | 17.0 | 1 | 1 |
| Crowd | 17.0 | 1 | 1 |
| Python Machine Learning, 2nd edition | 17.0 | 1 | 1 |
| Cerebras | 17.0 | 1 | 1 |
| DGX SaturnV Volta | 17.0 | 1 | 1 |
| Atlantic Council | 16.7 | 0 | 4 |
| Illumio | 16.7 | 0 | 4 |
| AI accelerator | 16.7 | 0 | 4 |
| Mistral Vibe | 16.7 | 0 | 4 |
| GitHub | 15.6 | 2 | 0 |
| Q11219 | 15.6 | 2 | 0 |
| Q11278 | 15.6 | 2 | 0 |
| Dell | 15.6 | 2 | 0 |
| Schneider Electric | 15.6 | 2 | 0 |
| Ericsson | 15.6 | 2 | 0 |
| Intel Technology Journal | 15.6 | 2 | 0 |
| Windows Glyph List 4 | 15.6 | 2 | 0 |
| Windows Installer | 15.6 | 2 | 0 |
| SPSS | 15.6 | 2 | 0 |
| Nvidia | 15.6 | 2 | 0 |
| DirectX | 15.6 | 2 | 0 |
| Robert Bosch | 15.6 | 2 | 0 |
| PCMan File Manager | 15.6 | 2 | 0 |
| Microsoft Paint | 15.6 | 2 | 0 |
| Akamai Technologies | 15.6 | 2 | 0 |
| CHKDSK | 15.6 | 2 | 0 |
| Teradata | 15.6 | 2 | 0 |
| Microsoft Digital Image | 15.6 | 2 | 0 |
| Windows Registry | 15.6 | 2 | 0 |
| Qualcomm | 15.6 | 2 | 0 |
| Tom's Hardware | 15.6 | 2 | 0 |
| OCRopus | 15.6 | 2 | 0 |
| Autodesk | 15.6 | 2 | 0 |
| Visual Basic for Applications | 15.6 | 2 | 0 |
| PhysX | 15.6 | 2 | 0 |
| NetApp | 15.6 | 2 | 0 |
| VMware Workstation Player | 15.6 | 2 | 0 |
| LabVIEW | 15.6 | 2 | 0 |
| Forrester | 15.6 | 2 | 0 |
| Bloomberg Businessweek | 15.6 | 2 | 0 |
| Ionos | 15.6 | 2 | 0 |
| Honeywell | 15.6 | 2 | 0 |
| Microsoft Dynamics NAV | 15.6 | 2 | 0 |
| Microsoft AutoRoute | 15.6 | 2 | 0 |
| VMware Fusion | 15.6 | 2 | 0 |
| Salesforce | 15.6 | 2 | 0 |
| IBM Informix | 15.6 | 2 | 0 |
| util-linux | 15.6 | 2 | 0 |
| Mike's New Car | 15.6 | 2 | 0 |
| Lightbeam (software) | 15.6 | 2 | 0 |
| CICS | 15.6 | 2 | 0 |
| IBM Rational DOORS | 15.6 | 2 | 0 |
| Intelligent Input Bus | 15.6 | 2 | 0 |
| Microsoft Virtual Server | 15.6 | 2 | 0 |
| Directorate-General for European Civil Protection and Humanitarian Aid Operations | 15.6 | 2 | 0 |
| Slade School of Fine Art | 15.6 | 2 | 0 |
| Executive Agency for Consumers, Health, Agriculture and Food | 15.6 | 2 | 0 |
| SlideShare | 15.6 | 2 | 0 |
| Foomatic | 15.6 | 2 | 0 |
| Journal of the ACM | 15.6 | 2 | 0 |
| Telephony Application Programming Interface | 15.6 | 2 | 0 |
| Rational Rhapsody | 15.6 | 2 | 0 |
| Oracle Application Server | 15.6 | 2 | 0 |
| Gummi | 15.6 | 2 | 0 |
| Siebel Systems | 15.6 | 2 | 0 |
| RTD info | 15.6 | 2 | 0 |
| Debian GNU/kFreeBSD | 15.6 | 2 | 0 |
| European Commissioner for Preparedness and Crisis Management | 15.6 | 2 | 0 |
| Bloomberg Terminal | 15.6 | 2 | 0 |
| CPLEX | 15.6 | 2 | 0 |
| Microsoft Student | 15.6 | 2 | 0 |
| Vantive | 15.6 | 2 | 0 |
| ARM Cortex-A | 15.6 | 2 | 0 |
| AutoCAD Architecture | 15.6 | 2 | 0 |
| EDGAR | 15.6 | 2 | 0 |
| Microsoft Layer for Unicode | 15.6 | 2 | 0 |
| Autodesk MotionBuilder | 15.6 | 2 | 0 |
| uPortal | 15.6 | 2 | 0 |
| VMware Server | 15.6 | 2 | 0 |
| IBM Configuration Management Version Control | 15.6 | 2 | 0 |
| Q4052640 | 15.6 | 2 | 0 |
| Yahoo! Finance | 15.6 | 2 | 0 |
| Wojciech Kopczuk | 15.6 | 2 | 0 |
| ACM Transactions on Mathematical Software | 15.6 | 2 | 0 |
| Beebdroid | 15.6 | 2 | 0 |
| CSC – IT Center for Science | 15.6 | 2 | 0 |
| Directorate-General for Education, Youth, Sport and Culture | 15.6 | 2 | 0 |
| Directorate-General for Economic and Financial Affairs | 15.6 | 2 | 0 |
| Directorate-General for Digital Services | 15.6 | 2 | 0 |
| Directorate-General for Financial Stability, Financial Services and Capital Markets Union | 15.6 | 2 | 0 |
| Frege | 15.6 | 2 | 0 |
| IntelliType | 15.6 | 2 | 0 |
| Java BluePrints | 15.6 | 2 | 0 |
| Marie Skłodowska-Curie Actions | 15.6 | 2 | 0 |
| Intel oneAPI Math Kernel Library | 15.6 | 2 | 0 |
| Microsoft Japan | 15.6 | 2 | 0 |
| Microsoft Search Server | 15.6 | 2 | 0 |
| Nimble Storage | 15.6 | 2 | 0 |
| Office for administration and payment of individual entitlements | 15.6 | 2 | 0 |
| Oracle Property Manager | 15.6 | 2 | 0 |
| Owl Lisp | 15.6 | 2 | 0 |
| Red Hat Virtualization | 15.6 | 2 | 0 |
| Q7715973 | 15.6 | 2 | 0 |
| UCL Faculty of Arts and Humanities | 15.6 | 2 | 0 |
| UCL Faculty of Life Sciences | 15.6 | 2 | 0 |
| UCL Queen Square Institute of Neurology | 15.6 | 2 | 0 |
| Q10984556 | 15.6 | 2 | 0 |
| Microsoft Pinyin IME | 15.6 | 2 | 0 |
| AutoKey | 15.6 | 2 | 0 |
| International Public Policy Review | 15.6 | 2 | 0 |
| ACM Transactions on Asian Language Information Processing | 15.6 | 2 | 0 |
| ACM Journal of Data and Information Quality | 15.6 | 2 | 0 |
| EU Transparency Register | 15.6 | 2 | 0 |
| Microsoft Movies & TV | 15.6 | 2 | 0 |
| Akka | 15.6 | 2 | 0 |
| Asus Memo Pad HD 7 | 15.6 | 2 | 0 |
| Microsoft Mobile | 15.6 | 2 | 0 |
| Nokia Fastlane | 15.6 | 2 | 0 |
| SchedMD | 15.6 | 2 | 0 |
| Tesla Supercharger network | 15.6 | 2 | 0 |
| Q18146823 | 15.6 | 2 | 0 |
| Q18168774 | 15.6 | 2 | 0 |
| Performance Analyzer | 15.6 | 2 | 0 |
| FreeSync | 15.6 | 2 | 0 |
| Graphics Core Next | 15.6 | 2 | 0 |
| FedRAMP | 15.6 | 2 | 0 |
| Oracle BlueKai Data Management Platform | 15.6 | 2 | 0 |
| Microsoft Lumia 950 XL | 15.6 | 2 | 0 |
| Data Analytics Library | 15.6 | 2 | 0 |
| SAP S/4HANA | 15.6 | 2 | 0 |
| Maps | 15.6 | 2 | 0 |
| Kaminari | 15.6 | 2 | 0 |
| Zephyr | 15.6 | 2 | 0 |
| Chakra | 15.6 | 2 | 0 |
| Windows Subsystem for Linux | 15.6 | 2 | 0 |
| Microsoft Entra ID | 15.6 | 2 | 0 |
| Azure Cognitive Search | 15.6 | 2 | 0 |
| European Commissioner for Financial Stability, Financial Services and Capital Markets Union | 15.6 | 2 | 0 |
| Microsoft Dynamics 365 | 15.6 | 2 | 0 |
| Axios | 15.6 | 2 | 0 |
| Forbes 30 Under 30 | 15.6 | 2 | 0 |
| ACM SIGCAS Computers and Society | 15.6 | 2 | 0 |
| MinIO | 15.6 | 2 | 0 |
| cligh | 15.6 | 2 | 0 |
| hidapi | 15.6 | 2 | 0 |
| libtelnet | 15.6 | 2 | 0 |
| llvm-libunwind | 15.6 | 2 | 0 |
| os-diskconfig-python-novaclient-ext | 15.6 | 2 | 0 |
| python-scsi | 15.6 | 2 | 0 |
| ucpp | 15.6 | 2 | 0 |
| Nokia (China) | 15.6 | 2 | 0 |
| ACM Transactions on Interactive Intelligent Systems | 15.6 | 2 | 0 |
| resvg | 15.6 | 2 | 0 |
| FER+ | 15.6 | 2 | 0 |
| CaosDB - Research Data Management for Complex, Changing, and Automated Research Workflows | 15.6 | 2 | 0 |
| Cardiovascular Disease Ontology | 15.6 | 2 | 0 |
| Vaccination Informed Consent Ontology | 15.6 | 2 | 0 |
| Nokia 6.1 Plus | 15.6 | 2 | 0 |
| Domoticz | 15.6 | 2 | 0 |
| Twitter developer code of conduct | 15.6 | 2 | 0 |
| Eric Salathé | 15.6 | 2 | 0 |
| Data protection law and the media | 15.6 | 2 | 0 |
| Project:Make Wikipedia blacker | 15.6 | 2 | 0 |
| adoc-mode | 15.6 | 2 | 0 |
| Microsoft Saudi | 15.6 | 2 | 0 |
| NASDAQ/Ngs (Global Select Market) | 15.6 | 2 | 0 |
| clinical LABoratory Ontology | 15.6 | 2 | 0 |
| Zebrafish Phenotype Ontology | 15.6 | 2 | 0 |
| University College London Health and Social Survey Research Group | 15.6 | 2 | 0 |
| wikibase-cli | 15.6 | 2 | 0 |
| ASUS VT168N | 15.6 | 2 | 0 |
| James B Robinson | 15.6 | 2 | 0 |
| Please, let’s not go back to normal | 15.6 | 2 | 0 |
| spdx-license-ids | 15.6 | 2 | 0 |
| arg | 15.6 | 2 | 0 |
| on-finished | 15.6 | 2 | 0 |
| which-pm-runs | 15.6 | 2 | 0 |
| ms | 15.6 | 2 | 0 |
| ini | 15.6 | 2 | 0 |
| domhandler | 15.6 | 2 | 0 |
| debug | 15.6 | 2 | 0 |
| is-callable | 15.6 | 2 | 0 |
| object-inspect | 15.6 | 2 | 0 |
| XState | 15.6 | 2 | 0 |
| is-date-object | 15.6 | 2 | 0 |
| trim-repeated | 15.6 | 2 | 0 |
| ua-parser-js | 15.6 | 2 | 0 |
| dom-serializer | 15.6 | 2 | 0 |
| is-regex | 15.6 | 2 | 0 |
| verror | 15.6 | 2 | 0 |
| seek-bzip | 15.6 | 2 | 0 |
| keyv | 15.6 | 2 | 0 |
| readable-stream | 15.6 | 2 | 0 |
| js-yaml | 15.6 | 2 | 0 |
| node-fetch | 15.6 | 2 | 0 |
| responselike | 15.6 | 2 | 0 |
| are-we-there-yet | 15.6 | 2 | 0 |
| clone-response | 15.6 | 2 | 0 |
| pako | 15.6 | 2 | 0 |
| common-tags | 15.6 | 2 | 0 |
| cacheable-request | 15.6 | 2 | 0 |
| graphql-playground-html | 15.6 | 2 | 0 |
| caniuse-lite | 15.6 | 2 | 0 |
| graphql-playground-middleware-express | 15.6 | 2 | 0 |
| trim-right | 15.6 | 2 | 0 |
| eslint-import-resolver-node | 15.6 | 2 | 0 |
| tabbable | 15.6 | 2 | 0 |
| eslint-module-utils | 15.6 | 2 | 0 |
| is-arguments | 15.6 | 2 | 0 |
| is-boolean-object | 15.6 | 2 | 0 |
| foreach | 15.6 | 2 | 0 |
| is-bigint | 15.6 | 2 | 0 |
| which-boxed-primitive | 15.6 | 2 | 0 |
| zen-observable-ts | 15.6 | 2 | 0 |
| react-datetime | 15.6 | 2 | 0 |
| csso | 15.6 | 2 | 0 |
| postcss-load-config | 15.6 | 2 | 0 |
| postcss-loader | 15.6 | 2 | 0 |
| socket.io-client | 15.6 | 2 | 0 |
| http-proxy-middleware | 15.6 | 2 | 0 |
| eslint-plugin-import | 15.6 | 2 | 0 |
| is-typed-array | 15.6 | 2 | 0 |
| jsx-ast-utils | 15.6 | 2 | 0 |
| eslint-plugin-jsx-a11y | 15.6 | 2 | 0 |
| string.prototype.matchall | 15.6 | 2 | 0 |
| eslint-plugin-react | 15.6 | 2 | 0 |
| gatsby-plugin-react-helmet-async | 15.6 | 2 | 0 |
| react-helmet-async | 15.6 | 2 | 0 |
| ACM Transactions on Parallel Computing | 15.6 | 2 | 0 |
| data center | 14.4 | 0 | 3 |
