| IBM | 60.0 | 36 | 0 |
| Claude | 51.1 | 4 | 4 |
| Microsoft | 47.1 | 16 | 0 |
| Data Analytics for Machine Learning | 40.3 | 5 | 1 |
| Apple Intelligence | 40.0 | 0 | 13 |
| Palantir Technologies | 39.8 | 10 | 0 |
| H2O | 34.9 | 2 | 2 |
| Salesforce | 34.9 | 2 | 2 |
| GPT-4 | 34.9 | 2 | 2 |
| Dolly | 34.9 | 2 | 2 |
| Aleph Alpha | 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 |
| Oracle CRM | 32.3 | 6 | 0 |
| Oracle Fusion Applications | 32.3 | 6 | 0 |
| SAP ERP | 29.8 | 5 | 0 |
| Oracle Database | 29.8 | 5 | 0 |
| Media Creation Tool | 29.8 | 5 | 0 |
| Contentsquare | 29.5 | 0 | 6 |
| IBM Configuration Management Version Control | 28.8 | 2 | 1 |
| OpenAI | 28.8 | 2 | 1 |
| bidirectional encoder representations from transformers | 28.8 | 2 | 1 |
| Microsoft Academic Graph | 28.8 | 2 | 1 |
| OneTrust | 28.8 | 2 | 1 |
| OpenAI OpCo | 28.8 | 2 | 1 |
| Hugging Face Hub | 28.8 | 2 | 1 |
| Microsoft Security Copilot | 28.8 | 2 | 1 |
| Comet | 28.8 | 2 | 1 |
| Q80689 | 26.7 | 4 | 0 |
| Oracle E-Business Suite | 26.7 | 4 | 0 |
| Microsoft Lumia 640 XL | 26.7 | 4 | 0 |
| Mistral Vibe | 24.4 | 0 | 4 |
| Microsoft Windows | 23.0 | 3 | 0 |
| JDeveloper | 23.0 | 3 | 0 |
| Jakarta EE | 23.0 | 3 | 0 |
| PricewaterhouseCoopers | 23.0 | 3 | 0 |
| Oracle SQL Developer | 23.0 | 3 | 0 |
| SAP NetWeaver Business Intelligence | 23.0 | 3 | 0 |
| Elasticsearch | 23.0 | 3 | 0 |
| Datalogix | 23.0 | 3 | 0 |
| IBM Bluemix | 23.0 | 3 | 0 |
| Microsoft Lumia 640 | 23.0 | 3 | 0 |
| Dataiku | 23.0 | 3 | 0 |
| Oracle ERP Cloud | 23.0 | 3 | 0 |
| Oracle Cloud Platform | 23.0 | 3 | 0 |
| Oracle HCM Cloud | 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 |
| Dataminr | 22.0 | 1 | 1 |
| Google | 22.0 | 1 | 1 |
| Seeing AI | 22.0 | 1 | 1 |
| Ryota Tomioka | 22.0 | 1 | 1 |
| Katherine A. Heller | 22.0 | 1 | 1 |
| GPT-2 | 22.0 | 1 | 1 |
| GPT-3 | 22.0 | 1 | 1 |
| Veritone Inc. | 22.0 | 1 | 1 |
| Road Roughness Estimation Using Machine Learning | 22.0 | 1 | 1 |
| Cohere | 22.0 | 1 | 1 |
| Machine Learning and Deep Learning -- A review for Ecologists | 22.0 | 1 | 1 |
| Anthropic | 22.0 | 1 | 1 |
| PaLM | 22.0 | 1 | 1 |
| NVIDIA A800 40GB Active GPU | 22.0 | 1 | 1 |
| DALL·E 3 | 22.0 | 1 | 1 |
| Gemma | 22.0 | 1 | 1 |
| Computing Power and the Governance of Artificial Intelligence | 22.0 | 1 | 1 |
| Premise Order Matters in Reasoning with Large Language Models | 22.0 | 1 | 1 |
| DBRX | 22.0 | 1 | 1 |
| Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data | 22.0 | 1 | 1 |
| Artificial Intelligence for Engineering Design, Analysis and Manufacturing | 21.0 | 0 | 3 |
| operational risk management | 21.0 | 0 | 3 |
| Perspectives in healthcare risk management | 21.0 | 0 | 3 |
| Model AI Governance Framework for Generative AI | 21.0 | 0 | 3 |
| Rudrendu Kumar Paul | 21.0 | 0 | 3 |
| Q11219 | 18.3 | 2 | 0 |
| Q11278 | 18.3 | 2 | 0 |
| Intel Technology Journal | 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 |
| Wolters Kluwer | 18.3 | 2 | 0 |
| Microsoft Paint | 18.3 | 2 | 0 |
| CHKDSK | 18.3 | 2 | 0 |
| Microsoft Digital Image | 18.3 | 2 | 0 |
| Windows Registry | 18.3 | 2 | 0 |
| Deloitte | 18.3 | 2 | 0 |
| KPMG | 18.3 | 2 | 0 |
| Visual Basic for Applications | 18.3 | 2 | 0 |
| Azure | 18.3 | 2 | 0 |
| Forrester | 18.3 | 2 | 0 |
| Microsoft Dynamics NAV | 18.3 | 2 | 0 |
| Microsoft AutoRoute | 18.3 | 2 | 0 |
| Salesforce | 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 |
| GFT Technologies | 18.3 | 2 | 0 |
| Telephony Application Programming Interface | 18.3 | 2 | 0 |
| Splunk Inc. | 18.3 | 2 | 0 |
| Rational Rhapsody | 18.3 | 2 | 0 |
| Oracle Application Server | 18.3 | 2 | 0 |
| IBM Power Systems | 18.3 | 2 | 0 |
| Siebel Systems | 18.3 | 2 | 0 |
| Business Standard | 18.3 | 2 | 0 |
| CPLEX | 18.3 | 2 | 0 |
| Microsoft Student | 18.3 | 2 | 0 |
| Vantive | 18.3 | 2 | 0 |
| Wolters Kluwer Deutschland | 18.3 | 2 | 0 |
| EDGAR | 18.3 | 2 | 0 |
| Microsoft Layer for Unicode | 18.3 | 2 | 0 |
| Yahoo! Finance | 18.3 | 2 | 0 |
| IntelliType | 18.3 | 2 | 0 |
| Java BluePrints | 18.3 | 2 | 0 |
| Intel oneAPI Math Kernel Library | 18.3 | 2 | 0 |
| Microsoft Japan | 18.3 | 2 | 0 |
| Microsoft Search Server | 18.3 | 2 | 0 |
| Nimble Storage | 18.3 | 2 | 0 |
| Oracle Property Manager | 18.3 | 2 | 0 |
| Q10984556 | 18.3 | 2 | 0 |
| Microsoft Pinyin IME | 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 |
| Performance Analyzer | 18.3 | 2 | 0 |
| Oracle BlueKai Data Management Platform | 18.3 | 2 | 0 |
| Microsoft Lumia 950 XL | 18.3 | 2 | 0 |
| Data Analytics Library | 18.3 | 2 | 0 |
| SAP S/4HANA | 18.3 | 2 | 0 |
| Elastic | 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 |
| Microsoft Dynamics 365 | 18.3 | 2 | 0 |
| Prometeia | 18.3 | 2 | 0 |
| SAS Institute | 18.3 | 2 | 0 |
| Oracle Cloud | 18.3 | 2 | 0 |
| CaosDB - Research Data Management for Complex, Changing, and Automated Research Workflows | 18.3 | 2 | 0 |
| Microsoft Saudi | 18.3 | 2 | 0 |
| IBM Cloud | 18.3 | 2 | 0 |
| Microsoft Mesh | 18.3 | 2 | 0 |
| BIS Quarterly Review | 18.3 | 2 | 0 |
| IBM Cloud Object Storage | 18.3 | 2 | 0 |
| Microsoft Lists | 18.3 | 2 | 0 |
| Microsoft Berlin | 18.3 | 2 | 0 |
| huggingface_hub | 18.3 | 2 | 0 |
| Antimalware Scan Interface | 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 |
| Sora | 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 |
| Capital One | 16.7 | 0 | 2 |
| Supply chain risk management | 16.7 | 0 | 2 |
| corporate governance of information technology | 16.7 | 0 | 2 |
| data lineage | 16.7 | 0 | 2 |
| David M. Blei | 16.7 | 0 | 2 |
| Pierre Baldi | 16.7 | 0 | 2 |
| training, validation, and test data sets | 16.7 | 0 | 2 |
| Artificial Intelligence | 16.7 | 0 | 2 |
| Chauncey Starr | 16.7 | 0 | 2 |
| Commission on Risk Assessment and Risk Management | 16.7 | 0 | 2 |
| Daniel S. Jurafsky | 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 |
| Journal of Experimental and Theoretical Artificial Intelligence | 16.7 | 0 | 2 |
| The Journal of Change Management | 16.7 | 0 | 2 |
| ISO/IEC 31010 | 16.7 | 0 | 2 |
| similarity learning | 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 |
| RankBrain | 16.7 | 0 | 2 |
