| Mistral Vibe | 64.0 | 6 | 2 |
| IBM | 60.0 | 36 | 0 |
| Notion | 58.4 | 4 | 2 |
| CustomGPT.ai | 54.7 | 3 | 2 |
| Zendesk | 50.0 | 2 | 2 |
| H2O | 50.0 | 2 | 2 |
| bidirectional encoder representations from transformers | 50.0 | 2 | 2 |
| GPT-4 | 50.0 | 2 | 2 |
| Dolly | 50.0 | 2 | 2 |
| Claude | 50.0 | 2 | 2 |
| Data Analytics for Machine Learning | 49.8 | 5 | 1 |
| Microsoft | 47.1 | 16 | 0 |
| n8n | 46.7 | 4 | 1 |
| TensorFlow.js | 43.2 | 1 | 2 |
| Hugging Face | 43.0 | 3 | 1 |
| Rudrendu Kumar Paul | 40.0 | 0 | 3 |
| Palantir Technologies | 39.8 | 10 | 0 |
| Microsoft Search Server | 38.3 | 2 | 1 |
| Fooocus | 38.3 | 2 | 1 |
| Hugging Face Hub | 38.3 | 2 | 1 |
| Microsoft Security Copilot | 38.3 | 2 | 1 |
| AI Assistant | 38.3 | 2 | 1 |
| Azure DevOps Server | 34.6 | 7 | 0 |
| Q18698690 | 34.6 | 7 | 0 |
| Microsoft SQL Server | 32.3 | 6 | 0 |
| Oracle CRM | 32.3 | 6 | 0 |
| Oracle Fusion Applications | 32.3 | 6 | 0 |
| Andrew McCallum | 31.7 | 0 | 2 |
| business process automation | 31.7 | 0 | 2 |
| Natural Language Engineering | 31.7 | 0 | 2 |
| Tree kernel | 31.7 | 0 | 2 |
| Eric P. Xing | 31.7 | 0 | 2 |
| J. Nathan Kutz | 31.7 | 0 | 2 |
| Dan Roth | 31.7 | 0 | 2 |
| Tommi S. Jaakkola | 31.7 | 0 | 2 |
| Detection of sentence boundaries and abbreviations in clinical narratives | 31.7 | 0 | 2 |
| Alec Radford | 31.7 | 0 | 2 |
| Semantically-based priors and nuanced knowledge core for Big Data, Social AI, and language understanding | 31.7 | 0 | 2 |
| Exploring Spanish health social media for detecting drug effects | 31.7 | 0 | 2 |
| The role of fine-grained annotations in supervised recognition of risk factors for heart disease from EHRs | 31.7 | 0 | 2 |
| Challenges in clinical natural language processing for automated disorder normalization | 31.7 | 0 | 2 |
| A Frequency-based Strategy of Obtaining Sentences from Clinical Data Repository for Crowdsourcing | 31.7 | 0 | 2 |
| Efficient identification of nationally mandated reportable cancer cases using natural language processing and machine learning. | 31.7 | 0 | 2 |
| Unsupervised Topic Modeling in a Large Free Text Radiology Report Repository | 31.7 | 0 | 2 |
| Finding Cervical Cancer Symptoms in Swedish Clinical Text using a Machine Learning Approach and NegEx | 31.7 | 0 | 2 |
| Predicting Health Care Utilization After Behavioral Health Referral Using Natural Language Processing and Machine Learning | 31.7 | 0 | 2 |
| Recognizing Disjoint Clinical Concepts in Clinical Text Using Machine Learning-based Methods | 31.7 | 0 | 2 |
| Methodological Issues in Predicting Pediatric Epilepsy Surgery Candidates Through Natural Language Processing and Machine Learning | 31.7 | 0 | 2 |
| Adapting existing natural language processing resources for cardiovascular risk factors identification in clinical notes | 31.7 | 0 | 2 |
| Using Machine Learning and Natural Language Processing Algorithms to Automate the Evaluation of Clinical Decision Support in Electronic Medical Record Systems | 31.7 | 0 | 2 |
| Information extraction from multi-institutional radiology reports | 31.7 | 0 | 2 |
| An Unsupervised Graph Based Continuous Word Representation Method for Biomedical Text Mining | 31.7 | 0 | 2 |
| Automated Learning of Temporal Expressions | 31.7 | 0 | 2 |
| Automatically Expanding the Synonym Set of SNOMED CT using Wikipedia | 31.7 | 0 | 2 |
| A new approach for cleansing geographical dataset using Levenshtein distance, prior knowledge and contextual information | 31.7 | 0 | 2 |
| Extracting Dependence Relations from Unstructured Medical Text | 31.7 | 0 | 2 |
| Development and evaluation of task-specific NLP framework in China | 31.7 | 0 | 2 |
| Automatic Detection of Skin and Subcutaneous Tissue Infections from Primary Care Electronic Medical Records | 31.7 | 0 | 2 |
| Automated Classification of Radiology Reports for Acute Lung Injury: Comparison of Keyword and Machine Learning Based Natural Language Processing Approaches. | 31.7 | 0 | 2 |
| Identification of Incidental Pulmonary Nodules in Free-text Radiology Reports: An Initial Investigation | 31.7 | 0 | 2 |
| Generation of Natural-Language Textual Summaries from Longitudinal Clinical Records | 31.7 | 0 | 2 |
| Predictive Analytics through Machine Learning in the clinical settings | 31.7 | 0 | 2 |
| Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach | 31.7 | 0 | 2 |
| Need of informatics in designing interoperable clinical registries | 31.7 | 0 | 2 |
| Artificial Intelligence in Medical Practice: The Question to the Answer? | 31.7 | 0 | 2 |
| Integrating Natural Language Processing and Machine Learning Algorithms to Categorize Oncologic Response in Radiology Reports | 31.7 | 0 | 2 |
| Fast Model Adaptation for Automated Section Classification in Electronic Medical Records | 31.7 | 0 | 2 |
| Machine learning-based detection of chemical risk | 31.7 | 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. | 31.7 | 0 | 2 |
| Using natural language processing and machine learning to identify gout flares from electronic clinical notes | 31.7 | 0 | 2 |
| A Novel Approach to Create a Machine Readable Concept Model for Validating SNOMED CT Concept Post-coordination | 31.7 | 0 | 2 |
| PyTorch | 31.7 | 0 | 2 |
| Claire Cardie | 31.7 | 0 | 2 |
| Hady Elsahar | 31.7 | 0 | 2 |
| Predictive modeling for odor character of a chemical using machine learning combined with natural language processing. | 31.7 | 0 | 2 |
| Dat Quoc Nguyen | 31.7 | 0 | 2 |
| Syntactic N-grams as machine learning features for natural language processing | 31.7 | 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 | 31.7 | 0 | 2 |
| Guest editorial: special issue on predictive analytics using machine learning | 31.7 | 0 | 2 |
| Applied Machine Learning predictive analytics to SQL Injection Attack detection and prevention | 31.7 | 0 | 2 |
| Extracting Biomarker Information Applying Natural Language Processing and Machine Learning | 31.7 | 0 | 2 |
| Biomarker information extraction tool (BIET) development using natural language processing and machine learning | 31.7 | 0 | 2 |
| Resolving "orphaned" non-specific structures using machine learning and natural language processing methods | 31.7 | 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 | 31.7 | 0 | 2 |
| Natural Language AI | 31.7 | 0 | 2 |
| Correction: Predictive modeling for odor character of a chemical using machine learning combined with natural language processing | 31.7 | 0 | 2 |
| Using natural language processing and machine learning to identify breast cancer local recurrence | 31.7 | 0 | 2 |