| Bullet | 14.4 | 0 | 3 |
| training, validation, and test data sets | 14.4 | 0 | 3 |
| edge computing | 14.4 | 0 | 3 |
| Ian T. Foster | 14.4 | 0 | 3 |
| Office of Critical Minerals and Energy Innovation | 14.4 | 0 | 3 |
| Artificial Intelligence for Engineering Design, Analysis and Manufacturing | 14.4 | 0 | 3 |
| Q16639197 | 14.4 | 0 | 3 |
| Perspectives in healthcare risk management | 14.4 | 0 | 3 |
| Dynamic VMs placement for energy efficiency by PSO in cloud computing | 14.4 | 0 | 3 |
| Large-scale machine learning based on functional networks for biomedical big data with high performance computing platforms | 14.4 | 0 | 3 |
| Integrating multiple scientific computing needs via a Private Cloud infrastructure | 14.4 | 0 | 3 |
| Empirical evaluation of a cloud computing information security governance framework | 14.4 | 0 | 3 |
| Optimal and suboptimal resource allocation techniques in cloud computing data centers | 14.4 | 0 | 3 |
| A data center network featuring low latency and energy efficiency based on all optical core interconnect | 14.4 | 0 | 3 |
| Bridging the gap between human knowledge and machine learning | 14.4 | 0 | 3 |
| Government Cloud Computing and the Policies of Data Sovereignty | 14.4 | 0 | 3 |
| Government Cloud Computing and National Data Sovereignty | 14.4 | 0 | 3 |
| A Cloud Governance Framework for Cloud Computing : An Information Security Governance Perspective to Protect Cloud Users | 14.4 | 0 | 3 |
| DynaCool - Simulating Efficient Liquid Cooling for Current and Next Generation Large Scale Data Centres | 14.4 | 0 | 3 |
| Real-time New Zealand sign language translator using convolution neural network | 14.4 | 0 | 3 |
| Machine learning for energy-resource allocation, workflow scheduling and live migration in cloud computing: State-of-the-art survey | 14.4 | 0 | 3 |
| Energy efficiency in cloud computing data centers: a survey on software technologies | 14.4 | 0 | 3 |
| Analysis of methods for assessing the energy efficiency of data centers using the power usage effectiveness method | 14.4 | 0 | 3 |
| Software Orchestrated and Hardware Accelerated Artificial Intelligence: Toward Low Latency Edge Computing | 14.4 | 0 | 3 |
| The Use of Blockchain Technology in Public Sector Entities Management: An Example of Security and Energy Efficiency in Cloud Computing Data Processing | 14.4 | 0 | 3 |
| High Performance Computing (HPC) Data Center for Information as a Service (IaaS) Security Checklist: Cloud Data Governance | 14.4 | 0 | 3 |
| ISGcloud: a Security Governance Framework for Cloud Computing | 14.4 | 0 | 3 |
| Application and Prospect of Artificial Intelligence Technology in Energy Management and Optimization for Cloud Computing Data Center | 14.4 | 0 | 3 |
| Using a data processing unit (DPU) as a pre-processor for graphics processing unit (GPU) based machine learning | 14.4 | 0 | 3 |
| cybersecurity professional | 14.4 | 0 | 3 |
| Artificial Intelligence: An Energy Efficiency Tool for Enhanced High performance computing | 14.4 | 0 | 3 |
| Improvement of Energy Efficiency in Cloud Computing by Load Balancing Algorithm | 14.4 | 0 | 3 |
| Distributed machine learning load balancing strategy in cloud computing services | 14.4 | 0 | 3 |
| Providing a load balancing method based on dragonfly optimization algorithm for resource allocation in cloud computing | 14.4 | 0 | 3 |
| Resource Prediction for Big Data Processing in a Cloud Data Center : A Machine Learning Approach | 14.4 | 0 | 3 |
| Energy-efficient task offloading, load balancing, and resource allocation in mobile edge computing enabled IoT networks | 14.4 | 0 | 3 |
| A Secure Fuzzy based Enhanced Multi-Queue Job Scheduling Algorithm (EMQJSA) for Load Balancing in Cloud Computing | 14.4 | 0 | 3 |
| Approaches to Capacity Building for Machine Learning and Artificial Intelligence Applications in Health | 14.4 | 0 | 3 |
| Application Scenarios of Edge Computing in Conjunction with Cloud Computing and Artificial Intelligence | 14.4 | 0 | 3 |
| A comprehensive analysis of the role of artificial intelligence and machine learning in modern digital forensics and incident response | 14.4 | 0 | 3 |
| Load balancing Technique for Energy-Energy Efficiency in Cloud Computing | 14.4 | 0 | 3 |
| A Machine Learning Framework for Resource Allocation Assisted by Cloud Computing | 14.4 | 0 | 3 |
| An Adaptive Load Balancing Queue Based Resource Allocation Algorithm in Cloud Computing Environment | 14.4 | 0 | 3 |
| Resource Allocation for Stable LLM Training in Mobile Edge Computing | 14.4 | 0 | 3 |
| PREACT: Predictive Resource Allocation for Bursty Workloads in a Co-located Data Center | 14.4 | 0 | 3 |
| Method and system for artificial intelligence model training using a watermark-enabled kernel for a data processing accelerator | 14.4 | 0 | 3 |
| Model AI Governance Framework for Generative AI | 14.4 | 0 | 3 |
| Vanta | 14.4 | 0 | 3 |
| AFRICLOUD | 14.4 | 0 | 3 |
| Nayan Goel | 14.4 | 0 | 3 |
| AI data center | 14.4 | 0 | 3 |
| Rudrendu Kumar Paul | 14.4 | 0 | 3 |
| Orbital AI Data Center Speculation | 14.4 | 0 | 3 |
| Artificial Intelligence and Machine Learning in Telecommunications Revolutionizing Customer Experience and Enhancing Service Delivery | 14.4 | 0 | 3 |
| AI poisoning | 14.4 | 0 | 3 |
| Public Procurement Relationship | 14.4 | 0 | 3 |
| Tokens, Watts, and Geography: AI Infrastructure | 14.4 | 0 | 3 |
| Jack Dongarra | 11.4 | 0 | 2 |
| Donald Michie | 11.4 | 0 | 2 |
| computational science | 11.4 | 0 | 2 |
| emerging technology | 11.4 | 0 | 2 |
| London School of Economics and Political Science | 11.4 | 0 | 2 |
| distributed computing | 11.4 | 0 | 2 |
| computer cluster | 11.4 | 0 | 2 |
| Los Alamos National Laboratory | 11.4 | 0 | 2 |
| Deloitte | 11.4 | 0 | 2 |
| LeaseWeb | 11.4 | 0 | 2 |
| public contract | 11.4 | 0 | 2 |
| Animal Logic | 11.4 | 0 | 2 |
| Capita | 11.4 | 0 | 2 |
| Knowledge Engineering and Machine Learning Group | 11.4 | 0 | 2 |
| Argonne National Laboratory | 11.4 | 0 | 2 |
| Oak Ridge National Laboratory | 11.4 | 0 | 2 |
| colocation centre | 11.4 | 0 | 2 |
| Industrial Light & Magic | 11.4 | 0 | 2 |
| backpropagation | 11.4 | 0 | 2 |
| intelligent control | 11.4 | 0 | 2 |
| Blue Sky Studios | 11.4 | 0 | 2 |
| NXP Semiconductors | 11.4 | 0 | 2 |
| OpenNebula | 11.4 | 0 | 2 |
| high-performance computing | 11.4 | 0 | 2 |
| Idaho National Laboratory | 11.4 | 0 | 2 |
| infrastructure as a service | 11.4 | 0 | 2 |
| NVIDIA BR02 | 11.4 | 0 | 2 |
| job scheduler | 11.4 | 0 | 2 |
| power distribution unit | 11.4 | 0 | 2 |
| James Demmel | 11.4 | 0 | 2 |
| Sony Pictures Imageworks | 11.4 | 0 | 2 |
| National Laboratory of the Rockies | 11.4 | 0 | 2 |
| Amazon Elastic Compute Cloud | 11.4 | 0 | 2 |
| High Energy Accelerator Research Organization | 11.4 | 0 | 2 |
| government procurement in the European Union | 11.4 | 0 | 2 |
| power usage effectiveness | 11.4 | 0 | 2 |
| David M. Blei | 11.4 | 0 | 2 |
| intelligent tutoring system | 11.4 | 0 | 2 |
| Pierre Baldi | 11.4 | 0 | 2 |
| queries per second | 11.4 | 0 | 2 |
| Canadian Joint Incident Response Unit | 11.4 | 0 | 2 |
| Initiative For Open Authentication | 11.4 | 0 | 2 |
| United States Agency for Healthcare Research and Quality | 11.4 | 0 | 2 |
| Artificial Intelligence | 11.4 | 0 | 2 |
| Carl Kesselman | 11.4 | 0 | 2 |
| Computational Science & Discovery | 11.4 | 0 | 2 |
| Daniel S. Jurafsky | 11.4 | 0 | 2 |
| David A. Bader | 11.4 | 0 | 2 |
| early stopping | 11.4 | 0 | 2 |
| elasticity | 11.4 | 0 | 2 |
| Eric Horvitz | 11.4 | 0 | 2 |
| Federal Energy Management Program | 11.4 | 0 | 2 |
| Green Power Usage Effectiveness | 11.4 | 0 | 2 |
| High Performance Computing Act of 1991 | 11.4 | 0 | 2 |
| eventual consistency | 11.4 | 0 | 2 |
| Journal of Artificial Intelligence Research | 11.4 | 0 | 2 |
| Jungle computing | 11.4 | 0 | 2 |
| Ken Forbus | 11.4 | 0 | 2 |
| Kevin Leyton-Brown | 11.4 | 0 | 2 |
| Logicworks | 11.4 | 0 | 2 |
| Managed private cloud | 11.4 | 0 | 2 |
| Maryland Department of Emergency Management | 11.4 | 0 | 2 |
| National Oceanographic Data Center | 11.4 | 0 | 2 |
| Oak Ridge National Laboratory Distributed Active Archive Center | 11.4 | 0 | 2 |
| Operational efficiency | 11.4 | 0 | 2 |
| Real Time rendering | 11.4 | 0 | 2 |
| statistical relational learning | 11.4 | 0 | 2 |
| timeline of scientific computing | 11.4 | 0 | 2 |
| Category:Data centers | 11.4 | 0 | 2 |
| modular data center | 11.4 | 0 | 2 |
| National Laboratory of Atomic, Molecular and Optical Physics | 11.4 | 0 | 2 |
| private cloud | 11.4 | 0 | 2 |
| Applied Artificial Intelligence | 11.4 | 0 | 2 |
| Connection Science | 11.4 | 0 | 2 |
| Journal of Experimental and Theoretical Artificial Intelligence | 11.4 | 0 | 2 |
| H2O | 11.4 | 0 | 2 |
| network sovereignty | 11.4 | 0 | 2 |
| Expedient | 11.4 | 0 | 2 |
