| IBM | 60.0 | 36 | 0 |
| Microsoft | 47.1 | 16 | 0 |
| Data Analytics for Machine Learning | 46.4 | 5 | 2 |
| Apple Intelligence | 40.0 | 0 | 13 |
| Palantir Technologies | 39.8 | 10 | 0 |
| iTmethods Inc. | 39.7 | 3 | 2 |
| H2O | 34.9 | 2 | 2 |
| Feast | 34.9 | 2 | 2 |
| Reign | 34.9 | 2 | 2 |
| Azure DevOps Server | 34.6 | 7 | 0 |
| Q18698690 | 34.6 | 7 | 0 |
| Hugging Face | 33.5 | 3 | 1 |
| Atlan | 33.5 | 3 | 1 |
| Microsoft SQL Server | 32.3 | 6 | 0 |
| Media Creation Tool | 29.8 | 5 | 0 |
| Contentsquare | 29.5 | 0 | 6 |
| Q21447895 | 28.8 | 2 | 1 |
| bidirectional encoder representations from transformers | 28.8 | 2 | 1 |
| Microsoft Academic Graph | 28.8 | 2 | 1 |
| Dolly | 28.8 | 2 | 1 |
| Comet | 28.8 | 2 | 1 |
| Q80689 | 26.7 | 4 | 0 |
| Microsoft Lumia 640 XL | 26.7 | 4 | 0 |
| Fivetran | 26.7 | 4 | 0 |
| PyTorch | 24.4 | 0 | 4 |
| Mistral Vibe | 24.4 | 0 | 4 |
| Microsoft Windows | 23.0 | 3 | 0 |
| Q215273 | 23.0 | 3 | 0 |
| Anais da Academia Brasileira de Ciências | 23.0 | 3 | 0 |
| Arquivos Brasileiros De Cardiologia | 23.0 | 3 | 0 |
| Ciência Rural | 23.0 | 3 | 0 |
| Brazilian Dental Journal | 23.0 | 3 | 0 |
| Brazilian Journal of Chemical Engineering | 23.0 | 3 | 0 |
| IBM Bluemix | 23.0 | 3 | 0 |
| Microsoft Lumia 640 | 23.0 | 3 | 0 |
| G2 | 23.0 | 3 | 0 |
| Dataiku | 23.0 | 3 | 0 |
| Clinics | 23.0 | 3 | 0 |
| Revista brasileira de hematologia e hemoterapia | 23.0 | 3 | 0 |
| The Brazilian journal of infectious diseases : an official publication of the Brazilian Society of Infectious Diseases | 23.0 | 3 | 0 |
| Brazilian Journal of Poultry Science | 23.0 | 3 | 0 |
| Revista brasileira de sementes = Brazilian seed journal | 23.0 | 3 | 0 |
| Revista Brasileira de fruticultura | 23.0 | 3 | 0 |
| Revista de Microbiologia | 23.0 | 3 | 0 |
| Microsoft Docs | 23.0 | 3 | 0 |
| Microsoft Learn | 23.0 | 3 | 0 |
| Microsoft Typography | 23.0 | 3 | 0 |
| SAS Institute | 22.0 | 1 | 1 |
| Seeing AI | 22.0 | 1 | 1 |
| Ryota Tomioka | 22.0 | 1 | 1 |
| Kubeflow | 22.0 | 1 | 1 |
| cnvrg.io | 22.0 | 1 | 1 |
| KDnuggets | 22.0 | 1 | 1 |
| Seldon | 22.0 | 1 | 1 |
| wandb | 22.0 | 1 | 1 |
| Road Roughness Estimation Using Machine Learning | 22.0 | 1 | 1 |
| Machine Learning and Deep Learning -- A review for Ecologists | 22.0 | 1 | 1 |
| NVIDIA A800 40GB Active GPU | 22.0 | 1 | 1 |
| Computing Power and the Governance of Artificial Intelligence | 22.0 | 1 | 1 |
| Artificial Intelligence for Engineering Design, Analysis and Manufacturing | 21.0 | 0 | 3 |
| Data Science: Big Data, Machine Learning, and Artificial Intelligence | 21.0 | 0 | 3 |
| Data science, artificial intelligence, and machine learning: Opportunities for laboratory medicine and the value of positive regulation | 21.0 | 0 | 3 |
| Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases | 21.0 | 0 | 3 |
| Engineering and clinical use of artificial intelligence (AI) with machine learning and data science advancements: radiology leading the way for future | 21.0 | 0 | 3 |
| Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence | 21.0 | 0 | 3 |
| Machine learning model development with interactive model evaluation | 21.0 | 0 | 3 |
| Machine learning based concept drift detection for predictive maintenance | 21.0 | 0 | 3 |
| Expert Monitoring: Human-Centered Concept Drift Detection in Machine Learning Operations | 21.0 | 0 | 3 |
| Rudrendu Kumar Paul | 21.0 | 0 | 3 |
| Q11219 | 18.3 | 2 | 0 |
| Q11278 | 18.3 | 2 | 0 |
| Windows Glyph List 4 | 18.3 | 2 | 0 |
| Windows Installer | 18.3 | 2 | 0 |
| SPSS | 18.3 | 2 | 0 |
| Nvidia | 18.3 | 2 | 0 |
| DirectX | 18.3 | 2 | 0 |
| Microsoft Paint | 18.3 | 2 | 0 |
| Acta Botanica Brasilica | 18.3 | 2 | 0 |
| CHKDSK | 18.3 | 2 | 0 |
| Microsoft Digital Image | 18.3 | 2 | 0 |
| Windows Registry | 18.3 | 2 | 0 |
| Red Hat | 18.3 | 2 | 0 |
| Visual Basic for Applications | 18.3 | 2 | 0 |
| Azure | 18.3 | 2 | 0 |
| Microsoft Dynamics NAV | 18.3 | 2 | 0 |
| Microsoft AutoRoute | 18.3 | 2 | 0 |
| IBM Informix | 18.3 | 2 | 0 |
| CICS | 18.3 | 2 | 0 |
| IBM Rational DOORS | 18.3 | 2 | 0 |
| Microsoft Virtual Server | 18.3 | 2 | 0 |
| Telephony Application Programming Interface | 18.3 | 2 | 0 |
| Rational Rhapsody | 18.3 | 2 | 0 |
| IBM Power Systems | 18.3 | 2 | 0 |
| CPLEX | 18.3 | 2 | 0 |
| Microsoft Student | 18.3 | 2 | 0 |
| Brazilian Journal of Physics | 18.3 | 2 | 0 |
| Scientia Agricola | 18.3 | 2 | 0 |
| OpenShift | 18.3 | 2 | 0 |
| Eclética Química | 18.3 | 2 | 0 |
| Microsoft Layer for Unicode | 18.3 | 2 | 0 |
| IBM Configuration Management Version Control | 18.3 | 2 | 0 |
| Yahoo! Finance | 18.3 | 2 | 0 |
| Brazilian Journal of Biology | 18.3 | 2 | 0 |
| IntelliType | 18.3 | 2 | 0 |
| Revista Brasileira de Biologia | 18.3 | 2 | 0 |
| Microsoft Japan | 18.3 | 2 | 0 |
| Microsoft Search Server | 18.3 | 2 | 0 |
| Neotropical Ichthyology | 18.3 | 2 | 0 |
| Red Hat Virtualization | 18.3 | 2 | 0 |
| Revista Brasileira de Estudos de População | 18.3 | 2 | 0 |
| Brazilian Journal of Psychiatry | 18.3 | 2 | 0 |
| Revista Brasileira de Ciências Sociais | 18.3 | 2 | 0 |
| Revista Brasileira de Ensino de Física | 18.3 | 2 | 0 |
| Q10984556 | 18.3 | 2 | 0 |
| Microsoft Pinyin IME | 18.3 | 2 | 0 |
| Journal of Applied Oral Science | 18.3 | 2 | 0 |
| Arquivos de Neuro-Psiquiatria | 18.3 | 2 | 0 |
| Agora | 18.3 | 2 | 0 |
| Arquivos Brasileiros de Endocrinologia e Metabologia | 18.3 | 2 | 0 |
| Arquivos de Gastroenterologia | 18.3 | 2 | 0 |
| Arquivos Brasileiros de Oftalmologia | 18.3 | 2 | 0 |
| Arquivo Brasileiro de Medicina Veterinaria e Zootecnia | 18.3 | 2 | 0 |
| Kriterion: revista de filosofia | 18.3 | 2 | 0 |
| Alea | 18.3 | 2 | 0 |
| Bragantia | 18.3 | 2 | 0 |
| Ciencia e Tecnologia de Alimentos | 18.3 | 2 | 0 |
| Revista Brasileira de Zootecnia | 18.3 | 2 | 0 |
| Revista Latino-Americana de Efermagem | 18.3 | 2 | 0 |
| Revista de Psiquiatria Clinica | 18.3 | 2 | 0 |
