| IBM | 60.0 | 36 | 0 |
| Mistral Vibe | 56.7 | 6 | 4 |
| Microsoft | 47.1 | 16 | 0 |
| Data Analytics for Machine Learning | 46.4 | 5 | 2 |
| Claude | 42.6 | 2 | 4 |
| 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 |
| Microsoft Security Copilot | 34.9 | 2 | 2 |
| Azure DevOps Server | 34.6 | 7 | 0 |
| Q18698690 | 34.6 | 7 | 0 |
| Hugging Face | 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 |
| Microsoft Search Server | 28.8 | 2 | 1 |
| OpenAI | 28.8 | 2 | 1 |
| Microsoft Academic Graph | 28.8 | 2 | 1 |
| OpenAI OpCo | 28.8 | 2 | 1 |
| Atlas of AI, book review: Mapping out the total cost of artificial intelligence | 28.8 | 2 | 1 |
| NVIDIA Jetson Orin NX 16GB | 28.8 | 2 | 1 |
| Hugging Face Hub | 28.8 | 2 | 1 |
| BaiduWiki | 27.2 | 0 | 5 |
| Q80689 | 26.7 | 4 | 0 |
| Oracle E-Business Suite | 26.7 | 4 | 0 |
| Microsoft Lumia 640 XL | 26.7 | 4 | 0 |
| Microsoft Windows | 23.0 | 3 | 0 |
| JDeveloper | 23.0 | 3 | 0 |
| Jakarta EE | 23.0 | 3 | 0 |
| Q215273 | 23.0 | 3 | 0 |
| PricewaterhouseCoopers | 23.0 | 3 | 0 |
| Oracle SQL Developer | 23.0 | 3 | 0 |
| SAP NetWeaver Business Intelligence | 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 |
| 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 |
| Cohere | 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 |
| Artificial Intelligence for Engineering Design, Analysis and Manufacturing | 21.0 | 0 | 3 |
| Eric P. Xing | 21.0 | 0 | 3 |
| Proceedings. IEEE Workshop on Applications of Computer Vision | 21.0 | 0 | 3 |
| Machine learning in cell biology – teaching computers to recognize phenotypes | 21.0 | 0 | 3 |
| Artificial Intelligence in Medical Practice: The Question to the Answer? | 21.0 | 0 | 3 |
| Machine learning-based detection of chemical risk | 21.0 | 0 | 3 |
| Machine learning in computer vision | 21.0 | 0 | 3 |
| 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 | 21.0 | 0 | 3 |
| Kairntech SAS | 21.0 | 0 | 3 |
| A Study Using Artificial Intelligence to Identify Adults With Complex Perianal Fistulas Associated With Crohn's Disease | 21.0 | 0 | 3 |
| prompt engineering | 21.0 | 0 | 3 |
| Vision and Language | 21.0 | 0 | 3 |
| Data transformation and knowledge retrieval for humanitarian crisis response | 21.0 | 0 | 3 |
| Medical AI Security and Data Privacy in the Age of Computer Vision | 21.0 | 0 | 3 |
| Implementing artificial intelligence agents to perform machine learning tasks using predictive analytics to leverage ensemble policies for maximizing | 21.0 | 0 | 3 |
| SinaLab | 21.0 | 0 | 3 |
| retrieval-augmented generation | 21.0 | 0 | 3 |
| Artificial intelligence for health message generation: an empirical study using a large language model (LLM) and prompt engineering | 21.0 | 0 | 3 |
| PNS266 LANDSCAPE ANALYSIS OF IMPACT OF MACHINE LEARNING, NATURAL LANGUAGE PROCESSING, ARTIFICIAL INTELLIGENCE AND BLOCKCHAIN TECHNOLOGY ON LEVERAGING REAL WORLD EVIDENCE (RWE) | 21.0 | 0 | 3 |
| Q136153857 | 21.0 | 0 | 3 |
| Nayan Goel | 21.0 | 0 | 3 |
| Toward Greener Matrix Operations by Lossless Compressed Formats | 21.0 | 0 | 3 |
| Rudrendu Kumar Paul | 21.0 | 0 | 3 |
| CustomGPT.ai | 21.0 | 0 | 3 |
| Victor Hugo Villafañe Aguilar | 21.0 | 0 | 3 |
| Digital Clouds | 21.0 | 0 | 3 |
| Surogate | 21.0 | 0 | 3 |
| SOFI AI Tech Solution Inc. | 21.0 | 0 | 3 |
| Q11219 | 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 |
| Microsoft Digital Image | 18.3 | 2 | 0 |
| Red Hat | 18.3 | 2 | 0 |
| Ernst & Young | 18.3 | 2 | 0 |
| Azure | 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 |
| 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 |
| CPLEX | 18.3 | 2 | 0 |
| Microsoft Student | 18.3 | 2 | 0 |
| ZDNET | 18.3 | 2 | 0 |
| Vantive | 18.3 | 2 | 0 |
| OpenShift | 18.3 | 2 | 0 |
| Microsoft Layer for Unicode | 18.3 | 2 | 0 |
| Creatio | 18.3 | 2 | 0 |
| IBM Configuration Management Version Control | 18.3 | 2 | 0 |
| IntelliType | 18.3 | 2 | 0 |
| Java BluePrints | 18.3 | 2 | 0 |
| Microsoft Japan | 18.3 | 2 | 0 |
| Nimble Storage | 18.3 | 2 | 0 |
| Oracle Property Manager | 18.3 | 2 | 0 |
| Red Hat Virtualization | 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 |
| SAP S/4HANA | 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 |
| Axios | 18.3 | 2 | 0 |
| Forbes 30 Under 30 | 18.3 | 2 | 0 |
| Bubble | 18.3 | 2 | 0 |
| SAS Institute | 18.3 | 2 | 0 |
| Oracle Cloud | 18.3 | 2 | 0 |
| Microsoft Saudi | 18.3 | 2 | 0 |
| IBM Cloud | 18.3 | 2 | 0 |
| Microsoft Mesh | 18.3 | 2 | 0 |
| IBM Cloud Object Storage | 18.3 | 2 | 0 |
| Brian Proffitt | 18.3 | 2 | 0 |
| Microsoft Lists | 18.3 | 2 | 0 |
| Microsoft Berlin | 18.3 | 2 | 0 |
| Freshworks | 18.3 | 2 | 0 |
| Mistral AI | 18.3 | 2 | 0 |
| huggingface_hub | 18.3 | 2 | 0 |
| Sora | 18.3 | 2 | 0 |
| artificial intelligence | 16.7 | 0 | 2 |
| Peter Norvig | 16.7 | 0 | 2 |
| 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 |
| natural language understanding | 16.7 | 0 | 2 |
| International Journal of Computer Vision | 16.7 | 0 | 2 |
| Andrei Broder | 16.7 | 0 | 2 |
| David M. Blei | 16.7 | 0 | 2 |
| Pierre Baldi | 16.7 | 0 | 2 |
| Graph cuts in computer vision | 16.7 | 0 | 2 |