| recovery as a service | 11.4 | 0 | 2 |
| similarity learning | 11.4 | 0 | 2 |
| Tencent Cloud | 11.4 | 0 | 2 |
| deductive classifier | 11.4 | 0 | 2 |
| vanishing gradient problem | 11.4 | 0 | 2 |
| Eric P. Xing | 11.4 | 0 | 2 |
| Michael J. Kearns | 11.4 | 0 | 2 |
| Peter Ungaro | 11.4 | 0 | 2 |
| government procurement | 11.4 | 0 | 2 |
| AHaH Computing–From Metastable Switches to Attractors to Machine Learning | 11.4 | 0 | 2 |
| Data residency | 11.4 | 0 | 2 |
| RankBrain | 11.4 | 0 | 2 |
| Nervana Systems | 11.4 | 0 | 2 |
| infrastructure as code | 11.4 | 0 | 2 |
| Hartree Centre | 11.4 | 0 | 2 |
| Certificate of public convenience and necessity | 11.4 | 0 | 2 |
| Reproducible Research in Computational Science | 11.4 | 0 | 2 |
| Probabilistic machine learning and artificial intelligence | 11.4 | 0 | 2 |
| Galaxy CloudMan: delivering cloud compute clusters | 11.4 | 0 | 2 |
| Next Generation Distributed Computing for Cancer Research | 11.4 | 0 | 2 |
| Learning classification models with soft-label information | 11.4 | 0 | 2 |
| Computational opportunities for remote collaboration and capacity building afforded by Web 2.0 and cloud computing | 11.4 | 0 | 2 |
| A survey on security issues in service delivery models of cloud computing | 11.4 | 0 | 2 |
| Synthesis lectures on artificial intelligence and machine learning | 11.4 | 0 | 2 |
| LifeWorks | 11.4 | 0 | 2 |
| The African Field Epidemiology Network--networking for effective field epidemiology capacity building and service delivery | 11.4 | 0 | 2 |
| Vehicle Technologies Office | 11.4 | 0 | 2 |
| A global machine learning based scoring function for protein structure prediction | 11.4 | 0 | 2 |
| PCP-ML: Protein characterization package for machine learning | 11.4 | 0 | 2 |
| Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises | 11.4 | 0 | 2 |
| Detecting Falls with Wearable Sensors Using Machine Learning Techniques | 11.4 | 0 | 2 |
| High throughput transmission optical projection tomography using low cost graphics processing unit | 11.4 | 0 | 2 |
| Developing a data governance model in health care | 11.4 | 0 | 2 |
| Visual Cloud | 11.4 | 0 | 2 |
| Application of machine learning algorithms for clinical predictive modeling: a data-mining approach in SCT | 11.4 | 0 | 2 |
| Automated method for extraction of lung tumors using a machine learning classifier with knowledge of radiation oncologists on data sets of planning CT and FDG-PET/CT images | 11.4 | 0 | 2 |
| Fusing Dual-Event Data Sets for Mycobacterium tuberculosis Machine Learning Models and Their Evaluation | 11.4 | 0 | 2 |
| Integrating machine learning techniques into robust data enrichment approach and its application to gene expression data | 11.4 | 0 | 2 |
| Low latency and efficient optical flow control for intra data center networks | 11.4 | 0 | 2 |
| Evaluation of various machine learning methods to predict vision-related quality of life from visual field data and visual acuity in patients with glaucoma | 11.4 | 0 | 2 |
| Transfer learning based clinical concept extraction on data from multiple sources | 11.4 | 0 | 2 |
| Are Bigger Data Sets Better for Machine Learning? Fusing Single-Point and Dual-Event Dose Response Data for Mycobacterium tuberculosis | 11.4 | 0 | 2 |
| Improving peak detection in high-resolution LC/MS metabolomics data using preexisting knowledge and machine learning approach | 11.4 | 0 | 2 |
| Analysis of cytokine release assay data using machine learning approaches | 11.4 | 0 | 2 |
| Applications of Machine Learning and Data Mining Methods to Detect Associations of Rare and Common Variants with Complex Traits | 11.4 | 0 | 2 |
| "Big data" - large data, a lot of knowledge? | 11.4 | 0 | 2 |
| Frank Pasquale | 11.4 | 0 | 2 |
| Leveraging the Power of High Performance Computing for Next Generation Sequencing Data Analysis: Tricks and Twists from a High Throughput Exome Workflow | 11.4 | 0 | 2 |
| Dependable data centers. Providing reliable power and cooling to this key hospital asset. | 11.4 | 0 | 2 |
| High throughput optimization of stem cell microenvironments. | 11.4 | 0 | 2 |
| Using Machine Learning Methods to Predict Experimental High Throughput Screening Data | 11.4 | 0 | 2 |
| Save energy and $$$ in the data center. The cabling installed today has to meet the requirements of the cabling for tomorrow. Most data centers are planning for speeds of 10-Gigabit Ethernet. | 11.4 | 0 | 2 |
| Sensing data centres for energy efficiency | 11.4 | 0 | 2 |
| Computational identification of surrogate genes for prostate cancer phases using machine learning and molecular network analysis | 11.4 | 0 | 2 |
| Getting started with cloud computing: offloading ancillary applications helps data center expand without adding cost or staff | 11.4 | 0 | 2 |
| Eoulsan: a cloud computing-based framework facilitating high throughput sequencing analyses. | 11.4 | 0 | 2 |
| Demand action, not assumption: A data governance model at Vanderbilt University Medical Center supports patient safety by providing complete and accurate information | 11.4 | 0 | 2 |
| CARAT-GxG: CUDA-Accelerated Regression Analysis Toolkit for Large-Scale Gene–Gene Interaction with GPU Computing System | 11.4 | 0 | 2 |
| Combining Phylogenetic Profiling-Based and Machine Learning-Based Techniques to Predict Functional Related Proteins | 11.4 | 0 | 2 |
| Machine learning in cell biology – teaching computers to recognize phenotypes | 11.4 | 0 | 2 |
| DR-Predictor: Incorporating Flexible Docking with Specialized Electronic Reactivity and Machine Learning Techniques to Predict CYP-Mediated Sites of Metabolism | 11.4 | 0 | 2 |
| Bioinformatics pipelines for targeted resequencing and whole-exome sequencing of human and mouse genomes: a virtual appliance approach for instant deployment | 11.4 | 0 | 2 |
| A review of machine learning methods to predict the solubility of overexpressed recombinant proteins in Escherichia coli | 11.4 | 0 | 2 |
| Efficient design of meganucleases using a machine learning approach | 11.4 | 0 | 2 |
| Prediction of hepatitis C virus interferon/ribavirin therapy outcome based on viral nucleotide attributes using machine learning algorithms | 11.4 | 0 | 2 |
| In Silico Machine Learning Methods in Drug Development | 11.4 | 0 | 2 |
| High-performance computing, high-speed networks, and configurable computing environments: progress toward fully distributed computing | 11.4 | 0 | 2 |
| Efficient Nash Equilibrium Resource Allocation Based on Game Theory Mechanism in Cloud Computing by Using Auction | 11.4 | 0 | 2 |
| Risk management and disaster recovery planning for online libraries | 11.4 | 0 | 2 |
| Fullrmc, a rigid body Reverse Monte Carlo modeling package enabled with machine learning and artificial intelligence | 11.4 | 0 | 2 |
| Machine Learning Algorithms Outperform Conventional Regression Models in Predicting Development of Hepatocellular Carcinoma | 11.4 | 0 | 2 |
| Artificial intelligence and robotics in high throughput post-genomics | 11.4 | 0 | 2 |
| REEF: Retainable Evaluator Execution Framework | 11.4 | 0 | 2 |
| Simulation of biological evolution and machine learning. I. Selection of self-reproducing numeric patterns by data processing machines, effects of hereditary control, mutation type and crossing | 11.4 | 0 | 2 |
| Chemogenomics: a discipline at the crossroad of high throughput technologies, biomarker research, combinatorial chemistry, genomics, cheminformatics, bioinformatics and artificial intelligence | 11.4 | 0 | 2 |
| A Method for the Evaluation of Image Quality According to the Recognition Effectiveness of Objects in the Optical Remote Sensing Image Using Machine Learning Algorithm | 11.4 | 0 | 2 |
| Medical decision support using machine learning for early detection of late-onset neonatal sepsis | 11.4 | 0 | 2 |
| Context matters in NGO-government contracting for health service delivery: a case study from Pakistan | 11.4 | 0 | 2 |
| Review of machine learning and signal processing techniques for automated electrode selection in high-density microelectrode arrays | 11.4 | 0 | 2 |
| Machine Learning and Tubercular Drug Target Recognition | 11.4 | 0 | 2 |
| Analysis of MicroRNA Expression Using Machine Learning | 11.4 | 0 | 2 |
| Predicting essential genes for identifying potential drug targets in Aspergillus fumigatus | 11.4 | 0 | 2 |
| Class probability estimation for medical studies | 11.4 | 0 | 2 |
| Machine Learning-Based Methods for Prediction of Linear B-Cell Epitopes | 11.4 | 0 | 2 |
| Hybrid Machine Learning Technique for Forecasting Dhaka Stock Market Timing Decisions | 11.4 | 0 | 2 |
| Cyber Risk Management for Critical Infrastructure: A Risk Analysis Model and Three Case Studies | 11.4 | 0 | 2 |
| Satellite Data and Machine Learning for Weather Risk Management and Food Security | 11.4 | 0 | 2 |
| Machine Learning in the Rational Design of Antimicrobial Peptides | 11.4 | 0 | 2 |