| Anna Rumshisky | 31.7 | 0 | 2 |
| Identification of suicidal behavior among psychiatrically hospitalized adolescents using natural language processing and machine learning of electronic health records | 31.7 | 0 | 2 |
| Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke | 31.7 | 0 | 2 |
| Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE study | 31.7 | 0 | 2 |
| Sameer Singh | 31.7 | 0 | 2 |
| Machine Learning and Knowledge Extraction | 31.7 | 0 | 2 |
| Mohit Bansal | 31.7 | 0 | 2 |
| Identification of Patients Admitted With COPD Exacerbations and Predicting Readmission Risk Using Machine Learning | 31.7 | 0 | 2 |
| Machine learning for predictive analytics in medicine: real opportunity or overblown hype? | 31.7 | 0 | 2 |
| Letter to the Editor. Importance of calibration assessment in machine learning-based predictive analytics | 31.7 | 0 | 2 |
| Predictive analytics by deep machine learning: A call for next-gen tools to improve health care | 31.7 | 0 | 2 |
| Predicting Intensive Care Unit admission among patients presenting to the emergency department using machine learning and natural language processing | 31.7 | 0 | 2 |
| Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes | 31.7 | 0 | 2 |
| Natural language processing and machine learning to identify alcohol misuse from the electronic health record in trauma patients: development and internal validation | 31.7 | 0 | 2 |
| Technology opportunity discovery by structuring user needs based on natural language processing and machine learning | 31.7 | 0 | 2 |
| Machine Learning and Natural Language Processing for Geolocation-Centric Monitoring and Characterization of Opioid-Related Social Media Chatter | 31.7 | 0 | 2 |
| Machine Learning, Predictive Analytics, and Clinical Practice: Can the Past Inform the Present? | 31.7 | 0 | 2 |
| Risk of mortality and cardiopulmonary arrest in critical patients presenting to the emergency department using machine learning and natural language processing | 31.7 | 0 | 2 |
| Predictive analytics and machine learning in stroke and neurovascular medicine | 31.7 | 0 | 2 |
| Development of Big Data Predictive Analytics Model for Disease Prediction using Machine learning Technique | 31.7 | 0 | 2 |
| Machine learning-based preoperative predictive analytics for lumbar spinal stenosis | 31.7 | 0 | 2 |
| Supervised Machine Learning Predictive Analytics For Triple-Negative Breast Cancer Death Outcomes | 31.7 | 0 | 2 |
| Machine Learning, Natural Language Processing, and the Electronic Health Record: Innovations in Mental Health Services Research | 31.7 | 0 | 2 |
| Machine Vision Methods, Natural Language Processing, and Machine Learning Algorithms for Automated Dispersion Plot Analysis and Chemical Identification from Complex Mixtures | 31.7 | 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 | 31.7 | 0 | 2 |
| A Computable Phenotype for Acute Respiratory Distress Syndrome Using Natural Language Processing and Machine Learning | 31.7 | 0 | 2 |
| Automating Ischemic Stroke Subtype Classification Using Machine Learning and Natural Language Processing | 31.7 | 0 | 2 |
| Using Machine Learning Applied to Real-World Healthcare Data for Predictive Analytics: An Applied Example in Bariatric Surgery | 31.7 | 0 | 2 |
| Development of a global infectious disease activity database using natural language processing, machine learning, and human expertise | 31.7 | 0 | 2 |
| Automated Detection of Radiology Reports that Require Follow-up Imaging Using Natural Language Processing Feature Engineering and Machine Learning Classification | 31.7 | 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 | 31.7 | 0 | 2 |
| Development of machine learning-based preoperative predictive analytics for unruptured intracranial aneurysm surgery: a pilot study | 31.7 | 0 | 2 |
| Finding warning markers: Leveraging natural language processing and machine learning technologies to detect risk of school violence | 31.7 | 0 | 2 |
| Integrated Natural Language Processing and Machine Learning Models for Standardizing Radiotherapy Structure Names | 31.7 | 0 | 2 |
| CLINICAL CHARACTERISTICS AND PROGNOSTIC FACTORS FOR ICU ADMISSION OF PATIENTS WITH COVID-19 USING MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING | 31.7 | 0 | 2 |
| Machine learning and natural language processing methods to identify ischemic stroke, acuity and location from radiology reports | 31.7 | 0 | 2 |
| Machine learning and natural language processing in psychotherapy research: Alliance as example use case | 31.7 | 0 | 2 |
| Natural language processing and machine learning to enable automatic extraction and classification of patients' smoking status from electronic medical records | 31.7 | 0 | 2 |
| Identifying Goals of Care Conversations in the Electronic Health Record, Using Natural Language Processing and Machine Learning | 31.7 | 0 | 2 |
| Raymond J. Mooney | 31.7 | 0 | 2 |
| Social Reminiscence in Older Adults' Everyday Conversations: Automated Detection Using Natural Language Processing and Machine Learning | 31.7 | 0 | 2 |
| Clinical concept normalization with a hybrid natural language processing system combining multilevel matching and machine learning ranking | 31.7 | 0 | 2 |
| Estimating Nonfatal Gunshot Injury Locations With Natural Language Processing and Machine Learning Models | 31.7 | 0 | 2 |
| Natural language processing with machine learning to predict outcomes after ovarian cancer surgery | 31.7 | 0 | 2 |
| Kairntech SAS | 31.7 | 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 | 31.7 | 0 | 2 |
| Gerald Francis DeJong, II | 31.7 | 0 | 2 |
| Predictive article recommendation using natural language processing and machine learning to support evidence updates in domain-specific knowledge graphs | 31.7 | 0 | 2 |
| The present and future state of machine learning for predictive analytics in surgery | 31.7 | 0 | 2 |
| Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing | 31.7 | 0 | 2 |
| Byron Wallace | 31.7 | 0 | 2 |
| Natural language processing and entrustable professional activity text feedback in surgery: A machine learning model of resident autonomy | 31.7 | 0 | 2 |
| John E. Miller | 31.7 | 0 | 2 |
| Challenges and Solutions to Employing Natural Language Processing and Machine Learning to Measure Patients' Health Literacy and Physician Writing Complexity: The ECLIPPSE Study | 31.7 | 0 | 2 |
| Prediction of Stroke Outcome Using Natural Language Processing-Based Machine Learning of Radiology Report of Brain MRI | 31.7 | 0 | 2 |