| training, validation, and test data sets | 16.7 | 0 | 2 |
| Andrew McCallum | 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 |
| geometric feature learning | 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 |
| Piotr Indyk | 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 |
| Natural Language Engineering | 16.7 | 0 | 2 |
| Moses Charikar | 16.7 | 0 | 2 |
| Lenhart Schubert | 16.7 | 0 | 2 |
| Tree kernel | 16.7 | 0 | 2 |
| similarity learning | 16.7 | 0 | 2 |
| vanishing gradient problem | 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 |
| Dan Roth | 16.7 | 0 | 2 |
| RankBrain | 16.7 | 0 | 2 |
| Nervana Systems | 16.7 | 0 | 2 |
| Regina Barzilay | 16.7 | 0 | 2 |
| High-throughput analysis of behavior for drug discovery | 16.7 | 0 | 2 |
| Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | 16.7 | 0 | 2 |
| Tommi S. Jaakkola | 16.7 | 0 | 2 |
| Probabilistic machine learning and artificial intelligence | 16.7 | 0 | 2 |
| Detection of sentence boundaries and abbreviations in clinical narratives | 16.7 | 0 | 2 |
| Challenges and Practical Approaches with Word Sense Disambiguation of Acronyms and Abbreviations in the Clinical Domain | 16.7 | 0 | 2 |
| Application of the SP theory of intelligence to the understanding of natural vision and the development of computer vision | 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 |
| 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 |
| Semantically-based priors and nuanced knowledge core for Big Data, Social AI, and language understanding | 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 |
| A review on machine learning principles for multi-view biological data integration | 16.7 | 0 | 2 |
| Computational identification of surrogate genes for prostate cancer phases using machine learning and molecular network analysis | 16.7 | 0 | 2 |
| AISO: Annotation of Image Segments with Ontologies | 16.7 | 0 | 2 |
| Combining Phylogenetic Profiling-Based and Machine Learning-Based Techniques to Predict Functional Related Proteins | 16.7 | 0 | 2 |
| Semi-supervised learning of causal relations in biomedical scientific discourse | 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 |
| Comparison and combination of several MeSH indexing approaches | 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 |
| Exploring Spanish health social media for detecting drug effects | 16.7 | 0 | 2 |
| The role of fine-grained annotations in supervised recognition of risk factors for heart disease from EHRs | 16.7 | 0 | 2 |
| Challenges in clinical natural language processing for automated disorder normalization | 16.7 | 0 | 2 |
| A Frequency-based Strategy of Obtaining Sentences from Clinical Data Repository for Crowdsourcing | 16.7 | 0 | 2 |
| Fullrmc, a rigid body Reverse Monte Carlo modeling package enabled with machine learning and artificial intelligence | 16.7 | 0 | 2 |
| Natural language processing in psychiatry. Artificial intelligence technology and psychopathology | 16.7 | 0 | 2 |
| Efficient identification of nationally mandated reportable cancer cases using natural language processing and machine learning. | 16.7 | 0 | 2 |
| Machine Learning and Computer Vision System for Phenotype Data Acquisition and Analysis in Plants | 16.7 | 0 | 2 |
| Machine Learning Algorithms Outperform Conventional Regression Models in Predicting Development of Hepatocellular Carcinoma | 16.7 | 0 | 2 |
| Unsupervised Topic Modeling in a Large Free Text Radiology Report Repository | 16.7 | 0 | 2 |
| Finding Cervical Cancer Symptoms in Swedish Clinical Text using a Machine Learning Approach and NegEx | 16.7 | 0 | 2 |
| Predicting Health Care Utilization After Behavioral Health Referral Using Natural Language Processing and Machine Learning | 16.7 | 0 | 2 |
| Recognizing Disjoint Clinical Concepts in Clinical Text Using Machine Learning-based Methods | 16.7 | 0 | 2 |
| Methodological Issues in Predicting Pediatric Epilepsy Surgery Candidates Through Natural Language Processing and Machine Learning | 16.7 | 0 | 2 |
| Adapting existing natural language processing resources for cardiovascular risk factors identification in clinical notes | 16.7 | 0 | 2 |
| Using Machine Learning and Natural Language Processing Algorithms to Automate the Evaluation of Clinical Decision Support in Electronic Medical Record Systems | 16.7 | 0 | 2 |
| Automated analysis of retinal imaging using machine learning techniques for computer vision | 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 |
| Information extraction from multi-institutional radiology reports | 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 |
| An Unsupervised Graph Based Continuous Word Representation Method for Biomedical Text Mining | 16.7 | 0 | 2 |
| Automated Learning of Temporal Expressions | 16.7 | 0 | 2 |
| Automatically Expanding the Synonym Set of SNOMED CT using Wikipedia | 16.7 | 0 | 2 |
| A new approach for cleansing geographical dataset using Levenshtein distance, prior knowledge and contextual information | 16.7 | 0 | 2 |
| The Mutual Inspirations of Machine Learning and Neuroscience | 16.7 | 0 | 2 |
| Frame semantics-based study of verbs across medical genres | 16.7 | 0 | 2 |