| Potential Application of Machine Learning in Health Outcomes Research and Some Statistical Cautions | 11.4 | 0 | 2 |
| The application of machine learning to the modelling of percutaneous absorption: An overview and guide | 11.4 | 0 | 2 |
| Snore related signals processing in a private cloud computing system | 11.4 | 0 | 2 |
| The Mutual Inspirations of Machine Learning and Neuroscience | 11.4 | 0 | 2 |
| Computational Science and Engineering Online (CSE-Online): a cyber-infrastructure for scientific computing | 11.4 | 0 | 2 |
| Machine learning applications in genetics and genomics | 11.4 | 0 | 2 |
| Machine learning methods for the classification of gliomas: Initial results using features extracted from MR spectroscopy | 11.4 | 0 | 2 |
| Classification of lung cancer using ensemble-based feature selection and machine learning methods | 11.4 | 0 | 2 |
| Redefining climate regions in the United States of America using satellite remote sensing and machine learning for public health applications | 11.4 | 0 | 2 |
| Comparison and Validation of Injury Risk Classifiers for Advanced Automated Crash Notification Systems | 11.4 | 0 | 2 |
| Research and Evaluations of the Health Aspects of Disasters, Part VIII: Risk, Risk Reduction, Risk Management, and Capacity Building | 11.4 | 0 | 2 |
| Using Incident Response Trees as a Tool for Risk Management of Online Financial Services | 11.4 | 0 | 2 |
| Update on EPA's ToxCast program: providing high throughput decision support tools for chemical risk management. | 11.4 | 0 | 2 |
| National Laboratory for High Performance Computing | 11.4 | 0 | 2 |
| Deep into the Brain: Artificial Intelligence in Stroke Imaging | 11.4 | 0 | 2 |
| Supervised machine learning and active learning in classification of radiology reports | 11.4 | 0 | 2 |
| KI – Künstliche Intelligenz | 11.4 | 0 | 2 |
| The Human Behaviour-Change Project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation | 11.4 | 0 | 2 |
| Rapid estimation of compost enzymatic activity by spectral analysis method combined with machine learning | 11.4 | 0 | 2 |
| What subject matter questions motivate the use of machine learning approaches compared to statistical models for probability prediction? | 11.4 | 0 | 2 |
| TRC Companies | 11.4 | 0 | 2 |
| Automatic recognition of disorders, findings, pharmaceuticals and body structures from clinical text: An annotation and machine learning study | 11.4 | 0 | 2 |
| Using machine learning to blend human and robot controls for assisted wheelchair navigation | 11.4 | 0 | 2 |
| Predictability of intracranial pressure level in traumatic brain injury: features extraction, statistical analysis and machine learning-based evaluation | 11.4 | 0 | 2 |
| Clinical Data Processing Tools: A Machine Learning Resource | 11.4 | 0 | 2 |
| Artificial Intelligence in Medical Practice: The Question to the Answer? | 11.4 | 0 | 2 |
| Artificial intelligence expert systems with neural network machine learning may assist decision-making for extractions in orthodontic treatment planning | 11.4 | 0 | 2 |
| Use of Artificial Intelligence and Machine Learning Algorithms with Gene Expression Profiling to Predict Recurrent Nonmuscle Invasive Urothelial Carcinoma of the Bladder | 11.4 | 0 | 2 |
| HClass: Automatic classification tool for health pathologies using artificial intelligence techniques | 11.4 | 0 | 2 |
| Improving Hospital-Wide Early Resource Allocation through Machine Learning | 11.4 | 0 | 2 |
| In silico prediction of anti-malarial hit molecules based on machine learning methods | 11.4 | 0 | 2 |
| Machine learning-based detection of chemical risk | 11.4 | 0 | 2 |
| Quantifying surgical complexity with machine learning: Looking beyond patient factors to improve surgical models | 11.4 | 0 | 2 |
| A computational visual saliency model based on statistics and machine learning | 11.4 | 0 | 2 |
| Probability estimation and machine learning—Editorial | 11.4 | 0 | 2 |
| Computer aided diagnosis of degenerative intervertebral disc diseases from lumbar MR images | 11.4 | 0 | 2 |
| Artificial intelligence to assist clinical diagnosis in medicine | 11.4 | 0 | 2 |
| Uncertainty quantification and integration of machine learning techniques for predicting acid rock drainage chemistry: A probability bounds approach | 11.4 | 0 | 2 |
| Utility of Vital Signs, Heart Rate Variability and Complexity, and Machine Learning for Identifying the Need for Lifesaving Interventions in Trauma Patients | 11.4 | 0 | 2 |
| Development and validation of a machine learning algorithm and hybrid system to predict the need for life-saving interventions in trauma patients | 11.4 | 0 | 2 |
| Classification of mysticete sounds using machine learning techniques | 11.4 | 0 | 2 |
| Machine learning approach to an otoneurological classification problem | 11.4 | 0 | 2 |
| Editorial: Charting Chemical Space: Challenges and Opportunities for Artificial Intelligence and Machine Learning | 11.4 | 0 | 2 |
| Diagnosing shock via artificial intelligence: applying machine learning techniques to medicine | 11.4 | 0 | 2 |
| Graph algorithms for machine learning: a case-control study based on prostate cancer populations and high throughput transcriptomic data | 11.4 | 0 | 2 |
| Machine Learning and Artificial Intelligence in Radiology | 11.4 | 0 | 2 |
| On the Fuzziness of Machine Learning, Neural Networks, and Artificial Intelligence in Radiation Oncology. | 11.4 | 0 | 2 |
| RFC 8257: Data Center TCP (DCTCP): TCP Congestion Control for Data Centers | 11.4 | 0 | 2 |
| Cost efficiency in maternal and child health and family planning service delivery in Bangladesh: implications for NGOs | 11.4 | 0 | 2 |
| Cerebellarlike corrective model inference engine for manipulation tasks | 11.4 | 0 | 2 |
| Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success | 11.4 | 0 | 2 |
| Q48699785 | 11.4 | 0 | 2 |
| Government Cloud Computing Policies: Potential Opportunities for Advancing Military Biomedical Research | 11.4 | 0 | 2 |
| Machine Learning, Natural Language Programming, and Electronic Health Records: the next step in the Artificial Intelligence Journey? | 11.4 | 0 | 2 |
| Machine learning & artificial intelligence in the quantum domain: a review of recent progress | 11.4 | 0 | 2 |
| Data Science: Big Data, Machine Learning, and Artificial Intelligence | 11.4 | 0 | 2 |
| Journal of Risk and Financial Management | 11.4 | 0 | 2 |
| Journal of Manufacturing and Materials Processing | 11.4 | 0 | 2 |
| Advances in Distributed Computing and Artificial Intelligence Journal | 11.4 | 0 | 2 |
| GPU Acceleration of Melody Accurate Matching in Query-by-Humming | 11.4 | 0 | 2 |
| A Multianalyzer Machine Learning Model for Marine Heterogeneous Data Schema Mapping | 11.4 | 0 | 2 |
| Chemogenomics: a discipline at the crossroad of high throughput technologies, biomarker research, combinatorial chemistry, genomics, cheminformatics, bioinformatics and artificial intelligence | 11.4 | 0 | 2 |
| Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What Do These Terms Mean and How Will They Impact Health Care? | 11.4 | 0 | 2 |
| Toward Augmented Radiologists: Changes in Radiology Education in the Era of Machine Learning and Artificial Intelligence | 11.4 | 0 | 2 |
| Predicting Treatment Response to Intra-arterial Therapies for Hepatocellular Carcinoma with the Use of Supervised Machine Learning-An Artificial Intelligence Concept | 11.4 | 0 | 2 |
| HT-Paxos: high throughput state-machine replication protocol for large clustered data centers | 11.4 | 0 | 2 |
| A reasonable approach to security. Quantitative risk management goes beyond HIPAA compliance | 11.4 | 0 | 2 |
| Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks (SNPD/SAWN'05) | 11.4 | 0 | 2 |
| Load Balancing in Cloud Computing Environment Using Improved Weighted Round Robin Algorithm for Nonpreemptive Dependent Tasks | 11.4 | 0 | 2 |
| TechSutram | 11.4 | 0 | 2 |
| Artificial intelligence, machine learning and the evolution of healthcare: A bright future or cause for concern? | 11.4 | 0 | 2 |
| Gender bias in artificial intelligence: the need for diversity and gender theory in machine learning | 11.4 | 0 | 2 |
| CSIRO Scientific Computing | 11.4 | 0 | 2 |
| Reconfigurable very high throughput low latency VLSI (FPGA) design architecture of CRC 32 | 11.4 | 0 | 2 |
| DuerOS | 11.4 | 0 | 2 |
| Appian Corporation | 11.4 | 0 | 2 |
| Single Sign-On in Cloud Computing Scenarios: A Research Proposal | 11.4 | 0 | 2 |
| Mobile-Edge Computing and Internet of Things for Consumers: Part II: Energy efficiency, connectivity, and economic development | 11.4 | 0 | 2 |
| Artificial intelligence for designing user profiling system for cloud computing security: Experiment | 11.4 | 0 | 2 |
| Reproducible experiments on dynamic resource allocation in cloud data centers | 11.4 | 0 | 2 |