| Brain-Age Prediction Using Shallow Machine Learning: Predictive Analytics Competition 2019 | 31.7 | 0 | 2 |
| Improving ED Emergency Severity Index Acuity Assignment Using Machine Learning and Clinical Natural Language Processing | 31.7 | 0 | 2 |
| Machine learning and natural language processing (NLP) approach to predict early progression to first-line treatment in real-world hormone receptor-positive (HR+)/HER2-negative advanced breast cancer patients | 31.7 | 0 | 2 |
| Evidence of Gender Differences in the Diagnosis and Management of Coronavirus Disease 2019 Patients: An Analysis of Electronic Health Records Using Natural Language Processing and Machine Learning | 31.7 | 0 | 2 |
| A Study Using Artificial Intelligence to Identify Adults With Complex Perianal Fistulas Associated With Crohn's Disease | 31.7 | 0 | 2 |
| Machine Learning with Spark™ and Python® | 31.7 | 0 | 2 |
| A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies | 31.7 | 0 | 2 |
| Speech and Language Processing | 31.7 | 0 | 2 |
| Automating incidental findings in radiology reports using natural language processing and machine learning to identify and classify pulmonary nodules | 31.7 | 0 | 2 |
| Automate incidental findings in radiology reports using natural language processing and machine learning to identify and classify lung nodules | 31.7 | 0 | 2 |
| TXTWerk: Natural Language Processing with Wikidata Knowledge Graphs – Examples and lessons learned | 31.7 | 0 | 2 |
| Computational Phenotyping and Phenome-wide Association Studies: Leveraging Machine Learning and Natural Language Processing to Understand Electronic Health Record Data | 31.7 | 0 | 2 |
| Machine learning in medicine: a practical introduction to natural language processing | 31.7 | 0 | 2 |
| Compilation of parasitic immunogenic proteins from 30 years of published research using machine learning and natural language processing | 31.7 | 0 | 2 |
| Framework for Sentiment Classification for Morphologically Rich Languages: A Case Study for Sinhala | 31.7 | 0 | 2 |
| Data transformation and knowledge retrieval for humanitarian crisis response | 31.7 | 0 | 2 |
| SQL Injection Attacks Predictive Analytics Using Supervised Machine Learning Techniques | 31.7 | 0 | 2 |
| Identification of muscle-invasion status in bladder cancer patients using natural language processing and machine learning. | 31.7 | 0 | 2 |
| Automatically Detect Software Security Vulnerabilities Based on Natural Language Processing Techniques and Machine Learning Algorithms | 31.7 | 0 | 2 |
| Data Privacy and Trustworthy Machine Learning | 31.7 | 0 | 2 |
| A predictive analytics approach for stroke prediction using machine learning and neural networks | 31.7 | 0 | 2 |
| Intelligent RFQ Summarization Using Natural Language Processing, Text Mining, and Machine Learning Techniques | 31.7 | 0 | 2 |
| Predicting mortality in both diabetes and open-source clinical datasets from free text entries using machine learning (natural language processing) | 31.7 | 0 | 2 |
| Machine Learning and Data Privacy in Digital Advertising | 31.7 | 0 | 2 |
| Analysis on Integrating Machine Learning with Blockchain to Ensure Data Privacy | 31.7 | 0 | 2 |
| Machine learning concepts for correlated Big Data privacy | 31.7 | 0 | 2 |
| Natural language processing (NLP) and machine learning (ML) model for predicting CMS OP-35 categories among patients receiving chemotherapy. | 31.7 | 0 | 2 |
| A Gentle Introduction to Machine Learning for Natural Language Processing: How to Start in 16 Practical Steps | 31.7 | 0 | 2 |
| PMU95 BCBSLA APPROACH USING NATURAL LANGUAGE PROCESSING (NLP) AND MACHINE LEARNING TO PREDICT THE RISK OF HOSPITALIZATIONS | 31.7 | 0 | 2 |
| Methods for Extracting Treatment Patterns for Renal Cell Carcinoma (RCC) from Social Media (SM) Forums Using Natural Language Processing (NLP) and Machine Learning (ML) | 31.7 | 0 | 2 |
| A digital analysis system of patents integrating natural language processing and machine learning | 31.7 | 0 | 2 |
| Limitations of Legal Regulations for Data Processing Performed by Machine Learning Algorithm : With Respect to Data Privacy and Anti-Discrimination | 31.7 | 0 | 2 |
| Detection of Fake Job Postings by Utilizing Machine Learning and Natural Language Processing Approaches | 31.7 | 0 | 2 |
| Applying machine learning and natural language processing to detect phishing email | 31.7 | 0 | 2 |
| From distributed machine learning to federated learning: In the view of data privacy and security | 31.7 | 0 | 2 |
| Generating corpus for training and validating machine learning model for natural language processing | 31.7 | 0 | 2 |
| Auto scaling a distributed predictive analytics system with machine learning | 31.7 | 0 | 2 |
| Secure machine learning workflow automation using isolated resources | 31.7 | 0 | 2 |
| Conversation space artifact generation using natural language processing, machine learning, and ontology-based techniques | 31.7 | 0 | 2 |
| Analyzing software test failures using natural language processing and machine learning | 31.7 | 0 | 2 |
| Implementing artificial intelligence agents to perform machine learning tasks using predictive analytics to leverage ensemble policies for maximizing | 31.7 | 0 | 2 |
| Call for Papers:2019 2 International Conference on Machine Learning and Natural Language Processing | 31.7 | 0 | 2 |
| Call for Papers: The 2 International Conference on Machine Learning and Natural Language Processing （MLNLP 2019） | 31.7 | 0 | 2 |
| Call for Papers: 2020 3 International Conference on Machine Learning and Natural Language Processing | 31.7 | 0 | 2 |
| Methods of Entity Relation Extraction Based on Natural Language Processing and Machine Learning | 31.7 | 0 | 2 |
| Machine Learning and Natural Language Processing for Prediction of Human Factors in Aviation Incident Reports | 31.7 | 0 | 2 |
| Shared prediction engine for machine learning model deployment | 31.7 | 0 | 2 |
| Utilizing a machine learning model and natural language processing to manage and allocate tasks | 31.7 | 0 | 2 |
| SinaLab | 31.7 | 0 | 2 |
| Systems and methods for utilizing machine learning and natural language processing to provide a dual-panel user interface | 31.7 | 0 | 2 |
| System and method for using machine learning supporting natural language processing analysis | 31.7 | 0 | 2 |
| Algorithmic and meta-algorithmic machine learning natural language processing approaches for stakeholder requirements classification | 31.7 | 0 | 2 |
| Machine Learning Driven Mental Stress Detection on Reddit Posts Using Natural Language Processing | 31.7 | 0 | 2 |