| Risk Assessment and Risk Management of Nanomaterials in the Workplace: Translating Research to Practice | 16.7 | 0 | 2 |
| Nervana Systems | 16.7 | 0 | 2 |
| A public health context for residual risk assessment and risk management under the clean air act | 16.7 | 0 | 2 |
| A multidisciplinary approach to therapeutic risk management of the suicidal patient | 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 |
| 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 |
| Heat wave hazard classification and risk assessment using artificial intelligence fuzzy logic | 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 |
| Assessing the fit of biotic ligand model validation data in a risk management decision context | 16.7 | 0 | 2 |
| Frank Pasquale | 16.7 | 0 | 2 |
| Managing the unmanageable: risk assessment and risk management in contemporary professional practice | 16.7 | 0 | 2 |
| Risk assessment and risk management implications of hormesis | 16.7 | 0 | 2 |
| Trends in risk assessment and risk management | 16.7 | 0 | 2 |
| Computational identification of surrogate genes for prostate cancer phases using machine learning and molecular network analysis | 16.7 | 0 | 2 |
| From Risk Assessment to Risk Management: Matching Interventions to Adolescent Offenders' Strengths and Vulnerabilities | 16.7 | 0 | 2 |
| The Role of Toxicological Science in Risk Assessment and Risk Management | 16.7 | 0 | 2 |
| Combining Phylogenetic Profiling-Based and Machine Learning-Based Techniques to Predict Functional Related Proteins | 16.7 | 0 | 2 |
| Alzheimer's disease risk assessment using large-scale machine learning methods | 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 |
| Aspects of risk assessment and risk management of nosocomial transmission of classical and variant Creutzfeldt-Jakob disease with special attention to German regulations | 16.7 | 0 | 2 |
| Fullrmc, a rigid body Reverse Monte Carlo modeling package enabled with machine learning and artificial intelligence | 16.7 | 0 | 2 |
| Can Systematic Reviews Inform GMO Risk Assessment and Risk Management? | 16.7 | 0 | 2 |
| Current approaches to cyanotoxin risk assessment and risk management around the globe | 16.7 | 0 | 2 |
| Machine Learning Algorithms Outperform Conventional Regression Models in Predicting Development of Hepatocellular Carcinoma | 16.7 | 0 | 2 |
| Risk assessment and risk management of noncriteria pollutants | 16.7 | 0 | 2 |
| Risk Assessment and Hierarchical Risk Management of Enterprises in Chemical Industrial Parks Based on Catastrophe Theory | 16.7 | 0 | 2 |
| Risk Assessment/Risk Management of Motor Vehicle Emissions | 16.7 | 0 | 2 |
| Clinical Evaluation of a Novel and Mobile Autism Risk Assessment | 16.7 | 0 | 2 |
| General introduction to risk assessment and risk management | 16.7 | 0 | 2 |
| Men having sex with men donor deferral risk assessment: an analysis using risk management principles | 16.7 | 0 | 2 |
| Discussion on the boundary of risk assessment and risk management | 16.7 | 0 | 2 |
| Risk management and risk assessment of novel plant foods: concepts and principles. | 16.7 | 0 | 2 |
| Sharing risk management: an implementation model for cardiovascular absolute risk assessment and management in Australian general practice | 16.7 | 0 | 2 |
| Omnibus Risk Assessment via Accelerated Failure Time Kernel Machine Modeling | 16.7 | 0 | 2 |
| Machine Learning Analysis of the Relationship Between Changes in Immunological Parameters and Changes in Resistance to Listeria monocytogenes: A New Approach for Risk Assessment and Systems Immunology | 16.7 | 0 | 2 |
| Health benefits of 'grow your own' food in urban areas: implications for contaminated land risk assessment and risk management? | 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 |
| Towards improving cardiovascular risk management in patients with rheumatoid arthritis: the need for accurate risk assessment | 16.7 | 0 | 2 |
| Medical decision support using machine learning for early detection of late-onset neonatal sepsis | 16.7 | 0 | 2 |
| Research on risk assessment and risk management: future directions | 16.7 | 0 | 2 |
| Travel risk assessment and risk management | 16.7 | 0 | 2 |
| Therapeutic risk management of the suicidal patient: augmenting clinical suicide risk assessment with structured instruments | 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 |
| Suicide risk assessment and suicide risk formulation: essential components of the therapeutic risk management model | 16.7 | 0 | 2 |
| Hybrid Machine Learning Technique for Forecasting Dhaka Stock Market Timing Decisions | 16.7 | 0 | 2 |
| Version 3 of the Historical‐Clinical‐Risk Management‐20 (HCR‐20V3): Relevance to Violence Risk Assessment and Management in Forensic Conditional Release Contexts | 16.7 | 0 | 2 |
| An ecosystem services approach to pesticide risk assessment and risk management of non-target terrestrial plants: recommendations from a SETAC Europe workshop | 16.7 | 0 | 2 |
| Satellite Data and Machine Learning for Weather Risk Management and Food Security | 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 |
| Risk assessment of sewer condition using artificial intelligence tools: application to the SANEST sewer system. | 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 |
| Food and feed chemical contaminants in the European Union: Regulatory, scientific, and technical issues concerning chemical contaminants occurrence, risk assessment, and risk management in the European Union | 16.7 | 0 | 2 |
| Violence Risk Assessment and Management in Outpatient Clinical Practice | 16.7 | 0 | 2 |
| Integration of QbD risk assessment tools and overall risk management | 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 |
| Foundational issues in risk assessment and risk management | 16.7 | 0 | 2 |
| Quantifying risk and accuracy in cancer risk assessment: the process and its role in risk management problem-solving | 16.7 | 0 | 2 |
| Risk assessment, risk management and risk-based monitoring following a reported accidental release of poliovirus in Belgium, September to November 2014 | 16.7 | 0 | 2 |