| Brazilian Journal of Microbiology | 18.3 | 2 | 0 |
| Mana | 18.3 | 2 | 0 |
| Revista Brasileira de Entomologia | 18.3 | 2 | 0 |
| Memórias do Instituto Oswaldo Cruz | 18.3 | 2 | 0 |
| Iheringia. Série Zoologia | 18.3 | 2 | 0 |
| Neotropical Entomology | 18.3 | 2 | 0 |
| Sao Paulo Medical Journal | 18.3 | 2 | 0 |
| Revista Arvore: Brazilian Journal of Forest Science | 18.3 | 2 | 0 |
| Microsoft Movies & TV | 18.3 | 2 | 0 |
| Microsoft Mobile | 18.3 | 2 | 0 |
| Q18146823 | 18.3 | 2 | 0 |
| Q18168774 | 18.3 | 2 | 0 |
| Nauplius | 18.3 | 2 | 0 |
| Microsoft Lumia 950 XL | 18.3 | 2 | 0 |
| Windows Subsystem for Linux | 18.3 | 2 | 0 |
| Microsoft Entra ID | 18.3 | 2 | 0 |
| Azure Cognitive Search | 18.3 | 2 | 0 |
| Pró-fono : revista de atualização científica | 18.3 | 2 | 0 |
| Cadernos de saúde pública | 18.3 | 2 | 0 |
| Jornal de pediatria | 18.3 | 2 | 0 |
| International Brazilian Journal of Urology | 18.3 | 2 | 0 |
| Revista do Instituto de Medicina Tropical de São Paulo | 18.3 | 2 | 0 |
| Revista da Associação Médica Brasileira | 18.3 | 2 | 0 |
| Revista da Sociedade Brasileira de Medicina Tropical | 18.3 | 2 | 0 |
| Ciência & saude coletiva | 18.3 | 2 | 0 |
| Revista brasileira de anestesiologia | 18.3 | 2 | 0 |
| Revista brasileira de ginecologia e obstetrícia : revista da Federação Brasileira das Sociedades de Ginecologia e Obstetrícia | 18.3 | 2 | 0 |
| Brazilian Oral Research | 18.3 | 2 | 0 |
| Microsoft Dynamics 365 | 18.3 | 2 | 0 |
| Revista de odontologia da Universidade de Sao Paulo | 18.3 | 2 | 0 |
| Revista do Hospital das Clinicas | 18.3 | 2 | 0 |
| História, Ciências, Saúde-Manguinhos | 18.3 | 2 | 0 |
| Pesquisa Odontológica Brasileira | 18.3 | 2 | 0 |
| Revista brasileira de ciencia do solo | 18.3 | 2 | 0 |
| Brazilian Journal of Botany | 18.3 | 2 | 0 |
| Psicologia em estudo | 18.3 | 2 | 0 |
| Tempo (Rio de Janeiro, Brazil) | 18.3 | 2 | 0 |
| Brazilian journal of veterinary research and animal science | 18.3 | 2 | 0 |
| Educacao & sociedade | 18.3 | 2 | 0 |
| Jornal brasileiro de patologia e medicina laboratorial | 18.3 | 2 | 0 |
| Educação em Revista | 18.3 | 2 | 0 |
| Bubble | 18.3 | 2 | 0 |
| SAS Institute | 18.3 | 2 | 0 |
| CaosDB - Research Data Management for Complex, Changing, and Automated Research Workflows | 18.3 | 2 | 0 |
| Planta Daninha | 18.3 | 2 | 0 |
| Pesquisa Operacional | 18.3 | 2 | 0 |
| Estudos de Psicologia (Campinas) | 18.3 | 2 | 0 |
| Psicologia USP | 18.3 | 2 | 0 |
| Dados: Revista de Ciências Sociais | 18.3 | 2 | 0 |
| Cadernos Cedes | 18.3 | 2 | 0 |
| Revista de Psiquiatria do Rio Grande do Sul | 18.3 | 2 | 0 |
| História | 18.3 | 2 | 0 |
| Revista da Faculdade de Educação | 18.3 | 2 | 0 |
| Jornal de Pneumologia | 18.3 | 2 | 0 |
| Psicologia: Teoria e Pesquisa | 18.3 | 2 | 0 |
| Lua Nova: Revista de Cultura e Política | 18.3 | 2 | 0 |
| Cerâmica | 18.3 | 2 | 0 |
| Estudos de Psicologia (Natal) | 18.3 | 2 | 0 |
| Revista de Nutrição | 18.3 | 2 | 0 |
| Revista Latinoamericana de Psicopatologia Fundamental | 18.3 | 2 | 0 |
| Psicologia | 18.3 | 2 | 0 |
| Avaliação: Revista da Avaliação da Educação Superior | 18.3 | 2 | 0 |
| Interface: Comunicação, Saúde, Educação | 18.3 | 2 | 0 |
| Ciência e Agrotecnologia | 18.3 | 2 | 0 |
| Sociologias | 18.3 | 2 | 0 |
| Revista Brasileira de Saúde Materno Infantil | 18.3 | 2 | 0 |
| Jornal Vascular Brasileiro | 18.3 | 2 | 0 |
| Perspectivas em Ciência da Informação | 18.3 | 2 | 0 |
| Microsoft Saudi | 18.3 | 2 | 0 |
| Revista Escola de Minas | 18.3 | 2 | 0 |
| Sao Paulo em Perspectiva | 18.3 | 2 | 0 |
| IBM Cloud | 18.3 | 2 | 0 |
| Estudos Afro-Asiáticos | 18.3 | 2 | 0 |
| Microsoft Mesh | 18.3 | 2 | 0 |
| IBM Cloud Object Storage | 18.3 | 2 | 0 |
| Branwen R Drew | 18.3 | 2 | 0 |
| Brian Proffitt | 18.3 | 2 | 0 |
| Microsoft Lists | 18.3 | 2 | 0 |
| Anyscale, Inc. | 18.3 | 2 | 0 |
| Microsoft Berlin | 18.3 | 2 | 0 |
| huggingface_hub | 18.3 | 2 | 0 |
| Antimalware Scan Interface | 18.3 | 2 | 0 |
| Anais da Sociedade Entomológica do Brasil | 18.3 | 2 | 0 |
| SPARQL Generation: an analysis on fine-tuning OpenLLaMA for Question Answering over a Life Science Knowledge Graph | 18.3 | 2 | 0 |
| Donald Michie | 16.7 | 0 | 2 |
| emerging technology | 16.7 | 0 | 2 |
| Knowledge Engineering and Machine Learning Group | 16.7 | 0 | 2 |
| Atlantic Council | 16.7 | 0 | 2 |
| backpropagation | 16.7 | 0 | 2 |
| intelligent control | 16.7 | 0 | 2 |
| Decision Intelligence | 16.7 | 0 | 2 |
| David M. Blei | 16.7 | 0 | 2 |
| concept drift | 16.7 | 0 | 2 |
| Pierre Baldi | 16.7 | 0 | 2 |
| grid search | 16.7 | 0 | 2 |
| training, validation, and test data sets | 16.7 | 0 | 2 |
| Artificial Intelligence | 16.7 | 0 | 2 |
| Daniel S. Jurafsky | 16.7 | 0 | 2 |
| early stopping | 16.7 | 0 | 2 |
| Eric Horvitz | 16.7 | 0 | 2 |
| Journal of Artificial Intelligence Research | 16.7 | 0 | 2 |
| Ken Forbus | 16.7 | 0 | 2 |
| Kevin Leyton-Brown | 16.7 | 0 | 2 |
| statistical relational learning | 16.7 | 0 | 2 |
| Applied Artificial Intelligence | 16.7 | 0 | 2 |
| Connection Science | 16.7 | 0 | 2 |
| Statistical Analysis and Data Mining | 16.7 | 0 | 2 |
| Journal of Experimental and Theoretical Artificial Intelligence | 16.7 | 0 | 2 |
| Lise Getoor | 16.7 | 0 | 2 |
| similarity learning | 16.7 | 0 | 2 |
| vanishing gradient problem | 16.7 | 0 | 2 |
| Eric P. Xing | 16.7 | 0 | 2 |
| Michael J. Kearns | 16.7 | 0 | 2 |
| J. Nathan Kutz | 16.7 | 0 | 2 |
| AHaH Computing–From Metastable Switches to Attractors to Machine Learning | 16.7 | 0 | 2 |
| feature engineering | 16.7 | 0 | 2 |
| RankBrain | 16.7 | 0 | 2 |
| Nervana Systems | 16.7 | 0 | 2 |
| Probabilistic machine learning and artificial intelligence | 16.7 | 0 | 2 |
| Learning classification models with soft-label information | 16.7 | 0 | 2 |
| Synthesis lectures on artificial intelligence and machine learning | 16.7 | 0 | 2 |
| Arthur Zimek | 16.7 | 0 | 2 |