| Using social media, machine learning and natural language processing to map multiple recreational beneficiaries | 31.7 | 0 | 2 |
| Algorithmic and meta-algorithmic machine learning natural language processing approaches for stakeholder requirements classification | 31.7 | 0 | 2 |
| Searching for chromate replacements using natural language processing and machine learning algorithms | 31.7 | 0 | 2 |
| Industry 4.0 oriented predictive analytics of cardiovascular diseases using machine learning, hyperparameter tuning and ensemble techniques | 31.7 | 0 | 2 |
| Blending citizen science with natural language processing and machine learning: Understanding the experience of living with multiple sclerosis | 31.7 | 0 | 2 |
| Generating knowledge graphs by employing Natural Language Processing and Machine Learning techniques within the scholarly domain | 31.7 | 0 | 2 |
| Improved prediction of drug-induced liver injury literature using natural language processing and machine learning methods | 31.7 | 0 | 2 |
| Early Prediction of 30-Day ICU Re-admissions Using Natural Language Processing and Machine Learning | 31.7 | 0 | 2 |
| Intelligent compilation of patent summaries using machine learning and natural language processing techniques | 31.7 | 0 | 2 |
| Jianfeng Gao | 31.7 | 0 | 2 |
| Automated Modular Data Analysis and Visualization System with Predictive Analytics Using Machine Learning for Agriculture field | 31.7 | 0 | 2 |
| Predictive Analytics In Weather Forecasting Using Machine Learning Algorithms | 31.7 | 0 | 2 |
| PNS266 LANDSCAPE ANALYSIS OF IMPACT OF MACHINE LEARNING, NATURAL LANGUAGE PROCESSING, ARTIFICIAL INTELLIGENCE AND BLOCKCHAIN TECHNOLOGY ON LEVERAGING REAL WORLD EVIDENCE (RWE) | 31.7 | 0 | 2 |
| Sentiment Analysis in Product Reviews using Natural Language Processing and Machine Learning | 31.7 | 0 | 2 |
| Identifying individual expectations in service recovery through natural language processing and machine learning | 31.7 | 0 | 2 |
| MalDy: Portable, data-driven malware detection using natural language processing and machine learning techniques on behavioral analysis reports | 31.7 | 0 | 2 |
| 100. Starving For Support: Natural Language Processing And Machine Learning Analysis of Anorexia Nervosa In Pro-Eating Disorder Communities | 31.7 | 0 | 2 |
| Arabic Natural Language Processing and Machine Learning-Based Systems | 31.7 | 0 | 2 |
| PRM85 - MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING TO POWER LITERATURE SEARCH FOR TIMELY, MEANINGFUL RESULTS | 31.7 | 0 | 2 |
| Practical AI: Machine Learning, Data Science | 31.7 | 0 | 2 |
| Automating the assessment of multicultural orientation through machine learning and natural language processing. | 31.7 | 0 | 2 |
| AI Chatbot using Machine Learning | 31.7 | 0 | 2 |
| Machine learning and predictive analytics aid in individualizing risk profiles for patients undergoing minimally invasive left pancreatectomy | 31.7 | 0 | 2 |
| Optimizing Churn Identification in Telecommunications Using Natural Language Processing and XG Boost Machine Learning Paradigm | 31.7 | 0 | 2 |
| Machine learning and predictive analytics provide individualized risk profiles for patients undergoing minimally invasive left pancreatectomy | 31.7 | 0 | 2 |
| Machine learning and natural language processing on the patent corpus: Data, tools, and new measures | 31.7 | 0 | 2 |
| Utilizing Natural Language Processing and Machine Learning to Create a Better Member Experience: Blue Cross Blue Shield of Louisiana (BCBSLA) Innovation in Action | 31.7 | 0 | 2 |
| An Executive Guide to AI, Machine Learning, and Generative AI—With Some Help From ChatGPT and Bard | 31.7 | 0 | 2 |
| Natural Language Processing and Machine Learning Techniques in Real World (Law and Health) | 31.7 | 0 | 2 |
| Leveraging Machine Learning and Natural Language Processing for Predicting the Crime Rate: Reach 360 | 31.7 | 0 | 2 |
| Natural language processing based machine learning psychological emotion analysis method | 31.7 | 0 | 2 |
| Machine learning drug discovery based on graph neural network and large language model | 31.7 | 0 | 2 |
| Vocational Domain Identification with Machine Learning and Natural Language Processing on Wikipedia Text: Error Analysis and Class Balancing | 31.7 | 0 | 2 |
| An Automated Literature Review Tool (LiteRev) for Streamlining and Accelerating Research Using Natural Language Processing and Machine Learning: Descriptive Performance Evaluation Study | 31.7 | 0 | 2 |
| Machine learning and natural language processing for automating software testing (tutorial) | 31.7 | 0 | 2 |
| Harnessing Machine Learning and Generative AI: A New Era in Online Tutoring Systems | 31.7 | 0 | 2 |
| Machine Learning and Natural Language Processing Algorithms in the Remote Mobile Medical Diagnosis System of Internet Hospitals | 31.7 | 0 | 2 |
| Automated Examination System using Machine Learning and Natural Language Processing | 31.7 | 0 | 2 |
| A Data-Driven Analytical Framework for ESG-based Stock Investment Analytics using Machine Learning and Natural Language Processing | 31.7 | 0 | 2 |
| System Integration of Neocortex, a Unique, Scalable AI Platform | 31.7 | 0 | 2 |
| Study on Intelligent Scoring of English Composition Based on Machine Learning from the Perspective of Natural Language Processing | 31.7 | 0 | 2 |
| From Attack Trees to Attack-Defense Trees with Generative AI & Natural Language Processing | 31.7 | 0 | 2 |
| Blazing a New Trail in ERP Integration with NLP and Generative AI through APIs: a fraud examination perspective | 31.7 | 0 | 2 |
| The Generative AI Deployment Rush: How to Democratize the Politics of Pace | 31.7 | 0 | 2 |
| The Evolution of Natural Language Processing: from Rules Through Neural Networks to Generative AI. What Does the Future Hold? | 31.7 | 0 | 2 |
| María Grandury | 31.7 | 0 | 2 |
| Exploring Generative AI and Natural Language Processing to Develop Search Strategies for Systematic Reviews | 31.7 | 0 | 2 |
| A Custom Generative AI Chatbot as a Course Resource | 31.7 | 0 | 2 |
| Model AI Governance Framework for Generative AI | 31.7 | 0 | 2 |
| Digital workflows | 31.7 | 0 | 2 |
| Q135256182 | 31.7 | 0 | 2 |
| Q136303331 | 31.7 | 0 | 2 |
| Q136797233 | 31.7 | 0 | 2 |
| Q136833216 | 31.7 | 0 | 2 |
| Q136858296 | 31.7 | 0 | 2 |
| Q136908105 | 31.7 | 0 | 2 |
| Nayan Goel | 31.7 | 0 | 2 |
| Machine Learning for Everyone: Practical AI Applications | 31.7 | 0 | 2 |
| GenAI.mil | 31.7 | 0 | 2 |
| Covid-19 Vaccine Stance Detection using Natural Language Processing and Machine Learning Algorithms. | 31.7 | 0 | 2 |
