| IBM | 60.0 | 36 | 0 |
| Microsoft | 47.1 | 16 | 0 |
| Data Analytics for Machine Learning | 47.0 | 5 | 1 |
| H2O | 45.6 | 2 | 2 |
| Machine Learning and Knowledge Extraction | 45.6 | 2 | 2 |
| Anomaly Detection In IoT Sensor Data Using Machine Learning Techniques For Predictive Maintenance In Smart Grids | 40.0 | 0 | 4 |
| Palantir Technologies | 39.8 | 10 | 0 |
| WinCC | 38.8 | 1 | 2 |
| bidirectional encoder representations from transformers | 35.5 | 2 | 1 |
| Dolly | 35.5 | 2 | 1 |
| NVIDIA Jetson Orin NX 16GB | 35.5 | 2 | 1 |
| TrendMiner | 35.5 | 2 | 1 |
| Azure DevOps Server | 34.6 | 7 | 0 |
| Q18698690 | 34.6 | 7 | 0 |
| predictive maintenance | 34.5 | 0 | 3 |
| JAMA Surgery | 34.5 | 0 | 3 |
| Intelligent Single-Board Computer for Industry 4.0: Efficient Real-Time Monitoring System for Anomaly Detection in CNC Machines | 34.5 | 0 | 3 |
| ICS for multivariate functional anomaly detection with applications to predictive maintenance and quality control | 34.5 | 0 | 3 |
| Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry | 34.5 | 0 | 3 |
| The quality management ecosystem for predictive maintenance in the Industry 4.0 era | 34.5 | 0 | 3 |
| Enhancing data quality and process optimization for smart manufacturing lines in industry 4.0 scenarios | 34.5 | 0 | 3 |
| Q136467205 | 34.5 | 0 | 3 |
| AiiACo | 34.5 | 0 | 3 |
| Microsoft SQL Server | 32.3 | 6 | 0 |
| Oracle CRM | 32.3 | 6 | 0 |
| Oracle Fusion Applications | 32.3 | 6 | 0 |
| SAP ERP | 29.8 | 5 | 0 |
| Oracle Database | 29.8 | 5 | 0 |
| Media Creation Tool | 29.8 | 5 | 0 |
| Rockwell Automation | 28.7 | 1 | 1 |
| MindSphere | 28.7 | 1 | 1 |
| Ryota Tomioka | 28.7 | 1 | 1 |
| Samsara | 28.7 | 1 | 1 |
| Road Roughness Estimation Using Machine Learning | 28.7 | 1 | 1 |
| Machine Learning and Deep Learning -- A review for Ecologists | 28.7 | 1 | 1 |
| NVIDIA A800 40GB Active GPU | 28.7 | 1 | 1 |
| NVIDIA Jetson AGX Orin 64GB | 28.7 | 1 | 1 |
| NVIDIA Jetson Orin NX 8GB | 28.7 | 1 | 1 |
| NVIDIA Jetson Orin Nano 8GB | 28.7 | 1 | 1 |
| Q135647703 | 28.7 | 1 | 1 |
| Q136408639 | 28.7 | 1 | 1 |
| Q137059343 | 28.7 | 1 | 1 |
| Machine Learning Methods and Tools | 28.7 | 1 | 1 |
| quality management | 27.3 | 0 | 2 |
| water quality management | 27.3 | 0 | 2 |
| Emerson Electric | 27.3 | 0 | 2 |
| Coherent Solutions | 27.3 | 0 | 2 |
| Quality Management Journal | 27.3 | 0 | 2 |
| J. Nathan Kutz | 27.3 | 0 | 2 |
| Launching total quality management in the Bureau of Mines: a case study. Quality improvement report: October 1990 through September 1992 | 27.3 | 0 | 2 |
| Machine Learning Methods in Systematic Reviews: Identifying Quality Improvement Intervention Evaluations | 27.3 | 0 | 2 |
| How Compliance Measures, Behavior Modification, and Continuous Quality Improvement Led to Routine HIV Screening in an Emergency Department in Brooklyn, New York | 27.3 | 0 | 2 |
| Quality assurance, quality management or quality control? | 27.3 | 0 | 2 |
| How to use continuous quality improvement theory and statistical quality control tools in a multispecialty clinic | 27.3 | 0 | 2 |
| Use of total quality management sparks staff nurse participation in continuous quality improvement. | 27.3 | 0 | 2 |
| Anomaly detection based on sensor data in petroleum industry applications | 27.3 | 0 | 2 |
| A Hierarchical Classification and Segmentation Scheme for Processing Sensor Data | 27.3 | 0 | 2 |
| Effective Sensor Selection and Data Anomaly Detection for Condition Monitoring of Aircraft Engines | 27.3 | 0 | 2 |
| Machine learning for large-scale wearable sensor data in Parkinson's disease: Concepts, promises, pitfalls, and futures | 27.3 | 0 | 2 |
| Improving data quality control in quality improvement projects | 27.3 | 0 | 2 |
| The impact of the International Atomic Energy Agency (IAEA) program on radiation and tissue banking in Uruguay: development of tissues quality control and quality management system in the National Multi-Tissue Bank of Uruguay | 27.3 | 0 | 2 |
| Architecture, cost-model and customization of real-time monitoring systems based on mobile biological sensor data-streams | 27.3 | 0 | 2 |
| Development of a continuous quality improvement/total quality management program for medication use monitoring. | 27.3 | 0 | 2 |
| Informatics, imaging, and healthcare quality management: imaging quality improvement opportunities and lessons learned form HCFA's Health Care Quality Improvement Program | 27.3 | 0 | 2 |
| Use of Machine Learning Classifiers and Sensor Data to Detect Neurological Deficit in Stroke Patients. | 27.3 | 0 | 2 |
| Quantitative research versus quality assurance, quality improvement, total quality management, and continuous quality improvement | 27.3 | 0 | 2 |
| Assessing the impact of continuous quality improvement/total quality management: concept versus implementation | 27.3 | 0 | 2 |
| How changing quality management influenced PGME accreditation: a focus on decentralization and quality improvement. | 27.3 | 0 | 2 |
| RNA-SeQC: RNA-seq metrics for quality control and process optimization | 27.3 | 0 | 2 |
| Achieving the Health Care Financing Administration limits by quality improvement and quality control. A real-world example | 27.3 | 0 | 2 |
| The Effect of 5S-Continuous Quality Improvement-Total Quality Management Approach on Staff Motivation, Patients' Waiting Time and Patient Satisfaction with Services at Hospitals in Uganda | 27.3 | 0 | 2 |
| Deepening our understanding of quality improvement in Europe (DUQuE): overview of a study of hospital quality management in seven countries | 27.3 | 0 | 2 |
| Liquid-based Papanicolaou tests in endometrial carcinoma diagnosis. Performance, error root cause analysis, and quality improvement | 27.3 | 0 | 2 |
| Perspectives on Quality Control, Risk Management, and Analytical Quality Management | 27.3 | 0 | 2 |
| Measuring Endoscopic Performance for Colorectal Cancer Prevention Quality Improvement in a Gastroenterology Practice | 27.3 | 0 | 2 |
| Wake Up Safe and root cause analysis: quality improvement in pediatric anesthesia | 27.3 | 0 | 2 |
| Improving rates of cotrimoxazole prophylaxis in resource-limited settings: implementation of a quality improvement approach | 27.3 | 0 | 2 |
| Advanced Practice Quality Improvement: Beyond the Radiology Department | 27.3 | 0 | 2 |
| Quality control review: implementing a scientifically based quality control system | 27.3 | 0 | 2 |
| Continuous Quality Improvement, Total Quality Management, and Reengineering: One Hospital's Continuous Quality Improvement Journey | 27.3 | 0 | 2 |
| EMQIT: a machine learning approach for energy based PWM matrix quality improvement | 27.3 | 0 | 2 |
| Integrating multisensor satellite data merging and image reconstruction in support of machine learning for better water quality management | 27.3 | 0 | 2 |
| Triage quality control is missing tools-a new observation technique for ED quality improvement | 27.3 | 0 | 2 |
| Six Sigma Quality Management System and Design of Risk-based Statistical Quality Control | 27.3 | 0 | 2 |
| Quality management in musculoskeletal imaging: form, content, and diagnosis of knee MRI reports and effectiveness of three different quality improvement measures | 27.3 | 0 | 2 |
| Quality control and quality management of alternate-site testing. | 27.3 | 0 | 2 |
| Business process quality management: a step beyond continuous quality improvement. | 27.3 | 0 | 2 |
| Advancing the epidemiology of injury and methods of quality control: ACEs as an outcomes-based system for quality improvement | 27.3 | 0 | 2 |
| Quality assurance in the mycobacteriology laboratory. Quality control, quality improvement, and proficiency testing | 27.3 | 0 | 2 |
| TQM (total quality management): a glimpse at the future of quality control. | 27.3 | 0 | 2 |
| Impact of Process Optimization and Quality Improvement Measures on Neonatal Feeding Outcomes at an All‐Referral Neonatal Intensive Care Unit | 27.3 | 0 | 2 |
| Root cause analysis in infusion nursing: applying quality improvement tools for adverse events | 27.3 | 0 | 2 |
| Real-Time Analysis on Drug-Antibody Ratio of Antibody-Drug Conjugates for Synthesis, Process Optimization, and Quality Control | 27.3 | 0 | 2 |
| The presence of total quality management and continuous quality improvement processes in California public health clinics | 27.3 | 0 | 2 |
