| Data Analytics for Machine Learning | 61.5 | 5 | 2 |
| IBM | 60.0 | 36 | 0 |
| Fivetran | 58.4 | 4 | 2 |
| Feast | 50.0 | 2 | 2 |
| Microsoft | 47.1 | 16 | 0 |
| TensorFlow.js | 43.2 | 1 | 2 |
| Eric P. Xing | 43.0 | 3 | 1 |
| Hugging Face | 43.0 | 3 | 1 |
| data warehouse | 40.0 | 0 | 3 |
| Arm Holdings | 40.0 | 0 | 3 |
| Reproducible data science over data lakes: replayable data pipelines with Bauplan and Nessie | 40.0 | 0 | 3 |
| Rudrendu Kumar Paul | 40.0 | 0 | 3 |
| Teradata | 38.3 | 2 | 1 |
| Presto | 38.3 | 2 | 1 |
| Qlik Sense | 38.3 | 2 | 1 |
| bidirectional encoder representations from transformers | 38.3 | 2 | 1 |
| SeaweedFS | 38.3 | 2 | 1 |
| Trino | 38.3 | 2 | 1 |
| Dolly | 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 |
| Q639194 | 31.7 | 0 | 2 |
| Baker Hughes | 31.7 | 0 | 2 |
| data mart | 31.7 | 0 | 2 |
| extract, transform, load | 31.7 | 0 | 2 |
| OLAP cube | 31.7 | 0 | 2 |
| Decision Intelligence | 31.7 | 0 | 2 |
| BigQuery | 31.7 | 0 | 2 |
| early stopping | 31.7 | 0 | 2 |
| Category:Data warehousing | 31.7 | 0 | 2 |
| Statistical Analysis and Data Mining | 31.7 | 0 | 2 |
| Lise Getoor | 31.7 | 0 | 2 |
| H2O | 31.7 | 0 | 2 |
| vanishing gradient problem | 31.7 | 0 | 2 |
| Template:Data warehouse | 31.7 | 0 | 2 |
| J. Nathan Kutz | 31.7 | 0 | 2 |
| feature engineering | 31.7 | 0 | 2 |
| Arthur Zimek | 31.7 | 0 | 2 |
| Providing data science support for systems pharmacology and its implications to drug discovery | 31.7 | 0 | 2 |
| Geminivirus data warehouse: a database enriched with machine learning approaches | 31.7 | 0 | 2 |
| Feature engineering combined with machine learning and rule-based methods for structured information extraction from narrative clinical discharge summaries | 31.7 | 0 | 2 |
| Machine learning and data science in soft materials engineering | 31.7 | 0 | 2 |
| PyTorch | 31.7 | 0 | 2 |
| The Journal of Finance and Data Science | 31.7 | 0 | 2 |
| Data Science: Big Data, Machine Learning, and Artificial Intelligence | 31.7 | 0 | 2 |
| International Journal of Data Science and Analytics | 31.7 | 0 | 2 |
| 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA) | 31.7 | 0 | 2 |
| Q57201105 | 31.7 | 0 | 2 |
| Data Science in the Research Domain Criteria Era: Relevance of Machine Learning to the Study of Stress Pathology, Recovery, and Resilience | 31.7 | 0 | 2 |
| A General Feature Engineering Wrapper for Machine Learning Using $$\epsilon $$ -Lexicase Survival | 31.7 | 0 | 2 |
| Experiences with distributed computing for meteorological applications: grid computing and cloud computing | 31.7 | 0 | 2 |
| Cross-Platform Normalization Enables Machine Learning Model Training On Microarray And RNA-Seq Data Simultaneously | 31.7 | 0 | 2 |
| Topological Data Analysis and Machine Learning for Recognizing Atmospheric River Patterns in Large Climate Datasets | 31.7 | 0 | 2 |
| Next Generation Workload Management System For Big Data on Heterogeneous Distributed Computing | 31.7 | 0 | 2 |
| [Dedicated to Prof. T. Okada and Prof. T. Nishioka: data science in chemistry]Visualizing Individual and Region-specific Microbial–metabolite Relations by Important Variable Selection Using Machine Learning Approaches | 31.7 | 0 | 2 |
| A Tutorial on Machine Learning and Data Science Tools with Python | 31.7 | 0 | 2 |
| Erratum to: Improving official statistics in emerging markets using machine learning and mobile phone data | 31.7 | 0 | 2 |
| Improving official statistics in emerging markets using machine learning and mobile phone data | 31.7 | 0 | 2 |
| A map of global peatland distribution created using machine learning for use in terrestrial ecosystem and earth system models | 31.7 | 0 | 2 |
| Machine learning and data science in materials design: a themed collection | 31.7 | 0 | 2 |
| Topological data analysis and machine learning for recognizing atmospheric river patterns in large climate datasets | 31.7 | 0 | 2 |
| Bridging the gap between human knowledge and machine learning | 31.7 | 0 | 2 |
| Data science, artificial intelligence, and machine learning: Opportunities for laboratory medicine and the value of positive regulation | 31.7 | 0 | 2 |
| Machine Learning and Knowledge Extraction | 31.7 | 0 | 2 |
| data analyst | 31.7 | 0 | 2 |
| Toward collaborative open data science in metabolomics using Jupyter Notebooks and cloud computing | 31.7 | 0 | 2 |
| Data Science and Machine Learning in Anesthesiology | 31.7 | 0 | 2 |
| Targeted Workup after Initial Febrile Urinary Tract Infection: Using a Novel Machine Learning Model to Identify Children Most Likely to Benefit from Voiding Cystourethrogram | 31.7 | 0 | 2 |
| Reducing the Concepts of Data Science and Machine Learning to Tools for the Bench Chemist | 31.7 | 0 | 2 |
| A modeling and machine learning approach to ECG feature engineering for the detection of ischemia using pseudo-ECG | 31.7 | 0 | 2 |
| Prediction model development of late-onset preeclampsia using machine learning-based methods | 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 |
| Medhere: A Smartwatch-based Medication Adherence Monitoring System using Machine Learning and Distributed Computing | 31.7 | 0 | 2 |
| Feature engineering with clinical expert knowledge: A case study assessment of machine learning model complexity and performance | 31.7 | 0 | 2 |