| Toward Greener Matrix Operations by Lossless Compressed Formats | 31.7 | 0 | 2 |
| AiiACo | 31.7 | 0 | 2 |
| Algorithmic Governance In Sport | 31.7 | 0 | 2 |
| iTmethods Inc. | 31.7 | 0 | 2 |
| Reign | 31.7 | 0 | 2 |
| Computational Metabolomics: Discovery of New Molecules to Actionable Insights | 31.7 | 0 | 2 |
| geogen gialon | 31.7 | 0 | 2 |
| AgenticAssure | 31.7 | 0 | 2 |
| SixDegree | 31.7 | 0 | 2 |
| Anove International | 31.7 | 0 | 2 |
| GLM (AI) | 31.7 | 0 | 2 |
| SOFI AI Tech Solution Inc. | 31.7 | 0 | 2 |
| Ryota Tomioka | 31.5 | 1 | 1 |
| Crowd | 31.5 | 1 | 1 |
| Python Machine Learning, 2nd edition | 31.5 | 1 | 1 |
| Katherine A. Heller | 31.5 | 1 | 1 |
| GPT-2 | 31.5 | 1 | 1 |
| GPT-3 | 31.5 | 1 | 1 |
| GitHub Actions | 31.5 | 1 | 1 |
| Road Roughness Estimation Using Machine Learning | 31.5 | 1 | 1 |
| Machine Learning and Deep Learning -- A review for Ecologists | 31.5 | 1 | 1 |
| Microsoft Copilot | 31.5 | 1 | 1 |
| PaLM | 31.5 | 1 | 1 |
| ChatGPT in education | 31.5 | 1 | 1 |
| aTrain | 31.5 | 1 | 1 |
| DBRX | 31.5 | 1 | 1 |
| Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World Data | 31.5 | 1 | 1 |
| OpenAI o3-mini | 31.5 | 1 | 1 |
| Computing Education in the Era of Generative AI | 31.5 | 1 | 1 |
| Q135228377 | 31.5 | 1 | 1 |
| GPT-5 | 31.5 | 1 | 1 |
| Q135647703 | 31.5 | 1 | 1 |
| Q136408639 | 31.5 | 1 | 1 |
| GPT-5.1 | 31.5 | 1 | 1 |
| Q137059343 | 31.5 | 1 | 1 |
| Ontology2Graph | 31.5 | 1 | 1 |
| LeRobot | 31.5 | 1 | 1 |
| Machine Learning Methods and Tools | 31.5 | 1 | 1 |
| krish567366 / mrce-plus | 31.5 | 1 | 1 |
| krish567366 / automl_self_improvement | 31.5 | 1 | 1 |
| entanglement-enhanced-nlp | 31.5 | 1 | 1 |
| mrce-plus | 31.5 | 1 | 1 |
| quantum-data-embedding-suite | 31.5 | 1 | 1 |
| SAP ERP | 29.8 | 5 | 0 |
| Oracle Database | 29.8 | 5 | 0 |
| Media Creation Tool | 29.8 | 5 | 0 |
| Q80689 | 26.7 | 4 | 0 |
| Atlassian | 26.7 | 4 | 0 |
| Oracle E-Business Suite | 26.7 | 4 | 0 |
| Disease Ontology | 26.7 | 4 | 0 |
| Microsoft Lumia 640 XL | 26.7 | 4 | 0 |
| Libertinus | 26.7 | 4 | 0 |
| Microsoft Windows | 23.0 | 3 | 0 |
| JDeveloper | 23.0 | 3 | 0 |
| Jakarta EE | 23.0 | 3 | 0 |
| Oracle SQL Developer | 23.0 | 3 | 0 |
| SAP NetWeaver Business Intelligence | 23.0 | 3 | 0 |
| Elasticsearch | 23.0 | 3 | 0 |
| Ledger | 23.0 | 3 | 0 |
| Datalogix | 23.0 | 3 | 0 |
| IBM Bluemix | 23.0 | 3 | 0 |
| Microsoft Lumia 640 | 23.0 | 3 | 0 |
| G2 | 23.0 | 3 | 0 |
| Dataiku | 23.0 | 3 | 0 |
| InfraKit | 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 |
| emacs-gnuplot | 23.0 | 3 | 0 |
| Microsoft Learn | 23.0 | 3 | 0 |
| Microsoft Typography | 23.0 | 3 | 0 |
| Q381 | 20.0 | 0 | 1 |
| machine learning | 20.0 | 0 | 1 |
| IPv6 rapid deployment | 20.0 | 0 | 1 |
| natural language processing | 20.0 | 0 | 1 |
| word-sense disambiguation | 20.0 | 0 | 1 |
| Schneider Electric | 20.0 | 0 | 1 |
| University of Texas at Austin | 20.0 | 0 | 1 |
| ProSiebenSat.1 Media SE | 20.0 | 0 | 1 |
| stop word | 20.0 | 0 | 1 |
| David Haussler | 20.0 | 0 | 1 |
| Terry Winograd | 20.0 | 0 | 1 |
| Peter Norvig | 20.0 | 0 | 1 |
| Q92894 | 20.0 | 0 | 1 |
| Corinna Cortes | 20.0 | 0 | 1 |
| Donald Michie | 20.0 | 0 | 1 |
| Yoav Freund | 20.0 | 0 | 1 |
| Leslie Valiant | 20.0 | 0 | 1 |
| Weka | 20.0 | 0 | 1 |
| emerging technology | 20.0 | 0 | 1 |
| lazy learning | 20.0 | 0 | 1 |
| explanation-based learning | 20.0 | 0 | 1 |
| privacy | 20.0 | 0 | 1 |
| artificial neural network | 20.0 | 0 | 1 |
| deep learning | 20.0 | 0 | 1 |
| digital literacy | 20.0 | 0 | 1 |
| ensemble learning | 20.0 | 0 | 1 |
| Ubuntu One | 20.0 | 0 | 1 |
| AFNLP | 20.0 | 0 | 1 |
| Jensen Huang | 20.0 | 0 | 1 |
| supervised learning | 20.0 | 0 | 1 |
| pattern recognition | 20.0 | 0 | 1 |
| John Hopfield | 20.0 | 0 | 1 |
| feature selection | 20.0 | 0 | 1 |
| information privacy | 20.0 | 0 | 1 |
| probably approximately correct learning | 20.0 | 0 | 1 |
| boosting | 20.0 | 0 | 1 |
| Kodak | 20.0 | 0 | 1 |
| single sign-on | 20.0 | 0 | 1 |
| European School of Management and Technology | 20.0 | 0 | 1 |
| Delta rule | 20.0 | 0 | 1 |
| ELIZA | 20.0 | 0 | 1 |
| Knowledge Engineering and Machine Learning Group | 20.0 | 0 | 1 |
| Central Authentication Service | 20.0 | 0 | 1 |
| Amazon Mechanical Turk | 20.0 | 0 | 1 |
| backpropagation | 20.0 | 0 | 1 |
| bootstrap aggregating | 20.0 | 0 | 1 |
| reinforcement learning | 20.0 | 0 | 1 |
| intelligent control | 20.0 | 0 | 1 |
| chatbot | 20.0 | 0 | 1 |
| Latent semantic indexing | 20.0 | 0 | 1 |
| Data Protection Directive | 20.0 | 0 | 1 |
| scikit-learn | 20.0 | 0 | 1 |
| semi-supervised learning | 20.0 | 0 | 1 |
| predictive analytics | 20.0 | 0 | 1 |
| natural language understanding | 20.0 | 0 | 1 |
| Cluster labeling | 20.0 | 0 | 1 |
| Comcast | 20.0 | 0 | 1 |
| Bullet | 20.0 | 0 | 1 |
| conditional random field | 20.0 | 0 | 1 |
| self-organizing map | 20.0 | 0 | 1 |
| unsupervised learning | 20.0 | 0 | 1 |
| Lexical Markup Framework | 20.0 | 0 | 1 |
| linear discriminant analysis | 20.0 | 0 | 1 |
| Enterprise search | 20.0 | 0 | 1 |
| Data Privacy Day | 20.0 | 0 | 1 |
| data cap | 20.0 | 0 | 1 |
| version space | 20.0 | 0 | 1 |
| OpenSocial | 20.0 | 0 | 1 |
| customer analytics | 20.0 | 0 | 1 |
| Natural Language Toolkit | 20.0 | 0 | 1 |
| Journal of Machine Learning Research | 20.0 | 0 | 1 |
| latent semantic analysis | 20.0 | 0 | 1 |
| Conference on Neural Information Processing Systems | 20.0 | 0 | 1 |
| language technology | 20.0 | 0 | 1 |
| Rapid Deployment Unit Water Supply | 20.0 | 0 | 1 |
| Rapid Deployment Unit Search and Rescue | 20.0 | 0 | 1 |
| sentiment analysis | 20.0 | 0 | 1 |
| Shogun | 20.0 | 0 | 1 |
| ReadSoft | 20.0 | 0 | 1 |
| computational learning theory | 20.0 | 0 | 1 |
| Viola–Jones object detection framework | 20.0 | 0 | 1 |
| artificial immune system | 20.0 | 0 | 1 |
| lemmatisation | 20.0 | 0 | 1 |
| Linear classifier | 20.0 | 0 | 1 |
| Decision Intelligence | 20.0 | 0 | 1 |
| Apache Solr | 20.0 | 0 | 1 |
| Junction tree algorithm | 20.0 | 0 | 1 |
| ATALA | 20.0 | 0 | 1 |
| Andrei Broder | 20.0 | 0 | 1 |
| David M. Blei | 20.0 | 0 | 1 |
| European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases | 20.0 | 0 | 1 |
| Jean-Pierre Chanod | 20.0 | 0 | 1 |
| LRE Map | 20.0 | 0 | 1 |
| Michael I. Jordan | 20.0 | 0 | 1 |
| Orange | 20.0 | 0 | 1 |
| Pierre Baldi | 20.0 | 0 | 1 |
| Robert Schapire | 20.0 | 0 | 1 |
| Tanagra | 20.0 | 0 | 1 |
| Usama Fayyad | 20.0 | 0 | 1 |
| grid search | 20.0 | 0 | 1 |
| Q3632688 | 20.0 | 0 | 1 |
| Stuart J. Russell | 20.0 | 0 | 1 |