| Does SNOMED CT post-coordination scale? | 16.7 | 0 | 2 |
| What's in a class? Lessons learnt from the ICD - SNOMED CT harmonisation | 16.7 | 0 | 2 |
| The influence of similarity between concepts in evolving biomedical ontologies for mapping adaptation | 16.7 | 0 | 2 |
| Using TimeML to support the modeling of computerized clinical guidelines | 16.7 | 0 | 2 |
| Extracting Dependence Relations from Unstructured Medical Text | 16.7 | 0 | 2 |
| Development and evaluation of task-specific NLP framework in China | 16.7 | 0 | 2 |
| Automatic Detection of Skin and Subcutaneous Tissue Infections from Primary Care Electronic Medical Records | 16.7 | 0 | 2 |
| Machine learning applications in genetics and genomics | 16.7 | 0 | 2 |
| Exploiting parallel corpora to scale up multilingual biomedical terminologies | 16.7 | 0 | 2 |
| Machine learning methods for the classification of gliomas: Initial results using features extracted from MR spectroscopy | 16.7 | 0 | 2 |
| Assessment of beer quality based on foamability and chemical composition using computer vision algorithms, near infrared spectroscopy and machine learning algorithms | 16.7 | 0 | 2 |
| Computer vision for high content screening | 16.7 | 0 | 2 |
| Computational Analysis of Behavior | 16.7 | 0 | 2 |
| Computer vision and machine learning for robust phenotyping in genome-wide studies | 16.7 | 0 | 2 |
| Classification of lung cancer using ensemble-based feature selection and machine learning methods | 16.7 | 0 | 2 |
| Machine learning and computer vision approaches for phenotypic profiling | 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 |
| Computer vision and artificial intelligence in mammography | 16.7 | 0 | 2 |
| explainable AI | 16.7 | 0 | 2 |
| Automated Classification of Radiology Reports for Acute Lung Injury: Comparison of Keyword and Machine Learning Based Natural Language Processing Approaches. | 16.7 | 0 | 2 |
| Identification of Incidental Pulmonary Nodules in Free-text Radiology Reports: An Initial Investigation | 16.7 | 0 | 2 |
| Modeling false positive error making patterns in radiology trainees for improved mammography education | 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 |
| William T. Freeman | 16.7 | 0 | 2 |
| Generation of Natural-Language Textual Summaries from Longitudinal Clinical Records | 16.7 | 0 | 2 |
| Predictive Analytics through Machine Learning in the clinical settings | 16.7 | 0 | 2 |
| Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach | 16.7 | 0 | 2 |
| Need of informatics in designing interoperable clinical registries | 16.7 | 0 | 2 |
| Integrating Natural Language Processing and Machine Learning Algorithms to Categorize Oncologic Response in Radiology Reports | 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 |
| Fast Model Adaptation for Automated Section Classification in Electronic Medical Records | 16.7 | 0 | 2 |
| In silico prediction of anti-malarial hit molecules based on machine learning methods | 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 |
| Automatic classification of written descriptions by healthy adults: An overview of the application of natural language processing and machine learning techniques to clinical discourse analysis. | 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 |
| Using natural language processing and machine learning to identify gout flares from electronic clinical notes | 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 |
| Unsupervised multiple kernel learning for heterogeneous data integration | 16.7 | 0 | 2 |
| A Novel Approach to Create a Machine Readable Concept Model for Validating SNOMED CT Concept Post-coordination | 16.7 | 0 | 2 |
| PyTorch | 16.7 | 0 | 2 |
| Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success | 16.7 | 0 | 2 |
| Claire Cardie | 16.7 | 0 | 2 |
| Hady Elsahar | 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 |
| Protecting Your Patients' Interests in the Era of Big Data, Artificial Intelligence, and Predictive Analytics | 16.7 | 0 | 2 |
| Automatic variance analysis of multistage care pathways | 16.7 | 0 | 2 |
| Real-time monitoring of clinical processes using complex event processing and transition systems | 16.7 | 0 | 2 |
| A Multianalyzer Machine Learning Model for Marine Heterogeneous Data Schema Mapping | 16.7 | 0 | 2 |
| Advances in Natural Language Processing: 4th International Conference, EsTAL 2004, Alicante, Spain, October 20-22, 2004. Proceedings | 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 |
| Serge Belongie | 16.7 | 0 | 2 |
| Assessment of Beer Quality Based on a Robotic Pourer, Computer Vision, and Machine Learning Algorithms Using Commercial Beers | 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 |
| Computer Vision, Graphics, and Image Processing | 16.7 | 0 | 2 |
| How Artificial Intelligence Can Improve Our Understanding of the Genes Associated with Endometriosis: Natural Language Processing of the PubMed Database. | 16.7 | 0 | 2 |
| Predictive modeling for odor character of a chemical using machine learning combined with natural language processing. | 16.7 | 0 | 2 |