| A global machine learning based scoring function for protein structure prediction | 16.7 | 0 | 2 |
| PCP-ML: Protein characterization package for machine learning | 16.7 | 0 | 2 |
| Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises | 16.7 | 0 | 2 |
| Detecting Falls with Wearable Sensors Using Machine Learning Techniques | 16.7 | 0 | 2 |
| Application of machine learning algorithms for clinical predictive modeling: a data-mining approach in SCT | 16.7 | 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 | 16.7 | 0 | 2 |
| Fusing Dual-Event Data Sets for Mycobacterium tuberculosis Machine Learning Models and Their Evaluation | 16.7 | 0 | 2 |
| Integrating machine learning techniques into robust data enrichment approach and its application to gene expression data | 16.7 | 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 | 16.7 | 0 | 2 |
| Transfer learning based clinical concept extraction on data from multiple sources | 16.7 | 0 | 2 |
| Are Bigger Data Sets Better for Machine Learning? Fusing Single-Point and Dual-Event Dose Response Data for Mycobacterium tuberculosis | 16.7 | 0 | 2 |
| Improving peak detection in high-resolution LC/MS metabolomics data using preexisting knowledge and machine learning approach | 16.7 | 0 | 2 |
| Analysis of cytokine release assay data using machine learning approaches | 16.7 | 0 | 2 |
| Applications of Machine Learning and Data Mining Methods to Detect Associations of Rare and Common Variants with Complex Traits | 16.7 | 0 | 2 |
| "Big data" - large data, a lot of knowledge? | 16.7 | 0 | 2 |
| Frank Pasquale | 16.7 | 0 | 2 |
| Providing data science support for systems pharmacology and its implications to drug discovery | 16.7 | 0 | 2 |
| Computational identification of surrogate genes for prostate cancer phases using machine learning and molecular network analysis | 16.7 | 0 | 2 |
| Combining Phylogenetic Profiling-Based and Machine Learning-Based Techniques to Predict Functional Related Proteins | 16.7 | 0 | 2 |
| Machine learning in cell biology – teaching computers to recognize phenotypes | 16.7 | 0 | 2 |
| DR-Predictor: Incorporating Flexible Docking with Specialized Electronic Reactivity and Machine Learning Techniques to Predict CYP-Mediated Sites of Metabolism | 16.7 | 0 | 2 |
| A review of machine learning methods to predict the solubility of overexpressed recombinant proteins in Escherichia coli | 16.7 | 0 | 2 |
| Efficient design of meganucleases using a machine learning approach | 16.7 | 0 | 2 |
| Prediction of hepatitis C virus interferon/ribavirin therapy outcome based on viral nucleotide attributes using machine learning algorithms | 16.7 | 0 | 2 |
| In Silico Machine Learning Methods in Drug Development | 16.7 | 0 | 2 |
| Fullrmc, a rigid body Reverse Monte Carlo modeling package enabled with machine learning and artificial intelligence | 16.7 | 0 | 2 |
| Feature engineering combined with machine learning and rule-based methods for structured information extraction from narrative clinical discharge summaries | 16.7 | 0 | 2 |
| Machine Learning Algorithms Outperform Conventional Regression Models in Predicting Development of Hepatocellular Carcinoma | 16.7 | 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 | 16.7 | 0 | 2 |
| Medical decision support using machine learning for early detection of late-onset neonatal sepsis | 16.7 | 0 | 2 |
| Review of machine learning and signal processing techniques for automated electrode selection in high-density microelectrode arrays | 16.7 | 0 | 2 |
| Machine Learning and Tubercular Drug Target Recognition | 16.7 | 0 | 2 |
| Analysis of MicroRNA Expression Using Machine Learning | 16.7 | 0 | 2 |
| Predicting essential genes for identifying potential drug targets in Aspergillus fumigatus | 16.7 | 0 | 2 |
| Class probability estimation for medical studies | 16.7 | 0 | 2 |
| Machine Learning-Based Methods for Prediction of Linear B-Cell Epitopes | 16.7 | 0 | 2 |
| Hybrid Machine Learning Technique for Forecasting Dhaka Stock Market Timing Decisions | 16.7 | 0 | 2 |
| Machine Learning in the Rational Design of Antimicrobial Peptides | 16.7 | 0 | 2 |
| Potential Application of Machine Learning in Health Outcomes Research and Some Statistical Cautions | 16.7 | 0 | 2 |
| The application of machine learning to the modelling of percutaneous absorption: An overview and guide | 16.7 | 0 | 2 |
| The Mutual Inspirations of Machine Learning and Neuroscience | 16.7 | 0 | 2 |
| Machine learning applications in genetics and genomics | 16.7 | 0 | 2 |
| Machine learning methods for the classification of gliomas: Initial results using features extracted from MR spectroscopy | 16.7 | 0 | 2 |
| Classification of lung cancer using ensemble-based feature selection and machine learning methods | 16.7 | 0 | 2 |
| Redefining climate regions in the United States of America using satellite remote sensing and machine learning for public health applications | 16.7 | 0 | 2 |
| Comparison and Validation of Injury Risk Classifiers for Advanced Automated Crash Notification Systems | 16.7 | 0 | 2 |
| Catia Pesquita | 16.7 | 0 | 2 |
| Deep into the Brain: Artificial Intelligence in Stroke Imaging | 16.7 | 0 | 2 |
| Supervised machine learning and active learning in classification of radiology reports | 16.7 | 0 | 2 |
| KI – Künstliche Intelligenz | 16.7 | 0 | 2 |
| The Human Behaviour-Change Project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation | 16.7 | 0 | 2 |
| Rapid estimation of compost enzymatic activity by spectral analysis method combined with machine learning | 16.7 | 0 | 2 |
| What subject matter questions motivate the use of machine learning approaches compared to statistical models for probability prediction? | 16.7 | 0 | 2 |
| Automatic recognition of disorders, findings, pharmaceuticals and body structures from clinical text: An annotation and machine learning study | 16.7 | 0 | 2 |