| Provision of risk management and risk assessment information: the role of the pharmacist | 16.7 | 0 | 2 |
| Reflections on uncertainty in risk assessment and risk management by the Society of Environmental Toxicology and Chemistry (SETAC) precautionary principle workgroup. | 16.7 | 0 | 2 |
| Characterizing environmental harm: developments in an approach to strategic risk assessment and risk management | 16.7 | 0 | 2 |
| explainable AI | 16.7 | 0 | 2 |
| Interindividual variations in susceptibility and sensitivity: linking risk assessment and risk management | 16.7 | 0 | 2 |
| Hexavalent chromium-contaminated soils: options for risk assessment and risk management | 16.7 | 0 | 2 |
| Failures in risk assessment and risk management for cosmetic preservatives in Europe and the impact on public health | 16.7 | 0 | 2 |
| Risk assessment and clinical risk management: the lessons from recent inquiries | 16.7 | 0 | 2 |
| Research Areas in Relation To Risk Management and Risk Assessment | 16.7 | 0 | 2 |
| The duty of care 2: risk assessment and risk management | 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 |
| Integrated risk assessment or integrated risk management? | 16.7 | 0 | 2 |
| Rapid estimation of compost enzymatic activity by spectral analysis method combined with machine learning | 16.7 | 0 | 2 |
| Risk assessment and risk management at the Canadian Food Inspection Agency (CFIA): a perspective on the monitoring of foods for chemical residues | 16.7 | 0 | 2 |
| Regulatory approach on environmental risk assessment. Risk management recommendations, reasonable and prudent alternatives. | 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 |
| Conscious worst case definition for risk assessment, part I: a knowledge mapping approach for defining most critical risk factors in integrative risk management of chemicals and nanomaterials. | 16.7 | 0 | 2 |
| Risk assessment and risk management according to the HACCP (Hazard Analysis and Critical Control Point) concept: a concept for safe foods | 16.7 | 0 | 2 |
| The socio-hygienic monitoring as an integral system for health risk assessment and risk management at the regional level | 16.7 | 0 | 2 |
| Polychlorinated biphenyls and Hudson River white perch: implications for population-level ecological risk assessment and risk management | 16.7 | 0 | 2 |
| Risk assessment and risk management in Japan | 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 |
| The role of scientific research in risk assessment and risk management decisions | 16.7 | 0 | 2 |
| Gail model risk assessment and risk perceptions | 16.7 | 0 | 2 |
| Characterizing uncertainty when evaluating risk management metrics: risk assessment modeling of Listeria monocytogenes contamination in ready-to-eat deli meats. | 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 |
| Wall-based measurement features provides an improved IVUS coronary artery risk assessment when fused with plaque texture-based features during machine learning paradigm | 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 |
| PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound: A link between carotid and coronary grayscale plaque morphology. | 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 |
| Risk assessment in ovarian hyperstimulation syndrome (OHS) using the machine learning system (Decision Master) in 155 in-vitro fertilisations and embryo-transfer (IVF/ET) cycles with a long stimulation protocol | 16.7 | 0 | 2 |
| Using the Bayesian network relative risk model risk assessment process to evaluate management alternatives for the South River and upper Shenandoah River, Virginia | 16.7 | 0 | 2 |
| Making the relationship between risk assessment and risk management more intimate | 16.7 | 0 | 2 |
| Probabilistic risk assessment based model validation method using Bayesian network | 16.7 | 0 | 2 |
| Guidance on a harmonised framework for pest risk assessment and the identification and evaluation of pest risk management options by EFSA | 16.7 | 0 | 2 |
| Risk assessment of the oriental chestnut gall wasp,Dryocosmus kuriphilusfor the EU territory and identification and evaluation of risk management options | 16.7 | 0 | 2 |
| Risk assessment ofGibberella circinatafor the EU territory and identification and evaluation of risk management options | 16.7 | 0 | 2 |
| Pest risk assessment ofMonilinia fructicolafor the EU territory and identification and evaluation of risk management options | 16.7 | 0 | 2 |
| Scientific Opinion updating the evaluation of the environmental risk assessment and risk management recommendations on insect resistant genetically modified maize 1507 for cultivation | 16.7 | 0 | 2 |
| Statement supplementing the evaluation of the environmental risk assessment and risk management recommendations on insect resistant genetically modified maize Bt11 for cultivation | 16.7 | 0 | 2 |
| Scientific Opinion updating the risk assessment conclusions and risk management recommendations on the genetically modified insect resistant maize 1507 | 16.7 | 0 | 2 |
| Scientific Opinion supplementing the conclusions of the environmental risk assessment and risk management recommendations on the genetically modified insect resistant maize 1507 for cultivation | 16.7 | 0 | 2 |
| Scientific Opinion supplementing the conclusions of the environmental risk assessment and risk management recommendations for the cultivation of the genetically modified insect resistant maize Bt11 and MON 810 | 16.7 | 0 | 2 |
| Scientific Opinion updating the risk assessment conclusions and risk management recommendations on the genetically modified insect resistant maize MON 810 | 16.7 | 0 | 2 |
| Scientific Opinion updating the risk assessment conclusions and risk management recommendations on the genetically modified insect resistant maize Bt11 | 16.7 | 0 | 2 |
| Statement supplementing the environmental risk assessment conclusions and risk management recommendations on genetically modified insect‐resistant maize 59122 for cultivation in the light of new scientific information on non‐target organisms and r... | 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 |
| PyTorch | 16.7 | 0 | 2 |