| Imperfect Information Dynamic Stackelberg Game Based Resource Allocation Using Hidden Markov for Cloud Computing | 11.4 | 0 | 2 |
| A two-time-scale load balancing framework for minimizing electricity bills of Internet Data Centers | 11.4 | 0 | 2 |
| Minimizing Electricity Bills for Geographically Distributed Data Centers with Renewable and Cooling Aware Load Balancing | 11.4 | 0 | 2 |
| Automation, machine learning, and artificial intelligence in echocardiography: A brave new world | 11.4 | 0 | 2 |
| Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing | 11.4 | 0 | 2 |
| Rule-based Machine Learning Methods for Functional Prediction | 11.4 | 0 | 2 |
| VMInformant: an instrumented virtual machine to support trustworthy cloud computing | 11.4 | 0 | 2 |
| Machine Learning Approaches for Early DRG Classification and Resource Allocation | 11.4 | 0 | 2 |
| Integration of High-Performance Computing into Cloud Computing Services | 11.4 | 0 | 2 |
| Peering Into the Black Box of Artificial Intelligence: Evaluation Metrics of Machine Learning Methods | 11.4 | 0 | 2 |
| Big Data Processing for Pervasive Environment in Cloud Computing | 11.4 | 0 | 2 |
| How Bioethics Can Shape Artificial Intelligence and Machine Learning | 11.4 | 0 | 2 |
| Distributed Computing and Artificial Intelligence, 13th International Conference | 11.4 | 0 | 2 |
| Manufacturing capacity planning and the value of multi-stage stochastic programming under Markovian demand | 11.4 | 0 | 2 |
| Training for capacity building of extension personnel for improving efficiency of knowledge transfer to farming communities | 11.4 | 0 | 2 |
| Towards Economic Fairness for Big Data Processing in Pay-as-You-Go Cloud Computing | 11.4 | 0 | 2 |
| Machine learning in computer vision | 11.4 | 0 | 2 |
| Reliability and high availability in cloud computing environments: a reference roadmap | 11.4 | 0 | 2 |
| Performance evaluation and analysis of load balancing algorithms in cloud computing environments | 11.4 | 0 | 2 |
| Cloud light weight: A new solution for load balancing in cloud computing | 11.4 | 0 | 2 |
| Performance Factors of Cloud Computing Data Centers Using [(M/G/1) : (∞/Gdmodel)] Queuing Systems | 11.4 | 0 | 2 |
| MACHINE LEARNING OF MORPHOSYNTACTIC STRUCTURE: LEMMATIZING UNKNOWN SLOVENE WORDS | 11.4 | 0 | 2 |
| Improving Big Data Centers Energy Efficiency: Traffic Based Model and Method | 11.4 | 0 | 2 |
| Distributed Computing and Artificial Intelligence | 11.4 | 0 | 2 |
| Peter Van Gemmeren | 11.4 | 0 | 2 |
| Extraction of Structured Information by Machine Learning Using Community Information | 11.4 | 0 | 2 |
| Data processing performance analysis for ultrafast electron beam X-ray CT using parallel processing hardware architectures | 11.4 | 0 | 2 |
| Feasibility of Implementing Multi-factor Authentication Schemes in Mobile Cloud Computing | 11.4 | 0 | 2 |
| Experiences with distributed computing for meteorological applications: grid computing and cloud computing | 11.4 | 0 | 2 |
| Cross-Platform Normalization Enables Machine Learning Model Training On Microarray And RNA-Seq Data Simultaneously | 11.4 | 0 | 2 |
| Evaluation of Load Balancing Performance of Parallel Processing Linear Time-Delay Systems | 11.4 | 0 | 2 |
| Artificial Intelligence Techniques for Flood Risk Management in Urban Environments | 11.4 | 0 | 2 |
| Minimizing total busy time in offline parallel scheduling with application to energy efficiency in cloud computing | 11.4 | 0 | 2 |
| Environment-conscious scheduling of HPC applications on distributed Cloud-oriented data centers | 11.4 | 0 | 2 |
| Information security governance practices in critical infrastructure organizations: A socio-technical and institutional logic perspective | 11.4 | 0 | 2 |
| Optimizing Network-Aware Resource Allocation in Cloud Data Centers | 11.4 | 0 | 2 |
| Argument Based Machine Learning Applied to Law | 11.4 | 0 | 2 |
| FIELDED MACHINE LEARNING SYSTEM FOR VOCATIONAL COUNSELLING | 11.4 | 0 | 2 |
| Optimizing resource allocation for elastic security VNFs in the SDNFV-enabled cloud computing | 11.4 | 0 | 2 |
| Energy efficiency evaluation of CO 2 transcritical refrigeration cycles for telecommunication and data centres | 11.4 | 0 | 2 |
| Pathological brain detection in MRI scanning via Hu moment invariants and machine learning | 11.4 | 0 | 2 |
| Risk management and dynamic network performance: an illustration using a dual banking system | 11.4 | 0 | 2 |
| PREDICTING STUDENTS' PERFORMANCE IN DISTANCE LEARNING USING MACHINE LEARNING TECHNIQUES | 11.4 | 0 | 2 |
| Where do machine learning and human-computer interaction meet? | 11.4 | 0 | 2 |
| INDUSTRIAL EXPERT SYSTEM ACQUIRED BY MACHINE LEARNING | 11.4 | 0 | 2 |
| MACHINE LEARNING GOES TO THE BANK | 11.4 | 0 | 2 |
| MACHINE LEARNING IN HYBRID HIERARCHICAL AND PARTIAL-ORDER PLANNERS FOR MANUFACTURING DOMAINS | 11.4 | 0 | 2 |
| Machine Learning Applications in Baseball: A Systematic Literature Review | 11.4 | 0 | 2 |
| Id+: Enhancing medical knowledge acquisition with machine learning | 11.4 | 0 | 2 |
| Machine learning meets human-computer interaction - introduction to the special issue | 11.4 | 0 | 2 |
| Machine learning: A tool to support usability? | 11.4 | 0 | 2 |
| Robotics and computer vision techniques combined with non-invasive consumer biometrics to assess quality traits from beer foamability using machine learning: A potential for artificial intelligence applications | 11.4 | 0 | 2 |
| Machine learning for map interpretation: An intelligent tool for environmental planning | 11.4 | 0 | 2 |
| MACHINE LEARNING TECHNIQUES FOR ACQUIRING NEW KNOWLEDGE IN IMAGE TRACKING | 11.4 | 0 | 2 |
| Multiphysics simulations: challenges and opportunities | 11.4 | 0 | 2 |
| Using Java for distributed computing in the Gaia satellite data processing | 11.4 | 0 | 2 |
| Cloud computing for geodetic imaging data processing, analysis, and modeling | 11.4 | 0 | 2 |
| Mirror, Mirror in the Data Center: Remote Data Copy For Disaster Recovery | 11.4 | 0 | 2 |
| Data Processing Disaster Recovery Planning Considerations for Tandem Networks | 11.4 | 0 | 2 |
| Artificial intelligence machine learning-based coronary CT fractional flow reserve (CT-FFR): Impact of iterative and filtered back projection reconstruction techniques | 11.4 | 0 | 2 |
| A Governance Framework for ICT Supply Chain Risk Management | 11.4 | 0 | 2 |
| Artificial intelligence, machine learning and health systems | 11.4 | 0 | 2 |
| Machine Learning Aided Scheme for Load Balancing in Dense IoT Networks | 11.4 | 0 | 2 |
| Proposing a Machine Learning Approach to Analyze and Predict Employment and its Factors | 11.4 | 0 | 2 |
| Machine Learning Paradigms for Modeling Spatial and Temporal Information in Multimedia Data Mining | 11.4 | 0 | 2 |
| An Ecological Risk Management and Capacity Building Model | 11.4 | 0 | 2 |
| Virtual Enterprise Risk Management Using Artificial Intelligence | 11.4 | 0 | 2 |
| A Secure Multi-Tier Mobile Edge Computing Model for Data Processing Offloading Based on Degree of Trust | 11.4 | 0 | 2 |
| Cloud Computing and Dynamic Resource Allocation for Multimedia Applications | 11.4 | 0 | 2 |
| Knowledge Transfer Between Artificial Intelligence Systems | 11.4 | 0 | 2 |
| Managing a tier-2 computer centre with a private cloud infrastructure | 11.4 | 0 | 2 |
| Resource Allocation and Scheduling in Cloud Computing: Policy and Algorithm | 11.4 | 0 | 2 |
| Optics in data center: Improving scalability and energy efficiency | 11.4 | 0 | 2 |
| Energy-Efficient Multi-Job Scheduling Model for Cloud Computing and Its Genetic Algorithm | 11.4 | 0 | 2 |
| Managing Energy Efficiency in the Cloud Computing Environment Using SNMPv3: A Quantitative Analysis of Processing and Power Usage | 11.4 | 0 | 2 |
| Critical Issues for Data Center Energy Efficiency | 11.4 | 0 | 2 |
| Parallel Implementation of a Recursive Least-Squares Neural Network Training Method on the Intel iPSC/2 | 11.4 | 0 | 2 |
| Application Scheduling in Mobile Cloud Computing with Load Balancing | 11.4 | 0 | 2 |
| A Decentralized Virtual Machine Migration Approach of Data Centers for Cloud Computing | 11.4 | 0 | 2 |
| B3: Fuzzy-Based Data Center Load Optimization in Cloud Computing | 11.4 | 0 | 2 |
| A multi-factor monitoring fault tolerance model based on a GPU cluster for big data processing | 11.4 | 0 | 2 |
| Study on the Effectiveness of the Investment Strategy Based on a Classifier with Rules Adapted by Machine Learning | 11.4 | 0 | 2 |
| Estimated Interval-Based Checkpointing (EIC) on Spot Instances in Cloud Computing | 11.4 | 0 | 2 |
| Property-Based Anonymous Attestation in Trusted Cloud Computing | 11.4 | 0 | 2 |
| A Game Theory Approach to Fair and Efficient Resource Allocation in Cloud Computing | 11.4 | 0 | 2 |
| public procurement law | 11.4 | 0 | 2 |