| Artificial intelligence, machine learning and the evolution of healthcare: A bright future or cause for concern? | 16.7 | 0 | 2 |
| Dat Quoc Nguyen | 16.7 | 0 | 2 |
| Gender bias in artificial intelligence: the need for diversity and gender theory in machine learning | 16.7 | 0 | 2 |
| Syntactic N-grams as machine learning features for natural language processing | 16.7 | 0 | 2 |
| TensorFlow.js | 16.7 | 0 | 2 |
| DuerOS | 16.7 | 0 | 2 |
| Predictive Analytics and Modeling Employing Machine Learning Technology: The Next Step in Data Sharing, Analysis and Individualized Counseling Explored with A Large, Prospective Prenatal Hydronephrosis Database | 16.7 | 0 | 2 |
| Kristen Grauman | 16.7 | 0 | 2 |
| Guest editorial: special issue on predictive analytics using machine learning | 16.7 | 0 | 2 |
| Automation, machine learning, and artificial intelligence in echocardiography: A brave new world | 16.7 | 0 | 2 |
| Rule-based Machine Learning Methods for Functional Prediction | 16.7 | 0 | 2 |
| Applied Machine Learning predictive analytics to SQL Injection Attack detection and prevention | 16.7 | 0 | 2 |
| Extracting Biomarker Information Applying Natural Language Processing and Machine Learning | 16.7 | 0 | 2 |
| Biomarker information extraction tool (BIET) development using natural language processing and machine learning | 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 |
| Hybridization of Convergent Photogrammetry, Computer Vision, and Artificial Intelligence for Digital Documentation of Cultural Heritage - A Case Study: The Magdalena Palace | 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 |
| COMPUTER VISION INSPECTION OF ELLIPTICAL PROFILES | 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 |
| 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 |
| Hand Gesture Recognition System Based in Computer Vision and Machine Learning | 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 |
| 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 |
| 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 |
| Resolving "orphaned" non-specific structures using machine learning and natural language processing methods | 16.7 | 0 | 2 |
| Using a Natural Language Processing and Machine Learning Algorithm Program to Analyze Inter-Radiologist Report Style Variation and Compare Variation Between Radiologists When Using Highly Structured Versus More Free Text Reporting | 16.7 | 0 | 2 |
| Data integration strategies for predictive analytics in precision medicine | 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 |
| Florian Jug | 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 |
| A learning-based thresholding method customizable to computer vision applications | 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 |
| Identification of freezer burn on frozen salmon surface using hyperspectral imaging and computer vision combined with machine learning algorithm | 16.7 | 0 | 2 |
| Introduction to the special section on Artificial Intelligence and Computer Vision | 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 |
| Structuring Neural Networks for More Explainable Predictions | 16.7 | 0 | 2 |
| Explainable and Interpretable Models in Computer Vision and Machine Learning | 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 |
| Natural Language AI | 16.7 | 0 | 2 |
| Correction: Predictive modeling for odor character of a chemical using machine learning combined with natural language processing | 16.7 | 0 | 2 |
| Craig Knoblock | 16.7 | 0 | 2 |
| Notion | 16.7 | 0 | 2 |
| Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective | 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 |
| Using natural language processing and machine learning to identify breast cancer local recurrence | 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 |
| Machine learning methods for omics data integration | 16.7 | 0 | 2 |
| Anna Rumshisky | 16.7 | 0 | 2 |
| bidirectional encoder representations from transformers | 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 |
| Could advances in representation learning in Artificial Intelligence provide the new paradigm for data integration in drug discovery? | 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 |
| Predicting Mortality in the Surgical Intensive Care Unit Using Artificial Intelligence and Natural Language Processing of Physician Documentation | 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 |
| Bedside Computer Vision - Moving Artificial Intelligence from Driver Assistance to Patient Safety | 16.7 | 0 | 2 |
| Prediction of pork loin quality using online computer vision system and artificial intelligence model | 16.7 | 0 | 2 |
| Representation Learning: A Unified Deep Learning Framework for Automatic Prostate MR Segmentation | 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 |
| Identification of suicidal behavior among psychiatrically hospitalized adolescents using natural language processing and machine learning of electronic health records | 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 |
| Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke | 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 |
| Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE study | 16.7 | 0 | 2 |
| Sameer Singh | 16.7 | 0 | 2 |
| Machine Learning and Knowledge Extraction | 16.7 | 0 | 2 |
| Artificial Intelligence/Machine Learning Modeling on Time to Palliative Care Review in an Inpatient Hospital Population | 16.7 | 0 | 2 |
| Christoph Lampert | 16.7 | 0 | 2 |
| Mohit Bansal | 16.7 | 0 | 2 |
| Identification of Patients Admitted With COPD Exacerbations and Predicting Readmission Risk Using Machine Learning | 16.7 | 0 | 2 |
| Michael J Brooks | 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 |
| Machine learning for predictive analytics in medicine: real opportunity or overblown hype? | 16.7 | 0 | 2 |
| Hongtu Zhu | 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 |
| Letter to the Editor. Importance of calibration assessment in machine learning-based predictive analytics | 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 |
| Predictive analytics by deep machine learning: A call for next-gen tools to improve health care | 16.7 | 0 | 2 |
| The Impact of Artificial Intelligence and Machine Learning in Radiation Therapy: Considerations for Future Curriculum Enhancement | 16.7 | 0 | 2 |
| Predicting Intensive Care Unit admission among patients presenting to the emergency department using machine learning and natural language processing | 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 |
| Otoscopic diagnosis using computer vision: An automated machine learning approach | 16.7 | 0 | 2 |
| Application of artificial intelligence (AI) in Radiotherapy workflow: Paradigm shift in Precision Radiotherapy using Machine Learning | 16.7 | 0 | 2 |
| Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes | 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 |
| Improving eQTL Analysis Using a Machine Learning Approach for Data Integration: A Logistic Model Tree Solution | 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 |
| Artificial Intelligence Algorithms and Natural Language Processing for the Recognition of Syncope Patients on Emergency Department Medical Records | 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 |
| Natural language processing and machine learning to identify alcohol misuse from the electronic health record in trauma patients: development and internal validation | 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 |
| Technology opportunity discovery by structuring user needs based on natural language processing and machine learning | 16.7 | 0 | 2 |
| Machine Learning and Natural Language Processing for Geolocation-Centric Monitoring and Characterization of Opioid-Related Social Media Chatter | 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 |
| Artificial Intelligence/Machine Learning in Diabetes Care | 16.7 | 0 | 2 |
| Machine Learning, Predictive Analytics, and Clinical Practice: Can the Past Inform the Present? | 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 |
| Risk of mortality and cardiopulmonary arrest in critical patients presenting to the emergency department using machine learning and natural language processing | 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 |
| Predictive analytics and machine learning in stroke and neurovascular medicine | 16.7 | 0 | 2 |
| Artificial Intelligence and Machine Learning: A New Disruptive Force in Orthopaedics | 16.7 | 0 | 2 |
| Development of Big Data Predictive Analytics Model for Disease Prediction using Machine learning Technique | 16.7 | 0 | 2 |
| Machine learning-based preoperative predictive analytics for lumbar spinal stenosis | 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 |
| Supervised Machine Learning Predictive Analytics For Triple-Negative Breast Cancer Death Outcomes | 16.7 | 0 | 2 |
| Machine Learning, Natural Language Processing, and the Electronic Health Record: Innovations in Mental Health Services Research | 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 |
| 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 |
| Identification of disease-associated loci using machine learning for genotype and network data integration | 16.7 | 0 | 2 |
| Machine Vision Methods, Natural Language Processing, and Machine Learning Algorithms for Automated Dispersion Plot Analysis and Chemical Identification from Complex Mixtures | 16.7 | 0 | 2 |
| Development of machine learning and natural language processing algorithms for preoperative prediction and automated identification of intraoperative vascular injury in anterior lumbar spine surgery | 16.7 | 0 | 2 |
| Computer vision and artificial intelligence are emerging diagnostic tools for the clinical microbiologist | 16.7 | 0 | 2 |
| Applied machine learning and artificial intelligence in rheumatology | 16.7 | 0 | 2 |
| A Computable Phenotype for Acute Respiratory Distress Syndrome Using Natural Language Processing and Machine Learning | 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 |
| Automating Ischemic Stroke Subtype Classification Using Machine Learning and Natural Language Processing | 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 |