| Derivation of endogenous equivalent values to support risk assessment and risk management decisions for an endogenous carcinogen: Ethylene oxide | 16.7 | 0 | 2 |
| Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success | 16.7 | 0 | 2 |
| Machine learning can classify vital sign alerts as real or artifact in online continuous monitoring data. | 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 |
| Data Science: Big Data, Machine Learning, and Artificial Intelligence | 16.7 | 0 | 2 |
| Examining the relationship between risk assessment and risk management in mental health. | 16.7 | 0 | 2 |
| Journal of Risk and Financial Management | 16.7 | 0 | 2 |
| Spatial health risk assessment and hierarchical risk management for mercury in soils from a typical contaminated site, China | 16.7 | 0 | 2 |
| Using landscape ecology to focus ecological risk assessment and guide risk management decision-making | 16.7 | 0 | 2 |
| Risk Assessment and Risk Management of Chemicals in China | 16.7 | 0 | 2 |
| Risk assessment of Giardia in rivers of southern China based on continuous monitoring | 16.7 | 0 | 2 |
| A Multianalyzer Machine Learning Model for Marine Heterogeneous Data Schema Mapping | 16.7 | 0 | 2 |
| Risk assessment and clinical risk management | 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 |
| Determination of a risk management primer at petroleum-contaminated sites: Developing new human health risk assessment strategy | 16.7 | 0 | 2 |
| Risk Assessment for Parents Who Suspect Their Child Has Autism Spectrum Disorder: Machine Learning Approach. | 16.7 | 0 | 2 |
| Artificial intelligence, machine learning and the evolution of healthcare: A bright future or cause for concern? | 16.7 | 0 | 2 |
| AIR Worldwide | 16.7 | 0 | 2 |
| Gender bias in artificial intelligence: the need for diversity and gender theory in machine learning | 16.7 | 0 | 2 |
| Risk assessment and risk management of violent reoffending among prisoners | 16.7 | 0 | 2 |
| TensorFlow.js | 16.7 | 0 | 2 |
| DuerOS | 16.7 | 0 | 2 |
| Trait-based risk assessment for invasive species: high performance across diverse taxonomic groups, geographic ranges and machine learning/statistical tools | 16.7 | 0 | 2 |
| Studying Stress in Ecological Systems: Implications for Ecological Risk Assessment and Risk Management | 16.7 | 0 | 2 |
| Fine Particulate Matter, Risk Assessment, and Risk Management | 16.7 | 0 | 2 |
| The [R]Evolving Relationship Between Risk Assessment and Risk Management | 16.7 | 0 | 2 |
| Risk assessment and risk management: a primer for marine scientists | 16.7 | 0 | 2 |
| Environmental risk management for radiological accidents: Integrating risk assessment and decision analysis for remediation at different spatial scales | 16.7 | 0 | 2 |
| Automation, machine learning, and artificial intelligence in echocardiography: A brave new world | 16.7 | 0 | 2 |
| On the Significance of “The Red Book” in the Evolution of Risk Assessment and Risk Management | 16.7 | 0 | 2 |
| Introduction: With a summary of the findings and recommendations of the commission on risk assessment and risk management | 16.7 | 0 | 2 |
| Rule-based Machine Learning Methods for Functional Prediction | 16.7 | 0 | 2 |
| Managing Data Retention Policies at Scale | 16.7 | 0 | 2 |
| Peering Into the Black Box of Artificial Intelligence: Evaluation Metrics of Machine Learning Methods | 16.7 | 0 | 2 |
| How Bioethics Can Shape Artificial Intelligence and Machine Learning | 16.7 | 0 | 2 |
| Big-data and machine learning to revamp computational toxicology and its use in risk assessment | 16.7 | 0 | 2 |
| Machine learning in computer vision | 16.7 | 0 | 2 |
| A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination | 16.7 | 0 | 2 |
| The iPrevent Online Breast Cancer Risk Assessment and Risk Management Tool: Usability and Acceptability Testing. (Preprint) | 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 |
| Artificial Intelligence Techniques for Flood Risk Management in Urban Environments | 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 |
| The iPrevent Online Breast Cancer Risk Assessment and Risk Management Tool: Usability and Acceptability Testing | 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 |
| Risk assessment and risk management: Developing a model of shared learning in clinical practice | 16.7 | 0 | 2 |
| Machine Learning for Nanomaterial Toxicity Risk Assessment | 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 |
| A Governance Framework for ICT Supply Chain Risk Management | 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 |
| Virtual Enterprise Risk Management Using Artificial Intelligence | 16.7 | 0 | 2 |
| Separating Risk Assessment from Risk Management Poses Legal and Ethical Problems in Person-Centred Care | 16.7 | 0 | 2 |
| Current Advances, Trends and Challenges of Machine Learning and Knowledge Extraction: From Machine Learning to Explainable AI | 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 |
| Risk assessment and risk management for safe foods: Assessment needs inclusion of variability and uncertainty, management needs discrete decisions | 16.7 | 0 | 2 |
| Risk identification, risk assessment, and risk management of abusable drug formulations | 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 |
| Patient safety risk assessment and risk management: A review on Indian hospitals | 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 |
| 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 |
| Artificial intelligence for cancer-associated thrombosis risk assessment - Author's reply | 16.7 | 0 | 2 |
| Artificial intelligence for cancer-associated thrombosis risk assessment | 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 |
| 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 |
| Approach to the Evaluation of a Method for the Adoption of Information Technology Governance, Risk Management and Compliance in the Swiss Hospital Environment | 16.7 | 0 | 2 |
| Improving hazard characterization in microbial risk assessment using next generation sequencing data and machine learning: Predicting clinical outcomes in shigatoxigenic Escherichia coli | 16.7 | 0 | 2 |