| Avoiding common pitfalls in machine learning omic data science | 31.7 | 0 | 2 |
| David S. Matteson | 31.7 | 0 | 2 |
| Big-Data Science in Porous Materials: Materials Genomics and Machine Learning | 31.7 | 0 | 2 |
| Proceedings of the ... International Conference on Data Science and Advanced Analytics. IEEE International Conference on Data Science and Advanced Analytics | 31.7 | 0 | 2 |
| HealtheDataLab - a cloud computing solution for data science and advanced analytics in healthcare with application to predicting multi-center pediatric readmissions | 31.7 | 0 | 2 |
| Machine Learning-Based Signal Quality Evaluation of Single-Period Radial Artery Pulse Waves: Model Development and Validation | 31.7 | 0 | 2 |
| Integration of Unstructured Data into a Clinical Data Warehouse for Kidney Transplant Screening - Challenges & Solutions | 31.7 | 0 | 2 |
| Data science and machine learning, mathematical and statistical methods | 31.7 | 0 | 2 |
| Surfing the Data Pipeline with Python | 31.7 | 0 | 2 |
| Belkacem Chikhaoui | 31.7 | 0 | 2 |
| Prediction of Cardiac Arrest in the Emergency Department Based on Machine Learning and Sequential Characteristics: Model Development and Retrospective Clinical Validation Study | 31.7 | 0 | 2 |
| Machine Learning Modelling and Feature Engineering in Seismology Experiment | 31.7 | 0 | 2 |
| Machine Learning to Predict Mortality and Critical Events in COVID-19 Positive New York City Patients: A Cohort Study | 31.7 | 0 | 2 |
| Blood Uric Acid Prediction With Machine Learning: Model Development and Performance Comparison | 31.7 | 0 | 2 |
| Probabilistic forecasting of surgical case duration using machine learning: model development and validation | 31.7 | 0 | 2 |
| Structural Disparities in Data Science: A Prolegomenon for the Future of Machine Learning | 31.7 | 0 | 2 |
| The role of data science and machine learning in Health Professions Education: practical applications, theoretical contributions, and epistemic beliefs | 31.7 | 0 | 2 |
| Institution-Specific Machine Learning Models for Prehospital Assessment to Predict Hospital Admission: Prediction Model Development Study | 31.7 | 0 | 2 |
| Machine Learning Approach to Reduce Alert Fatigue Using a Disease Medication-Related Clinical Decision Support System: Model Development and Validation | 31.7 | 0 | 2 |
| Yishay Mansour | 31.7 | 0 | 2 |
| International Conference on Data Science and Advanced Analytics | 31.7 | 0 | 2 |
| International Conference on Machine Learning, Optimization, and Data Science | 31.7 | 0 | 2 |
| 5th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2018, Turin, Italy, October 1-3, 2018 | 31.7 | 0 | 2 |
| Machine Learning, Optimization, and Data Science - 5th International Conference, LOD 2019, Siena, Italy, September 10-13, 2019 | 31.7 | 0 | 2 |
| 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015, Campus des Cordeliers, Paris, France, October 19-21, 2015 | 31.7 | 0 | 2 |
| International Conference on Data Science and Advanced Analytics, DSAA 2014, Shanghai, China, October 30 - November 1, 2014 | 31.7 | 0 | 2 |
| Jupyter for data science exploratory analysis, statistical modeling, machine learning, and data visualization with Jupyter | 31.7 | 0 | 2 |
| Rapid discovery of novel prophages using biological feature engineering and machine learning | 31.7 | 0 | 2 |
| Machine learning in coupled wildfire-water supply risk assessment: Data science toolkit | 31.7 | 0 | 2 |
| SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle | 31.7 | 0 | 2 |
| Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases | 31.7 | 0 | 2 |
| A Link between Machine Learning and Optimization in Ground-Motion Model Development: Weighted Mixed-Effects Regression with Data-Driven Probabilistic Earthquake Classification | 31.7 | 0 | 2 |
| Data Science | 31.7 | 0 | 2 |
| model persistence | 31.7 | 0 | 2 |
| Introducing students to machine learning with decision trees using CODAP and Jupyter Notebooks | 31.7 | 0 | 2 |
| Anatomy of a Data Science Software Toolkit That Uses Machine Learning to Aid ‘Bench-to-Bedside’ Medical Research—With Essential Concepts of Data Mining and Analysis Explained | 31.7 | 0 | 2 |
| Photometric Redshifts With Machine Learning, Lights and Shadows on a Complex Data Science Use Case | 31.7 | 0 | 2 |
| Migrating from a Centralized Data Warehouse to a Decentralized Data Platform Architecture | 31.7 | 0 | 2 |
| Finding Related Tables in Data Lakes for Interactive Data Science | 31.7 | 0 | 2 |
| A Machine Learning Classifier Improves Mortality Prediction Compared With Pediatric Logistic Organ Dysfunction-2 Score: Model Development and Validation | 31.7 | 0 | 2 |
| Predicting Kidney Graft Survival Using Machine Learning Methods: Prediction Model Development and Feature Significance Analysis Study | 31.7 | 0 | 2 |
| PHOTONAI-A Python API for rapid machine learning model development | 31.7 | 0 | 2 |
| A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation | 31.7 | 0 | 2 |
| Feature engineering solution with structured query language analytic functions in detecting electricity frauds using machine learning | 31.7 | 0 | 2 |
| Engineering and clinical use of artificial intelligence (AI) with machine learning and data science advancements: radiology leading the way for future | 31.7 | 0 | 2 |
| Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation | 31.7 | 0 | 2 |
| Benefits of using data warehousing and data mining tools | 31.7 | 0 | 2 |
| Quantifying Uncertainty in Machine Learning-Based Power Outage Prediction Model Training: A Tool for Sustainable Storm Restoration | 31.7 | 0 | 2 |
| TEACHING AND LEARNING DATA-DRIVEN MACHINE LEARNING WITH EDUCATIONALLY DESIGNED JUPYTER NOTEBOOKS | 31.7 | 0 | 2 |
| Eric Biernat, Michel Lutz, 2017, <i>Data science : fondamentaux et études de cas, Machine learning avec Python et R</i>, Paris, Eyrolles, 296 p. | 31.7 | 0 | 2 |
| CLASSIFICATION COMPLEX QUERY SQL FOR DATA LAKE MANAGEMENT USING MACHINE LEARNING | 31.7 | 0 | 2 |
| Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence | 31.7 | 0 | 2 |
| Python the game changer in the field of Machine Learning, Data Science and IoT: A Review | 31.7 | 0 | 2 |
| Feature-engine: A Python package for feature engineering for machine learning | 31.7 | 0 | 2 |
| MLxtend: Providing machine learning and data science utilities and extensions to Python’s scientific computing stack | 31.7 | 0 | 2 |
| Modern Machine Learning Technologies and Data Science Workshop. Proc. 3rd International Workshop (MoMLeT&DS 2021). Volume I: Main Conference | 31.7 | 0 | 2 |
| Proceedings of the 2nd International Workshop on Modern Machine Learning Technologies and Data Science (MoMLeT+DS 2020). Volume I: Main Conference | 31.7 | 0 | 2 |
| Modern Machine Learning Technologies and Data Science Workshop. Proc. 3rd International Workshop (MoMLeT&DS 2021). Volume I: Main Conference | 31.7 | 0 | 2 |
| 2nd International Workshop on Modern Machine Learning Technologies and Data Science | 31.7 | 0 | 2 |
| Building and Accelerating a Declarative Platform for Machine Learning Model Serving | 31.7 | 0 | 2 |
| Using Jupyter Notebooks for re-training machine learning models | 31.7 | 0 | 2 |
| The Role of Ecosystem Data Governance in Adoption of Data Platforms by Internet-of-Things Data Providers: Case of Dutch Horticulture Industry | 31.7 | 0 | 2 |
| Heart disease prediction using entropy based feature engineering and ensembling of machine learning classifiers | 31.7 | 0 | 2 |
| A photovoltaic power prediction approach enhanced by feature engineering and stacked machine learning model | 31.7 | 0 | 2 |
| Towards the automated evaluation of product packaging in the Food&Beverage sector through data science/machine learning methods | 31.7 | 0 | 2 |
| Prediction of Diabetic Retinopathy Using Health Records With Machine Learning Classifiers and Data Science | 31.7 | 0 | 2 |
| Machine Learning Algorithms to Classify Future Returns Using Structured and Unstructured Data | 31.7 | 0 | 2 |
| Species Distribution Modelling via Feature Engineering and Machine Learning for Pelagic Fishes in the Mediterranean Sea | 31.7 | 0 | 2 |
| Structured data vs. unstructured data in machine learning prediction models for suicidal behaviors: A systematic review and meta-analysis | 31.7 | 0 | 2 |
| A Platform to Help in Generating Code for Machine Learning and Data Science Projects | 31.7 | 0 | 2 |
| Inclusion of data uncertainty in machine learning and its application in geodetic data science, with case studies for the prediction of Earth orientation parameters and GNSS station coordinate time series | 31.7 | 0 | 2 |
| Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research | 31.7 | 0 | 2 |
| Corporate Data Ethics: Data Governance Transformations for the Age of Advanced Analytics and AI | 31.7 | 0 | 2 |
| Data Warehouse Signature: High Performance Evaluation for Implementing Security Issues in Data Warehouses through a New Framework | 31.7 | 0 | 2 |
| Data Warehouse Signature: A Framework for Implementing Security Issues in Data Warehouses | 31.7 | 0 | 2 |
| Data Science Data Governance [AI Ethics] | 31.7 | 0 | 2 |
| Modern Machine Learning Technologies and Data Science Workshop MoMLeT&DS 2022 | 31.7 | 0 | 2 |
| Modern Machine Learning Technologies and Data Science Workshop MoMLeT&DS 2022 | 31.7 | 0 | 2 |
| An automatic sediment-facies classification approach using machine learning and feature engineering | 31.7 | 0 | 2 |
| Machine learning model training for reviewing documents | 31.7 | 0 | 2 |
| Interactive machine learning model development | 31.7 | 0 | 2 |
| Lane-changing Decision Model Development by Combining Rules Abstract and Machine Learning Technique | 31.7 | 0 | 2 |
| Application of Unstructured Data Platform in Teaching Practice——Taking the Parasaga Digital Resource Cloud Service Platform as an Example | 31.7 | 0 | 2 |
| Research on the Data Lake Architecture of Integrating Multi-Source Heterogeneous Data Governance | 31.7 | 0 | 2 |
| Business-driven Government Big Data Platform Data Governance | 31.7 | 0 | 2 |
| Machine learning model development with interactive model building | 31.7 | 0 | 2 |
| Machine learning model development with interactive model evaluation | 31.7 | 0 | 2 |
| Electronic Medical Record–Based Machine Learning Approach to Predict the Risk of 30-Day Adverse Cardiac Events After Invasive Coronary Treatment: Machine Learning Model Development and Validation | 31.7 | 0 | 2 |
| Data governance operations in highly distributed data platforms | 31.7 | 0 | 2 |