| Using machine learning to blend human and robot controls for assisted wheelchair navigation | 16.7 | 0 | 2 |
| Predictability of intracranial pressure level in traumatic brain injury: features extraction, statistical analysis and machine learning-based evaluation | 16.7 | 0 | 2 |
| Artificial Intelligence in Medical Practice: The Question to the Answer? | 16.7 | 0 | 2 |
| Machine learning and data science in soft materials engineering | 16.7 | 0 | 2 |
| Artificial intelligence expert systems with neural network machine learning may assist decision-making for extractions in orthodontic treatment planning | 16.7 | 0 | 2 |
| Use of Artificial Intelligence and Machine Learning Algorithms with Gene Expression Profiling to Predict Recurrent Nonmuscle Invasive Urothelial Carcinoma of the Bladder | 16.7 | 0 | 2 |
| HClass: Automatic classification tool for health pathologies using artificial intelligence techniques | 16.7 | 0 | 2 |
| In silico prediction of anti-malarial hit molecules based on machine learning methods | 16.7 | 0 | 2 |
| Machine learning-based detection of chemical risk | 16.7 | 0 | 2 |
| Quantifying surgical complexity with machine learning: Looking beyond patient factors to improve surgical models | 16.7 | 0 | 2 |
| A computational visual saliency model based on statistics and machine learning | 16.7 | 0 | 2 |
| Probability estimation and machine learning—Editorial | 16.7 | 0 | 2 |
| Computer aided diagnosis of degenerative intervertebral disc diseases from lumbar MR images | 16.7 | 0 | 2 |
| Artificial intelligence to assist clinical diagnosis in medicine | 16.7 | 0 | 2 |
| Uncertainty quantification and integration of machine learning techniques for predicting acid rock drainage chemistry: A probability bounds approach | 16.7 | 0 | 2 |
| Utility of Vital Signs, Heart Rate Variability and Complexity, and Machine Learning for Identifying the Need for Lifesaving Interventions in Trauma Patients | 16.7 | 0 | 2 |
| Development and validation of a machine learning algorithm and hybrid system to predict the need for life-saving interventions in trauma patients | 16.7 | 0 | 2 |
| Classification of mysticete sounds using machine learning techniques | 16.7 | 0 | 2 |
| Machine learning approach to an otoneurological classification problem | 16.7 | 0 | 2 |
| Editorial: Charting Chemical Space: Challenges and Opportunities for Artificial Intelligence and Machine Learning | 16.7 | 0 | 2 |
| Diagnosing shock via artificial intelligence: applying machine learning techniques to medicine | 16.7 | 0 | 2 |
| Machine Learning and Artificial Intelligence in Radiology | 16.7 | 0 | 2 |
| On the Fuzziness of Machine Learning, Neural Networks, and Artificial Intelligence in Radiation Oncology. | 16.7 | 0 | 2 |
| Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success | 16.7 | 0 | 2 |
| The Journal of Finance and Data Science | 16.7 | 0 | 2 |
| The ACR Data Science Institute and AI Advisory Group: Harnessing the Power of Artificial Intelligence to Improve Patient Care | 16.7 | 0 | 2 |
| Machine Learning, Natural Language Programming, and Electronic Health Records: the next step in the Artificial Intelligence Journey? | 16.7 | 0 | 2 |
| Machine learning & artificial intelligence in the quantum domain: a review of recent progress | 16.7 | 0 | 2 |
| A Multianalyzer Machine Learning Model for Marine Heterogeneous Data Schema Mapping | 16.7 | 0 | 2 |
| Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What Do These Terms Mean and How Will They Impact Health Care? | 16.7 | 0 | 2 |
| Toward Augmented Radiologists: Changes in Radiology Education in the Era of Machine Learning and Artificial Intelligence | 16.7 | 0 | 2 |
| Predicting Treatment Response to Intra-arterial Therapies for Hepatocellular Carcinoma with the Use of Supervised Machine Learning-An Artificial Intelligence Concept | 16.7 | 0 | 2 |
| Physical Model Evaluation of Topical Prodrug Delivery—simultaneous Transport and Bioconversion of vidarabine-5′-valerate I: Physical Model Development | 16.7 | 0 | 2 |
| International Journal of Data Science and Analytics | 16.7 | 0 | 2 |
| Artificial intelligence, machine learning and the evolution of healthcare: A bright future or cause for concern? | 16.7 | 0 | 2 |
| Gender bias in artificial intelligence: the need for diversity and gender theory in machine learning | 16.7 | 0 | 2 |
| TensorFlow.js | 16.7 | 0 | 2 |
| DuerOS | 16.7 | 0 | 2 |
| Ursa Labs | 16.7 | 0 | 2 |
| Automation, machine learning, and artificial intelligence in echocardiography: A brave new world | 16.7 | 0 | 2 |
| Q57201105 | 16.7 | 0 | 2 |
| Data Science in the Research Domain Criteria Era: Relevance of Machine Learning to the Study of Stress Pathology, Recovery, and Resilience | 16.7 | 0 | 2 |
| Rule-based Machine Learning Methods for Functional Prediction | 16.7 | 0 | 2 |
| A General Feature Engineering Wrapper for Machine Learning Using $$\epsilon $$ -Lexicase Survival | 16.7 | 0 | 2 |
| Peering Into the Black Box of Artificial Intelligence: Evaluation Metrics of Machine Learning Methods | 16.7 | 0 | 2 |
| Data science and artificial intelligence to improve clinical practice and research | 16.7 | 0 | 2 |
| Informatics, Data Science, and Artificial Intelligence | 16.7 | 0 | 2 |
| How Bioethics Can Shape Artificial Intelligence and Machine Learning | 16.7 | 0 | 2 |
| Machine learning in computer vision | 16.7 | 0 | 2 |
| MACHINE LEARNING OF MORPHOSYNTACTIC STRUCTURE: LEMMATIZING UNKNOWN SLOVENE WORDS | 16.7 | 0 | 2 |
| Extraction of Structured Information by Machine Learning Using Community Information | 16.7 | 0 | 2 |
| Cross-Platform Normalization Enables Machine Learning Model Training On Microarray And RNA-Seq Data Simultaneously | 16.7 | 0 | 2 |
| Argument Based Machine Learning Applied to Law | 16.7 | 0 | 2 |
| FIELDED MACHINE LEARNING SYSTEM FOR VOCATIONAL COUNSELLING | 16.7 | 0 | 2 |