| D-MSR: a distributed network management scheme for real-time monitoring and process control applications in wireless industrial automation. | 27.3 | 0 | 2 |
| Evidence of disparity in the application of quality improvement efforts for the treatment of acute myocardial infarction: The American College of Cardiology's Guidelines Applied in Practice Initiative in Michigan | 27.3 | 0 | 2 |
| Total quality management and statistical quality control: practical applications to waste stream management | 27.3 | 0 | 2 |
| The big picture. Total quality management and continuous quality improvement | 27.3 | 0 | 2 |
| Quality Control for High-Throughput Imaging Experiments Using Machine Learning in Cellprofiler | 27.3 | 0 | 2 |
| Explaining the Success or Failure of Quality Improvement Initiatives in Long-Term Care Organizations From a Dynamic Perspective | 27.3 | 0 | 2 |
| Defect Detection and Segmentation Framework for Remote Field Eddy Current Sensor Data. | 27.3 | 0 | 2 |
| Automatic Classification of Sub-Techniques in Classical Cross-Country Skiing Using a Machine Learning Algorithm on Micro-Sensor Data | 27.3 | 0 | 2 |
| Real-Time Monitoring and Analysis of Zebrafish Electrocardiogram with Anomaly Detection. | 27.3 | 0 | 2 |
| Building a Culture of Continuous Quality Improvement in an Academic Radiology Department | 27.3 | 0 | 2 |
| PyTorch | 27.3 | 0 | 2 |
| On the Comparison of Wearable Sensor Data Fusion to a Single Sensor Machine Learning Technique in Fall Detection | 27.3 | 0 | 2 |
| An educational model to introduce staff nurses to continuous quality improvement/total quality management concepts | 27.3 | 0 | 2 |
| Root Cause Analysis of Quality Defects Using HPLC-MS Fingerprint Knowledgebase for Batch-to-batch Quality Control of Herbal Drugs | 27.3 | 0 | 2 |
| Transition to quality improvement: adapting the quality management plan. | 27.3 | 0 | 2 |
| Quality improvement through root cause analysis. | 27.3 | 0 | 2 |
| Total quality management: defining a process for quality improvement | 27.3 | 0 | 2 |
| Total quality management in a 300-bed community hospital: the quality improvement process translated to health care | 27.3 | 0 | 2 |
| Technology's Role in Quality Improvement and Operational Efficiency | 27.3 | 0 | 2 |
| Quality control in FISH as part of a laboratory's quality management system | 27.3 | 0 | 2 |
| Root Cause Analysis and Actions for the Prevention of Medical Errors: Quality Improvement and Resident Education | 27.3 | 0 | 2 |
| Improved detection of chemical substances from colorimetric sensor data using probabilistic machine learning | 27.3 | 0 | 2 |
| Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants. | 27.3 | 0 | 2 |
| Combining Fog Computing with Sensor Mote Machine Learning for Industrial IoT. | 27.3 | 0 | 2 |
| TensorFlow.js | 27.3 | 0 | 2 |
| Internal quality control and external quality management of data in practice | 27.3 | 0 | 2 |
| INDUSTRY 4.0: A REVIEW ON INDUSTRIAL AUTOMATION AND ROBOTIC | 27.3 | 0 | 2 |
| Smart manufacturing, manufacturing intelligence and demand-dynamic performance | 27.3 | 0 | 2 |
| Initiating predictive maintenance for a conveyor motor in a bottling plant using industry 4.0 concepts | 27.3 | 0 | 2 |
| Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging | 27.3 | 0 | 2 |
| Data Quality Improvement in Clinical Databases Using Statistical Quality Control | 27.3 | 0 | 2 |
| Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing | 27.3 | 0 | 2 |
| Combining Knowledge Modeling and Machine Learning for Alarm Root Cause Analysis | 27.3 | 0 | 2 |
| Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection | 27.3 | 0 | 2 |
| Real-Time Monitoring of Mobile Biological Sensor Data-Streams: Architecture and Cost-Model | 27.3 | 0 | 2 |
| A novel anomaly detection algorithm for sensor data under uncertainty | 27.3 | 0 | 2 |
| Anomaly Detection in Streaming Sensor Data | 27.3 | 0 | 2 |
| Anomaly Detection in Streaming Sensor Data | 27.3 | 0 | 2 |
| Anomaly Detection in Streaming Sensor Data | 27.3 | 0 | 2 |
| Wearable Sensor Data to Track Subject-Specific Movement Patterns Related to Clinical Outcomes Using a Machine Learning Approach | 27.3 | 0 | 2 |
| DAQUA-MASS: An ISO 8000-61 Based Data Quality Management Methodology for Sensor Data | 27.3 | 0 | 2 |
| Short-Term Speed Prediction Using Remote Microwave Sensor Data: Machine Learning versus Statistical Model | 27.3 | 0 | 2 |
| Applying machine learning on sensor data for irrigation recommendations: revealing the agronomist’s tacit knowledge | 27.3 | 0 | 2 |
| A quality inspection system for resistance seam welds in endless production of steel coils using anomaly detection techniques | 27.3 | 0 | 2 |
| Machine Learning for Predictive Maintenance: A Multiple Classifier Approach | 27.3 | 0 | 2 |
| Contextual anomaly detection framework for big sensor data | 27.3 | 0 | 2 |
| Microfluidic chip designs process optimization and dimensional quality control | 27.3 | 0 | 2 |
| Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging | 27.3 | 0 | 2 |
| Anomaly detection for machine learning redshifts applied to SDSS galaxies | 27.3 | 0 | 2 |
| Machine Learning-Based Sensor Data Modeling Methods for Power Transformer PHM | 27.3 | 0 | 2 |
| Multi-Sensor Data Fusion for Real-Time Surface Quality Control in Automated Machining Systems | 27.3 | 0 | 2 |
| Machine learning methods for wind turbine condition monitoring: A review | 27.3 | 0 | 2 |
| A critical review of smart manufacturing & Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs) | 27.3 | 0 | 2 |
| Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants | 27.3 | 0 | 2 |
| Systematic serendipity: a test of unsupervised machine learning as a method for anomaly detection | 27.3 | 0 | 2 |
| A Survey of the Advancing Use and Development of Machine Learning in Smart Manufacturing | 27.3 | 0 | 2 |
| EdgeX Foundry | 27.3 | 0 | 2 |
| Total quality management/continuous quality improvement in business and health care | 27.3 | 0 | 2 |
| Run-rejection survey to assess benefits of training in concepts of quality control and total quality management | 27.3 | 0 | 2 |
| Total quality management and continuous quality improvement: an introduction for surgeons | 27.3 | 0 | 2 |
| Implementation of unit-based continuous quality improvement approach to quality management: applying concepts to practice | 27.3 | 0 | 2 |
| Organization of pharmacy production quality control during the introduction of the complex quality management system for pharmacy production and drug supply | 27.3 | 0 | 2 |
| Understanding patient-centered care in the context of total quality management and continuous quality improvement | 27.3 | 0 | 2 |
| Integrating quality assurance and total quality management/quality improvement | 27.3 | 0 | 2 |
| Total quality management (TQM) and continuous quality improvement (CQI) | 27.3 | 0 | 2 |
| Incorporation of continuous quality improvement in a hospital dietary department's quality management program | 27.3 | 0 | 2 |
| 3D seismic geometry quality control and corrections by applying machine learning | 27.3 | 0 | 2 |
| The design of a visual display for the presentation of statistical quality control information to operators on the plant floor | 27.3 | 0 | 2 |
| Modern techniques for quality control and quality management in gynecological health care | 27.3 | 0 | 2 |
| Integration of quality improvement with existing quality assessment/quality management processes | 27.3 | 0 | 2 |
| Total quality management: the results of quality improvement teams | 27.3 | 0 | 2 |
| Total quality management: needed research on the structural and cultural dimensions of quality improvement in health care organizations | 27.3 | 0 | 2 |
| Revenue enhancement through total quality management/continuous quality improvement (TQM/CQI) in outpatient coding and billing | 27.3 | 0 | 2 |
| Serum tumour markers: from quality control to total quality management | 27.3 | 0 | 2 |
| Visioning, reengineering, and continuous quality improvement: parts of the quality management whole | 27.3 | 0 | 2 |
| The perceived effectiveness of total quality management as a tool for quality improvement in emergency medicine | 27.3 | 0 | 2 |
| Conceiving and implementing an ISO 9001:2000 quality management system: quality improvement and efficiency increase over 3 years at the Dept. of Ophthalmology, Sultan Qaboos University in Oman | 27.3 | 0 | 2 |