| Automated Detection of Macular Diseases by Optical Coherence Tomography and Artificial Intelligence Machine Learning of Optical Coherence Tomography Images | 16.7 | 0 | 2 |
| Patient centered care for prostate cancer-how can artificial intelligence and machine learning help make the right decision for the right patient? | 16.7 | 0 | 2 |
| Artificial Intelligence and Machine Learning in Endocrinology and Metabolism: The Dawn of a New Era | 16.7 | 0 | 2 |
| Data science, artificial intelligence, and machine learning: Opportunities for laboratory medicine and the value of positive regulation | 16.7 | 0 | 2 |
| Brief Online Help-seeking Barrier Reduction Intervention | 16.7 | 0 | 2 |
| Machine Learning and Knowledge Extraction | 16.7 | 0 | 2 |
| Contribution of Virtual Reality and Modelling in Falling Risk Assessment in Elderly and Parkinson's Disease Patients | 16.7 | 0 | 2 |
| Machine Learning Models for Genetic Risk Assessment of Infants with Non-syndromic Orofacial Cleft. | 16.7 | 0 | 2 |
| Machine Learning Application for Rupture Risk Assessment in Small-Sized Intracranial Aneurysm. | 16.7 | 0 | 2 |
| Application of Genomic Techniques and Image Processing Using Artificial Intelligence to Obtain a Predictor Model Risk of Melanoma | 16.7 | 0 | 2 |
| 1380GCC: Prospective Study of GP-88 Blood Test in Healthy Women With Baseline Gail Model Risk Assessment Undergoing Screening for Breast Cancer | 16.7 | 0 | 2 |
| Artificial Intelligence/Machine Learning Modeling on Time to Palliative Care Review in an Inpatient Hospital Population | 16.7 | 0 | 2 |
| Multimedia Risk Assessment for Environmental Risk Management | 16.7 | 0 | 2 |
| Risk Assessment and Risk Management in Japan | 16.7 | 0 | 2 |
| The IXth UOEH International Symposium and the First Pan Pacific Cooperative Symposium. Industrialization and emerging environmental health issues--risk assessment and risk management. 2-6 October, 1989, Kitakyushu, Japan | 16.7 | 0 | 2 |
| Industrialization and emerging environmental health issues: risk assessment and risk management. Proceedings of the IXth UOEH International Symposium and The First Pan Pacific Cooperative Symposium | 16.7 | 0 | 2 |
| Risk assessment and risk management. International Symposium on Chemical Mixtures: Risk Assessment and Management. The Jerry F. Stara Memorial Symposium. June 7-9, 1988, Cincinnati, Ohio. Proceedings | 16.7 | 0 | 2 |
| The real role of risk assessment in cancer risk management | 16.7 | 0 | 2 |
| Symposium on safety assessment: the interface between science, law and regulation. Introductory remarks to session on risk assessment and risk management | 16.7 | 0 | 2 |
| Environmental risks of chemicals and genetically modified organisms: a comparison. Part II: Sustainability and precaution in risk assessment and risk management | 16.7 | 0 | 2 |
| Response to the June 13, 1996, draft report of the Commission on Risk Assessment and Risk Management | 16.7 | 0 | 2 |
| Risk assessment and risk management implications of hormesis | 16.7 | 0 | 2 |
| Risk assessment and risk management | 16.7 | 0 | 2 |
| Risk assessment and risk management of vitamins and minerals | 16.7 | 0 | 2 |
| Analysis of risk assessment and risk management processes in the derivation of maximum levels for environmental contaminants in food | 16.7 | 0 | 2 |
| Should routine screening by mammography be replaced by a more selective service of risk assessment/risk management? | 16.7 | 0 | 2 |
| The EU existing chemicals regulation: A suitable tool for environmental risk assessment and risk management? | 16.7 | 0 | 2 |
| ES&T Views: Risk assessment: A tool for risk management | 16.7 | 0 | 2 |
| ES&T Series: Cancer Risk Assessment. 5. The Risk Management-risk assessment interface | 16.7 | 0 | 2 |
| Foundational Issues in Risk Assessment and Risk Management | 16.7 | 0 | 2 |
| Risk assessment and risk management of chemical exposures in agriculture | 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 |
| Quantitative Health Risk Assessment of Cryptosporidium in Rivers of Southern China Based on Continuous Monitoring | 16.7 | 0 | 2 |
| Thinking on risk assessment and risk management of post-marketing Chinese medicine | 16.7 | 0 | 2 |
| Urban pesticide risk assessment and risk management: Get involved | 16.7 | 0 | 2 |
| From risk assessment to risk management | 16.7 | 0 | 2 |
| The TTC Approach in Practice and its Impact on Risk Assessment and Risk Management in Food Safety. A Regulatory Toxicologist's Perspective | 16.7 | 0 | 2 |
| Risk assessment and risk management of mycotoxins | 16.7 | 0 | 2 |
| Improving Patient Safety in the Inpatient Setting Through Risk Assessment and Mitigation | 16.7 | 0 | 2 |
| Sugars and health – risk assessment to risk management | 16.7 | 0 | 2 |
| Evaluation of machine learning algorithms for improved risk assessment for Down's syndrome | 16.7 | 0 | 2 |
| Differences between staff groups in perception of risk assessment and risk management of inappropriate sexual behaviour in patients with traumatic brain injury | 16.7 | 0 | 2 |
| Artificial Intelligence and Machine Learning: Opportunities for Radiologists in Training | 16.7 | 0 | 2 |
| Policy Implications of Artificial Intelligence and Machine Learning in Diabetes Management | 16.7 | 0 | 2 |
| 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 |
| Artificial Intelligence and Machine Learning: Will Clinical Pharmacologists Be Needed in the Next Decade? The John Henry Question | 16.7 | 0 | 2 |
| Machine learning techniques in cardiac risk assessment | 16.7 | 0 | 2 |
| Systematic benefit-risk assessment for buprenorphine implant: a semiquantitative method to support risk management | 16.7 | 0 | 2 |
| Cervical vertebral maturation assessment on lateral cephalometric radiographs using artificial intelligence: comparison of machine learning classifier models | 16.7 | 0 | 2 |
| The Impact of Artificial Intelligence and Machine Learning in Radiation Therapy: Considerations for Future Curriculum Enhancement | 16.7 | 0 | 2 |
| Artificial Intelligence for chemical risk assessment | 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 |