| Concept Drift Detection with Clustering via Statistical Change Detection Methods | 16.7 | 0 | 2 |
| Pathological brain detection in MRI scanning via Hu moment invariants and machine learning | 16.7 | 0 | 2 |
| PREDICTING STUDENTS' PERFORMANCE IN DISTANCE LEARNING USING MACHINE LEARNING TECHNIQUES | 16.7 | 0 | 2 |
| Where do machine learning and human-computer interaction meet? | 16.7 | 0 | 2 |
| INDUSTRIAL EXPERT SYSTEM ACQUIRED BY MACHINE LEARNING | 16.7 | 0 | 2 |
| MACHINE LEARNING GOES TO THE BANK | 16.7 | 0 | 2 |
| MACHINE LEARNING IN HYBRID HIERARCHICAL AND PARTIAL-ORDER PLANNERS FOR MANUFACTURING DOMAINS | 16.7 | 0 | 2 |
| Machine Learning Applications in Baseball: A Systematic Literature Review | 16.7 | 0 | 2 |
| Id+: Enhancing medical knowledge acquisition with machine learning | 16.7 | 0 | 2 |
| Machine learning meets human-computer interaction - introduction to the special issue | 16.7 | 0 | 2 |
| Machine learning: A tool to support usability? | 16.7 | 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 | 16.7 | 0 | 2 |
| Machine learning for map interpretation: An intelligent tool for environmental planning | 16.7 | 0 | 2 |
| MACHINE LEARNING TECHNIQUES FOR ACQUIRING NEW KNOWLEDGE IN IMAGE TRACKING | 16.7 | 0 | 2 |
| Topological Data Analysis and Machine Learning for Recognizing Atmospheric River Patterns in Large Climate Datasets | 16.7 | 0 | 2 |
| An ice sheet model validation framework for the Greenland ice sheet | 16.7 | 0 | 2 |
| Artificial intelligence machine learning-based coronary CT fractional flow reserve (CT-FFR): Impact of iterative and filtered back projection reconstruction techniques | 16.7 | 0 | 2 |
| Artificial intelligence, machine learning and health systems | 16.7 | 0 | 2 |
| Proposing a Machine Learning Approach to Analyze and Predict Employment and its Factors | 16.7 | 0 | 2 |
| Machine Learning Paradigms for Modeling Spatial and Temporal Information in Multimedia Data Mining | 16.7 | 0 | 2 |
| [Dedicated to Prof. T. Okada and Prof. T. Nishioka: data science in chemistry]Visualizing Individual and Region-specific Microbial–metabolite Relations by Important Variable Selection Using Machine Learning Approaches | 16.7 | 0 | 2 |
| A Tutorial on Machine Learning and Data Science Tools with Python | 16.7 | 0 | 2 |
| Study on the Effectiveness of the Investment Strategy Based on a Classifier with Rules Adapted by Machine Learning | 16.7 | 0 | 2 |
| Narrow Artificial Intelligence with Machine Learning for Real-Time Estimation of a Mobile Agent’s Location Using Hidden Markov Models | 16.7 | 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 | 16.7 | 0 | 2 |
| Big Data Analytics, Machine Learning, and Artificial Intelligence in Next-Generation Wireless Networks | 16.7 | 0 | 2 |
| Model updating mechanism of concept drift detection in data stream based on classifier pool | 16.7 | 0 | 2 |
| Erratum to: Improving official statistics in emerging markets using machine learning and mobile phone data | 16.7 | 0 | 2 |
| Improving official statistics in emerging markets using machine learning and mobile phone data | 16.7 | 0 | 2 |
| In defence of machine learning: Debunking the myths of artificial intelligence | 16.7 | 0 | 2 |
| Applying machine learning to programming by demonstration | 16.7 | 0 | 2 |
| Adaptive kinetic structural behavior through machine learning: Optimizing the process of kinematic transformation using artificial neural networks | 16.7 | 0 | 2 |
| Concept Drift Detection Using Online Histogram-Based Bayesian Classifiers | 16.7 | 0 | 2 |
| A map of global peatland distribution created using machine learning for use in terrestrial ecosystem and earth system models | 16.7 | 0 | 2 |
| New ethical challenges of digital technologies, machine learning and artificial intelligence in public health: a call for papers | 16.7 | 0 | 2 |
| Assessing the Role of Artificial Intelligence (AI) in Clinical Oncology: Utility of Machine Learning in Radiotherapy Target Volume Delineation | 16.7 | 0 | 2 |
| Down the deep rabbit hole: Untangling deep learning from machine learning and artificial intelligence | 16.7 | 0 | 2 |
| Adaptive concept drift detection | 16.7 | 0 | 2 |
| Artificial intelligence and machine learning in wound care-The wounded machine! | 16.7 | 0 | 2 |
| The role of artificial intelligence and machine learning in harmonization of high-resolution post-mortem MRI (virtopsy) with respect to brain microstructure | 16.7 | 0 | 2 |
| Artificial intelligence and machine learning | 16.7 | 0 | 2 |
| Artificial intelligence and machine learning in haematology | 16.7 | 0 | 2 |
| Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018 | 16.7 | 0 | 2 |
| From Machine Learning to Artificial Intelligence Applications in Cardiac Care | 16.7 | 0 | 2 |
| Artificial intelligence and machine learning | applications in musculoskeletal physiotherapy | 16.7 | 0 | 2 |
| An artificial intelligence atomic force microscope enabled by machine learning | 16.7 | 0 | 2 |
| Artificial Intelligence Applied to Osteoporosis: A Performance Comparison of Machine Learning Algorithms in Predicting Fragility Fractures From MRI Data | 16.7 | 0 | 2 |
| Artificial intelligence, machine learning, neural networks, and deep learning: Futuristic concepts for new dental diagnosis | 16.7 | 0 | 2 |
| Machine learning: applications of artificial intelligence to imaging and diagnosis | 16.7 | 0 | 2 |
| The machine learning approach: Artificial intelligence is coming to support critical clinical thinking | 16.7 | 0 | 2 |
| Pathogenesis-based treatments in primary Sjogren's syndrome using artificial intelligence and advanced machine learning techniques: a systematic literature review | 16.7 | 0 | 2 |
| The growing role of machine learning and artificial intelligence in developmental medicine | 16.7 | 0 | 2 |
| The power and limitations of machine learning and artificial intelligence in cardiac CT | 16.7 | 0 | 2 |