| On Using Statistical Process Control Charts to Analyze the Impact of Quality Improvement Interventions | 27.3 | 0 | 2 |
| Integrating quality improvement into a family medicine clerkship | 27.3 | 0 | 2 |
| A systemic approach to quality improvement in public health services | 27.3 | 0 | 2 |
| Quality Improvement of Capsular Polysaccharide in Streptococcus pneumoniae by Purification Process Optimization | 27.3 | 0 | 2 |
| Predictive Maintenance with Sensor Data Analytics on a Raspberry Pi-Based Experimental Platform | 27.3 | 0 | 2 |
| Deep Learning for Industrial Computer Vision Quality Control in the Printing Industry 4.0. | 27.3 | 0 | 2 |
| Patient Safety/Quality Improvement Primer, Part II: Prevention of Harm Through Root Cause Analysis and Action (RCA2) | 27.3 | 0 | 2 |
| [Chinese consensus of early colorectal cancer screening (2019, Shanghai)] | 27.3 | 0 | 2 |
| A Novel Sensor Data Pre-Processing Methodology for the Internet of Things Using Anomaly Detection and Transfer-By-Subspace-Similarity Transformation | 27.3 | 0 | 2 |
| Anomaly Detection for Resonant New Physics with Machine Learning | 27.3 | 0 | 2 |
| Election forensics: Using machine learning and synthetic data for possible election anomaly detection | 27.3 | 0 | 2 |
| Process Optimization and Digital Quality Improvement to Enhance Timely Initiation of Epidural Infusions and Postoperative Pain Control | 27.3 | 0 | 2 |
| Better Pairing Propofol Volume With Procedural Needs: A Propofol Waste Reduction Quality Improvement Project | 27.3 | 0 | 2 |
| Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning | 27.3 | 0 | 2 |
| Adaptive Information Visualization for Maritime Traffic Stream Sensor Data with Parallel Context Acquisition and Machine Learning | 27.3 | 0 | 2 |
| A Novel Blockchain Framework for Industrial IoT Edge Computing | 27.3 | 0 | 2 |
| Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions | 27.3 | 0 | 2 |
| The 60-Minute Root Cause Analysis: A Workshop to Engage Interdisciplinary Clinicians in Quality Improvement | 27.3 | 0 | 2 |
| A Machine Learning-Based Method for Automated Blockchain Transaction Signing Including Personalized Anomaly Detection | 27.3 | 0 | 2 |
| [A liver biopsy specimen model to study the quality improvement and process optimization through failure mode and effects analysis] | 27.3 | 0 | 2 |
| Machine Learning Algorithms Can Use Wearable Sensor Data to Accurately Predict Six-Week Patient-Reported Outcome Scores Following Joint Replacement in a Prospective Trial | 27.3 | 0 | 2 |
| Establishment of an Automatic Real-Time Monitoring System for Irrigation Water Quality Management | 27.3 | 0 | 2 |
| An Industrial Digitalization Platform for Condition Monitoring and Predictive Maintenance of Pumping Equipment | 27.3 | 0 | 2 |
| The Application of Machine Learning to Quality Improvement Through the Lens of the Radiology Value Network | 27.3 | 0 | 2 |
| Supervised machine learning quality control for magnetic resonance artifacts in neonatal data sets | 27.3 | 0 | 2 |
| INFERRING ADL BEHAVIORS FROM HIGH DENSITY, AMBIENT, INTERNET OF THINGS SENSOR DATA USING MACHINE LEARNING | 27.3 | 0 | 2 |
| Quality Control, Quality Assurance, and Quality Improvement-What is the Difference and Why Should Compounding Pharmacies Care? | 27.3 | 0 | 2 |
| Comparison Analysis of Machine Learning Techniques for Photovoltaic Prediction Using Weather Sensor Data | 27.3 | 0 | 2 |
| Yield prediction by machine learning from UAS-based mulit-sensor data fusion in soybean | 27.3 | 0 | 2 |
| Patient safety and quality improvement: Ethical principles for a regulatory approach to bias in healthcare machine learning | 27.3 | 0 | 2 |
| Digital Technologies-Enabled Smart Manufacturing and Industry 4.0 in the Post-COVID-19 Era: Lessons Learnt from a Pandemic | 27.3 | 0 | 2 |
| Hierarchical Anomaly Detection Model for In-Vehicle Networks Using Machine Learning Algorithms | 27.3 | 0 | 2 |
| Supervised Machine Learning Applied to Wearable Sensor Data Can Accurately Classify Functional Fitness Exercises Within a Continuous Workout | 27.3 | 0 | 2 |
| Gas-phase volatilomic approaches for quality control of brewing hops based on simultaneous GC-MS-IMS and machine learning | 27.3 | 0 | 2 |
| Fast Scanning Probe Microscopy via Machine Learning: Non-Rectangular Scans with Compressed Sensing and Gaussian Process Optimization | 27.3 | 0 | 2 |
| Mind-evolution-based Machine Learning for Dynamic Quality Control of Raw Material Cement | 27.3 | 0 | 2 |
| Implementation and Promotion of Quality Control Circle: A Starter for Quality Improvement in Chinese Hospitals | 27.3 | 0 | 2 |
| Quality Management and Quality Control in Hospitals’ Pathologic Department in Hebei Province | 27.3 | 0 | 2 |
| A Multitiered Solution for Anomaly Detection in Edge Computing for Smart Meters | 27.3 | 0 | 2 |
| A Genetic Attack Against Machine Learning Classifiers to Steal Biometric Actigraphy Profiles from Health Related Sensor Data | 27.3 | 0 | 2 |
| Machine learning from wristband sensor data for wearable, noninvasive seizure forecasting | 27.3 | 0 | 2 |
| Machine Learning Approach to Predict Risk of 90-Day Hospital Readmissions in Patients With Atrial Fibrillation: Implications for Quality Improvement in Healthcare | 27.3 | 0 | 2 |
| Mind the Queue: A Case Study in Visualizing Heterogeneous Behavioral Patterns in Livestock Sensor Data Using Unsupervised Machine Learning Techniques | 27.3 | 0 | 2 |
| Predictive Maintenance Scheduling with Failure Rate Described by Truncated Normal Distribution | 27.3 | 0 | 2 |
| An Ensemble-Based Approach to Anomaly Detection in Marine Engine Sensor Streams for Efficient Condition Monitoring and Analysis | 27.3 | 0 | 2 |
| Quantized Convolutional Neural Network Implementation on a Parallel-Connected Memristor Crossbar Array for Edge AI Platforms | 27.3 | 0 | 2 |
| Document AI | 27.3 | 0 | 2 |
| Models and algorithms for throughput improvement problem of serial production lines via downtime reduction | 27.3 | 0 | 2 |
| Sustainability Transition in Industry 4.0 and Smart Manufacturing with the Triple-Layered Business Model Canvas | 27.3 | 0 | 2 |
| A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modeling With Application in Industry 4.0 | 27.3 | 0 | 2 |
| Enabling robotic adaptive behaviour capabilities for new Industry 4.0 automated quality inspection paradigms | 27.3 | 0 | 2 |
| PingCAP | 27.3 | 0 | 2 |
| Enhanced Changeover Detection in Industry 4.0 Environments with Machine Learning | 27.3 | 0 | 2 |
| Anomaly detection and condition monitoring of wind turbine gearbox based on LSTM-FS and transfer learning | 27.3 | 0 | 2 |
| On the use of a full stack hardware/software infrastructure for sensor data fusion and fault prediction in industry 4.0 | 27.3 | 0 | 2 |
| seqQscorer: automated quality control of next-generation sequencing data using machine learning | 27.3 | 0 | 2 |
| Intrusion Detection in Internet of Things Systems: A Review on Design Approaches Leveraging Multi-Access Edge Computing, Machine Learning, and Datasets | 27.3 | 0 | 2 |
| Anomaly detection using edge computing in video surveillance system: review | 27.3 | 0 | 2 |
| Machine Learning Techniques for Anomaly Detection: An Overview | 27.3 | 0 | 2 |
| Food waste reduction and economic savings in times of crisis: The potential of machine learning methods to plan guest attendance in Swedish public catering during the Covid-19 pandemic | 27.3 | 0 | 2 |
| Benchmark Database for Process Optimization and Quality Control of Clinical Cancer Panel Sequencing | 27.3 | 0 | 2 |
| 5G Multi-Tier Handover with Multi-Access Edge Computing: A Deep Learning Approach | 27.3 | 0 | 2 |
| A Framework for Anomaly Detection under Dynamic and Distributed Scenarios | 27.3 | 0 | 2 |
| PSXI-22 Prediction quality of cattle behavior traits evaluated through different cross-validation strategies using wearable sensor data and machine learning algorithms | 27.3 | 0 | 2 |
| Analysis on Changes between New Quality Management Principles and Old Quality Control Principles of Accounting Firms Based on Python Text Mining | 27.3 | 0 | 2 |
| The Role of Machine Learning Algorithms in Materials Science: A State of Art Review on Industry 4.0 | 27.3 | 0 | 2 |
| Research on Pose Recognition Algorithm for Sports Players Based on Machine Learning of Sensor Data | 27.3 | 0 | 2 |
| Enabling Industrial IoT as a Service with Multi-Access Edge Computing | 27.3 | 0 | 2 |