| Proceedings of the Modern Machine Learning Technologies and Data Science Workshop (MoMLeT&DS 2023) | 31.7 | 0 | 2 |
| Modern Machine Learning Technologies and Data Science Workshop (MoMLeT&DS 2023) | 31.7 | 0 | 2 |
| Assessing the Potential of using Sentinel-1 and 2 or high-resolution aerial imagery data with Machine Learning and Data Science Techniques to Model Peatland Restoration Progress – a Northern Scotland case study | 31.7 | 0 | 2 |
| Predicting machine learning or deep learning model training time | 31.7 | 0 | 2 |
| Shared prediction engine for machine learning model deployment | 31.7 | 0 | 2 |
| Systems and methods for accelerating model training in machine learning | 31.7 | 0 | 2 |
| Automated server workload management using machine learning | 31.7 | 0 | 2 |
| Glycowork: A Python package for glycan data science and machine learning | 31.7 | 0 | 2 |
| Data harvesting for machine learning model training | 31.7 | 0 | 2 |
| IP packet-level encrypted traffic classification using machine learning with a light weight feature engineering method | 31.7 | 0 | 2 |
| METADATA MANAGEMENT FOR DATA WAREHOUSING: AN OVERVIEW | 31.7 | 0 | 2 |
| Machine Learning Meets Number Theory: The Data Science of Birch–Swinnerton-Dyer | 31.7 | 0 | 2 |
| On data lake architectures and metadata management | 31.7 | 0 | 2 |
| Machine learning model registry | 31.7 | 0 | 2 |
| A generic metadata management model for heterogeneous sources in a data warehouse | 31.7 | 0 | 2 |
| File and metadata management for BESIII distributed computing | 31.7 | 0 | 2 |
| System and method for identifying business logic and data lineage with machine learning | 31.7 | 0 | 2 |
| Journal of Statistics and Data Science Education | 31.7 | 0 | 2 |
| Tabular data generation for machine learning model training system | 31.7 | 0 | 2 |
| Tabular data generation with attention for machine learning model training system | 31.7 | 0 | 2 |
| Plinius: Secure and Persistent Machine Learning Model Training | 31.7 | 0 | 2 |
| Columnar storage and processing of unstructured data | 31.7 | 0 | 2 |
| A Survey on Recent Advancements in Auto-Machine Learning with a Focus on Feature Engineering | 31.7 | 0 | 2 |
| Machine learning based analytics platform | 31.7 | 0 | 2 |
| Scaling Data Science Solutions with Semantics and Machine Learning: Bosch Case. | 31.7 | 0 | 2 |
| Machine Learning-Based Prediction of Post-Thrombotic Syndrome: A Model Development and Validation Study | 31.7 | 0 | 2 |
| Credit Card Fraud Detection using Machine Learning and Data Science | 31.7 | 0 | 2 |
| Topological feature engineering for machine learning based halide perovskite materials design | 31.7 | 0 | 2 |
| How to Accelerate R&D and Optimize Experiment Planning with Machine Learning and Data Science | 31.7 | 0 | 2 |
| The Data Warehouse Toolkit | 31.7 | 0 | 2 |
| Prognostic model development for classification of colorectal adenocarcinoma by using machine learning model based on feature selection technique boruta | 31.7 | 0 | 2 |
| Impact of the WampServer application in Blended learning considering data science, machine learning, and neural networks | 31.7 | 0 | 2 |
| Fishery R&D Big Data Platform and Metadata Management Strategy | 31.7 | 0 | 2 |
| Machine learning model development for predicting road transport GHG emissions in Canada | 31.7 | 0 | 2 |
| Research on Approaches for Computer Aided Detection of Casting Defects in X-ray Images with Feature Engineering and Machine Learning | 31.7 | 0 | 2 |
| Introducing Technical Indicators to Electricity Price Forecasting: A Feature Engineering Study for Linear, Ensemble, and Deep Machine Learning Models | 31.7 | 0 | 2 |
| Creating adaptive predictions for packaging-critical quality parameters using advanced analytics and machine learning | 31.7 | 0 | 2 |
| Interatomic Potential Model Development: Finite‐Temperature Dynamics Machine Learning | 31.7 | 0 | 2 |
| Feature Engineering of Solid‐State Crystalline Lattices for Machine Learning | 31.7 | 0 | 2 |
| Tensile property prediction by feature engineering guided machine learning in reduced activation ferritic/martensitic steels | 31.7 | 0 | 2 |
| Pipe thinning model development for direct current potential drop data with machine learning approach | 31.7 | 0 | 2 |
| Russian Court Decisions Data Analysis Using Distributed Computing and Machine Learning to Improve Lawmaking and Law Enforcement | 31.7 | 0 | 2 |
| The Implication of Statistical Analysis and Feature Engineering for Model Building Using Machine Learning Algorithms | 31.7 | 0 | 2 |
| Data Governance is Key to Interpretation: Reconceptualizing Data in Data Science | 31.7 | 0 | 2 |
| Introduction to the Issue on Data Science: Machine Learning for Audio Signal Processing | 31.7 | 0 | 2 |
| DDoS attack detection with feature engineering and machine learning: the framework and performance evaluation | 31.7 | 0 | 2 |
| Customer churn prediction in telecom using machine learning in big data platform | 31.7 | 0 | 2 |
| Machine Learning Model Training and Practice: A Study on Constructing a Novel Drug Detection System | 31.7 | 0 | 2 |
| New paradigm for watershed model development by coupling machine learning algorithm and mechanistic model | 31.7 | 0 | 2 |
| Practical AI: Machine Learning, Data Science | 31.7 | 0 | 2 |
| Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study | 31.7 | 0 | 2 |
| Panoramic imaging errors in machine learning model development: a systematic review | 31.7 | 0 | 2 |
| Heat Source Model Development for Thermal Analysis of Laser Powder Bed Fusion Using Bayesian Optimization and Machine Learning | 31.7 | 0 | 2 |
| Proposing Enhanced Feature Engineering and a Selection Model for Machine Learning Processes | 31.7 | 0 | 2 |
| An open dataset of data lineage graphs for data governance research | 31.7 | 0 | 2 |
| A breast cancer-specific combinational QSAR model development using machine learning and deep learning approaches | 31.7 | 0 | 2 |
| IMPLEMENTING DATA SCIENCE FOR RAINFALL PREDICTION WITH VARIABLE PARAMETERS THROUGH MACHINE LEARNING AND ADVANCED BIG DATA TOOLS. | 31.7 | 0 | 2 |
| Comparison of open educational resources services to host your MOOC – Data Science, Data Analytics and Machine Learning Consulting in Koblenz Germany | 31.7 | 0 | 2 |
| Determining causal relationships in leadership research using Machine Learning: The powerful synergy of experiments and data science | 31.7 | 0 | 2 |
| Next steps to a modular machine learning-based data pipeline for automated snow avalanche detection in the Austrian Alps | 31.7 | 0 | 2 |
| Towards Feature Engineering with Human and AI’s Knowledge: Understanding Data Science Practitioners’ Perceptions in Human&AI-Assisted Feature Engineering Design | 31.7 | 0 | 2 |
| TLS Encrypted Application Classification Using Machine Learning with Flow Feature Engineering | 31.7 | 0 | 2 |
| Climate Science, Data Science and Distributed Computing to Build Teen Students' Positive Perceptions of CS | 31.7 | 0 | 2 |
| The Hopsworks Feature Store for Machine Learning | 31.7 | 0 | 2 |
| Memory Usage Prediction of HPC Workloads Using Feature Engineering and Machine Learning | 31.7 | 0 | 2 |
| Landscape of High-Performance Python to Develop Data Science and Machine Learning Applications | 31.7 | 0 | 2 |
| Optimizing Data Pipelines for Machine Learning in Feature Stores | 31.7 | 0 | 2 |
| Data science system for developing machine learning models | 31.7 | 0 | 2 |
| Big Data Platforms and Tools for Data Analytics in the Data Science Engineering Curriculum | 31.7 | 0 | 2 |
| Research on Real-time Processing and Stream Analysis of Unstructured Data Based on Big Data Platforms | 31.7 | 0 | 2 |
| Towards a More Generic and Elastic Metadata Management Model in a Data Lake Environment | 31.7 | 0 | 2 |
| Emerging health data platforms: From individual control to collective data governance | 31.7 | 0 | 2 |
| Infusing Data Science into Mechanical Engineering Curriculum with Course-Specific Machine Learning Modules | 31.7 | 0 | 2 |
| Learning from Machine Learning and Teaching with Machine Teaching: Using Lessons from Data Science to Enhance Collegiate Classrooms | 31.7 | 0 | 2 |
| Identifying the Best Admission Criteria for Data Science Using Machine Learning | 31.7 | 0 | 2 |
| Intraoperative Hypotension Prediction Model Based on Systematic Feature Engineering and Machine Learning | 31.7 | 0 | 2 |
| Machine Learning, Optimization, and Data Science - 8th International Workshop, LOD 2022, Certosa di Pontignano, Italy, September 19-22, 2022, Revised Selected Papers, Part I | 31.7 | 0 | 2 |
| Systems and methods for automated machine learning model training for a custom authored prompt | 31.7 | 0 | 2 |
| ScholarGPS | 31.7 | 0 | 2 |
| Q136300435 | 31.7 | 0 | 2 |
| Q136300437 | 31.7 | 0 | 2 |
| Q136451994 | 31.7 | 0 | 2 |
| Estimating and Projecting Environmental Indicators using Satellite Data and Machine Learning | 31.7 | 0 | 2 |
| Q136832750 | 31.7 | 0 | 2 |
| Q136907379 | 31.7 | 0 | 2 |
| Q136918367 | 31.7 | 0 | 2 |
| Q136927159 | 31.7 | 0 | 2 |
| Q136951579 | 31.7 | 0 | 2 |
| Feature Engineering: Preparing Data for Machine Learning | 31.7 | 0 | 2 |
| Data Science with Python: Practical Machine Learning | 31.7 | 0 | 2 |
| Human-in-the-loop: Towards label embeddings for assessing classification difficulty | 31.7 | 0 | 2 |
| LOD 2024 | 31.7 | 0 | 2 |
| Q138285438 | 31.7 | 0 | 2 |
| Abayomi Abiodun | 31.7 | 0 | 2 |
| Feature Engineering Delegation | 31.7 | 0 | 2 |
| Wardn Platform | 31.7 | 0 | 2 |
| Vendor Lock-In Dependency | 31.7 | 0 | 2 |
| Model Development and Internal Validation of a Machine Learning Risk Score for High Free Light Chain Myeloma | 31.7 | 0 | 2 |
| Machine learning feature engineering | 31.7 | 0 | 2 |
| User interface for machine learning feature engineering studio | 31.7 | 0 | 2 |
| Jeannette Wing | 31.5 | 1 | 1 |
| Snowflake Inc. | 31.5 | 1 | 1 |
| William W. Cohen | 31.5 | 1 | 1 |
| Chris Dyer | 31.5 | 1 | 1 |
| Ryota Tomioka | 31.5 | 1 | 1 |
| Python Machine Learning, 2nd edition | 31.5 | 1 | 1 |
| Apache Hudi | 31.5 | 1 | 1 |
| Maria-Florina Balcan | 31.5 | 1 | 1 |
| Andrew William Moore | 31.5 | 1 | 1 |
| Jupyter Notebook | 31.5 | 1 | 1 |
| Road Roughness Estimation Using Machine Learning | 31.5 | 1 | 1 |
| Onyxia | 31.5 | 1 | 1 |
| emacs-code-cells | 31.5 | 1 | 1 |
| Jupyter Notebooks using the British Library’s Digital Collections and Data | 31.5 | 1 | 1 |
| Restoring Execution Environments of Jupyter Notebooks | 31.5 | 1 | 1 |
| Machine Learning and Deep Learning -- A review for Ecologists | 31.5 | 1 | 1 |