| statistical semantics | 20.0 | 0 | 1 |
| FastICA | 20.0 | 0 | 1 |
| Constraint Grammar | 20.0 | 0 | 1 |
| GitHub | 18.3 | 2 | 0 |
| Q11219 | 18.3 | 2 | 0 |
| Q11278 | 18.3 | 2 | 0 |
| Windows Glyph List 4 | 18.3 | 2 | 0 |
| Windows Installer | 18.3 | 2 | 0 |
| SPSS | 18.3 | 2 | 0 |
| DirectX | 18.3 | 2 | 0 |
| PCMan File Manager | 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 |
| OCRopus | 18.3 | 2 | 0 |
| Visual Basic for Applications | 18.3 | 2 | 0 |
| Azure | 18.3 | 2 | 0 |
| Microsoft Dynamics NAV | 18.3 | 2 | 0 |
| Microsoft AutoRoute | 18.3 | 2 | 0 |
| Salesforce | 18.3 | 2 | 0 |
| IBM Informix | 18.3 | 2 | 0 |
| util-linux | 18.3 | 2 | 0 |
| Lightbeam (software) | 18.3 | 2 | 0 |
| CICS | 18.3 | 2 | 0 |
| IBM Rational DOORS | 18.3 | 2 | 0 |
| Intelligent Input Bus | 18.3 | 2 | 0 |
| Microsoft Virtual Server | 18.3 | 2 | 0 |
| Intuit | 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 |
| Gummi | 18.3 | 2 | 0 |
| Siebel Systems | 18.3 | 2 | 0 |
| CPLEX | 18.3 | 2 | 0 |
| Microsoft Student | 18.3 | 2 | 0 |
| Vantive | 18.3 | 2 | 0 |
| Microsoft Layer for Unicode | 18.3 | 2 | 0 |
| uPortal | 18.3 | 2 | 0 |
| IBM Configuration Management Version Control | 18.3 | 2 | 0 |
| Beebdroid | 18.3 | 2 | 0 |
| Box | 18.3 | 2 | 0 |
| Coveo | 18.3 | 2 | 0 |
| Frege | 18.3 | 2 | 0 |
| HubSpot | 18.3 | 2 | 0 |
| IntelliType | 18.3 | 2 | 0 |
| Java BluePrints | 18.3 | 2 | 0 |
| Microsoft Japan | 18.3 | 2 | 0 |
| Oracle Property Manager | 18.3 | 2 | 0 |
| Owl Lisp | 18.3 | 2 | 0 |
| QuickBooks | 18.3 | 2 | 0 |
| RingCentral | 18.3 | 2 | 0 |
| Workday, Inc. | 18.3 | 2 | 0 |
| Zscaler | 18.3 | 2 | 0 |
| Q10984556 | 18.3 | 2 | 0 |
| Microsoft Pinyin IME | 18.3 | 2 | 0 |
| AutoKey | 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 |
| Sprinklr | 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 |
| Maps | 18.3 | 2 | 0 |
| OpenAI | 18.3 | 2 | 0 |
| Elastic | 18.3 | 2 | 0 |
| Kaminari | 18.3 | 2 | 0 |
| Chakra | 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 |
| Forbes 30 Under 30 | 18.3 | 2 | 0 |
| Bubble | 18.3 | 2 | 0 |
| cligh | 18.3 | 2 | 0 |
| hidapi | 18.3 | 2 | 0 |
| libtelnet | 18.3 | 2 | 0 |
| llvm-libunwind | 18.3 | 2 | 0 |
| os-diskconfig-python-novaclient-ext | 18.3 | 2 | 0 |
| python-scsi | 18.3 | 2 | 0 |
| ucpp | 18.3 | 2 | 0 |
| SAS Institute | 18.3 | 2 | 0 |
| Oracle Cloud | 18.3 | 2 | 0 |
| resvg | 18.3 | 2 | 0 |
| FER+ | 18.3 | 2 | 0 |
| CaosDB - Research Data Management for Complex, Changing, and Automated Research Workflows | 18.3 | 2 | 0 |
| Cardiovascular Disease Ontology | 18.3 | 2 | 0 |
| Vaccination Informed Consent Ontology | 18.3 | 2 | 0 |
| Domoticz | 18.3 | 2 | 0 |
| Twitter developer code of conduct | 18.3 | 2 | 0 |
| Microsoft Academic Graph | 18.3 | 2 | 0 |
| adoc-mode | 18.3 | 2 | 0 |
| monday.com | 18.3 | 2 | 0 |
| Microsoft Saudi | 18.3 | 2 | 0 |
| NASDAQ/Ngs (Global Select Market) | 18.3 | 2 | 0 |
| clinical LABoratory Ontology | 18.3 | 2 | 0 |
| Zebrafish Phenotype Ontology | 18.3 | 2 | 0 |
| wikibase-cli | 18.3 | 2 | 0 |
| spdx-license-ids | 18.3 | 2 | 0 |
| arg | 18.3 | 2 | 0 |
| on-finished | 18.3 | 2 | 0 |
| which-pm-runs | 18.3 | 2 | 0 |
| ms | 18.3 | 2 | 0 |
| ini | 18.3 | 2 | 0 |
| domhandler | 18.3 | 2 | 0 |
| debug | 18.3 | 2 | 0 |
| is-callable | 18.3 | 2 | 0 |
| object-inspect | 18.3 | 2 | 0 |
| XState | 18.3 | 2 | 0 |
| is-date-object | 18.3 | 2 | 0 |
| trim-repeated | 18.3 | 2 | 0 |
| ua-parser-js | 18.3 | 2 | 0 |
| dom-serializer | 18.3 | 2 | 0 |
| is-regex | 18.3 | 2 | 0 |
| verror | 18.3 | 2 | 0 |
| seek-bzip | 18.3 | 2 | 0 |
| keyv | 18.3 | 2 | 0 |
| readable-stream | 18.3 | 2 | 0 |
| js-yaml | 18.3 | 2 | 0 |
| node-fetch | 18.3 | 2 | 0 |
| responselike | 18.3 | 2 | 0 |
| are-we-there-yet | 18.3 | 2 | 0 |
| clone-response | 18.3 | 2 | 0 |
| pako | 18.3 | 2 | 0 |
| common-tags | 18.3 | 2 | 0 |
| cacheable-request | 18.3 | 2 | 0 |
| graphql-playground-html | 18.3 | 2 | 0 |
| caniuse-lite | 18.3 | 2 | 0 |
| graphql-playground-middleware-express | 18.3 | 2 | 0 |
| trim-right | 18.3 | 2 | 0 |
| eslint-import-resolver-node | 18.3 | 2 | 0 |
| tabbable | 18.3 | 2 | 0 |
| eslint-module-utils | 18.3 | 2 | 0 |
| is-arguments | 18.3 | 2 | 0 |
| is-boolean-object | 18.3 | 2 | 0 |
| foreach | 18.3 | 2 | 0 |
| is-bigint | 18.3 | 2 | 0 |
| which-boxed-primitive | 18.3 | 2 | 0 |
| zen-observable-ts | 18.3 | 2 | 0 |
| react-datetime | 18.3 | 2 | 0 |
| csso | 18.3 | 2 | 0 |
| postcss-load-config | 18.3 | 2 | 0 |
| postcss-loader | 18.3 | 2 | 0 |
| socket.io-client | 18.3 | 2 | 0 |
| http-proxy-middleware | 18.3 | 2 | 0 |
| eslint-plugin-import | 18.3 | 2 | 0 |
| is-typed-array | 18.3 | 2 | 0 |
| jsx-ast-utils | 18.3 | 2 | 0 |
| eslint-plugin-jsx-a11y | 18.3 | 2 | 0 |
| string.prototype.matchall | 18.3 | 2 | 0 |
| eslint-plugin-react | 18.3 | 2 | 0 |
| gatsby-plugin-react-helmet-async | 18.3 | 2 | 0 |
| react-helmet-async | 18.3 | 2 | 0 |
| IBM Cloud | 18.3 | 2 | 0 |
| JupyterLab | 18.3 | 2 | 0 |
| dungeon-mode | 18.3 | 2 | 0 |
| containerd | 18.3 | 2 | 0 |
| Libkiwix | 18.3 | 2 | 0 |
| FreeQDA | 18.3 | 2 | 0 |
| Microsoft Mesh | 18.3 | 2 | 0 |
| csharp-mode | 18.3 | 2 | 0 |
| arduino-mode | 18.3 | 2 | 0 |
| EXWM | 18.3 | 2 | 0 |
| subed | 18.3 | 2 | 0 |
| pdf-tools | 18.3 | 2 | 0 |
| SeaweedFS | 18.3 | 2 | 0 |
| js2-mode | 18.3 | 2 | 0 |
| IBM Cloud Object Storage | 18.3 | 2 | 0 |
| CTO: Core Ontology of Clinical Trials | 18.3 | 2 | 0 |
| Proof General | 18.3 | 2 | 0 |
| Emacs PHP Mode | 18.3 | 2 | 0 |
| Intel Math Kernel Library for Deep Neural Networks | 18.3 | 2 | 0 |
| GNUe DCL | 18.3 | 2 | 0 |
| emacs-bind-key | 18.3 | 2 | 0 |
| Salesforce | 18.3 | 2 | 0 |
| Microsoft Lists | 18.3 | 2 | 0 |
| OpenAI OpCo | 18.3 | 2 | 0 |
| Anyscale, Inc. | 18.3 | 2 | 0 |
| GNUstep Project Center | 18.3 | 2 | 0 |
| Julia Enhancement Proposal | 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 |
| Antimalware Scan Interface | 18.3 | 2 | 0 |
| Perplexity AI | 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 |
| Forbes ASAP | 18.3 | 2 | 0 |
| SambaNova Systems | 18.3 | 2 | 0 |
| The Internet Protocol Journal | 18.3 | 2 | 0 |
| emacs-ledger-mode | 18.3 | 2 | 0 |
| Aider | 18.3 | 2 | 0 |
| Forbes Best-In-State Wealth Advisor | 18.3 | 2 | 0 |
| Forbes Top 250 Wealth Advisor | 18.3 | 2 | 0 |
| NeoWiki | 18.3 | 2 | 0 |
| Q138493851 | 18.3 | 2 | 0 |
| Xamun Technologies Limited | 18.3 | 2 | 0 |
| krish567366 / OpenTX | 18.3 | 2 | 0 |
| krish567366 / AlphaForge | 18.3 | 2 | 0 |
| krish567366 / Vision-Sphere | 18.3 | 2 | 0 |
| krish567366 / Federated-AI-Network | 18.3 | 2 | 0 |