| State of the art on the initiatives and activities relevant to risk assessment and risk management of nanotechnologies in the food and agriculture sectors | 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 |
| Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness | 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 |
| A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes | 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 |
| Artificial Intelligence and Machine Learning in the Identification of Authentic and Fake Data Presentation | 16.7 | 0 | 2 |
| 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 |
| Machine Learning Approach to Inpatient Violence Risk Assessment Using Routinely Collected Clinical Notes in Electronic Health Records | 16.7 | 0 | 2 |
| Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review | 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 |
| Risk assessment for intraabdominal injury following blunt trauma in children: Derivation and validation of a machine learning model | 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 |
| Safeguards for the use of artificial intelligence and machine learning in global health | 16.7 | 0 | 2 |
| The need for a system view to regulate artificial intelligence/machine learning-based software as medical device | 16.7 | 0 | 2 |
| How current risk assessment and risk management methods for drinking water in The Netherlands cover the WHO water safety plan approach | 16.7 | 0 | 2 |
| Applied machine learning and artificial intelligence in rheumatology | 16.7 | 0 | 2 |
| Comparing different venous thromboembolism risk assessment machine learning models in Chinese patients | 16.7 | 0 | 2 |
| 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 |
| Artificial Intelligence versus Doctors' Intelligence: A Glance on Machine Learning Benefaction in Electrocardiography | 16.7 | 0 | 2 |
| 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 |
| 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 |
| Promising Artificial Intelligence-Machine Learning-Deep Learning Algorithms in Ophthalmology | 16.7 | 0 | 2 |
| Randomized controlled trial of an online machine learning-driven risk assessment and intervention platform for increasing the use of crisis services | 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 |
| Artificial Intelligence and Machine Learning | 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 |
| 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 |
| Occurrence and risk assessment of multiclass endocrine disrupting compounds in an urban tropical river and a proposed risk management and monitoring framework | 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 |
| Machine learning approaches for risk assessment of peripherally inserted Central catheter-related vein thrombosis in hospitalized patients with cancer | 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 |
| 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 |
| Artificial Intelligence and Machine Learning for HIV Prevention: Emerging Approaches to Ending the Epidemic | 16.7 | 0 | 2 |
| Ethics Implications of the Use of Artificial Intelligence in Violence Risk Assessment | 16.7 | 0 | 2 |
| Applications of artificial intelligence and machine learning in respiratory medicine | 16.7 | 0 | 2 |
| A machine learning approach to risk assessment for alcohol withdrawal syndrome | 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 |
| Early risk assessment for COVID-19 patients from emergency department data using machine learning | 16.7 | 0 | 2 |
| The Use of Artificial Intelligence and Deep Machine Learning in Oncologic Histopathology | 16.7 | 0 | 2 |
| Continuous monitoring of suspended sediment concentrations using image analytics and deriving inherent correlations by machine learning | 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 |
| Machine Learning and Artificial Intelligence in Pediatric Research: Current State, Future Prospects, and Examples in Perioperative and Critical Care | 16.7 | 0 | 2 |
| 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 |
| Role of Artificial Intelligence and Machine Learning in Nanosafety | 16.7 | 0 | 2 |
| Artificial Intelligence-Based Multimodal Risk Assessment Model for Surgical Site Infection (AMRAMS): Development and Validation Study | 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 |
| 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 |
| Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology | 16.7 | 0 | 2 |
| Q97454550 | 16.7 | 0 | 2 |
| 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 |
| Keeping the "Human in the Loop" in the Age of Artificial Intelligence : Accompanying Commentary for "Correcting the Brain?" by Rainey and Erden | 16.7 | 0 | 2 |
| Challenges of machine learning model validation using correlated behaviour data: Evaluation of cross-validation strategies and accuracy measures | 16.7 | 0 | 2 |
| Therapeutic Risk Management for Violence: Clinical Risk Assessment | 16.7 | 0 | 2 |
| A Clinician's Guide to Artificial Intelligence: How to Critically Appraise Machine Learning Studies | 16.7 | 0 | 2 |
| Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study | 16.7 | 0 | 2 |
| Machine Learning Approaches for Fracture Risk Assessment: A Comparative Analysis of Genomic and Phenotypic Data in 5130 Older Men | 16.7 | 0 | 2 |
| Use of artificial intelligence and machine learning for estimating malignancy risk of thyroid nodules | 16.7 | 0 | 2 |
| Two-stage artificial intelligence model for jointly measurement of atherosclerotic wall thickness and plaque burden in carotid ultrasound: A screening tool for cardiovascular/stroke risk assessment | 16.7 | 0 | 2 |
| Artificial Intelligence and Machine Learning in Radiology: Current State and Considerations for Routine Clinical Implementation | 16.7 | 0 | 2 |
| Use of Machine Learning and Artificial Intelligence to Drive Personalized Medicine Approaches for Spine Care | 16.7 | 0 | 2 |
| 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 |
| Bias and ethical considerations in machine learning and the automation of perioperative risk assessment | 16.7 | 0 | 2 |