| Comparable Study of Statistical Tests for Virtual Concept Drift Detection | 16.7 | 0 | 2 |
| Machine learning and data science in materials design: a themed collection | 16.7 | 0 | 2 |
| Topological data analysis and machine learning for recognizing atmospheric river patterns in large climate datasets | 16.7 | 0 | 2 |
| Parallel dynamic data-driven model for concept drift detection and prediction | 16.7 | 0 | 2 |
| Bridging the gap between human knowledge and machine learning | 16.7 | 0 | 2 |
| Inducing diagnostic rules for glomerular disease with the DLG machine learning algorithm | 16.7 | 0 | 2 |
| Learning design concepts using machine learning techniques | 16.7 | 0 | 2 |
| HumAIn | 16.7 | 0 | 2 |
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| Artificial Intelligence and Machine Learning in Endocrinology and Metabolism: The Dawn of a New Era | 16.7 | 0 | 2 |
| Machine Learning and Knowledge Extraction | 16.7 | 0 | 2 |
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| Artificial Intelligence/Machine Learning Modeling on Time to Palliative Care Review in an Inpatient Hospital Population | 16.7 | 0 | 2 |
| Histopathology Images Based Survival Prediction of Glioma Patients Using Artificial Intelligence | 16.7 | 0 | 2 |
| MR Based Survival Prediction of Glioma Patients Using Artificial Intelligence | 16.7 | 0 | 2 |
| Histopathology Images Based Prediction of Molecular Pathology in Glioma Using Artificial Intelligence | 16.7 | 0 | 2 |
| MR Based Prediction of Molecular Pathology in Glioma Using Artificial Intelligence | 16.7 | 0 | 2 |
| Vanderbilt Department of Biomedical Engineering | 16.7 | 0 | 2 |
| Artificial Intelligence and Machine Learning: Opportunities for Radiologists in Training | 16.7 | 0 | 2 |
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| Artificial Intelligence in Diagnosis of DFNA9 | 16.7 | 0 | 2 |
| Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches | 16.7 | 0 | 2 |
| Machine learning and artificial intelligence in the service of medicine: Necessity or potentiality? | 16.7 | 0 | 2 |
| Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine | 16.7 | 0 | 2 |
| Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence | 16.7 | 0 | 2 |
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| The Impact of Artificial Intelligence and Machine Learning in Radiation Therapy: Considerations for Future Curriculum Enhancement | 16.7 | 0 | 2 |
| Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods | 16.7 | 0 | 2 |
| Intelligent Artificial Intelligence: Present Considerations and Future Implications of Machine Learning Applied to Electrocardiogram Interpretation | 16.7 | 0 | 2 |
| Application of artificial intelligence (AI) in Radiotherapy workflow: Paradigm shift in Precision Radiotherapy using Machine Learning | 16.7 | 0 | 2 |
| Artificial intelligence and machine learning in emergency medicine | 16.7 | 0 | 2 |
| Artificial intelligence and machine learning in respiratory medicine | 16.7 | 0 | 2 |
| Payment Reform in the Era of Advanced Diagnostics, Artificial Intelligence, and Machine Learning | 16.7 | 0 | 2 |
| Artificial Intelligence for Aortic Pressure Waveform Analysis During Coronary Angiography: Machine Learning for Patient Safety | 16.7 | 0 | 2 |
| Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine | 16.7 | 0 | 2 |
| A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases | 16.7 | 0 | 2 |
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| Data Science and Machine Learning in Anesthesiology | 16.7 | 0 | 2 |
| Artificial intelligence and machine learning to fight COVID-19 | 16.7 | 0 | 2 |
| Artificial Intelligence and Machine learning based prediction of resistant and susceptible mutations in Mycobacterium tuberculosis | 16.7 | 0 | 2 |
| Identifying tuberculous pleural effusion using artificial intelligence machine learning algorithms | 16.7 | 0 | 2 |
| Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness | 16.7 | 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 | 16.7 | 0 | 2 |
| Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics | 16.7 | 0 | 2 |
| Artificial Intelligence and Machine Learning in Cardiovascular Healthcare | 16.7 | 0 | 2 |
| Machine Learning and Artificial Intelligence in Neurocritical Care: a Specialty-Wide Disruptive Transformation or a Strategy for Success | 16.7 | 0 | 2 |
| Concept drift detection on social network data using cross-recurrence quantification analysis | 16.7 | 0 | 2 |
| Artificial Intelligence/Machine Learning in Diabetes Care | 16.7 | 0 | 2 |
| Lifecycle Regulation of Artificial Intelligence- and Machine Learning-Based Software Devices in Medicine | 16.7 | 0 | 2 |
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| Artificial Intelligence Applications in Type 2 Diabetes Mellitus Care: Focus on Machine Learning Methods | 16.7 | 0 | 2 |
| The role of artificial intelligence and machine learning in predicting orthopaedic outcomes | 16.7 | 0 | 2 |
| Advanced Editorial to announce a JCAMD Special Issue on Artificial Intelligence and Machine Learning | 16.7 | 0 | 2 |
| Artificial Intelligence and Machine Learning: A New Disruptive Force in Orthopaedics | 16.7 | 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 | 16.7 | 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?" | 16.7 | 0 | 2 |
| Artificial Intelligence: A New Tool in Operating Room Management. Role of Machine Learning Models in Operating Room Optimization | 16.7 | 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?" | 16.7 | 0 | 2 |
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| Artificial intelligence, machine learning and the pediatric airway | 16.7 | 0 | 2 |
| A new era: artificial intelligence and machine learning in prostate cancer | 16.7 | 0 | 2 |
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| A(eye): A Review of Current Applications of Artificial Intelligence and Machine Learning in Ophthalmology | 16.7 | 0 | 2 |