| Using Machine Learning Applied to Real-World Healthcare Data for Predictive Analytics: An Applied Example in Bariatric Surgery | 16.7 | 0 | 2 |
| Development of a global infectious disease activity database using natural language processing, machine learning, and human expertise | 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 approaches using natural language processing to advance EHR-based clinical research | 16.7 | 0 | 2 |
| Artificial intelligence and machine learning in clinical development: a translational perspective | 16.7 | 0 | 2 |
| Spatial Variability of Aroma Profiles of Cocoa Trees Obtained through Computer Vision and Machine Learning Modelling: A Cover Photography and High Spatial Remote Sensing Application | 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 |
| 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 |
| Using automated computer vision and machine learning to code facial expressions of affect and arousal: Implications for emotion dysregulation research | 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 |
| Realtime Indoor Workout Analysis Using Machine Learning & Computer Vision | 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 |
| Interactive Machine Learning for Laboratory Data Integration | 16.7 | 0 | 2 |
| Supervised Machine Learning Based Multi-Task Artificial Intelligence Classification of Retinopathies | 16.7 | 0 | 2 |
| Open Source Infrastructure for Health Care Data Integration and Machine Learning Analyses | 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 |
| Computer vision and machine learning enabled soybean root phenotyping pipeline | 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 |
| 495 Prediction of pork loin quality using online computer vision system and artificial intelligence model | 16.7 | 0 | 2 |
| Artificial Intelligence and Machine Learning for HIV Prevention: Emerging Approaches to Ending the Epidemic | 16.7 | 0 | 2 |
| The Development of the Military Service Identification Tool: Identifying Military Veterans in a Clinical Research Database Using Natural Language Processing and Machine Learning | 16.7 | 0 | 2 |
| Development of machine learning-based preoperative predictive analytics for unruptured intracranial aneurysm surgery: a pilot study | 16.7 | 0 | 2 |
| Finding warning markers: Leveraging natural language processing and machine learning technologies to detect risk of school violence | 16.7 | 0 | 2 |
| Integrated Natural Language Processing and Machine Learning Models for Standardizing Radiotherapy Structure Names | 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 |
| CLINICAL CHARACTERISTICS AND PROGNOSTIC FACTORS FOR ICU ADMISSION OF PATIENTS WITH COVID-19 USING MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING | 16.7 | 0 | 2 |
| The Use of Artificial Intelligence and Deep Machine Learning in Oncologic Histopathology | 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 |
| Shaogang Gong | 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 |
| Machine learning and natural language processing methods to identify ischemic stroke, acuity and location from radiology reports | 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 |
| Machine learning and natural language processing in psychotherapy research: Alliance as example use case | 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 |
| Pupil Localisation and Eye Centre Estimation Using Machine Learning and Computer Vision | 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 |
| Automated Measurement of Lumbar Lordosis on Radiographs Using Machine Learning and Computer Vision | 16.7 | 0 | 2 |
| Natural language processing and machine learning to enable automatic extraction and classification of patients' smoking status from electronic medical records | 16.7 | 0 | 2 |
| A Clinician's Guide to Artificial Intelligence: How to Critically Appraise Machine Learning Studies | 16.7 | 0 | 2 |
| Use of artificial intelligence and machine learning for estimating malignancy risk of thyroid nodules | 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 |
| Data Integration Using Advances in Machine Learning in Drug Discovery and Molecular Biology | 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 |
| The present and future role of artificial intelligence and machine learning in anesthesiology | 16.7 | 0 | 2 |
| Protecting Data Privacy in the Age of AI-Enabled Ophthalmology | 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 |
| Identifying Goals of Care Conversations in the Electronic Health Record, Using Natural Language Processing and Machine Learning | 16.7 | 0 | 2 |
| Machine learning and artificial intelligence to aid climate change research and preparedness | 16.7 | 0 | 2 |
| Data governance: Organizing data for trustworthy Artificial Intelligence | 16.7 | 0 | 2 |
| Artificial Intelligence Predictive Analytics in the Management of Outpatient MRI Appointment No-Shows | 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 |
| Raymond J. Mooney | 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 |