| 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 |
| A nationwide artificial intelligence risk assessment for primary prevention of cardiometabolic diseases | 16.7 | 0 | 2 |
| Machine learning analysis of serum biomarkers for cardiovascular risk assessment in chronic kidney disease | 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 |
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| Writing the paper “Unveiling artificial intelligence: an insight into ethics and applications in anesthesia” implementing the large language model ChatGPT: a qualitative study | 16.7 | 0 | 2 |
| Jie Huang | 16.7 | 0 | 2 |
| Machine learning-based regional scale intelligent modeling of building information for natural hazard risk management | 16.7 | 0 | 2 |
| Modelling change management and risk management in a financial organization due to information system adoption | 16.7 | 0 | 2 |
| Scientific Applications Of Change Management and Version Control Booana Koteska and Anastas Eishev | 16.7 | 0 | 2 |
| Can artificial intelligence-strengthened ChatGPT or other large language models transform nucleic acid research? | 16.7 | 0 | 2 |
| “Knock, Knock … Who’s There?” ChatGPT and Artificial Intelligence-Powered Large Language Models: Reflections on Potential Impacts Within Health and Physical Education Teacher Education | 16.7 | 0 | 2 |
| Economic Risk Management in the Era of Artificial Intelligence: Typical Cases Study | 16.7 | 0 | 2 |
| Q122271027 | 16.7 | 0 | 2 |
| ChatGPT, Large Language Models, and Generative AI as Future Augments of Surgical Cancer Care | 16.7 | 0 | 2 |
| Hyperscale artificial intelligence and machine learning infrastructure | 16.7 | 0 | 2 |
| Q122642749 | 16.7 | 0 | 2 |
| System and method for blockchain transaction risk management using machine learning | 16.7 | 0 | 2 |
| Using machine learning in physics-based simulation of fire | 16.7 | 0 | 2 |
| The Educational Applications and Innovative Explorations of Machine Learning in the View of Artificial Intelligence | 16.7 | 0 | 2 |
| <scp>ChatGPT</scp> and a new academic reality: <scp>Artificial Intelligence‐written</scp> research papers and the ethics of the large language models in scholarly publishing | 16.7 | 0 | 2 |
| Enterprise data security storage integrating blockchain and artificial intelligence technology in property and resource risk management | 16.7 | 0 | 2 |
| Transforming OMFS through Artificial Intelligence and Machine Learning | 16.7 | 0 | 2 |
| System and methods for amalgamation of artificial intelligence (AI) and machine learning (ML) in test creation, execution, and prediction | 16.7 | 0 | 2 |
| Justice by Algorithm: Are Artificial Intelligence Risk Assessment Tools Biased Against Minorities? | 16.7 | 0 | 2 |
| System and method for identifying business logic and data lineage with machine learning | 16.7 | 0 | 2 |
| Factors Influence the Willingness to Implement and Develop Intelligent Systems that can Rely on Artificial Intelligence, Machine Learning, IoT or Blockchain | 16.7 | 0 | 2 |
| Wiki45 | 16.7 | 0 | 2 |
| The rise of artificial intelligence: addressing the impact of large language models such as ChatGPT on scientific publications | 16.7 | 0 | 2 |
| Advances in test automation for software with special focus on artificial intelligence and machine learning | 16.7 | 0 | 2 |
| Reciprocal human machine learning | 16.7 | 0 | 2 |
| Wireless device power optimization utilizing artificial intelligence and/or machine learning | 16.7 | 0 | 2 |
| Gemini | 16.7 | 0 | 2 |
| The Use of Machine Learning for Image Analysis Artificial Intelligence in Clinical Microbiology | 16.7 | 0 | 2 |
| Determining sequences of interactions, process extraction, and robot generation using artificial intelligence / machine learning models | 16.7 | 0 | 2 |
| Determining sequences of interactions, process extraction, and robot generation using artificial intelligence / machine learning models | 16.7 | 0 | 2 |
| Artificial intelligence/machine learning driven assessment system for a community of electrical equipment users | 16.7 | 0 | 2 |
| Advancing health through artificial intelligence/machine learning: The critical importance of multidisciplinary collaboration | 16.7 | 0 | 2 |
| Submodularity In Machine Learning and Artificial Intelligence | 16.7 | 0 | 2 |
| Radio access network service mediated enhanced session records for artificial intelligence or machine learning | 16.7 | 0 | 2 |
| Methods and systems for merging outputs of candidate and job-matching artificial intelligence engines executing machine learning-based models | 16.7 | 0 | 2 |
| Method and system for applying data retention policies in a computing platform | 16.7 | 0 | 2 |
| Coastal Flood risk assessment using ensemble multi-criteria decision-making with machine learning approaches | 16.7 | 0 | 2 |
| FDA-cleared artificial intelligence and machine learning-based medical devices and their 510(k) predicate networks | 16.7 | 0 | 2 |
| Multi-zone optimisation of high-rise buildings using artificial intelligence for sustainable metropolises. Part 1: Background, methodology, setup, and machine learning results | 16.7 | 0 | 2 |
| Utilizing Artificial Intelligence and Machine Learning to FacilitateAchieving Carbon Neutrality | 16.7 | 0 | 2 |
| Computer-based systems configured for machine learning version control of digital objects and methods of use thereof | 16.7 | 0 | 2 |
| BÜYÜK VERİ ANALİZİNDE YAPAY ZEKÂ VE MAKİNE ÖĞRENMESİ UYGULAMALARI - ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING APPLICATIONS IN BIG DATA ANALYSIS | 16.7 | 0 | 2 |
| Sensors, Machine learning, and Artificial intelligence in Real Time Fire Science | 16.7 | 0 | 2 |
| RISK MANAGEMENT IN THE CONTEXT OF MULTI-RISK ASSESSMENT | 16.7 | 0 | 2 |
| A Layered, Hybrid Machine Learning Analytic Workflow for Mouse Risk Assessment Behavior | 16.7 | 0 | 2 |
| Artificial intelligence for health message generation: an empirical study using a large language model (LLM) and prompt engineering | 16.7 | 0 | 2 |
| Risk Management in the Artificial Intelligence Act | 16.7 | 0 | 2 |