| alphaforge | 18.3 | 2 | 0 |
| Transformation Operating Framework | 18.3 | 2 | 0 |
| Java | 11.5 | 1 | 0 |
| Visual Basic | 11.5 | 1 | 0 |
| French Wikipedia | 11.5 | 1 | 0 |
| Q11222 | 11.5 | 1 | 0 |
| Q11226 | 11.5 | 1 | 0 |
| Q11230 | 11.5 | 1 | 0 |
| Windows Server 2008 | 11.5 | 1 | 0 |
| Windows Server 2003 | 11.5 | 1 | 0 |
| XNU | 11.5 | 1 | 0 |
| Solaris | 11.5 | 1 | 0 |
| Microsoft SharePoint | 11.5 | 1 | 0 |
| Oracle Corporation | 11.5 | 1 | 0 |
| Windows Mobile | 11.5 | 1 | 0 |
| Windows Home Server | 11.5 | 1 | 0 |
| Age of Empires II: The Age of Kings | 11.5 | 1 | 0 |
| PL/SQL | 11.5 | 1 | 0 |
| Oracle Linux | 11.5 | 1 | 0 |
| Q47604 | 11.5 | 1 | 0 |
| IBM Lotus Sametime | 11.5 | 1 | 0 |
| IBM WebSphere Application Server | 11.5 | 1 | 0 |
| Nasdaq | 11.5 | 1 | 0 |
| Windows 95 | 11.5 | 1 | 0 |
| Oracle WebLogic Server | 11.5 | 1 | 0 |
| Larry Ellison | 11.5 | 1 | 0 |
| Simon Peyton Jones | 11.5 | 1 | 0 |
| Security-Enhanced Linux | 11.5 | 1 | 0 |
| arXiv | 11.5 | 1 | 0 |
| IBM Lotus SmartSuite | 11.5 | 1 | 0 |
| Bing Maps Platform | 11.5 | 1 | 0 |
| Mongoose | 11.5 | 1 | 0 |
| Jakarta Transactions | 11.5 | 1 | 0 |
| Q173395 | 11.5 | 1 | 0 |
| Gartner | 11.5 | 1 | 0 |
| GNUstep | 11.5 | 1 | 0 |
| Jakarta Server Pages | 11.5 | 1 | 0 |
| Java Platform, Micro Edition | 11.5 | 1 | 0 |
| Microsoft Visio | 11.5 | 1 | 0 |
| System Center Operations Manager | 11.5 | 1 | 0 |
| Synaptic Package Manager | 11.5 | 1 | 0 |
| PL/I | 11.5 | 1 | 0 |
| Q223653 | 11.5 | 1 | 0 |
| Microsoft Defender Antivirus | 11.5 | 1 | 0 |
| Windows XP Professional x64 Edition | 11.5 | 1 | 0 |
| Q260180 | 11.5 | 1 | 0 |
| Group Policy | 11.5 | 1 | 0 |
| MSX BASIC | 11.5 | 1 | 0 |
| IBM AIX | 11.5 | 1 | 0 |
| Notepad | 11.5 | 1 | 0 |
| System File Checker | 11.5 | 1 | 0 |
| rTorrent | 11.5 | 1 | 0 |
| Oracle Financial Services Software | 11.5 | 1 | 0 |
| VSE | 11.5 | 1 | 0 |
| mod_wsgi | 11.5 | 1 | 0 |
| id Tech 4 | 11.5 | 1 | 0 |
| cryptlib | 11.5 | 1 | 0 |
| pkgsrc | 11.5 | 1 | 0 |
| Tenés Empanadas Graciela | 11.5 | 1 | 0 |
| TopLink | 11.5 | 1 | 0 |
| ActiveSync | 11.5 | 1 | 0 |
| Active Server Pages | 11.5 | 1 | 0 |
| Q368338 | 11.5 | 1 | 0 |
| Connect:Direct | 11.5 | 1 | 0 |
| PR Newswire | 11.5 | 1 | 0 |
| SAP NetWeaver Application Server | 11.5 | 1 | 0 |
| SquashFS | 11.5 | 1 | 0 |
| Microsoft Office 2008 for Mac | 11.5 | 1 | 0 |
| Office 2004 for Mac | 11.5 | 1 | 0 |
| Microsoft Office for Mac 2011 | 11.5 | 1 | 0 |
| Mobile Information Device Profile | 11.5 | 1 | 0 |
| IBM Db2 | 11.5 | 1 | 0 |
| American Megatrends | 11.5 | 1 | 0 |
| Microsoft Surface | 11.5 | 1 | 0 |
| Q483881 | 11.5 | 1 | 0 |
| Q484892 | 11.5 | 1 | 0 |
| Windows NT 4.0 | 11.5 | 1 | 0 |
| Microsoft Silverlight | 11.5 | 1 | 0 |
| The World's Billionaires | 11.5 | 1 | 0 |
| IBM Rational Application Developer | 11.5 | 1 | 0 |
| Nasdaq-100 | 11.5 | 1 | 0 |
| Age of Empires: The Rise of Rome | 11.5 | 1 | 0 |
| Polkit | 11.5 | 1 | 0 |
| SAP | 11.5 | 1 | 0 |
| Java Native Access | 11.5 | 1 | 0 |
| Project Gotham Racing | 11.5 | 1 | 0 |
| IBM Rational ClearCase | 11.5 | 1 | 0 |
| PowerDesigner | 11.5 | 1 | 0 |
| FiveThirtyEight | 11.5 | 1 | 0 |
| TechRadar | 11.5 | 1 | 0 |
| Age of Empires II: The Conquerors | 11.5 | 1 | 0 |
| Nmap | 11.5 | 1 | 0 |
| Arabic Typesetting | 11.5 | 1 | 0 |
| Tenchu Z | 11.5 | 1 | 0 |
| LevelDB | 11.5 | 1 | 0 |
| Microsoft Forefront Threat Management Gateway | 11.5 | 1 | 0 |
| Q636192 | 11.5 | 1 | 0 |
| Microsoft Office 2007 | 11.5 | 1 | 0 |
| Internet Explorer 5 | 11.5 | 1 | 0 |
| Sysinternals | 11.5 | 1 | 0 |
| Lotus 1-2-3 | 11.5 | 1 | 0 |
| Windows Media Center | 11.5 | 1 | 0 |
| Microsoft Plus! | 11.5 | 1 | 0 |
| Art Technology Group | 11.5 | 1 | 0 |
| Windows Fundamentals for Legacy PCs | 11.5 | 1 | 0 |
| Java Database Connectivity | 11.5 | 1 | 0 |
| Fail2ban | 11.5 | 1 | 0 |
| Windows Mail | 11.5 | 1 | 0 |
| Java Development Kit | 11.5 | 1 | 0 |
| Windows Virtual PC | 11.5 | 1 | 0 |
| Blackbox | 11.5 | 1 | 0 |
| Cisco IOS | 11.5 | 1 | 0 |
| Jakarta Messaging | 11.5 | 1 | 0 |
| Nokia Software Updater | 11.5 | 1 | 0 |
| Microsoft 365 | 11.5 | 1 | 0 |
| Lumia series | 11.5 | 1 | 0 |
| Berkeley DB | 11.5 | 1 | 0 |
| JRockit | 11.5 | 1 | 0 |
| K virtual machine | 11.5 | 1 | 0 |
| International Components for Unicode | 11.5 | 1 | 0 |
| GlusterFS | 11.5 | 1 | 0 |
| GNU Linear Programming Kit | 11.5 | 1 | 0 |
| Q840410 | 11.5 | 1 | 0 |
| Entity Framework | 11.5 | 1 | 0 |
| Media Player Classic | 11.5 | 1 | 0 |
| Windows Genuine Advantage | 11.5 | 1 | 0 |
| Modernizr | 11.5 | 1 | 0 |
| Microsoft JhengHei | 11.5 | 1 | 0 |
| BitLocker | 11.5 | 1 | 0 |
| Microsoft YaHei | 11.5 | 1 | 0 |
| Meiryo | 11.5 | 1 | 0 |
| Boost | 11.5 | 1 | 0 |
| Zen Cart | 11.5 | 1 | 0 |
| Microsoft BizTalk Server | 11.5 | 1 | 0 |
| Lotus Software | 11.5 | 1 | 0 |
| Windows SideShow | 11.5 | 1 | 0 |
| Microsoft Expression Encoder | 11.5 | 1 | 0 |
| Freelancer | 11.5 | 1 | 0 |
| Tesseract | 11.5 | 1 | 0 |
| SAP HANA | 11.5 | 1 | 0 |
| PHP-Nuke | 11.5 | 1 | 0 |
| Forbes | 11.5 | 1 | 0 |
| Java Foundation Classes | 11.5 | 1 | 0 |
| Filesystem in Userspace | 11.5 | 1 | 0 |
| Microsoft Web Platform Installer | 11.5 | 1 | 0 |
| Open XML Paper Specification | 11.5 | 1 | 0 |
| BigPark | 11.5 | 1 | 0 |
| SQL Server Express | 11.5 | 1 | 0 |
| Cisco Unified Communications Manager | 11.5 | 1 | 0 |
| The World's Most Powerful People | 11.5 | 1 | 0 |
| Microsoft InterConnect | 11.5 | 1 | 0 |
| Age of Mythology: The Titans | 11.5 | 1 | 0 |
| flex | 11.5 | 1 | 0 |
| Internet Explorer for UNIX | 11.5 | 1 | 0 |
| Arora | 11.5 | 1 | 0 |
| Q1063566 | 11.5 | 1 | 0 |
| Windows Nashville | 11.5 | 1 | 0 |
| Windows Driver Frameworks | 11.5 | 1 | 0 |
| Windows Media Audio 9 Lossless | 11.5 | 1 | 0 |
| Windows Firewall | 11.5 | 1 | 0 |
| IBM Lotus Domino | 11.5 | 1 | 0 |
| Q1071107 | 11.5 | 1 | 0 |
| Windows Metafile | 11.5 | 1 | 0 |
| Microsoft SharePoint Designer | 11.5 | 1 | 0 |
| Windows DVD Maker | 11.5 | 1 | 0 |
| Windows Embedded Compact 7 | 11.5 | 1 | 0 |
| Microsoft Works | 11.5 | 1 | 0 |
| Confluence | 11.5 | 1 | 0 |
| StumpWM | 11.5 | 1 | 0 |
| Java Advanced Imaging | 11.5 | 1 | 0 |
| Red5 | 11.5 | 1 | 0 |
| Core fonts for the Web | 11.5 | 1 | 0 |
| Forbes Global 2000 | 11.5 | 1 | 0 |
| Zoho Office Suite | 11.5 | 1 | 0 |
| Crystal Reports | 11.5 | 1 | 0 |
| GCompris | 11.5 | 1 | 0 |
| Cairo | 11.5 | 1 | 0 |
| ELinks | 11.5 | 1 | 0 |
| Cω | 11.5 | 1 | 0 |
| Conary | 11.5 | 1 | 0 |
| Windows Embedded CE 6.0 | 11.5 | 1 | 0 |
| Windows Mobile Device Center | 11.5 | 1 | 0 |
| Microsoft Office 2003 | 11.5 | 1 | 0 |