| New Phenotypes for Sepsis: The Promise and Problem of Applying Machine Learning and Artificial Intelligence in Clinical Research | 16.7 | 0 | 2 |
| The doctor will see you now: How machine learning and artificial intelligence can extend our understanding and treatment of asthma | 16.7 | 0 | 2 |
| Reducing the Concepts of Data Science and Machine Learning to Tools for the Bench Chemist | 16.7 | 0 | 2 |
| The coming era of artificial intelligence in biological data science | 16.7 | 0 | 2 |
| Artificial intelligence and machine learning in clinical development: a translational perspective | 16.7 | 0 | 2 |
| Artificial intelligence and avian influenza: Using machine learning to enhance active surveillance for avian influenza viruses | 16.7 | 0 | 2 |
| Artificial intelligence in dermato-oncology: a joint clinical and data science perspective | 16.7 | 0 | 2 |
| Promising Artificial Intelligence-Machine Learning-Deep Learning Algorithms in Ophthalmology | 16.7 | 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 | 16.7 | 0 | 2 |
| Radiomics and Artificial Intelligence for Biomarker and Prediction Model Development in Oncology | 16.7 | 0 | 2 |
| Artificial Intelligence and Machine Learning | 16.7 | 0 | 2 |
| Ripe for Disruption? Adopting Nurse-Led Data Science and Artificial Intelligence to Predict and Reduce Hospital-Acquired Outcomes in the Learning Health System | 16.7 | 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" | 16.7 | 0 | 2 |
| A modeling and machine learning approach to ECG feature engineering for the detection of ischemia using pseudo-ECG | 16.7 | 0 | 2 |
| Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning | 16.7 | 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 | 16.7 | 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 | 16.7 | 0 | 2 |
| Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review | 16.7 | 0 | 2 |
| Artificial intelligence, machine learning, and the human interface in medicine: Is there a sweet spot for oral and maxillofacial radiology? | 16.7 | 0 | 2 |
| A Novel Concept Drift Detection Method for Incremental Learning in Nonstationary Environments | 16.7 | 0 | 2 |
| A Machine Learning Approach for the Detection and Characterization of Illicit Drug Dealers on Instagram: Model Evaluation Study | 16.7 | 0 | 2 |
| Artificial Intelligence in Medical Education: Best Practices Using Machine Learning to Assess Surgical Expertise in Virtual Reality Simulation | 16.7 | 0 | 2 |
| Prediction model development of late-onset preeclampsia using machine learning-based methods | 16.7 | 0 | 2 |
| Supervised Machine Learning Based Multi-Task Artificial Intelligence Classification of Retinopathies | 16.7 | 0 | 2 |
| Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions | 16.7 | 0 | 2 |
| Artificial intelligence and machine learning for predicting acute kidney injury in severely burned patients: A proof of concept | 16.7 | 0 | 2 |
| Artificial intelligence and machine learning in spine research | 16.7 | 0 | 2 |
| Artificial intelligence, machine learning and deep learning: definitions and differences | 16.7 | 0 | 2 |
| Commentary: Rise of machine learning and artificial intelligence in ophthalmology | 16.7 | 0 | 2 |
| Artificial Intelligence and Machine Learning in Anesthesiology | 16.7 | 0 | 2 |
| Radiogenomics in Medulloblastoma: Can the Human Brain Compete with Artificial Intelligence and Machine Learning? | 16.7 | 0 | 2 |
| Automated Detection of Radiology Reports that Require Follow-up Imaging Using Natural Language Processing Feature Engineering and Machine Learning Classification | 16.7 | 0 | 2 |
| Artificial Intelligence and Machine Learning in Lower Extremity Arthroplasty: A Review | 16.7 | 0 | 2 |
| Predicting vital sign deterioration with artificial intelligence or machine learning | 16.7 | 0 | 2 |
| Coverage of ethics within the artificial intelligence and machine learning academic literature: The case of disabled people | 16.7 | 0 | 2 |
| Strengths, Weaknesses, Opportunities, and Threats Analysis of Artificial Intelligence and Machine Learning Applications in Radiology | 16.7 | 0 | 2 |
| Feature engineering with clinical expert knowledge: A case study assessment of machine learning model complexity and performance | 16.7 | 0 | 2 |
| Avoiding common pitfalls in machine learning omic data science | 16.7 | 0 | 2 |
| Artificial Intelligence and Machine Learning for HIV Prevention: Emerging Approaches to Ending the Epidemic | 16.7 | 0 | 2 |
| Applications of artificial intelligence and machine learning in respiratory medicine | 16.7 | 0 | 2 |
| Artificial Intelligence and Machine Learning in Radiology Education Is Ready for Prime Time | 16.7 | 0 | 2 |
| Artificial Intelligence and Machine Learning to Accelerate Translational Research: Proceedings of a Workshop—in Brief | 16.7 | 0 | 2 |
| David S. Matteson | 16.7 | 0 | 2 |
| The Use of Artificial Intelligence and Deep Machine Learning in Oncologic Histopathology | 16.7 | 0 | 2 |
| Artificial Intelligence-Based Neural Network for the Diagnosis of Diabetes: Model Development | 16.7 | 0 | 2 |
| The Use of Artificial Intelligence (AI) Machine Learning to Determine Myocyte Damage in Cardiac Transplant Acute Cellular Rejection | 16.7 | 0 | 2 |
| Artificial intelligence and machine learning in nephropathology | 16.7 | 0 | 2 |
| Reporting and Implementing Interventions Involving Machine Learning and Artificial Intelligence | 16.7 | 0 | 2 |
| Editorial. Machine learning and artificial intelligence applied to the diagnosis and management of Cushing disease | 16.7 | 0 | 2 |
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| Brave New Surgical Innovations: The Impact of Bioprinting, Machine Learning, and Artificial Intelligence in Craniofacial Surgery | 16.7 | 0 | 2 |
| Artificial Intelligence in Subarachnoid Hemorrhage | 16.7 | 0 | 2 |