| On Security Management: Improving Energy Efficiency, Decreasing Negative Environmental Impact, and Reducing Financial Costs for Data Centers | 11.4 | 0 | 2 |
| Performance Analysis of Heterogeneous Data Centers in Cloud Computing Using a Complex Queuing Model | 11.4 | 0 | 2 |
| Optimization Approach for Resource Allocation on Cloud Computing for IoT | 11.4 | 0 | 2 |
| Design of High Throughput and Cost-Efficient Data Center Networks | 11.4 | 0 | 2 |
| An Architecture of IoT Service Delegation and Resource Allocation Based on Collaboration between Fog and Cloud Computing | 11.4 | 0 | 2 |
| A Safety Resource Allocation Mechanism against Connection Fault for Vehicular Cloud Computing | 11.4 | 0 | 2 |
| Fairness-Aware and Energy Efficiency Resource Allocation in Multiuser OFDM Relaying System | 11.4 | 0 | 2 |
| A Hierarchical Load Balancing Strategy Considering Communication Delay Overhead for Large Distributed Computing Systems | 11.4 | 0 | 2 |
| A Game-Theoretic Based Resource Allocation Strategy for Cloud Computing Services | 11.4 | 0 | 2 |
| Narrow Artificial Intelligence with Machine Learning for Real-Time Estimation of a Mobile Agent’s Location Using Hidden Markov Models | 11.4 | 0 | 2 |
| ISS National Laboratory | 11.4 | 0 | 2 |
| The state-of-the-art on Intellectual Property Analytics (IPA): A literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data | 11.4 | 0 | 2 |
| A decision-based pre-emptive fair scheduling strategy to process cloud computing work-flows for sustainable enterprise management | 11.4 | 0 | 2 |
| Honey bee behavior inspired load balancing of tasks in cloud computing environments | 11.4 | 0 | 2 |
| Big Data Analytics, Machine Learning, and Artificial Intelligence in Next-Generation Wireless Networks | 11.4 | 0 | 2 |
| Machine learning classification methods in hyperspectral data processing for agricultural applications | 11.4 | 0 | 2 |
| Cloud computing and its interest in saving energy: the use case of a private cloud | 11.4 | 0 | 2 |
| Optimal multi-dimensional dynamic resource allocation in mobile cloud computing | 11.4 | 0 | 2 |
| A novel energy-efficient resource allocation algorithm based on immune clonal optimization for green cloud computing | 11.4 | 0 | 2 |
| Agent-based load balancing in Cloud data centers | 11.4 | 0 | 2 |
| Machine Learning with Sensitivity Analysis to Determine Key Factors Contributing to Energy Consumption in Cloud Data Centers | 11.4 | 0 | 2 |
| Trusted framework for online banking in public cloud using multi-factor authentication and privacy protection gateway | 11.4 | 0 | 2 |
| High availability in clouds: systematic review and research challenges | 11.4 | 0 | 2 |
| Energy efficiency-based joint spectrum handoff and resource allocation algorithm for heterogeneous CRNs | 11.4 | 0 | 2 |
| Erratum to: A survey of machine learning for big data processing | 11.4 | 0 | 2 |
| A survey of machine learning for big data processing | 11.4 | 0 | 2 |
| IaaSMon: Monitoring Architecture for Public Cloud Computing Data Centers | 11.4 | 0 | 2 |
| Developing a governance model for PPP infrastructure service delivery based on lessons from Eastern Australia | 11.4 | 0 | 2 |
| Distributed Computing in the 21st Century: Some Aspects of Cloud Computing | 11.4 | 0 | 2 |
| myTrustedCloud: Trusted Cloud Infrastructure for Security-critical Computation and Data Managment | 11.4 | 0 | 2 |
| Energy efficiency in data centres and the barriers to further improvements : an interdisciplinary investigation | 11.4 | 0 | 2 |
| Risk Management and Critical Infrastructure Protection: Assessing, Integrating, and Managing Threats, Vulnerabilities and Consequences | 11.4 | 0 | 2 |
| Resource allocation for phantom cellular networks: Energy efficiency vs spectral efficiency | 11.4 | 0 | 2 |
| Enhancement of Localization Systems in NLOS Urban Scenario with Multipath Ray Tracing Fingerprints and Machine Learning | 11.4 | 0 | 2 |
| In defence of machine learning: Debunking the myths of artificial intelligence | 11.4 | 0 | 2 |
| Applying machine learning to programming by demonstration | 11.4 | 0 | 2 |
| Google Cloud GPU | 11.4 | 0 | 2 |
| A demonstration of monitoring and measuring data centers for energy efficiency using opensource tools | 11.4 | 0 | 2 |
| On the energy efficiency of MapReduce shuffling operations in data centers | 11.4 | 0 | 2 |
| Adaptive kinetic structural behavior through machine learning: Optimizing the process of kinematic transformation using artificial neural networks | 11.4 | 0 | 2 |
| A survey on architectures and energy efficiency in Data Center Networks | 11.4 | 0 | 2 |
| A lightweight possession proof scheme for outsourced files in mobile cloud computing based on chameleon hash function | 11.4 | 0 | 2 |
| Implementing the Data Center Energy Productivity Metric in a High-Performance Computing Data Center | 11.4 | 0 | 2 |
| A Periodic Portfolio Scheduler for Scientific Computing in the Data Center | 11.4 | 0 | 2 |
| Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing | 11.4 | 0 | 2 |
| A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing | 11.4 | 0 | 2 |
| Optimal Load Balancing and Energy Cost Management for Internet Data Centers in Deregulated Electricity Markets | 11.4 | 0 | 2 |
| A survey on load balancing algorithms for virtual machines placement in cloud computing | 11.4 | 0 | 2 |
| Shangguang Wang | 11.4 | 0 | 2 |
| Cloud computing for small research groups in computational science and engineering: current status and outlook | 11.4 | 0 | 2 |
| data center management | 11.4 | 0 | 2 |
| MLOps | 11.4 | 0 | 2 |
| artificial intelligence in government | 11.4 | 0 | 2 |
| New ethical challenges of digital technologies, machine learning and artificial intelligence in public health: a call for papers | 11.4 | 0 | 2 |
| Structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure | 11.4 | 0 | 2 |
| Assessing the Role of Artificial Intelligence (AI) in Clinical Oncology: Utility of Machine Learning in Radiotherapy Target Volume Delineation | 11.4 | 0 | 2 |
| Council Regulation (EU) 2018/1488 | 11.4 | 0 | 2 |
| Risk Management in Video Game Development Projects | 11.4 | 0 | 2 |
| BCD: BigData, cloud computing and distributed computing | 11.4 | 0 | 2 |
| Power-Aware Multi-data Center Management Using Machine Learning | 11.4 | 0 | 2 |
| Towards energy-aware scheduling in data centers using machine learning | 11.4 | 0 | 2 |
| Resource Allocation for Cloud Computing | 11.4 | 0 | 2 |
| Assessing and forecasting energy efficiency on Cloud computing platforms | 11.4 | 0 | 2 |
| Toward business-driven risk management for Cloud computing | 11.4 | 0 | 2 |
| Down the deep rabbit hole: Untangling deep learning from machine learning and artificial intelligence | 11.4 | 0 | 2 |
| GPU accelerated low latency gravitational wave data processing | 11.4 | 0 | 2 |
| Monitoring of the data processing and simulated production at CMS with a web-based service: the Production Monitoring Platform (pMp) | 11.4 | 0 | 2 |
| Artificial intelligence and machine learning in wound care-The wounded machine! | 11.4 | 0 | 2 |
| The role of artificial intelligence and machine learning in harmonization of high-resolution post-mortem MRI (virtopsy) with respect to brain microstructure | 11.4 | 0 | 2 |
| Artificial intelligence and machine learning | 11.4 | 0 | 2 |
| Artificial intelligence and machine learning in haematology | 11.4 | 0 | 2 |
| Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018 | 11.4 | 0 | 2 |
| From Machine Learning to Artificial Intelligence Applications in Cardiac Care | 11.4 | 0 | 2 |
| Artificial intelligence and machine learning | applications in musculoskeletal physiotherapy | 11.4 | 0 | 2 |
| An artificial intelligence atomic force microscope enabled by machine learning | 11.4 | 0 | 2 |
| Artificial Intelligence Applied to Osteoporosis: A Performance Comparison of Machine Learning Algorithms in Predicting Fragility Fractures From MRI Data | 11.4 | 0 | 2 |
| Artificial intelligence, machine learning, neural networks, and deep learning: Futuristic concepts for new dental diagnosis | 11.4 | 0 | 2 |
| Machine learning: applications of artificial intelligence to imaging and diagnosis | 11.4 | 0 | 2 |
| The machine learning approach: Artificial intelligence is coming to support critical clinical thinking | 11.4 | 0 | 2 |
| ARTIFICIAL INTELLIGENCE AND BRAIN MECHANISMS. MEM RM-3522-PR | 11.4 | 0 | 2 |
| Pathogenesis-based treatments in primary Sjogren's syndrome using artificial intelligence and advanced machine learning techniques: a systematic literature review | 11.4 | 0 | 2 |
| The growing role of machine learning and artificial intelligence in developmental medicine | 11.4 | 0 | 2 |
| The power and limitations of machine learning and artificial intelligence in cardiac CT | 11.4 | 0 | 2 |
| Distributed Computing and Artificial Intelligence | 11.4 | 0 | 2 |