| Computational reproducibility of Jupyter notebooks from biomedical publications | 31.5 | 1 | 1 |
| Wikidata Lab XXXVI | 31.5 | 1 | 1 |
| Kusto Query Language | 31.5 | 1 | 1 |
| watsonx | 31.5 | 1 | 1 |
| aTrain | 31.5 | 1 | 1 |
| Q135647703 | 31.5 | 1 | 1 |
| Q136408639 | 31.5 | 1 | 1 |
| Q137059343 | 31.5 | 1 | 1 |
| LeRobot | 31.5 | 1 | 1 |
| Salt Lake Valley Fabric and Microsoft Data Platforms User Group | 31.5 | 1 | 1 |
| Hex | 31.5 | 1 | 1 |
| Machine Learning Methods and Tools | 31.5 | 1 | 1 |
| krish567366 / automl_self_improvement | 31.5 | 1 | 1 |
| quantum-data-embedding-suite | 31.5 | 1 | 1 |
| Oracle Database | 29.8 | 5 | 0 |
| Media Creation Tool | 29.8 | 5 | 0 |
| Q80689 | 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 |
| MinIO | 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 |
| Qlik | 23.0 | 3 | 0 |
| Oracle SQL Developer | 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 |
| 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 |
| Atlan | 23.0 | 3 | 0 |
| Dremio | 23.0 | 3 | 0 |
| machine learning | 20.0 | 0 | 1 |
| Cynthia Dwork | 20.0 | 0 | 1 |
| Nancy Lynch | 20.0 | 0 | 1 |
| Renée Miller | 20.0 | 0 | 1 |
| Matomo | 20.0 | 0 | 1 |
| Leslie Lamport | 20.0 | 0 | 1 |
| David Haussler | 20.0 | 0 | 1 |
| Q92894 | 20.0 | 0 | 1 |
| Corinna Cortes | 20.0 | 0 | 1 |
| Charles E. Leiserson | 20.0 | 0 | 1 |
| Bill Inmon | 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 |
| Duke University | 20.0 | 0 | 1 |
| London School of Economics and Political Science | 20.0 | 0 | 1 |
| distributed computing | 20.0 | 0 | 1 |
| artificial neural network | 20.0 | 0 | 1 |
| deep learning | 20.0 | 0 | 1 |
| computer cluster | 20.0 | 0 | 1 |
| Brother Bear | 20.0 | 0 | 1 |
| ensemble learning | 20.0 | 0 | 1 |
| Jensen Huang | 20.0 | 0 | 1 |
| supervised learning | 20.0 | 0 | 1 |
| Common Warehouse Metamodel | 20.0 | 0 | 1 |
| pattern recognition | 20.0 | 0 | 1 |
| Folding@home | 20.0 | 0 | 1 |
| John Hopfield | 20.0 | 0 | 1 |
| chief data officer | 20.0 | 0 | 1 |
| feature selection | 20.0 | 0 | 1 |
| probably approximately correct learning | 20.0 | 0 | 1 |
| Quorum | 20.0 | 0 | 1 |
| boosting | 20.0 | 0 | 1 |
| Ametek | 20.0 | 0 | 1 |
| Rutgers University | 20.0 | 0 | 1 |
| Delta rule | 20.0 | 0 | 1 |
| Knowledge Engineering and Machine Learning Group | 20.0 | 0 | 1 |
| Autodesk | 20.0 | 0 | 1 |
| online analytical processing | 20.0 | 0 | 1 |
| Aptiv | 20.0 | 0 | 1 |
| Distributed Computing Environment | 20.0 | 0 | 1 |
| FightAIDS@Home | 20.0 | 0 | 1 |
| Amazon Mechanical Turk | 20.0 | 0 | 1 |
| Project Athena | 20.0 | 0 | 1 |
| Atlassian | 20.0 | 0 | 1 |
| outlier | 20.0 | 0 | 1 |
| backpropagation | 20.0 | 0 | 1 |
| bootstrap aggregating | 20.0 | 0 | 1 |
| Barcelona Graduate School of Economics | 20.0 | 0 | 1 |
| Bath & Body Works | 20.0 | 0 | 1 |
| reinforcement learning | 20.0 | 0 | 1 |
| intelligent control | 20.0 | 0 | 1 |
| Drug Design and Optimization Lab | 20.0 | 0 | 1 |
| Adobe Flash Player | 20.0 | 0 | 1 |
| data governance | 20.0 | 0 | 1 |
| Courant Institute School of Mathematics, Computing, and Data Science | 20.0 | 0 | 1 |
| scikit-learn | 20.0 | 0 | 1 |
| HP Neoview | 20.0 | 0 | 1 |
| semi-supervised learning | 20.0 | 0 | 1 |
| WinFS | 20.0 | 0 | 1 |
| Q1107006 | 20.0 | 0 | 1 |
| Collatz Conjecture | 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 |
| unstructured data | 20.0 | 0 | 1 |
| PrimeGrid | 20.0 | 0 | 1 |
| unsupervised learning | 20.0 | 0 | 1 |
| data lineage | 20.0 | 0 | 1 |
| Q1172170 | 20.0 | 0 | 1 |
| pipeline | 20.0 | 0 | 1 |
| list of volunteer computing projects | 20.0 | 0 | 1 |
| Dijkstra Prize | 20.0 | 0 | 1 |
| linear discriminant analysis | 20.0 | 0 | 1 |
| SAP NetWeaver Business Intelligence | 20.0 | 0 | 1 |
| Electric Sheep | 20.0 | 0 | 1 |
| star schema | 20.0 | 0 | 1 |
| graphics pipeline | 20.0 | 0 | 1 |
| Fallacies of Distributed Computing | 20.0 | 0 | 1 |
| version space | 20.0 | 0 | 1 |
| leader election | 20.0 | 0 | 1 |
| PyCharm | 20.0 | 0 | 1 |
| operational data store | 20.0 | 0 | 1 |
| vendor lock-in | 20.0 | 0 | 1 |
| Gesellschaft für Klassifikation | 20.0 | 0 | 1 |
| Journal of Machine Learning Research | 20.0 | 0 | 1 |
| Intelligent workload management | 20.0 | 0 | 1 |
| tuple space | 20.0 | 0 | 1 |
| shared-nothing architecture | 20.0 | 0 | 1 |
| Matthias Jarke | 20.0 | 0 | 1 |
| Xgrid | 20.0 | 0 | 1 |
| Conference on Neural Information Processing Systems | 20.0 | 0 | 1 |
| fact table | 20.0 | 0 | 1 |
| Amazon Elastic Compute Cloud | 20.0 | 0 | 1 |
| Railway Markup Language | 20.0 | 0 | 1 |
| Rechenkraft.net | 20.0 | 0 | 1 |
| Shogun | 20.0 | 0 | 1 |
| volunteer computing | 20.0 | 0 | 1 |
| data science | 20.0 | 0 | 1 |
| École nationale supérieure des sciences applicatives et du risque | 20.0 | 0 | 1 |
| computational learning theory | 20.0 | 0 | 1 |
| Viola–Jones object detection framework | 20.0 | 0 | 1 |
| spatial data warehouse | 20.0 | 0 | 1 |
| artificial immune system | 20.0 | 0 | 1 |
| Virtual Shared Memory | 20.0 | 0 | 1 |
| Rectilinear Crossing Number | 20.0 | 0 | 1 |
| Linear classifier | 20.0 | 0 | 1 |
| DPAD | 20.0 | 0 | 1 |
| Fractal analysis | 20.0 | 0 | 1 |
| Junction tree algorithm | 20.0 | 0 | 1 |
| Arista Networks | 20.0 | 0 | 1 |
| Andrei Broder | 20.0 | 0 | 1 |