| Social Reminiscence in Older Adults' Everyday Conversations: Automated Detection Using Natural Language Processing and Machine Learning | 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 |
| Big data, machine learning and artificial intelligence: a neurologist's guide | 16.7 | 0 | 2 |
| Exploring the Potential of Artificial Intelligence and Machine Learning to Combat COVID-19 and Existing Opportunities for LMIC: A Scoping Review | 16.7 | 0 | 2 |
| Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19 | 16.7 | 0 | 2 |
| How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts | 16.7 | 0 | 2 |
| Big data, machine learning, and artificial intelligence: a field guide for neurosurgeons | 16.7 | 0 | 2 |
| Quantification of Advanced Dementia Patients' Engagement in Therapeutic Sessions: An Automatic Video Based Approach using Computer Vision and Machine Learning | 16.7 | 0 | 2 |
| Clinical concept normalization with a hybrid natural language processing system combining multilevel matching and machine learning ranking | 16.7 | 0 | 2 |
| An East Coast Perspective on Artificial Intelligence and Machine Learning: Part 1: Hemorrhagic Stroke Imaging and Triage | 16.7 | 0 | 2 |
| An East Coast Perspective on Artificial Intelligence and Machine Learning: Part 2: Ischemic Stroke Imaging and Triage | 16.7 | 0 | 2 |
| Artificial Intelligence Applications for Workflow, Process Optimization and Predictive Analytics | 16.7 | 0 | 2 |
| Estimating Nonfatal Gunshot Injury Locations With Natural Language Processing and Machine Learning Models | 16.7 | 0 | 2 |
| [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 |
| Artificial intelligence and machine learning for protein toxicity prediction using proteomics data | 16.7 | 0 | 2 |
| Natural language processing with machine learning to predict outcomes after ovarian cancer surgery | 16.7 | 0 | 2 |
| Artificial intelligence and machine learning in orthopedic surgery: a systematic review protocol | 16.7 | 0 | 2 |
| CLINICAL CHARACTERISTICS AND PROGNOSTIC FACTORS FOR ICU ADMISSION OF PATIENTS WITH COVID-19: A RETROSPECTIVE STUDY USING MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING | 16.7 | 0 | 2 |
| Artificial intelligence in dermatology: "unsupervised" versus "supervised" machine learning | 16.7 | 0 | 2 |
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| Artificial Intelligence Technology: Novel Strategy for Patent Dataset Creation Based on Machine Learning | 16.7 | 0 | 2 |
| Using Machine Learning to Code Occupational Surveillance Data: A Cooperative Effort between NIOSH and the Harvard Computer Society – Tech for Social Good Program | 16.7 | 0 | 2 |
| Quantum Machine Learning and the Realization of Artificial Intelligence:Philosophical Analysis Based on Computability and Computational Complexity | 16.7 | 0 | 2 |
| Machine Learning and Natural Language Processing for Prediction of Human Factors in Aviation Incident Reports | 16.7 | 0 | 2 |
| Artificial Intelligence and Machine Learning in Nuclear Medicine: Future Perspectives | 16.7 | 0 | 2 |
| Artificial intelligence natural language processing platform | 16.7 | 0 | 2 |
| System and methods for alert visualization and machine learning data pattern identification for explainable AI in alert processing | 16.7 | 0 | 2 |
| Using artificial intelligence and natural language processing for data collection in message oriented middleware frameworks | 16.7 | 0 | 2 |
| Predicting machine learning or deep learning model training time | 16.7 | 0 | 2 |
| Using artificial intelligence and natural language processing for data collection in message oriented middleware frameworks | 16.7 | 0 | 2 |
| Artificial Intelligence and Machine Learning:Algorithmic Foundations and Philosophical Perspectives | 16.7 | 0 | 2 |
| Automated data integration, reconciliation, and self healing using machine learning | 16.7 | 0 | 2 |
| Category:Works created using artificial intelligence | 16.7 | 0 | 2 |
| Shared prediction engine for machine learning model deployment | 16.7 | 0 | 2 |
| Enterprise deployment framework with artificial intelligence/machine learning | 16.7 | 0 | 2 |
| Analyzing a Python programming example of building an artificial intelligence machine learning model for detecting cyber-attacks: Benefits and challenges | 16.7 | 0 | 2 |
| Artificial intelligence and machine learning based product development | 16.7 | 0 | 2 |
| Natural language processing and artificial intelligence based search system | 16.7 | 0 | 2 |
| Artificial Intelligence, Blockchain, Machine Learning, and Customer Relationship Management | 16.7 | 0 | 2 |
| Utilizing a machine learning model and natural language processing to manage and allocate tasks | 16.7 | 0 | 2 |
| Artificial intelligence and machine learning based conversational agent | 16.7 | 0 | 2 |
| Software Testing: Issues and Challenges of Artificial Intelligence & Machine Learning | 16.7 | 0 | 2 |
| Systems and methods for accelerating model training in machine learning | 16.7 | 0 | 2 |
| Machine learning artificial intelligence system for predicting hours of operation | 16.7 | 0 | 2 |
| Machine learning artificial intelligence system for predicting popular hours | 16.7 | 0 | 2 |