| Anomaly detection-based condition monitoring | 27.3 | 0 | 2 |
| Author Correction: Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants | 27.3 | 0 | 2 |
| Machine learning iterative filtering algorithm for field defect detection in the process stage | 27.3 | 0 | 2 |
| $A^2$-LSTM for predictive maintenance of industrial equipment based on machine learning | 27.3 | 0 | 2 |
| Machine Learning Prediction of Clinical Trial Operational Efficiency | 27.3 | 0 | 2 |
| Integration of Multi-Sensor Data to Estimate Plot-Level Stem Volume Using Machine Learning Algorithms–Case Study of Evergreen Conifer Planted Forests in Japan | 27.3 | 0 | 2 |
| Design of a Digital Twin for an Industrial Vacuum Process: A Predictive Maintenance Approach | 27.3 | 0 | 2 |
| Condition Monitoring of DC-Link Capacitors Using Time–Frequency Analysis and Machine Learning Classification of Conducted EMI | 27.3 | 0 | 2 |
| Anomaly Detection and Classification in Water Distribution Networks Integrated with Hourly Nodal Water Demand Forecasting Models and Feature Extraction Technique | 27.3 | 0 | 2 |
| Machine Learning Models Applied to a GNSS Sensor Network for Automated Bridge Anomaly Detection | 27.3 | 0 | 2 |
| Soft Sensor Data Based Autonomous Detection of Aneurysm impacted coronary illness using machine learning algorithms | 27.3 | 0 | 2 |
| Geometrical defect detection for additive manufacturing with machine learning models | 27.3 | 0 | 2 |
| Real-time monitoring of fat crystallization using pulsed acoustic spectroscopy and supervised machine learning | 27.3 | 0 | 2 |
| Estimating the stiffness of kiwifruit based on the fusion of instantaneous tactile sensor data and machine learning schemes | 27.3 | 0 | 2 |
| Data fusion and machine learning for ship fuel efficiency modeling: Part III – Sensor data and meteorological data | 27.3 | 0 | 2 |
| EGD-SNet: A computational search engine for predicting an end-to-end machine learning pipeline for Energy Generation & Demand Forecasting | 27.3 | 0 | 2 |
| An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics | 27.3 | 0 | 2 |
| A novel method for Indoor Air Quality Control of Smart Homes using a Machine learning model | 27.3 | 0 | 2 |
| Geometrical defect detection on additive manufacturing parts with curvature feature and machine learning | 27.3 | 0 | 2 |
| Artificial Intelligence as a Process Optimization Driver under Industry 4.0 Framework and the Role of IIoT, a Bibliometric Analysis | 27.3 | 0 | 2 |
| Architecture Integration of 5G Networks and Time-Sensitive Networking with Edge Computing for Smart Manufacturing | 27.3 | 0 | 2 |
| Automatic Quality Control of Crowdsourced Rainfall Data With Multiple Noises: A Machine Learning Approach | 27.3 | 0 | 2 |
| A Robust Security Task Offloading in Industrial IoT-Enabled Distributed Multi-Access Edge Computing | 27.3 | 0 | 2 |
| HYBRID AERIAL SENSOR DATA AS BASIS FOR A GEOSPATIAL DIGITAL TWIN | 27.3 | 0 | 2 |
| A Review on Data-Driven Quality Prediction in the Production Process with Machine Learning for Industry 4.0 | 27.3 | 0 | 2 |
| Traceable machine learning real-time quality control based on patient data | 27.3 | 0 | 2 |
| Automatic Waveform Quality Control for Surface Waves Using Machine Learning | 27.3 | 0 | 2 |
| Digital twin and machine learning for decision support in thermal power plant with combustion engines | 27.3 | 0 | 2 |
| Secure sharing of big digital twin data for smart manufacturing based on blockchain | 27.3 | 0 | 2 |
| Correlating in-situ sensor data to defect locations and part quality for additively manufactured parts using machine learning | 27.3 | 0 | 2 |
| A study on quality control using delta data with machine learning technique | 27.3 | 0 | 2 |
| Solar farm voltage anomaly detection using high-resolution $\mu $PMU data-driven unsupervised machine learning | 27.3 | 0 | 2 |
| SeLoC-ML: Semantic Low-Code Engineering for Machine Learning Applications in Industrial IoT. | 27.3 | 0 | 2 |
| Business Methodology for the Application in University Environments of Predictive Machine Learning Models Based on an Ethical Taxonomy of the Student’s Digital Twin | 27.3 | 0 | 2 |
| Security Provisions in Smart Edge Computing Devices Using Blockchain and Machine Learning Algorithms: A Novel Approach | 27.3 | 0 | 2 |
| Anomaly detection for process monitoring based on machine learning technique | 27.3 | 0 | 2 |
| A Digital Twin for the Logistics System of a Manufacturing Enterprise Using Industrial IoT | 27.3 | 0 | 2 |
| Machine Learning Assisted PUF Calibration for Trustworthy Proof of Sensor Data in IoT | 27.3 | 0 | 2 |
| Data Evaluation and Enhancement for Quality Improvement of Machine Learning | 27.3 | 0 | 2 |
| Anomaly Detection in Radiation Sensor Data With Application to Transportation Security | 27.3 | 0 | 2 |
| Toward Smart Manufacturing Using Spiral Digital Twin Framework and Twinchain | 27.3 | 0 | 2 |
| A Digital Twin Based Industrial Automation and Control System Security Architecture | 27.3 | 0 | 2 |
| Using open-source microcontrollers to enable digital twin communication for smart manufacturing | 27.3 | 0 | 2 |
| Implementing an open-source sensor data ingestion, fusion, and analysis capabilities for smart manufacturing | 27.3 | 0 | 2 |
| Open source software for visualization and quality control of continuous hydrologic and water quality sensor data | 27.3 | 0 | 2 |
| Research on lightweight anomaly detection of multimedia traffic in edge computing | 27.3 | 0 | 2 |
| A machine learning framework to improve effluent quality control in wastewater treatment plants | 27.3 | 0 | 2 |
| A low-cost approach for soil moisture prediction using multi-sensor data and machine learning algorithm | 27.3 | 0 | 2 |
| Researching on Multiple Machine Learning for Anomaly Detection | 27.3 | 0 | 2 |
| Artificial intelligence-based condition monitoring and predictive maintenance framework for wind turbines | 27.3 | 0 | 2 |
| Encryption and Generation of Images for Privacy-Preserving Machine Learning in Smart Manufacturing | 27.3 | 0 | 2 |
| Collaborative Offloading Method for Digital Twin Empowered Cloud Edge Computing on Internet of Vehicles | 27.3 | 0 | 2 |
| Anomaly detection and troubleshooting system for a network using machine learning and/or artificial intelligence | 27.3 | 0 | 2 |
| driver of Industry 4.0 | 27.3 | 0 | 2 |
| Automated quality control methods for sensor data: a novel observatory approach | 27.3 | 0 | 2 |
| Condition Monitoring of a Gear Box by Acoustic Camera and Machine Learning Techniques | 27.3 | 0 | 2 |
| Data-Enabled Physics-Informed Machine Learning for Reduced-Order Modeling Digital Twin: Application to Nuclear Reactor Physics | 27.3 | 0 | 2 |
| Distributed machine learning for anomaly detection | 27.3 | 0 | 2 |
| Optimizing machine learning-based, edge computing networks | 27.3 | 0 | 2 |
| Short-long term anomaly detection in wireless sensor networks based on machine learning and multi-parameterized edit distance | 27.3 | 0 | 2 |
| Root cause analysis and automation using machine learning | 27.3 | 0 | 2 |
| The Application of "IE method" in Process Optimization of the Teaching Quality Management | 27.3 | 0 | 2 |
| Emergency Medical Services Demand Forecasting | 27.3 | 0 | 2 |
| Quality Control for Chinese Book Cataloguing in Public Library Based on the ISO Mode: ISO9001 Quality Management System of Anhui Library as an Example | 27.3 | 0 | 2 |
| Preprocessing sensor data for machine learning | 27.3 | 0 | 2 |
| On the Quality Control and Total Quality Management of Virtual Reference Service | 27.3 | 0 | 2 |
| Machine learning-based anomaly detection for human presence verification | 27.3 | 0 | 2 |
| Anomaly detection in time-series data using state inference and machine learning | 27.3 | 0 | 2 |
| Machine learning based predictive maintenance of equipment | 27.3 | 0 | 2 |
| Machine learning-based recommendation system for root cause analysis of service issues | 27.3 | 0 | 2 |
| Machine learning anomaly detection | 27.3 | 0 | 2 |
| Machine learning based test coverage in a production environment | 27.3 | 0 | 2 |
| Device manufacturing cycle time reduction using machine learning techniques | 27.3 | 0 | 2 |
| Study of Software Quality Improvement through Reliability Metrics Models and Root Cause Analysis Program | 27.3 | 0 | 2 |
| The digital twin in Industry 4.0: A wide‐angle perspective | 27.3 | 0 | 2 |