| Improving the energy efficiency of virtual data centers in an IT service provider through proactive fuzzy rules-based multicriteria decision making | 11.4 | 0 | 2 |
| Neural networks in distributed computing and artificial intelligence | 11.4 | 0 | 2 |
| Neural Systems in Distributed Computing and Artificial Intelligence | 11.4 | 0 | 2 |
| A low-level resource allocation in an agent-based Cloud Computing platform | 11.4 | 0 | 2 |
| Special issue on distributed computing and artificial intelligence | 11.4 | 0 | 2 |
| Special issue on distributed computing and artificial intelligence systems | 11.4 | 0 | 2 |
| +Cloud: A Virtual Organization of Multiagent System for Resource Allocation into a Cloud Computing Environment | 11.4 | 0 | 2 |
| Distributed Computing and Artificial Intelligence, 11th International Conference | 11.4 | 0 | 2 |
| Machine Learning-based CPS for Clustering High throughput Machining Cycle Conditions | 11.4 | 0 | 2 |
| Fair Resource Allocation in an Intrusion-Detection System for Edge Computing: Ensuring the Security of Internet of Things Devices | 11.4 | 0 | 2 |
| A machine learning approach for accelerating DNA sequence analysis | 11.4 | 0 | 2 |
| A Game-Theoretic Analysis of Energy Efficiency and Performance for Cloud Computing in Communication Networks | 11.4 | 0 | 2 |
| Inducing diagnostic rules for glomerular disease with the DLG machine learning algorithm | 11.4 | 0 | 2 |
| Learning design concepts using machine learning techniques | 11.4 | 0 | 2 |
| Design of a Two-Phase Gravity-Driven Micro-Scale Thermosyphon Cooling System for High-Performance Computing Data Centers | 11.4 | 0 | 2 |
| Unsupervised Power Modeling of Co-Allocated Workloads for Energy Efficiency in Data Centers | 11.4 | 0 | 2 |
| Leakage and Temperature Aware Server Control for Improving Energy Efficiency in Data Centers | 11.4 | 0 | 2 |
| Automated Detection of Macular Diseases by Optical Coherence Tomography and Artificial Intelligence Machine Learning of Optical Coherence Tomography Images | 11.4 | 0 | 2 |
| Patient centered care for prostate cancer-how can artificial intelligence and machine learning help make the right decision for the right patient? | 11.4 | 0 | 2 |
| Artificial Intelligence and Machine Learning in Endocrinology and Metabolism: The Dawn of a New Era | 11.4 | 0 | 2 |
| Data science, artificial intelligence, and machine learning: Opportunities for laboratory medicine and the value of positive regulation | 11.4 | 0 | 2 |
| Machine Learning and Knowledge Extraction | 11.4 | 0 | 2 |
| Artificial Intelligence/Machine Learning Modeling on Time to Palliative Care Review in an Inpatient Hospital Population | 11.4 | 0 | 2 |
| SkyNet: an efficient and robust neural network training tool for machine learning in astronomy | 11.4 | 0 | 2 |
| OpenCluster: a flexible distributed computing framework for astronomical data processing | 11.4 | 0 | 2 |
| Active Learning Solution on Distributed Edge Computing | 11.4 | 0 | 2 |
| Collaborative production and resource allocation: the consequences of prospective payment for hospital care | 11.4 | 0 | 2 |
| Histopathology Images Based Survival Prediction of Glioma Patients Using Artificial Intelligence | 11.4 | 0 | 2 |
| MR Based Survival Prediction of Glioma Patients Using Artificial Intelligence | 11.4 | 0 | 2 |
| Histopathology Images Based Prediction of Molecular Pathology in Glioma Using Artificial Intelligence | 11.4 | 0 | 2 |
| MR Based Prediction of Molecular Pathology in Glioma Using Artificial Intelligence | 11.4 | 0 | 2 |
| Composable disaggregated infrastructure | 11.4 | 0 | 2 |
| East-west traffic | 11.4 | 0 | 2 |
| Machine Learning in Rehabilitation Assessment for Thermal and Heart Rate Data Processing | 11.4 | 0 | 2 |
| Artificial Intelligence and Machine Learning: Opportunities for Radiologists in Training | 11.4 | 0 | 2 |
| Policy Implications of Artificial Intelligence and Machine Learning in Diabetes Management | 11.4 | 0 | 2 |
| Artificial Intelligence in Diagnosis of DFNA9 | 11.4 | 0 | 2 |
| Evaluating the Intention and Behaviour of Private Sector Participation in Healthcare Service Delivery via Public-Private Partnership: Evidence from China | 11.4 | 0 | 2 |
| Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches | 11.4 | 0 | 2 |
| Machine learning and artificial intelligence in the service of medicine: Necessity or potentiality? | 11.4 | 0 | 2 |
| Strategic public procurement regulatory compliance model with mediating effect of ethical behavior | 11.4 | 0 | 2 |
| Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine | 11.4 | 0 | 2 |
| Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence | 11.4 | 0 | 2 |
| Machine Learning and Understanding for Intelligent Extreme Scale Scientific Computing and Discovery. DOE Workshop Report, January 7–9, 2015, Rockville, MD | 11.4 | 0 | 2 |
| Artificial Intelligence and Machine Learning: Will Clinical Pharmacologists Be Needed in the Next Decade? The John Henry Question | 11.4 | 0 | 2 |
| Cervical vertebral maturation assessment on lateral cephalometric radiographs using artificial intelligence: comparison of machine learning classifier models | 11.4 | 0 | 2 |
| The Impact of Artificial Intelligence and Machine Learning in Radiation Therapy: Considerations for Future Curriculum Enhancement | 11.4 | 0 | 2 |
| Prioritization of Information Security Controls through Fuzzy AHP for Cloud Computing Networks and Wireless Sensor Networks | 11.4 | 0 | 2 |
| Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods | 11.4 | 0 | 2 |
| Intelligent Artificial Intelligence: Present Considerations and Future Implications of Machine Learning Applied to Electrocardiogram Interpretation | 11.4 | 0 | 2 |
| Application of artificial intelligence (AI) in Radiotherapy workflow: Paradigm shift in Precision Radiotherapy using Machine Learning | 11.4 | 0 | 2 |
| Accelerating single molecule localization microscopy through parallel processing on a high-performance computing cluster | 11.4 | 0 | 2 |
| Artificial intelligence and machine learning in emergency medicine | 11.4 | 0 | 2 |
| Artificial intelligence and machine learning in respiratory medicine | 11.4 | 0 | 2 |
| [Analysis on ophthalmic human resource allocation and service delivery at county level in Mainland China in 2014] | 11.4 | 0 | 2 |
| Payment Reform in the Era of Advanced Diagnostics, Artificial Intelligence, and Machine Learning | 11.4 | 0 | 2 |
| Artificial Intelligence for Aortic Pressure Waveform Analysis During Coronary Angiography: Machine Learning for Patient Safety | 11.4 | 0 | 2 |
| Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine | 11.4 | 0 | 2 |
| A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases | 11.4 | 0 | 2 |
| Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness | 11.4 | 0 | 2 |
| Uncertainty-Quantified Hybrid Machine Learning/Density Functional Theory High Throughput Screening Method for Crystals | 11.4 | 0 | 2 |
| Artificial intelligence and machine learning to fight COVID-19 | 11.4 | 0 | 2 |
| Artificial Intelligence and Machine learning based prediction of resistant and susceptible mutations in Mycobacterium tuberculosis | 11.4 | 0 | 2 |
| Identifying tuberculous pleural effusion using artificial intelligence machine learning algorithms | 11.4 | 0 | 2 |
| Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness | 11.4 | 0 | 2 |
| Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology | 11.4 | 0 | 2 |
| Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics | 11.4 | 0 | 2 |
| Optimization based resource and cooling management for a high performance computing data center | 11.4 | 0 | 2 |
| Artificial Intelligence and Machine Learning in Cardiovascular Healthcare | 11.4 | 0 | 2 |
| Machine Learning and Artificial Intelligence in Neurocritical Care: a Specialty-Wide Disruptive Transformation or a Strategy for Success | 11.4 | 0 | 2 |
| Artificial Intelligence/Machine Learning in Diabetes Care | 11.4 | 0 | 2 |
| Lifecycle Regulation of Artificial Intelligence- and Machine Learning-Based Software Devices in Medicine | 11.4 | 0 | 2 |
| Artificial Intelligence and Machine Learning in the Identification of Authentic and Fake Data Presentation | 11.4 | 0 | 2 |
| Artificial Intelligence Applications in Type 2 Diabetes Mellitus Care: Focus on Machine Learning Methods | 11.4 | 0 | 2 |
| The role of artificial intelligence and machine learning in predicting orthopaedic outcomes | 11.4 | 0 | 2 |
| Advanced Editorial to announce a JCAMD Special Issue on Artificial Intelligence and Machine Learning | 11.4 | 0 | 2 |
| Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm | 11.4 | 0 | 2 |
| Artificial Intelligence and Machine Learning: A New Disruptive Force in Orthopaedics | 11.4 | 0 | 2 |