| System and method for artificial intelligence based data integration of entities post market consolidation | 16.7 | 0 | 2 |
| Prevention of computer vision syndrome using explainable artificial intelligence | 16.7 | 0 | 2 |
| Systems and methods for hail damage verification on rooftops using computer vision and artificial intelligence | 16.7 | 0 | 2 |
| Methods and apparatus for performing machine learning to improve capabilities of an artificial intelligence (AI) entity used for online communications | 16.7 | 0 | 2 |
| Data harvesting for machine learning model training | 16.7 | 0 | 2 |
| Large language model artificial intelligence: the current state and future of ChatGPT in neuro-oncology publishing | 16.7 | 0 | 2 |
| Guidelines for Quality Assurance of Machine Learning-Based Artificial Intelligence | 16.7 | 0 | 2 |
| ChatGPT versus the neurosurgical written boards: a comparative analysis of artificial intelligence/machine learning performance on neurosurgical board–style questions | 16.7 | 0 | 2 |
| Systems and methods for hail damage verification on rooftops using computer vision and artificial intelligence | 16.7 | 0 | 2 |
| 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 |
| Computer vision and machine learning approaches for metadata enrichment to improve searchability of historical newspaper collections | 16.7 | 0 | 2 |
| Systems and methods for utilizing machine learning and natural language processing to provide a dual-panel user interface | 16.7 | 0 | 2 |
| ChatGPT and Other Natural Language Processing Artificial Intelligence Models in Adult Reconstruction | 16.7 | 0 | 2 |
| Can artificial intelligence-strengthened ChatGPT or other large language models transform nucleic acid research? | 16.7 | 0 | 2 |
| Systems and methods for hail damage verification on rooftops using computer vision and artificial intelligence | 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 |
| Will Affective Computing Emerge From Foundation Models and General Artificial Intelligence? A First Evaluation of ChatGPT | 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 |
| A New Approach of Educational Data Governance in the Intelligent Age: the Construction and Practice of Artificial Intelligence Educational Brain Model | 16.7 | 0 | 2 |
| Q122642749 | 16.7 | 0 | 2 |
| Machine learning model registry | 16.7 | 0 | 2 |
| System and method for using machine learning supporting natural language processing analysis | 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 |
| 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 |
| 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 |
| Personality Changes and Staring Spells in a 12-Year-Old Child: A Case Report Incorporating ChatGPT, a Natural Language Processing Tool Driven by Artificial Intelligence (AI) | 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 |
| The future of General Movement Assessment: The role of computer vision and machine learning – A scoping review | 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 |
| Aleph Alpha | 16.7 | 0 | 2 |
| Tabular data generation for machine learning model training system | 16.7 | 0 | 2 |
| Tabular data generation with attention for machine learning model training system | 16.7 | 0 | 2 |
| Artificial intelligence (AI) model training using cloud gaming network | 16.7 | 0 | 2 |
| INSIGHT. Intelligent Neural Systems as InteGrated Heritage Tools | 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 |
| Plinius: Secure and Persistent Machine Learning Model Training | 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 |
| Applied artificial intelligence technology for using natural language processing to train a natural language generation system with respect to date an | 16.7 | 0 | 2 |
| Applied artificial intelligence technology for using natural language processing to train a natural language generation system with respect to numeric | 16.7 | 0 | 2 |
| Applied artificial intelligence technology for building a knowledge base using natural language processing | 16.7 | 0 | 2 |
| FDA-cleared artificial intelligence and machine learning-based medical devices and their 510(k) predicate networks | 16.7 | 0 | 2 |
| Algorithmic and meta-algorithmic machine learning natural language processing approaches for stakeholder requirements classification | 16.7 | 0 | 2 |
| artificial intelligence in education | 16.7 | 0 | 2 |
| Machine Learning Driven Mental Stress Detection on Reddit Posts Using Natural Language Processing | 16.7 | 0 | 2 |
| Using social media, machine learning and natural language processing to map multiple recreational beneficiaries | 16.7 | 0 | 2 |
| Algorithmic and meta-algorithmic machine learning natural language processing approaches for stakeholder requirements classification | 16.7 | 0 | 2 |
| Searching for chromate replacements using natural language processing and machine learning algorithms | 16.7 | 0 | 2 |
| Industry 4.0 oriented predictive analytics of cardiovascular diseases using machine learning, hyperparameter tuning and ensemble techniques | 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 |