| Computer-based systems and/or computing devices configured for root cause analysis of computing incidents using machine learning | 27.3 | 0 | 2 |
| Constructing Production Scheduling Knowledge System ——A Machine Learning Method Based on Pattern Classification | 27.3 | 0 | 2 |
| Joint optimization of production quantity, quality control and predictive maintenance for production systems with multiple machines | 27.3 | 0 | 2 |
| Anomaly detection and reporting for machine learning models | 27.3 | 0 | 2 |
| Systems and methods for machine learning based equipment maintenance scheduling | 27.3 | 0 | 2 |
| Multistage quality control in manufacturing process using blockchain with machine learning technique | 27.3 | 0 | 2 |
| Steel company in Industry 4.0: diagnosis of changes in direction to smart manufacturing based on case study | 27.3 | 0 | 2 |
| Defect detection for multi-function devices using machine learning | 27.3 | 0 | 2 |
| Classification of Wheelchair Related Shoulder Loading Activities from Wearable Sensor Data: A Machine Learning Approach | 27.3 | 0 | 2 |
| Defect detection for multi-function devices using machine learning | 27.3 | 0 | 2 |
| Detecting, diagnosing, and directing solutions for source type mislabeling of machine data, including machine data that may contain PII, using machine | 27.3 | 0 | 2 |
| Method and system for performing automated root cause analysis of anomaly events in high-dimensional sensor data | 27.3 | 0 | 2 |
| Machine learning based crop water demand forecasting using minimum climatological data | 27.3 | 0 | 2 |
| Application of machine learning for fleet-based condition monitoring of ball screw drives in machine tools | 27.3 | 0 | 2 |
| A blockchain-enabled deep residual architecture for accountable, in-situ quality control in industry 4.0 with minimal latency | 27.3 | 0 | 2 |
| Detecting Video Game Player Burnout With the Use of Sensor Data and Machine Learning | 27.3 | 0 | 2 |
| Anomaly detection in encrypted HTTPS traffic using machine learning: a comparative analysis of feature selection techniques | 27.3 | 0 | 2 |
| Predictive maintenance and process supervision using a scalable industrial analytics platform | 27.3 | 0 | 2 |
| AI project of National Science and Technology Museum | 27.3 | 0 | 2 |
| Vibration-based railway track condition monitoring: A physics-based digital twin approach | 27.3 | 0 | 2 |
| Applied machine learning in mine safety and short-term underground mine production scheduling | 27.3 | 0 | 2 |
| Predictive maintenance in the Industry 4.0: A systematic literature review | 27.3 | 0 | 2 |
| A Machine Learning Approach for Anomaly Detection in Industrial Control Systems Based on Measurement Data | 27.3 | 0 | 2 |
| Machine learning models for automated anomaly detection for application infrastructure components | 27.3 | 0 | 2 |
| Blockchain-enabled edge computing method for production scheduling | 27.3 | 0 | 2 |
| Classifying pedestrian trajectories by Machine learning using laser sensor data | 27.3 | 0 | 2 |
| Comprehensive evaluation of machine learning models for predicting ship energy consumption based on onboard sensor data | 27.3 | 0 | 2 |
| Supervised multi-regional segmentation machine learning architecture for digital twin applications in coastal regions | 27.3 | 0 | 2 |
| Industry 4.0 oriented predictive analytics of cardiovascular diseases using machine learning, hyperparameter tuning and ensemble techniques | 27.3 | 0 | 2 |
| Machine learning-based defect detection of a specimen | 27.3 | 0 | 2 |
| Machine learning based anomaly detection for sedimentological data: Application to a Holocene multi-proxy paleoenvironmental reconstruction from Laguna Boquita, Jalisco, Mexico | 27.3 | 0 | 2 |
| Machine learning anomaly detection of process-loaded DLLs | 27.3 | 0 | 2 |
| RESEARCH ON KEY POINTS OF QUALITY INSPECTION OF AERIAL PHOTOGRAMMETRY RESULTS AND QUALITY IMPROVEMENT MEASURES | 27.3 | 0 | 2 |
| A study of micromanufacturing process fingerprints in micro-injection moulding for machine learning and Industry 4.0 applications | 27.3 | 0 | 2 |
| Digital Twin-Driven Machine Condition Monitoring: A Literature Review | 27.3 | 0 | 2 |
| Quality Control (QC) procedures for Australia’s National Reference Station’s sensor data—Comparing semi-autonomous systems to an expert oceanographer | 27.3 | 0 | 2 |
| ESB Platform Integrating Knime Data Mining Tool Oriented on Industry 4.0 Based on Artificial Neural Network Predictive Maintenance | 27.3 | 0 | 2 |
| Method and system for unsupervised anomaly detection and accountability with majority voting for high-dimensional sensor data | 27.3 | 0 | 2 |
| Precise Water Leak Detection Using Machine Learning and Real-Time Sensor Data | 27.3 | 0 | 2 |
| An AWS Machine Learning-Based Indirect Monitoring Method for Deburring in Aerospace Industries Towards Industry 4.0 | 27.3 | 0 | 2 |
| Evaluating Centralized and Heterarchical Control of Smart Manufacturing Systems in the Era of Industry 4.0 | 27.3 | 0 | 2 |
| Measuring water content of pressed beet pulp – Microwave transmission measurement for process optimization and quality improvement | 27.3 | 0 | 2 |
| Machine learning refinery sensor data to predict catalyst saturation levels | 27.3 | 0 | 2 |
| Machine Learning Approach Electric Appliance Consumption and Peak Demand Forecasting of Residential Customers Using Smart Meter Data | 27.3 | 0 | 2 |
| Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0 | 27.3 | 0 | 2 |
| Anomaly detection in wireless sensor network using machine learning algorithm | 27.3 | 0 | 2 |
| Predictive maintenance architecture development for nuclear infrastructure using machine learning | 27.3 | 0 | 2 |
| PulSec: Secure Element based framework for sensors anomaly detection in Industry 4.0 | 27.3 | 0 | 2 |
| Decision Making in Predictive Maintenance: Literature Review and Research Agenda for Industry 4.0 | 27.3 | 0 | 2 |
| Digital twin-enabled reconfigurable modeling for smart manufacturing systems | 27.3 | 0 | 2 |
| Machine Learning Framework for Predictive Maintenance in Milling | 27.3 | 0 | 2 |
| Dynamic Predictive Maintenance in industry 4.0 based on real time information: Case study in automotive industries | 27.3 | 0 | 2 |
| Contributions of Industry 4.0 to quality management - A SCOR perspective | 27.3 | 0 | 2 |
| Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case | 27.3 | 0 | 2 |
| Machine learning for quality control system | 27.3 | 0 | 2 |
| Lifelong Machine Learning and root cause analysis for large-scale cancer patient data | 27.3 | 0 | 2 |
| Machine learning applications to non-destructive defect detection in horticultural products | 27.3 | 0 | 2 |
| Enabling real-time road anomaly detection via mobile edge computing | 27.3 | 0 | 2 |
| Implementation of quality management system in health care of the Lipetsk region by optimizing internal quality control and safety of medical activities. Results and future prospects | 27.3 | 0 | 2 |
| A Mobile Health Approach to Improving Personal Exposure Assessment: Using Mobile Sensor Data and Machine Learning to Predict Key Microenvironments | 27.3 | 0 | 2 |
| Identification and classification of materials using machine vision and machine learning in the context of industry 4.0 | 27.3 | 0 | 2 |
| Anomaly Detection in Power Generation Plants Using Machine Learning and Neural Networks | 27.3 | 0 | 2 |
| The use of Digital Twin for predictive maintenance in manufacturing | 27.3 | 0 | 2 |
| Pharmaceutical quality control laboratory digital twin – A novel governance model for resource planning and scheduling | 27.3 | 0 | 2 |
| Data-driven smart manufacturing: Tool wear monitoring with audio signals and machine learning | 27.3 | 0 | 2 |
| A Study on Machine Learning-based Grass Demand Forecasting | 27.3 | 0 | 2 |
| Machine Learning approaches for Anomaly Detection in Multiphase Flow Meters | 27.3 | 0 | 2 |
| Application research of improved genetic algorithm based on machine learning in production scheduling | 27.3 | 0 | 2 |
| DT-II:Digital twin enhanced Industrial Internet reference framework towards smart manufacturing | 27.3 | 0 | 2 |
| Application framework of digital twin-driven product smart manufacturing system: A case study of aeroengine blade manufacturing | 27.3 | 0 | 2 |
| Special Issue on New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes | 27.3 | 0 | 2 |