| Artificial Intelligence and Arthroplasty at a Single Institution: Real-World Applications of Machine Learning to Big Data, Value-Based Care, Mobile Health, and Remote Patient Monitoring | 11.4 | 0 | 2 |
| Response to Letters to the Editor regarding the editorial "Artificial intelligence, machine learning, and the human interface in medicine: is there a sweet spot for oral and maxillofacial radiology?" | 11.4 | 0 | 2 |
| Artificial Intelligence: A New Tool in Operating Room Management. Role of Machine Learning Models in Operating Room Optimization | 11.4 | 0 | 2 |
| Response to Editorial "Artificial intelligence, machine learning, and the human interface in medicine: Is there a sweet spot for oral and maxillofacial radiology?" | 11.4 | 0 | 2 |
| Pragmatic considerations for fostering reproducible research in artificial intelligence | 11.4 | 0 | 2 |
| Safeguards for the use of artificial intelligence and machine learning in global health | 11.4 | 0 | 2 |
| The need for a system view to regulate artificial intelligence/machine learning-based software as medical device | 11.4 | 0 | 2 |
| Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective | 11.4 | 0 | 2 |
| Applied machine learning and artificial intelligence in rheumatology | 11.4 | 0 | 2 |
| Artificial intelligence, machine learning and the pediatric airway | 11.4 | 0 | 2 |
| DolphinNext: a distributed data processing platform for high throughput genomics | 11.4 | 0 | 2 |
| A new era: artificial intelligence and machine learning in prostate cancer | 11.4 | 0 | 2 |
| Artificial Intelligence versus Doctors' Intelligence: A Glance on Machine Learning Benefaction in Electrocardiography | 11.4 | 0 | 2 |
| A(eye): A Review of Current Applications of Artificial Intelligence and Machine Learning in Ophthalmology | 11.4 | 0 | 2 |
| New Phenotypes for Sepsis: The Promise and Problem of Applying Machine Learning and Artificial Intelligence in Clinical Research | 11.4 | 0 | 2 |
| The doctor will see you now: How machine learning and artificial intelligence can extend our understanding and treatment of asthma | 11.4 | 0 | 2 |
| Artificial intelligence and machine learning in clinical development: a translational perspective | 11.4 | 0 | 2 |
| Artificial intelligence and avian influenza: Using machine learning to enhance active surveillance for avian influenza viruses | 11.4 | 0 | 2 |
| Promising Artificial Intelligence-Machine Learning-Deep Learning Algorithms in Ophthalmology | 11.4 | 0 | 2 |
| Modeling Pinot Noir Aroma Profiles Based on Weather and Water Management Information Using Machine Learning Algorithms: A Vertical Vintage Analysis Using Artificial Intelligence | 11.4 | 0 | 2 |
| Stakeholder perspectives on public-private partnership in health service delivery in Sindh province of Pakistan: a qualitative study | 11.4 | 0 | 2 |
| Artificial Intelligence and Machine Learning | 11.4 | 0 | 2 |
| A Machine Learning Approach to Achieving Energy Efficiency in Relay-Assisted LTE-A Downlink System | 11.4 | 0 | 2 |
| Editorial for "Top 10 Reviewer Critiques of Radiology Artificial Intelligence (AI) Articles: Qualitative Thematic Analysis of Reviewer Critiques of Machine Learning / Deep Learning Manuscripts Submitted to JMRI" | 11.4 | 0 | 2 |
| Predicted Optimal Bifunctional Electrocatalysts for the Hydrogen Evolution Reaction and the Oxygen Evolution Reaction Using Chalcogenide Heterostructures Based on Machine Learning Analysis of in Silico Quantum Mechanics Based High Throughput Screeni | 11.4 | 0 | 2 |
| Effects of airflow on the thermal environment and energy efficiency in raised-floor data centers: A review | 11.4 | 0 | 2 |
| Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning | 11.4 | 0 | 2 |
| Real-Time Massive Vector Field Data Processing in Edge Computing | 11.4 | 0 | 2 |
| Selected Papers from the Workshop on Computational Biology: Joint with the International Joint Conference on Artificial Intelligence and the International Conference on Machine Learning, 2018 | 11.4 | 0 | 2 |
| Top 10 Reviewer Critiques of Radiology Artificial Intelligence (AI) Articles: Qualitative Thematic Analysis of Reviewer Critiques of Machine Learning/Deep Learning Manuscripts Submitted to JMRI | 11.4 | 0 | 2 |
| Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review | 11.4 | 0 | 2 |
| Artificial intelligence, machine learning, and the human interface in medicine: Is there a sweet spot for oral and maxillofacial radiology? | 11.4 | 0 | 2 |
| Artificial Intelligence in Medical Education: Best Practices Using Machine Learning to Assess Surgical Expertise in Virtual Reality Simulation | 11.4 | 0 | 2 |
| Supervised Machine Learning Based Multi-Task Artificial Intelligence Classification of Retinopathies | 11.4 | 0 | 2 |
| A Bilevel Optimization Approach for Joint Offloading Decision and Resource Allocation in Cooperative Mobile Edge Computing | 11.4 | 0 | 2 |
| A machine learning algorithm for high throughput identification of FTIR spectra: Application on microplastics collected in the Mediterranean Sea | 11.4 | 0 | 2 |
| Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions | 11.4 | 0 | 2 |
| Artificial intelligence and machine learning for predicting acute kidney injury in severely burned patients: A proof of concept | 11.4 | 0 | 2 |
| Orly Alter | 11.4 | 0 | 2 |
| Artificial intelligence and machine learning in spine research | 11.4 | 0 | 2 |
| Artificial intelligence, machine learning and deep learning: definitions and differences | 11.4 | 0 | 2 |
| Commentary: Rise of machine learning and artificial intelligence in ophthalmology | 11.4 | 0 | 2 |
| Artificial Intelligence and Machine Learning in Anesthesiology | 11.4 | 0 | 2 |
| Radiogenomics in Medulloblastoma: Can the Human Brain Compete with Artificial Intelligence and Machine Learning? | 11.4 | 0 | 2 |
| Artificial Intelligence and Machine Learning in Lower Extremity Arthroplasty: A Review | 11.4 | 0 | 2 |
| Predicting vital sign deterioration with artificial intelligence or machine learning | 11.4 | 0 | 2 |
| Medhere: A Smartwatch-based Medication Adherence Monitoring System using Machine Learning and Distributed Computing | 11.4 | 0 | 2 |
| Coverage of ethics within the artificial intelligence and machine learning academic literature: The case of disabled people | 11.4 | 0 | 2 |
| The BIPM Mycotoxin Metrology Capacity Building and Knowledge Transfer Program: Accurate Characterization of a Pure Aflatoxin B1 Material to Avoid Calibration Errors | 11.4 | 0 | 2 |
| Strengths, Weaknesses, Opportunities, and Threats Analysis of Artificial Intelligence and Machine Learning Applications in Radiology | 11.4 | 0 | 2 |
| DolphinNext: A Graphical User Interface for Distributed Data Processing of High Throughput Genomics | 11.4 | 0 | 2 |
| Artificial Intelligence and Machine Learning for HIV Prevention: Emerging Approaches to Ending the Epidemic | 11.4 | 0 | 2 |
| Applications of artificial intelligence and machine learning in respiratory medicine | 11.4 | 0 | 2 |
| Toward a Risk-Utility Data Governance Framework for Research Using Genomic and Phenotypic Data in Safe Havens: Multifaceted Review | 11.4 | 0 | 2 |
| The impact of knowledge transfer performance on the artificial intelligence industry innovation network: An empirical study of Chinese firms | 11.4 | 0 | 2 |
| Artificial Intelligence and Machine Learning in Radiology Education Is Ready for Prime Time | 11.4 | 0 | 2 |
| Manish Parashar | 11.4 | 0 | 2 |
| Artificial Intelligence and Machine Learning to Accelerate Translational Research: Proceedings of a Workshop—in Brief | 11.4 | 0 | 2 |
| Predicting the Growth and Trend of COVID-19 Pandemic using Machine Learning and Cloud Computing | 11.4 | 0 | 2 |
| The Use of Artificial Intelligence and Deep Machine Learning in Oncologic Histopathology | 11.4 | 0 | 2 |
| The Use of Artificial Intelligence (AI) Machine Learning to Determine Myocyte Damage in Cardiac Transplant Acute Cellular Rejection | 11.4 | 0 | 2 |
| Artificial intelligence and machine learning in nephropathology | 11.4 | 0 | 2 |
| Reporting and Implementing Interventions Involving Machine Learning and Artificial Intelligence | 11.4 | 0 | 2 |
| Editorial. Machine learning and artificial intelligence applied to the diagnosis and management of Cushing disease | 11.4 | 0 | 2 |
| Machine Learning and Artificial Intelligence in Pediatric Research: Current State, Future Prospects, and Examples in Perioperative and Critical Care | 11.4 | 0 | 2 |
| Brave New Surgical Innovations: The Impact of Bioprinting, Machine Learning, and Artificial Intelligence in Craniofacial Surgery | 11.4 | 0 | 2 |
| Artificial Intelligence in Subarachnoid Hemorrhage | 11.4 | 0 | 2 |
| Artificial intelligence for interpretation of segments of whole body MRI in CNO: pilot study comparing radiologists versus machine learning algorithm | 11.4 | 0 | 2 |
| Role of Artificial Intelligence and Machine Learning in Nanosafety | 11.4 | 0 | 2 |