| Artificial intelligence for interpretation of segments of whole body MRI in CNO: pilot study comparing radiologists versus machine learning algorithm | 16.7 | 0 | 2 |
| Big-Data Science in Porous Materials: Materials Genomics and Machine Learning | 16.7 | 0 | 2 |
| Role of Artificial Intelligence and Machine Learning in Nanosafety | 16.7 | 0 | 2 |
| Machine Learning-Based Signal Quality Evaluation of Single-Period Radial Artery Pulse Waves: Model Development and Validation | 16.7 | 0 | 2 |
| International Journal of Artificial Intelligence and Machine Learning | 16.7 | 0 | 2 |
| Transactions on Machine Learning and Artificial Intelligence | 16.7 | 0 | 2 |
| Data science and machine learning, mathematical and statistical methods | 16.7 | 0 | 2 |
| Machine learning and artificial intelligence in haematology | 16.7 | 0 | 2 |
| Artificial Intelligence for Prostate Cancer Treatment Planning | 16.7 | 0 | 2 |
| Artificial Intelligence and Machine Learning Applied at the Point of Care | 16.7 | 0 | 2 |
| Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine | 16.7 | 0 | 2 |
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| Computational Technology with Artificial Intelligence and Machine Learning: What Should a Cytologist Do with It? | 16.7 | 0 | 2 |
| Use of Machine Learning and Artificial Intelligence to predict SARS-CoV-2 infection from Full Blood Counts in a population | 16.7 | 0 | 2 |
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| Prediction of Cardiac Arrest in the Emergency Department Based on Machine Learning and Sequential Characteristics: Model Development and Retrospective Clinical Validation Study | 16.7 | 0 | 2 |
| Machine Learning Modelling and Feature Engineering in Seismology Experiment | 16.7 | 0 | 2 |
| Artificial Intelligence and Machine Learning in Radiology: Current State and Considerations for Routine Clinical Implementation | 16.7 | 0 | 2 |
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| Response Prediction to Neoadjuvant Chemoradiation in Esophageal Cancer Using Artificial Intelligence & Machine Learning | 16.7 | 0 | 2 |
| Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations | 16.7 | 0 | 2 |
| Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review | 16.7 | 0 | 2 |
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| The present and future role of artificial intelligence and machine learning in anesthesiology | 16.7 | 0 | 2 |
| Artificial Intelligence (AI) Based Machine Learning Models Predict Glucose Variability and Hypoglycaemia Risk in Patients with Type 2 Diabetes on a Multiple Drug Regimen who Fast during Ramadan (The PROFAST - IT Ramadan study) | 16.7 | 0 | 2 |
| Machine learning and artificial intelligence to aid climate change research and preparedness | 16.7 | 0 | 2 |
| Impact of Gene Biomarker Discovery Tools Based on Protein-Protein Interaction and Machine Learning on Performance of Artificial Intelligence Models in Predicting Clinical Stages of Breast Cancer | 16.7 | 0 | 2 |
| The Case for Algorithmic Stewardship for Artificial Intelligence and Machine Learning Technologies | 16.7 | 0 | 2 |
| Winnow Solutions | 16.7 | 0 | 2 |
| Artificial Intelligence in Global Ophthalmology: Using Machine Learning to Improve Cataract Surgery Outcomes at Ethiopian Outreaches | 16.7 | 0 | 2 |
| Addressing health disparities in the Food and Drug Administration's artificial intelligence and machine learning regulatory framework | 16.7 | 0 | 2 |
| Artificial Intelligence, Machine Learning, and Cardiovascular Disease | 16.7 | 0 | 2 |
| Author Correction: Artificial Intelligence and Machine learning based prediction of resistant and susceptible mutations in Mycobacterium tuberculosis | 16.7 | 0 | 2 |
| Patient generated health data and electronic health record integration in oncologic surgery: A call for artificial intelligence and machine learning | 16.7 | 0 | 2 |
| Construction of the Concept Drift Detection Model Based on the Information Entropy of Feature Distribution and Dynamic Weighting Algorithm | 16.7 | 0 | 2 |
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| Machine Learning to Predict Mortality and Critical Events in COVID-19 Positive New York City Patients: A Cohort Study | 16.7 | 0 | 2 |
| Blood Uric Acid Prediction With Machine Learning: Model Development and Performance Comparison | 16.7 | 0 | 2 |
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| [Artificial intelligence and machine learning in oncologic imaging] | 16.7 | 0 | 2 |
| Artificial Intelligence (AI) to the Rescue: Deploying Machine Learning to Bridge the Biorelevance Gap in Antioxidant Assays | 16.7 | 0 | 2 |
| Industry ties and evidence in public comments on the FDA framework for modifications to artificial intelligence/machine learning-based medical devices: a cross sectional study | 16.7 | 0 | 2 |
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| Kairntech SAS | 16.7 | 0 | 2 |
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| The role of data science and machine learning in Health Professions Education: practical applications, theoretical contributions, and epistemic beliefs | 16.7 | 0 | 2 |
| Letter to Editor: "Artificial Intelligence, Machine Learning, Deep Learning and Big Data Analytics for Resource Optimization in Surgery" | 16.7 | 0 | 2 |
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| Haoda Fu | 16.7 | 0 | 2 |
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| Damian Borth | 16.7 | 0 | 2 |
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| Ablation | 16.7 | 0 | 2 |
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| PolyAnalyst | 16.7 | 0 | 2 |
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| Machine Learning, Optimization, and Data Science - 5th International Conference, LOD 2019, Siena, Italy, September 10-13, 2019 | 16.7 | 0 | 2 |
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