| Generalized approach for multi-response machining process optimization using machine learning and evolutionary algorithms | 27.3 | 0 | 2 |
| Evaluation of in-mold sensors and machine data towards enhancing product quality and process monitoring via Industry 4.0 | 27.3 | 0 | 2 |
| A systematic literature review of machine learning methods applied to predictive maintenance | 27.3 | 0 | 2 |
| Machine learning based concept drift detection for predictive maintenance | 27.3 | 0 | 2 |
| Industrial IoT Sensor Data Federation in an Enterprise for an Early Failure Prediction | 27.3 | 0 | 2 |
| Data-driven predictive maintenance scheduling policies for railways | 27.3 | 0 | 2 |
| Process Modelling of Geothermal Drilling System Using Digital Twin for Real-Time Monitoring and Control | 27.3 | 0 | 2 |
| Machine learning-based design support system for the prediction of heterogeneous machine parameters in industry 4.0 | 27.3 | 0 | 2 |
| Development of a BIM-based holonic system for real-time monitoring of building operational efficiency | 27.3 | 0 | 2 |
| Anomaly Detection for Analysis of Annual Inventory Data: A Quality Control Approach | 27.3 | 0 | 2 |
| In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning | 27.3 | 0 | 2 |
| An IoT framework for Bio-medical sensor data acquisition and machine learning for early detection | 27.3 | 0 | 2 |
| Hierarchical Edge Computing: A Novel Multi-Source Multi-Dimensional Data Anomaly Detection Scheme for Industrial Internet of Things | 27.3 | 0 | 2 |
| Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn From a Digital Twin | 27.3 | 0 | 2 |
| A survey of machine learning methods applied to anomaly detection on drinking-water quality data | 27.3 | 0 | 2 |
| A predictive maintenance cost model for CNC SMEs in the era of industry 4.0 | 27.3 | 0 | 2 |
| Machine learning algorithms towards merging of mobile edge computing and Internet of Things | 27.3 | 0 | 2 |
| Condition Monitoring of Railway Tracks from Car-Body Vibration Using a Machine Learning Technique | 27.3 | 0 | 2 |
| Machine Learning and Quality Management of Quantitative Data - With an Application to Energy Finance | 27.3 | 0 | 2 |
| Fitness Evaluation System (FES) for Ailment Estimate by Machine Learning Over Big Health Sensor Data | 27.3 | 0 | 2 |
| Machine learning models to support reservoir production optimization | 27.3 | 0 | 2 |
| Unsupervised anomaly detection based on clustering methods and sensor data on a marine diesel engine | 27.3 | 0 | 2 |
| Methodology for enabling Digital Twin using advanced physics-based modelling in predictive maintenance | 27.3 | 0 | 2 |
| Predictive machine learning and data acquisition for power quality improvement in facts devices with optimum power flow control based on cross difference progression and coordination examining algorithm | 27.3 | 0 | 2 |
| DNS-ADVP: A Machine Learning Anomaly Detection and Visual Platform to Protect Top-Level Domain Name Servers Against DDoS Attacks | 27.3 | 0 | 2 |
| Machine Learning Based Integrated Feature Selection Approach for Improved Electricity Demand Forecasting in Decentralized Energy Systems | 27.3 | 0 | 2 |
| Methodology of overall equipment effectiveness calculation in the context of Industry 4.0 environment | 27.3 | 0 | 2 |
| A Smart Manufacturing Service System Based on Edge Computing, Fog Computing, and Cloud Computing | 27.3 | 0 | 2 |
| Effective and efficient network anomaly detection system using machine learning algorithm | 27.3 | 0 | 2 |
| Multistage Quality Control Using Machine Learning in the Automotive Industry | 27.3 | 0 | 2 |
| Software development for Industry 4.0 neuroprocessor industrial automation systems | 27.3 | 0 | 2 |
| A High-Frequency Data-Driven Machine Learning Approach for Demand Forecasting in Smart Cities | 27.3 | 0 | 2 |
| Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison | 27.3 | 0 | 2 |
| Predicting Traffic Phases from Car Sensor Data using Machine Learning | 27.3 | 0 | 2 |
| Towards Predicting System Disruption in Industry 4.0: Machine Learning-Based Approach | 27.3 | 0 | 2 |
| A comparison of fog and cloud computing cyber-physical interfaces for Industry 4.0 real-time embedded machine learning engineering applications | 27.3 | 0 | 2 |
| Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches | 27.3 | 0 | 2 |
| Automatic Detection of Major Freeway Congestion Events using Wireless Traffic Sensor Data: Machine Learning Approach | 27.3 | 0 | 2 |
| Simulation Modeling for Production Scheduling under Make-To-Order Production Environment : Focusing on the Flat Glass Production Environment | 27.3 | 0 | 2 |
| Anomaly detection in Skin Model Shapes using machine learning classifiers | 27.3 | 0 | 2 |
| MACHINE LEARNING FOR QOE PREDICTION AND ANOMALY DETECTION IN SELF-ORGANIZING MOBILE NETWORKING SYSTEMS | 27.3 | 0 | 2 |
| A novel feature extraction method of eddy current testing for defect detection based on machine learning | 27.3 | 0 | 2 |
| Industry 4.0: The use of industrial automation with the internet of things support in the control of electricity via local network in the city of Manaus-AM | 27.3 | 0 | 2 |
| Development of digital twin of CNC unit based on machine learning methods | 27.3 | 0 | 2 |
| Smart Manufacturing Scheduling With Edge Computing Using Multiclass Deep Q Network | 27.3 | 0 | 2 |
| A hybrid machine learning approach for enhanced anomaly detection in drinking water quality | 27.3 | 0 | 2 |
| Digital Twin Assisted VNF Mapping and Scheduling in SDN/NFV-Enabled Industrial IoT | 27.3 | 0 | 2 |
| Mechatronics applications of measurements for smart manufacturing in an industry 4.0 scenario | 27.3 | 0 | 2 |
| An Unsupervised Machine Learning Algorithm for Attack and Anomaly Detection in IoT Sensors | 27.3 | 0 | 2 |
| Application of machine learning in BGP anomaly detection | 27.3 | 0 | 2 |
| Demand forecasting in restaurants using machine learning and statistical analysis | 27.3 | 0 | 2 |
| Condition monitoring in Industry 4.0 production systems - the idea of computational intelligence methods application | 27.3 | 0 | 2 |
| Systems and methods for determining a risk score using machine learning based at least in part upon collected sensor data | 27.3 | 0 | 2 |
| Improving Accuracy of Sensor Data by Frequent Pattern Mining Algorithm Using Edge Computing | 27.3 | 0 | 2 |
| Proposed Model for Real-Time Anomaly Detection in Big IoT Sensor Data for Smart City | 27.3 | 0 | 2 |
| A Hybrid Computing Solution and Resource Scheduling Strategy for Edge Computing in Smart Manufacturing | 27.3 | 0 | 2 |
| Value of Customer Feedback in Quality Management for Construction Quality Improvement: Initial Perspective Review | 27.3 | 0 | 2 |
| Smart Condition Monitoring for Industry 4.0 Manufacturing Processes: An Ontology-Based Approach | 27.3 | 0 | 2 |
| Network Anomaly Detection by Means of Machine Learning: Random Forest Approach with Apache Spark | 27.3 | 0 | 2 |
| Edge Computing: A Promising Framework for Real-Time Fault Diagnosis and Dynamic Control of Rotating Machines Using Multi-Sensor Data | 27.3 | 0 | 2 |
| Enforcing Position-Based Confidentiality With Machine Learning Paradigm Through Mobile Edge Computing in Real-Time Industrial Informatics | 27.3 | 0 | 2 |
| Digital twin-driven multi-dimensional assembly error modeling and control for complex assembly process in Industry 4.0 | 27.3 | 0 | 2 |
| Utilizing machine learning techniques for network traffic anomaly detection | 27.3 | 0 | 2 |
| An online machine learning framework for early detection of product failures in an Industry 4.0 context | 27.3 | 0 | 2 |
| The Squeaky wheel: Machine learning for anomaly detection in subjective thermal comfort votes | 27.3 | 0 | 2 |
| Aggregated multi-attribute query processing in edge computing for industrial IoT applications | 27.3 | 0 | 2 |
| From Statistical Probability to Machine Learning Prediction Methods in Orofacial Pain and Headache Research: Evolution from Quality Control of Guinness Beer to IBM Innovations | 27.3 | 0 | 2 |
| Prototype Selection Method for Vehicle Condition Monitoring using Machine Learning | 27.3 | 0 | 2 |
| Shape Defect Detection using Local Standard Deviation and Rule-Based Classifier for Bottle Quality Inspection | 27.3 | 0 | 2 |
| A hybrid k-means-GMM machine learning technique for turbomachinery condition monitoring | 27.3 | 0 | 2 |