| Windows Messenger | 11.5 | 1 | 0 |
| Disk Cleanup | 11.5 | 1 | 0 |
| Microsoft Azure SQL Database | 11.5 | 1 | 0 |
| Windows Server Update Services | 11.5 | 1 | 0 |
| Windows Media | 11.5 | 1 | 0 |
| Windows Search | 11.5 | 1 | 0 |
| Microsoft Internet Explorer 2 | 11.5 | 1 | 0 |
| Microsoft Office XP | 11.5 | 1 | 0 |
| Oracle iPlanet Web Server | 11.5 | 1 | 0 |
| Frash | 11.5 | 1 | 0 |
| Duet | 11.5 | 1 | 0 |
| Duncan J. Watts | 11.5 | 1 | 0 |
| OpenSearch | 11.5 | 1 | 0 |
| read-only domain controller | 11.5 | 1 | 0 |
| Textile | 11.5 | 1 | 0 |
| Impossible Creatures | 11.5 | 1 | 0 |
| Q1322298 | 11.5 | 1 | 0 |
| Embeddable Linux Kernel Subset | 11.5 | 1 | 0 |
| Q1347061 | 11.5 | 1 | 0 |
| Jira | 11.5 | 1 | 0 |
| OpenSPARC | 11.5 | 1 | 0 |
| Essbase | 11.5 | 1 | 0 |
| Microsoft Messenger for Mac | 11.5 | 1 | 0 |
| Xarchiver | 11.5 | 1 | 0 |
| Mike Lazaridis | 11.5 | 1 | 0 |
| Windows Embedded Automotive | 11.5 | 1 | 0 |
| Fabasoft Mindbreeze | 11.5 | 1 | 0 |
| People | 11.5 | 1 | 0 |
| HipHop for PHP | 11.5 | 1 | 0 |
| Javadoc | 11.5 | 1 | 0 |
| Microsoft Deployment Toolkit | 11.5 | 1 | 0 |
| PC-MOS/386 | 11.5 | 1 | 0 |
| Microsoft Baseline Security Analyzer | 11.5 | 1 | 0 |
| Transaction Processing Facility | 11.5 | 1 | 0 |
| IBM Lotus Organizer | 11.5 | 1 | 0 |
| Yandex.Tank | 11.5 | 1 | 0 |
| Stackless Python | 11.5 | 1 | 0 |
| SAS Institute | 11.5 | 1 | 0 |
| GanttProject | 11.5 | 1 | 0 |
| GeoTIFF | 11.5 | 1 | 0 |
| Google Guice | 11.5 | 1 | 0 |
| Microsoft Expression Design | 11.5 | 1 | 0 |
| Media Player | 11.5 | 1 | 0 |
| hebOCR | 11.5 | 1 | 0 |
| Microsoft Dynamics 365 Sales | 11.5 | 1 | 0 |
| IBM Information Management System | 11.5 | 1 | 0 |
| JavaOne | 11.5 | 1 | 0 |
| Second Reality | 11.5 | 1 | 0 |
| LightDM | 11.5 | 1 | 0 |
| Publicis Sapient | 11.5 | 1 | 0 |
| Under a Killing Moon | 11.5 | 1 | 0 |
| Allegiance | 11.5 | 1 | 0 |
| Interactive Ruby Shell | 11.5 | 1 | 0 |
| Scuttle | 11.5 | 1 | 0 |
| Nokia Asha series | 11.5 | 1 | 0 |
| Microsoft Excel Viewer | 11.5 | 1 | 0 |
| IBM Canada | 11.5 | 1 | 0 |
| Windows Automated Installation Kit | 11.5 | 1 | 0 |
| Microsoft App-V | 11.5 | 1 | 0 |
| System Center Mobile Device Manager | 11.5 | 1 | 0 |
| Oracle Cloud File System | 11.5 | 1 | 0 |
| Java Media Framework | 11.5 | 1 | 0 |
| Julius | 11.5 | 1 | 0 |
| Mongrel | 11.5 | 1 | 0 |
| Outlook on the web | 11.5 | 1 | 0 |
| IBM Lotus Approach | 11.5 | 1 | 0 |
| Storage Technology Corporation | 11.5 | 1 | 0 |
| WEBrick | 11.5 | 1 | 0 |
| Microsoft Songsmith | 11.5 | 1 | 0 |
| LINBO | 11.5 | 1 | 0 |
| SQL Server Compact | 11.5 | 1 | 0 |
| Sympa | 11.5 | 1 | 0 |
| Oracle Application Development Framework | 11.5 | 1 | 0 |
| Microsoft MapPoint | 11.5 | 1 | 0 |
| Q1854343 | 11.5 | 1 | 0 |
| IBM Lotus Expeditor | 11.5 | 1 | 0 |
| IBM Lotus Freelance Graphics | 11.5 | 1 | 0 |
| IBM Lotus Word Pro | 11.5 | 1 | 0 |
| MS Sans Serif | 11.5 | 1 | 0 |
| Sylfaen | 11.5 | 1 | 0 |
| PeopleSoft | 11.5 | 1 | 0 |
| Microsoft Robotics Developer Studio | 11.5 | 1 | 0 |
| Microsoft WebMatrix | 11.5 | 1 | 0 |
| Microsoft Flight Simulator X | 11.5 | 1 | 0 |
| Nokia Suite | 11.5 | 1 | 0 |
| SAS | 11.5 | 1 | 0 |
| Spry framework | 11.5 | 1 | 0 |
| Oracle Call Interface | 11.5 | 1 | 0 |
| PGF/TikZ | 11.5 | 1 | 0 |
| System Center Data Protection Manager | 11.5 | 1 | 0 |
| Microsoft Analysis Services | 11.5 | 1 | 0 |
| JSDoc | 11.5 | 1 | 0 |
| Microsoft Entourage | 11.5 | 1 | 0 |
| Primavera | 11.5 | 1 | 0 |
| Rational Synergy | 11.5 | 1 | 0 |
| IBM Z | 11.5 | 1 | 0 |
| Windows Hardware Lab Kit | 11.5 | 1 | 0 |
| Tuxedo | 11.5 | 1 | 0 |
| Q2204758 | 11.5 | 1 | 0 |
| Q2204829 | 11.5 | 1 | 0 |
| SAP Solution Manager | 11.5 | 1 | 0 |
| SAP NetWeaver Portal | 11.5 | 1 | 0 |
| SAP NetWeaver Process Integration | 11.5 | 1 | 0 |
| SLIME | 11.5 | 1 | 0 |
| SQE | 11.5 | 1 | 0 |
| SQL Anywhere | 11.5 | 1 | 0 |
| Microsoft Security Development Lifecycle | 11.5 | 1 | 0 |
| JavaFX | 11.5 | 1 | 0 |
| Microsoft Flight | 11.5 | 1 | 0 |
| Microsoft Forefront | 11.5 | 1 | 0 |
| Microsoft Research Image Composite Editor | 11.5 | 1 | 0 |
| Basecamp | 11.5 | 1 | 0 |
| IBM Developer | 11.5 | 1 | 0 |
| IBM General Parallel File System | 11.5 | 1 | 0 |
| Seashore | 11.5 | 1 | 0 |
| Forbes 400 | 11.5 | 1 | 0 |
| JD Edwards | 11.5 | 1 | 0 |
| Microsoft Agent | 11.5 | 1 | 0 |
| UNIX System Services | 11.5 | 1 | 0 |
| Basecamp Classic | 11.5 | 1 | 0 |
| IBM WebSphere | 11.5 | 1 | 0 |
| Conky | 11.5 | 1 | 0 |
| Reconstructor | 11.5 | 1 | 0 |
| Q2567249 | 11.5 | 1 | 0 |
| Apple Productivity Experience Group | 11.5 | 1 | 0 |
| Windows SteadyState | 11.5 | 1 | 0 |
| YAM | 11.5 | 1 | 0 |
| Oracle Developer Studio | 11.5 | 1 | 0 |
| Windows Vista Starter | 11.5 | 1 | 0 |
| Windows XP Media Center Edition | 11.5 | 1 | 0 |
| Oracle Grid Engine | 11.5 | 1 | 0 |
| CGAL | 11.5 | 1 | 0 |
| Windows HPC Server 2008 | 11.5 | 1 | 0 |
| Agile Software Corporation | 11.5 | 1 | 0 |
| Kippo | 11.5 | 1 | 0 |
| Cisco Secure Client | 11.5 | 1 | 0 |
| Cisco Systems VPN Client | 11.5 | 1 | 0 |
| Cloudera | 11.5 | 1 | 0 |
| Windows XP Professional | 11.5 | 1 | 0 |
| Office Genuine Advantage | 11.5 | 1 | 0 |
| DuinOS | 11.5 | 1 | 0 |
| Dynamic Language Runtime | 11.5 | 1 | 0 |
| Windows Product Activation | 11.5 | 1 | 0 |
| Windows Enhanced Metafile | 11.5 | 1 | 0 |
| Ferite | 11.5 | 1 | 0 |
| Fontmatrix | 11.5 | 1 | 0 |
| Fraise | 11.5 | 1 | 0 |
| MBROLA | 11.5 | 1 | 0 |
| Gateway Load Balancing Protocol | 11.5 | 1 | 0 |
| HeeksCAD | 11.5 | 1 | 0 |
| MathJax | 11.5 | 1 | 0 |
| Hop | 11.5 | 1 | 0 |
| Q3157013 | 11.5 | 1 | 0 |
| Jappix | 11.5 | 1 | 0 |
| stet | 11.5 | 1 | 0 |
| uim | 11.5 | 1 | 0 |
| SAP Business One | 11.5 | 1 | 0 |
| Windows Embedded Industry | 11.5 | 1 | 0 |
| PlayReady | 11.5 | 1 | 0 |
| Microsoft Forefront Unified Access Gateway | 11.5 | 1 | 0 |
| Microsoft Office PerformancePoint Server | 11.5 | 1 | 0 |
| NanoJIT | 11.5 | 1 | 0 |
| OpenJPEG | 11.5 | 1 | 0 |
| Sun Java Communications Suite | 11.5 | 1 | 0 |
| Picty | 11.5 | 1 | 0 |
| Java Platform | 11.5 | 1 | 0 |
| PowerVM | 11.5 | 1 | 0 |
| Kimios | 11.5 | 1 | 0 |
| IBM Rational Software Modeler | 11.5 | 1 | 0 |
| Federated Wiki | 11.5 | 1 | 0 |
| Snappy | 11.5 | 1 | 0 |
| Oracle Spatial | 11.5 | 1 | 0 |
| Tilda | 11.5 | 1 | 0 |
| Q3569296 | 11.5 | 1 | 0 |
| MIX | 11.5 | 1 | 0 |
| Microsoft Customer Care Framework | 11.5 | 1 | 0 |
| Project Sylpheed | 11.5 | 1 | 0 |
| Q3853680 | 11.5 | 1 | 0 |
| Microsoft .NET | 11.5 | 1 | 0 |
| Microsoft Streets & Trips | 11.5 | 1 | 0 |
| Microsoft Voice Command | 11.5 | 1 | 0 |