| data.gouv.fr | 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 |
| GitHub | 18.3 | 2 | 0 |
| Q11219 | 18.3 | 2 | 0 |
| Q11278 | 18.3 | 2 | 0 |
| Nucleic Acids Research | 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 |
| Q229883 | 18.3 | 2 | 0 |
| PCMan File Manager | 18.3 | 2 | 0 |
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| 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 |
| Bernama | 18.3 | 2 | 0 |
| Microsoft Dynamics NAV | 18.3 | 2 | 0 |
| Microsoft AutoRoute | 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 |
| Telephony Application Programming Interface | 18.3 | 2 | 0 |
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| Oracle Application Server | 18.3 | 2 | 0 |
| IBM Power Systems | 18.3 | 2 | 0 |
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| Microsoft Layer for Unicode | 18.3 | 2 | 0 |
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| IBM Configuration Management Version Control | 18.3 | 2 | 0 |
| American Journal of Translational Research | 18.3 | 2 | 0 |
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| Biomedical Optics Express | 18.3 | 2 | 0 |
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| Frege | 18.3 | 2 | 0 |
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| HHpred / HHsearch | 18.3 | 2 | 0 |
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| Microsoft Search Server | 18.3 | 2 | 0 |
| Nimble Storage | 18.3 | 2 | 0 |
| Oracle Property Manager | 18.3 | 2 | 0 |
| Owl Lisp | 18.3 | 2 | 0 |
| Western Journal of Medicine | 18.3 | 2 | 0 |
| Trends in Amplification | 18.3 | 2 | 0 |
| Ulster Medical Journal | 18.3 | 2 | 0 |
| The AAPS Journal | 18.3 | 2 | 0 |
| Q10984556 | 18.3 | 2 | 0 |
| Microsoft Pinyin IME | 18.3 | 2 | 0 |
| AutoKey | 18.3 | 2 | 0 |
| Rice | 18.3 | 2 | 0 |
| AAPS PharmSciTech | 18.3 | 2 | 0 |
| International Journal of Mental Health Systems | 18.3 | 2 | 0 |
| Journal of Urban Health | 18.3 | 2 | 0 |
| Neural Development | 18.3 | 2 | 0 |
| Journal of the Society of Laparoendoscopic Surgeons | 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 |
| Q20641742 | 18.3 | 2 | 0 |
| Oracle BlueKai Data Management Platform | 18.3 | 2 | 0 |
| Microsoft Lumia 950 XL | 18.3 | 2 | 0 |
| Maps | 18.3 | 2 | 0 |
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| 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 |
| The Journal of clinical and aesthetic dermatology | 18.3 | 2 | 0 |
| Alcohol research : current reviews | 18.3 | 2 | 0 |
| Dermato-endocrinology | 18.3 | 2 | 0 |
| British Heart Journal | 18.3 | 2 | 0 |
| The western journal of emergency medicine | 18.3 | 2 | 0 |
| Psychiatry | 18.3 | 2 | 0 |
| Mycobiology | 18.3 | 2 | 0 |
| Eduard Hovy | 18.3 | 2 | 0 |
| Microsoft Dynamics 365 | 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 |
| 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 |
| Microsoft Saudi | 18.3 | 2 | 0 |
| clinical LABoratory Ontology | 18.3 | 2 | 0 |
| Zebrafish Phenotype Ontology | 18.3 | 2 | 0 |
| wikibase-cli | 18.3 | 2 | 0 |
| Confluent Inc. | 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 |
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| trim-repeated | 18.3 | 2 | 0 |
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| 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 |
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| graphql-playground-middleware-express | 18.3 | 2 | 0 |
| trim-right | 18.3 | 2 | 0 |
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| is-bigint | 18.3 | 2 | 0 |
| which-boxed-primitive | 18.3 | 2 | 0 |
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| http-proxy-middleware | 18.3 | 2 | 0 |
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| 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 |
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| dungeon-mode | 18.3 | 2 | 0 |
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| FreeQDA | 18.3 | 2 | 0 |
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| EXWM | 18.3 | 2 | 0 |
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| js2-mode | 18.3 | 2 | 0 |
| IBM Cloud Object Storage | 18.3 | 2 | 0 |
| Branwen R Drew | 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 |
| Microsoft Lists | 18.3 | 2 | 0 |
| GNUstep Project Center | 18.3 | 2 | 0 |
| Julia Enhancement Proposal | 18.3 | 2 | 0 |
| Microsoft Berlin | 18.3 | 2 | 0 |
| huggingface_hub | 18.3 | 2 | 0 |
| Antimalware Scan Interface | 18.3 | 2 | 0 |
| SPARQL Generation: an analysis on fine-tuning OpenLLaMA for Question Answering over a Life Science Knowledge Graph | 18.3 | 2 | 0 |
| Fooocus | 18.3 | 2 | 0 |
| emacs-ledger-mode | 18.3 | 2 | 0 |
| Hugging Face Hub | 18.3 | 2 | 0 |
| Aider | 18.3 | 2 | 0 |
| Microsoft Security Copilot | 18.3 | 2 | 0 |
| JuGloW | 18.3 | 2 | 0 |
| NeoWiki | 18.3 | 2 | 0 |
| AI Assistant | 18.3 | 2 | 0 |
| Q138493851 | 18.3 | 2 | 0 |
| Hex | 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 |
| Narad Bastola | 18.3 | 2 | 0 |
| Transformation Operating Framework | 18.3 | 2 | 0 |
| Q140053685 | 18.3 | 2 | 0 |
| Java | 11.5 | 1 | 0 |
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| Spanish 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 |
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| Portuguese Wikipedia | 11.5 | 1 | 0 |
| Mary Shaw | 11.5 | 1 | 0 |