| Dominant Expression of SAR Backscatter in Predicting Aboveground Biomass: Integrating Multi-Sensor Data and Machine Learning in Sikkim Himalaya | 27.3 | 0 | 2 |
| A Comprehensive Analysis of Machine Learning Models for Real Time Anomaly Detection in Internet of Things | 27.3 | 0 | 2 |
| Real-time hollow defect detection in tiles using on-device tiny machine learning | 27.3 | 0 | 2 |
| Machine Learning-Based Anomaly Detection for Load Forecasting Under Cyberattacks | 27.3 | 0 | 2 |
| Condition monitoring framework for damage identification in CFRP rotating shafts using Model-Driven Machine learning techniques | 27.3 | 0 | 2 |
| A Machine Learning-Based Digital Twin for Anti-Counterfeiting Applications With Copy Detection Patterns | 27.3 | 0 | 2 |
| Automatically generated quality control tables and quality improvement programs1 | 27.3 | 0 | 2 |
| Applying Machine Learning to Imbalanced Sensor Data | 27.3 | 0 | 2 |
| Tool condition monitoring in drilling processes using anomaly detection approaches based on control internal data | 27.3 | 0 | 2 |
| Anomaly Detection with Machine Learning and Graph Databases in Fraud Management | 27.3 | 0 | 2 |
| An approach to quality improvement of Nigerian university library services: A framework for effective quality management implementation | 27.3 | 0 | 2 |
| Dimensionality Reduction of Sensorial Features by Principal Component Analysis for ANN Machine Learning in Tool Condition Monitoring of CFRP Drilling | 27.3 | 0 | 2 |
| Machine Learning-Powered Anomaly Detection: Enhancing Data Security and Integrity | 27.3 | 0 | 2 |
| Real-Time Identification of Cyber-Physical Attacks on Water Distribution Systems via Machine Learning–Based Anomaly Detection Techniques | 27.3 | 0 | 2 |
| Study on the condition monitoring system for the sliding surface using machine learning | 27.3 | 0 | 2 |
| Predictive Maintenance and part quality control from joint product-process-machine requirements: application to a machine tool | 27.3 | 0 | 2 |
| Machine Learning and Deep Learning Techniques for Distributed Denial of Service Anomaly Detection in Software Defined Networks—Current Research Solutions | 27.3 | 0 | 2 |
| High-throughput image labeling and quality control for clinical trials using machine learning | 27.3 | 0 | 2 |
| Developing a semantic framework for categorizing IoT agriculture sensor data: A machine learning and web semantics approach | 27.3 | 0 | 2 |
| Anomaly Detection in Vehicle Traffic with Image Processing and Machine Learning | 27.3 | 0 | 2 |
| Impact of a Continuing Quality Control Review on Physics Quality Management and Radiation Safety Program in a Large Radiation Oncology Network: Multiyear Experience | 27.3 | 0 | 2 |
| From Raw Data to Smart Manufacturing: AI and Semantic Web of Things for Industry 4.0 | 27.3 | 0 | 2 |
| An application of machine learning and statistics to defect detection | 27.3 | 0 | 2 |
| Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0 | 27.3 | 0 | 2 |
| Anomaly Detection in Predictive Maintenance using Dynamic Time Warping | 27.3 | 0 | 2 |
| Short-term water demand forecasting using machine learning techniques | 27.3 | 0 | 2 |
| Anomaly detection system for data quality assurance in IoT infrastructures based on machine learning | 27.3 | 0 | 2 |
| Literature survey of chromosomes classification and anomaly detection using machine learning algorithms | 27.3 | 0 | 2 |
| Study on Machine Learning Based Intelligent Defect Detection System | 27.3 | 0 | 2 |
| Research on E-Commerce Commodity Demand Forecasting and Inventory Optimization Based on Time Series Forecasting Model | 27.3 | 0 | 2 |
| Opportunistic maintenance scheduling with stochastic opportunities duration in a predictive maintenance strategy | 27.3 | 0 | 2 |
| Generic Machine Learning Framework for Fully-Unsupervised Anomaly Detection with Contaminated Data | 27.3 | 0 | 2 |
| A Comparative Study of Anomaly Detection Techniques for IoT Security Using Adaptive Machine Learning for IoT Threats | 27.3 | 0 | 2 |
| Near Real-time Autonomous Quality Control for Streaming Environmental Sensor Data | 27.3 | 0 | 2 |
| Digital twin for condition monitoring of actuators | 27.3 | 0 | 2 |
| Quality management in the 21st century enterprises: Research pathway towards Industry 4.0 | 27.3 | 0 | 2 |
| Requirements for a Human-Centered Condition Monitoring in Modular Production Environments | 27.3 | 0 | 2 |
| A Machine Learning-Enhanced Digital Twin Approach for Human-Robot-Collaboration | 27.3 | 0 | 2 |
| A quest for waste reduction at institutions of higher learning: Investigating the integration of Six Sigma and Lean Six Sigma methodologies with total quality management | 27.3 | 0 | 2 |
| Foreword: Smart manufacturing, innovative product and service design to empower Industry 4.0 | 27.3 | 0 | 2 |
| A Review of Machine Learning Algorithms for estimating Critical Quality Attributes from Multi-Sensor Data | 27.3 | 0 | 2 |
| LAYERED MACHINE LEARNING FOR SHORT-TERM WATER DEMAND FORECASTING | 27.3 | 0 | 2 |
| Intelligent Computation Offloading Based on Digital Twin-Enabled 6G Industrial IoT | 27.3 | 0 | 2 |
| Development of Decision Support System (DSS) Framework for Predictive Maintenance of Rolling Element Bearing in the Emerging Era of Industry 4.0 | 27.3 | 0 | 2 |
| High-resolution mapping of seasonal snow cover extent in the Pamir Hindu Kush using machine learning-based integration of multi-sensor data | 27.3 | 0 | 2 |
| Resource Efficient Federated Learning and DAG Blockchain With Sharding in Digital Twin Driven Industrial IoT | 27.3 | 0 | 2 |
| Smart manufacturing analytics application for semi-continuous manufacturing process – a use case | 27.3 | 0 | 2 |
| Anomaly detection through keystroke and tap dynamics implemented via machine learning algorithms | 27.3 | 0 | 2 |
| Machine learning based feature extraction for quality control in a production line | 27.3 | 0 | 2 |
| Educational Programmes Quality Improvement with the Improvement of the University Quality Management System | 27.3 | 0 | 2 |
| Machine learning enabled Industrial IoT Security: Challenges, Trends and Solutions | 27.3 | 0 | 2 |
| Anomaly detection in wide area network meshes using two machine learning algorithms | 27.3 | 0 | 2 |
| Cluster identification of sensor data for predictive maintenance in a Selective Laser Melting machine tool | 27.3 | 0 | 2 |
| Digital twin-based WEEE recycling, recovery and remanufacturing in the background of Industry 4.0 | 27.3 | 0 | 2 |
| A Machine Learning Algorithm Based on Inverse Problems for Cyber Anomaly Detection | 27.3 | 0 | 2 |
| Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms | 27.3 | 0 | 2 |
| Augmented Data-Driven Machine Learning for Digital Twin of Stud Shear Connections | 27.3 | 0 | 2 |
| A Hybrid Approach to Improve the Video Anomaly Detection Performance of Pixel- and Frame-Based Techniques Using Machine Learning Algorithms | 27.3 | 0 | 2 |
| DEMAND FORECASTING IN THE CONTEXT OF COMPANY INVENTORY OPTIMIZATION | 27.3 | 0 | 2 |
| Comparative Analysis of Machine Learning Models for Predictive Maintenance of Ball Bearing Systems | 27.3 | 0 | 2 |
| Digital Twin Service towards Smart Manufacturing | 27.3 | 0 | 2 |
| Root cause analysis of failures and quality deviations in manufacturing using machine learning | 27.3 | 0 | 2 |
| Condition Monitoring of Single Point Cutting Tools Based on Machine Learning Approach | 27.3 | 0 | 2 |
| Determining directions of service quality management using online review mining with interpretable machine learning | 27.3 | 0 | 2 |
| Developing cyber-physical system and digital twin for smart manufacturing: Methodology and case study of continuous clarification | 27.3 | 0 | 2 |
| SGAD-GAN: Simultaneous Generation and Anomaly Detection for time-series sensor data with Generative Adversarial Networks | 27.3 | 0 | 2 |
| Machine learning and IoT – Based predictive maintenance approach for industrial applications | 27.3 | 0 | 2 |
| Enhancing titanium spacer defect detection through reinforcement learning-optimized digital twin and synthetic data generation | 27.3 | 0 | 2 |
| SCADA‐based wind turbine anomaly detection using Gaussian process models for wind turbine condition monitoring purposes | 27.3 | 0 | 2 |
| Performances of Machine Learning Algorithms for Binary Classification of Network Anomaly Detection System | 27.3 | 0 | 2 |
| LSTM-based autoencoder models for real-time quality control of wastewater treatment sensor data | 27.3 | 0 | 2 |