| Machine learning-enabled regional multi-hazards risk assessment considering social vulnerability | 16.7 | 0 | 2 |
| Artificial intelligence and Machine Learning for Real-world problems (A survey) | 16.7 | 0 | 2 |
| Mapping the Role and Impact of Artificial Intelligence and Machine Learning Applications in Supply Chain Digital Transformation: A Bibliometric Analysis | 16.7 | 0 | 2 |
| ALLaM | 16.7 | 0 | 2 |
| Template:Generative AI | 16.7 | 0 | 2 |
| The Use of Artificial Intelligence and Machine Learning in Creating a Roadmap Towards a Circular Economy for Plastics | 16.7 | 0 | 2 |
| GxP artificial intelligence / machine learning (AI/ML) platform | 16.7 | 0 | 2 |
| A Green information technology governance framework for eco-environmental risk mitigation | 16.7 | 0 | 2 |
| Artificial Intelligence Based Body Sensor Network Framework—Narrative Review: Proposing an End-to-End Framework using Wearable Sensors, Real-Time Location Systems and Artificial Intelligence/Machine Learning Algorithms for Data Collection, Data Mini | 16.7 | 0 | 2 |
| Transforming Assessment: The Impacts and Implications of Large Language Models and Generative AI | 16.7 | 0 | 2 |
| Atoosa Kasirzadeh | 16.7 | 0 | 2 |
| Artificial Intelligence, Machine Learning, and Digital Therapeutics in Palliative Care and Hospice: The Future of Compassionate Care or Rise of the Robots? (TH363) | 16.7 | 0 | 2 |
| Dynamic capabilities for firm performance under the information technology governance framework | 16.7 | 0 | 2 |
| Surveying the reach and maturity of machine learning and artificial intelligence in astronomy | 16.7 | 0 | 2 |
| Artificial intelligence in glaucoma: Assisted diagnosis and risk assessment | 16.7 | 0 | 2 |
| Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential | 16.7 | 0 | 2 |
| Adoption of Artificial Intelligence and Machine Learning Is Increasing, but Irrational Exuberance Remains | 16.7 | 0 | 2 |
| The Impact of Artificial Intelligence and Machine Learning on Physicians | 16.7 | 0 | 2 |
| AI slop | 16.7 | 0 | 2 |
| Risk Assessment, Health Impact Assessment, Risk Management: Loose Terminology Or More? | 16.7 | 0 | 2 |
| Risk Assessment, Health Impact Assessment, Risk Management: Time We Clarified | 16.7 | 0 | 2 |
| Special issue on artificial intelligence and machine learning for robotic manipulation | 16.7 | 0 | 2 |
| Model validation and model risk: reaching the end of the line? | 16.7 | 0 | 2 |
| Facies classification with different machine learning algorithm – An efficient artificial intelligence technique for improved classification | 16.7 | 0 | 2 |
| Emerging nutrient management databases and networks of networks will have broad applicability in future machine learning and artificial intelligence applications in soil and water conservation | 16.7 | 0 | 2 |
| In the Shadow of Artificial Intelligence: Examining Security Challenges, Attack Methodologies, and Vulnerabilities within Machine Learning Implementations | 16.7 | 0 | 2 |
| Do you know your customer? Bank risk assessment based on machine learning | 16.7 | 0 | 2 |
| Artificial Intelligence: the right to protection from discrimination caused by algorithms, machine learning and automated decision-making | 16.7 | 0 | 2 |
| Mathematical Foundations for Processing High Data Volume, Machine Learning, and Artificial Intelligence | 16.7 | 0 | 2 |
| A review of artificial intelligence based risk assessment methods for capturing complexity-risk interdependencies | 16.7 | 0 | 2 |
| Role of artificial intelligence and machine learning in ophthalmology | 16.7 | 0 | 2 |
| Machine learning based concept drift detection for predictive maintenance | 16.7 | 0 | 2 |
| Mary Shelley’s Frankenstein : The Link Between Frankenstein’s Creation of an Intelligent Being and Machine Learning of Artificial Intelligence | 16.7 | 0 | 2 |
| Artificial Intelligence and Machine Learning Algorithms For Informing the Diagnostic Process of Mild Cognitive Impairment and Dementia | 16.7 | 0 | 2 |
| Chemicals in California drinking water: source contaminants, risk assessment, risk management, and regulatory standards | 16.7 | 0 | 2 |
| Building engineering safety risk assessment and early warning mechanism construction based on distributed machine learning algorithm | 16.7 | 0 | 2 |
| Application of Machine Learning and Artificial Intelligence in Proxy Modeling for Fluid Flow in Porous Media | 16.7 | 0 | 2 |
| Deep Models, Machine Learning, and Artificial Intelligence Applications in National and International Security — Part Two | 16.7 | 0 | 2 |
| The Economics of Applications of Artificial Intelligence and Machine Learning in Agriculture | 16.7 | 0 | 2 |
| Artificial Intelligence – Machine Learning based Mental Health Diagnosis Automation | 16.7 | 0 | 2 |
| Hazardous substances and cancer incidence: introduction to the special issue on risk assessment and risk management | 16.7 | 0 | 2 |
| PNS266 LANDSCAPE ANALYSIS OF IMPACT OF MACHINE LEARNING, NATURAL LANGUAGE PROCESSING, ARTIFICIAL INTELLIGENCE AND BLOCKCHAIN TECHNOLOGY ON LEVERAGING REAL WORLD EVIDENCE (RWE) | 16.7 | 0 | 2 |
| Compliance monitoring in a regional context: revising seafood tissue monitoring for risk assessment | 16.7 | 0 | 2 |
| Machine Learning With Kernels for Portfolio Valuation and Risk Management | 16.7 | 0 | 2 |
| 475 ARTIFICIAL INTELLIGENCE WILL HELP IN DETERMINING THE NEED FOR ADDITIONAL SURGERY AFTER ENDOSCOPIC RESECTION OF T1 COLORECTAL CANCER –ANALYSIS BASED ON A BIG DATA FOR MACHINE LEARNING | 16.7 | 0 | 2 |
| System and method for incremental training of machine learning models in artificial intelligence systems, including incremental training using analysi | 16.7 | 0 | 2 |
| Flood Susceptibility Assessment by Using Bivariate Statistics and Machine Learning Models - A Useful Tool for Flood Risk Management | 16.7 | 0 | 2 |
| Machine Learning in Context, or Learning from LANDR: Artificial Intelligence and the Platformization of Music Mastering | 16.7 | 0 | 2 |
| Learning about risk: Machine learning for risk assessment | 16.7 | 0 | 2 |
| Artificial intelligence and machine learning for optical coherence tomography-based diagnosis in central serous chorioretinopathy | 16.7 | 0 | 2 |