| XNU | 11.5 | 1 | 0 |
| Mach | 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 |
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| Katia Sycara | 11.5 | 1 | 0 |
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| IBM WebSphere Application Server | 11.5 | 1 | 0 |
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| Windows 95 | 11.5 | 1 | 0 |
| Oracle WebLogic Server | 11.5 | 1 | 0 |
| Manuel Blum | 11.5 | 1 | 0 |
| Larry Ellison | 11.5 | 1 | 0 |
| Scott E. Fahlman | 11.5 | 1 | 0 |
| Edmund M. Clarke | 11.5 | 1 | 0 |
| Simon Peyton Jones | 11.5 | 1 | 0 |
| Luis von Ahn | 11.5 | 1 | 0 |
| Lenore Blum | 11.5 | 1 | 0 |
| Security-Enhanced Linux | 11.5 | 1 | 0 |
| arXiv | 11.5 | 1 | 0 |
| IBM Lotus SmartSuite | 11.5 | 1 | 0 |
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| 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 |
| Vertica | 11.5 | 1 | 0 |
| Q260180 | 11.5 | 1 | 0 |
| Group Policy | 11.5 | 1 | 0 |
| MSX BASIC | 11.5 | 1 | 0 |
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| 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 |
| javac | 11.5 | 1 | 0 |
| id Tech 4 | 11.5 | 1 | 0 |
| cryptlib | 11.5 | 1 | 0 |
| pkgsrc | 11.5 | 1 | 0 |
| Abstract Window Toolkit | 11.5 | 1 | 0 |
| Tenés Empanadas Graciela | 11.5 | 1 | 0 |
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| ActiveSync | 11.5 | 1 | 0 |
| Active Server Pages | 11.5 | 1 | 0 |
| Q368338 | 11.5 | 1 | 0 |
| Connect:Direct | 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 |
| HomoloGene | 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 |
| Apache Software Foundation | 11.5 | 1 | 0 |
| IBM Rational Application Developer | 11.5 | 1 | 0 |
| Age of Empires: The Rise of Rome | 11.5 | 1 | 0 |
| Integrated Lights-Out | 11.5 | 1 | 0 |
| Polkit | 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 |
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| TechRadar | 11.5 | 1 | 0 |
| Age of Empires II: The Conquerors | 11.5 | 1 | 0 |
| Apache License | 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 |
| 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 |
| ChorusOS | 11.5 | 1 | 0 |
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| 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 |
| ResearchGate | 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 |
| HP-UX | 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 |
| Blood | 11.5 | 1 | 0 |
| Boost | 11.5 | 1 | 0 |
| Zen Cart | 11.5 | 1 | 0 |
| GenBank | 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 |
| PHP-Nuke | 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 |
| Microsoft InterConnect | 11.5 | 1 | 0 |
| Java Secure Socket Extension | 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 |
| Java Virtual Machine Tools Interface | 11.5 | 1 | 0 |
| Microsoft Works | 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 |
| 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 |
| David S. Touretzky | 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 |
| Journal of Virology | 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 |
| Entrez | 11.5 | 1 | 0 |
| Q1347061 | 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 |
| Windows Embedded Automotive | 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 |
| GanttProject | 11.5 | 1 | 0 |
| GeoTIFF | 11.5 | 1 | 0 |
| William Sleator | 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 |
| 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 |
| JVCKenwood | 11.5 | 1 | 0 |
| Oracle Cloud File System | 11.5 | 1 | 0 |
| The Java Language Specification | 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 |
| Nyquist | 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 |
| Nanoscale Research Letters | 11.5 | 1 | 0 |
| Microsoft WebMatrix | 11.5 | 1 | 0 |
| Microsoft Flight Simulator X | 11.5 | 1 | 0 |
| Nokia Suite | 11.5 | 1 | 0 |
| eLife | 11.5 | 1 | 0 |
| PeerJ | 11.5 | 1 | 0 |
| Spry framework | 11.5 | 1 | 0 |
| Oracle Call Interface | 11.5 | 1 | 0 |
| Biophysical Journal | 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 |
| Journal of Cardiovascular Magnetic Resonance | 11.5 | 1 | 0 |
| Windows Hardware Lab Kit | 11.5 | 1 | 0 |
| Tuxedo | 11.5 | 1 | 0 |
| SLIME | 11.5 | 1 | 0 |
| SQE | 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 |
| Ralf Brown's Interrupt List | 11.5 | 1 | 0 |
| IBM Developer | 11.5 | 1 | 0 |
| IBM General Parallel File System | 11.5 | 1 | 0 |
| Seashore | 11.5 | 1 | 0 |
| JD Edwards | 11.5 | 1 | 0 |
| Microsoft Agent | 11.5 | 1 | 0 |
| UNIX System Services | 11.5 | 1 | 0 |
| OpenWindows | 11.5 | 1 | 0 |
| IBM WebSphere | 11.5 | 1 | 0 |
| Conky | 11.5 | 1 | 0 |
| Cancer Biology and Therapy | 11.5 | 1 | 0 |
| Q2554872 | 11.5 | 1 | 0 |
| Reconstructor | 11.5 | 1 | 0 |
| Q2567249 | 11.5 | 1 | 0 |
| Apple Productivity Experience Group | 11.5 | 1 | 0 |
| Cancer Imaging | 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 |
| Bioscience Reports | 11.5 | 1 | 0 |
| ACS Chemical Neuroscience | 11.5 | 1 | 0 |
| Arkivoc | 11.5 | 1 | 0 |
| Agile Software Corporation | 11.5 | 1 | 0 |
| Acta Histochemica et Cytochemica | 11.5 | 1 | 0 |
| Aging | 11.5 | 1 | 0 |
| Kippo | 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 |
| GeneReviews | 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 |