| Countering targeted cyber-physical attacks using anomaly detection in self-adaptive Industry 4.0 Systems | 27.3 | 0 | 2 |
| A Study on the Usability of Visualization by Data Type Collected in Smart Manufacturing Process-Focusing on the Learning Manufacturing AI Dataset Provided by KAMP- | 27.3 | 0 | 2 |
| Enhanced Quality Control in Pharmaceutical Applications by Combining Raman Spectroscopy and Machine Learning Techniques | 27.3 | 0 | 2 |
| Parametric and Generative Design techniques in mass-production environments as effective enablers of Industry 4.0 approaches in the Building Industry | 27.3 | 0 | 2 |
| THE USAGE OF TOTAL QUALITY MANAGEMENT (TQM) IN INDUSTRY 4.0 CONDITIONS | 27.3 | 0 | 2 |
| A digital twin for smart manufacturing of structural composites by liquid moulding | 27.3 | 0 | 2 |
| Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging. | 27.3 | 0 | 2 |
| Quantum machine learning for quantum anomaly detection | 27.3 | 0 | 2 |
| A scalable distributed machine learning approach for attack detection in edge computing environments | 27.3 | 0 | 2 |
| Optimising digital signal processor‐based defect detection in smart manufacturing with lightweight convolutional neural networks | 27.3 | 0 | 2 |
| Industrial IoT in 5G environment towards smart manufacturing | 27.3 | 0 | 2 |
| Hypernetwork-based manufacturing service scheduling for distributed and collaborative manufacturing operations towards smart manufacturing | 27.3 | 0 | 2 |
| Adopting New Machine Learning Approaches on Cox’s Partial Likelihood Parameter Estimation for Predictive Maintenance Decisions | 27.3 | 0 | 2 |
| Regional Tourism Demand Forecasting with Machine Learning Models: Gaussian Process Regression vs. Neural Network Models in a Multiple-Input Multiple-Output Setting | 27.3 | 0 | 2 |
| Utility companies strategy for short-term energy demand forecasting using machine learning based models | 27.3 | 0 | 2 |
| IO-Link Wireless enhanced factory automation communication for Industry 4.0 applications | 27.3 | 0 | 2 |
| Machine learning techniques for quality control in high conformance manufacturing environment | 27.3 | 0 | 2 |
| UNSUPERVISED MACHINE LEARNING FOR SEISMIC ANOMALY DETECTION: LOCAL OUTLIER FACTOR ALGORITHM TO INDONESIAN EARTHQUAKE DATA | 27.3 | 0 | 2 |
| UNSUPERVISED MACHINE LEARNING FOR SEISMIC ANOMALY DETECTION: ISOLATION FOREST ALGORITHM APPLICATION TO INDONESIAN EARTHQUAKE DATA | 27.3 | 0 | 2 |
| Training machine learning model based on training instances with: training instance input based on autonomous vehicle sensor data, and training instan | 27.3 | 0 | 2 |
| Hybrid modeling for grassland productivity prediction: A parametric and machine learning technique for grazing management with applicability to digital twin decision systems | 27.3 | 0 | 2 |
| Computation Offloading and Resource Allocation for Digital Twin-empowered Mobile Edge Computing | 27.3 | 0 | 2 |
| A performance measurement system for industry 4.0 enabled smart manufacturing system in SMMEs- A review and empirical investigation | 27.3 | 0 | 2 |
| Applying Time-Constraints Using Ontologies to Sensor Data for Predictive Maintenance | 27.3 | 0 | 2 |
| Blockchain-based Cybersecurity for Smart Manufacturing in Industry 4.0: Technology-Organization-Environment Approach | 27.3 | 0 | 2 |
| Digital Twin-based Condition Monitoring with Distributed Data Mapping of OPC UA and ISO 10303 STEP Standard | 27.3 | 0 | 2 |
| Anomaly Detection in Internet of Things Based on Logs Using Machine Learning and Deep Learning Techniques | 27.3 | 0 | 2 |
| Mobile Edge Computing and Machine Learning in the Internet of Unmanned Aerial Vehicles: A Survey | 27.3 | 0 | 2 |
| Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach | 27.3 | 0 | 2 |
| KDDT: Knowledge Distillation-Empowered Digital Twin for Anomaly Detection | 27.3 | 0 | 2 |
| Application of Artificial Intelligence in Condition Monitoring and Predictive Maintenance of Rail Transit Vehicle Equipment | 27.3 | 0 | 2 |
| Edge AI for Industry 4.0: An Internet of Things Approach | 27.3 | 0 | 2 |
| A Machine Learning Framework for Optimising Indoor Thermal Comfort and Air Quality through Sensor Data Streams | 27.3 | 0 | 2 |
| Sussex-Huawei Locomotion Recognition Using Machine Learning and Deep Learning with Multi-sensor data | 27.3 | 0 | 2 |
| SIURU: A Framework for Machine Learning Based Anomaly Detection in IoT Network Traffic | 27.3 | 0 | 2 |
| Time Series and Machine Learning methods for Demand Forecasting: a case study of machine seller in prefabricated concrete industry | 27.3 | 0 | 2 |
| Machine Learning Technique for Energy, Performance and Cost-Effective Resource Management in Multi-Access Edge Computing | 27.3 | 0 | 2 |
| Condition Monitoring of an Autonomous Electric Drive Train by Using Machine Learning Methods | 27.3 | 0 | 2 |
| Deep Learning or Classical Machine Learning? An Empirical Study on Log-Based Anomaly Detection | 27.3 | 0 | 2 |
| Combining traditional machine learning and anomaly detection for several imbalanced Android malware dataset's classification | 27.3 | 0 | 2 |
| The synergy of complex event processing and tiny machine learning in industrial IoT | 27.3 | 0 | 2 |
| Digital Twin of Intelligent Small Surface Defect Detection with Cyber-manufacturing Systems | 27.3 | 0 | 2 |
| Causal Neuro-Symbolic AI for Root Cause Analysis in Smart Manufacturing | 27.3 | 0 | 2 |
| The value of human data annotation for machine learning based anomaly detection in environmental systems | 27.3 | 0 | 2 |
| SentemQC - A novel and cost-efficient method for quality assurance and quality control of high-resolution frequency sensor data in fresh waters | 27.3 | 0 | 2 |
| Mistral Vibe | 27.3 | 0 | 2 |
| Digital Twin for Additive Manufacturing and Smart Manufacturing Education | 27.3 | 0 | 2 |
| Industry 4.0 Edge Computing Demonstration Projects for Manufacturing Technology Education | 27.3 | 0 | 2 |
| Implementation and Evaluation of a Predictive Maintenance Course Utilizing Machine Learning | 27.3 | 0 | 2 |
| Taking an Experiential Learning Approach to Industrial IoT Implementation for Smart Manufacturing through Course Work and University-Industry Partnerships | 27.3 | 0 | 2 |
| Educating the Workforce in Cyber and Smart Manufacturing for Industry 4.0 | 27.3 | 0 | 2 |
| Q135256168 | 27.3 | 0 | 2 |
| Q135257741 | 27.3 | 0 | 2 |
| Q135394646 | 27.3 | 0 | 2 |
| AI-Enabled Smart System for Continuous Monitoring of Neonatal Vital Signs in Intensive Care Unit | 27.3 | 0 | 2 |
| Q135458153 | 27.3 | 0 | 2 |
| Q136271673 | 27.3 | 0 | 2 |
| Q136292158 | 27.3 | 0 | 2 |
| Q136292166 | 27.3 | 0 | 2 |
| Q136292184 | 27.3 | 0 | 2 |
| Q136303136 | 27.3 | 0 | 2 |
| Q136791286 | 27.3 | 0 | 2 |
| Q136791893 | 27.3 | 0 | 2 |
| Q136792888 | 27.3 | 0 | 2 |
| Q136832503 | 27.3 | 0 | 2 |
| Q136834510 | 27.3 | 0 | 2 |
| Q136910871 | 27.3 | 0 | 2 |
| Q136918360 | 27.3 | 0 | 2 |
| Kushagra Thakur | 27.3 | 0 | 2 |
| Nayan Goel | 27.3 | 0 | 2 |
| Q137194653 | 27.3 | 0 | 2 |
| Q137215517 | 27.3 | 0 | 2 |
| Q137224307 | 27.3 | 0 | 2 |
| Q137263950 | 27.3 | 0 | 2 |
| Q137353125 | 27.3 | 0 | 2 |
| Q137353501 | 27.3 | 0 | 2 |
| Q137357660 | 27.3 | 0 | 2 |
| Q137359102 | 27.3 | 0 | 2 |
| Q137407346 | 27.3 | 0 | 2 |
| Causal Neuro-Symbolic AI for Root Cause Analysis in Smart Manufacturing | 27.3 | 0 | 2 |
| Rudrendu Kumar Paul | 27.3 | 0 | 2 |
| CustomGPT.ai | 27.3 | 0 | 2 |
| Digital Twin Divergence | 27.3 | 0 | 2 |
| Real-Time Digital Twin Synchronizer | 27.3 | 0 | 2 |
| Glápagos | 27.3 | 0 | 2 |
| Welotec | 27.3 | 0 | 2 |
| Center for Quality Management, Marine and Fishery Product Quality Control and Surveillance Agency | 27.3 | 0 | 2 |
| Vorel | 27.3 | 0 | 2 |
| DigitalPlace | 27.3 | 0 | 2 |
| SixDegree | 27.3 | 0 | 2 |
| General Electric | 26.7 | 4 | 0 |
| Q80689 | 26.7 | 4 | 0 |
| Oracle E-Business Suite | 26.7 | 4 | 0 |
| Microsoft Lumia 640 XL | 26.7 | 4 | 0 |
| Publications | 26.7 | 4 | 0 |
| Journal of Risk and Financial Management | 26.7 | 4 | 0 |
| Microsoft Windows | 23.0 | 3 | 0 |
| JDeveloper | 23.0 | 3 | 0 |
| Jakarta EE | 23.0 | 3 | 0 |
| PricewaterhouseCoopers | 23.0 | 3 | 0 |
| Oracle SQL Developer | 23.0 | 3 | 0 |
| SAP NetWeaver Business Intelligence | 23.0 | 3 | 0 |
| Datalogix | 23.0 | 3 | 0 |
| IBM Bluemix | 23.0 | 3 | 0 |
| Microsoft Lumia 640 | 23.0 | 3 | 0 |
| Dataiku | 23.0 | 3 | 0 |
| International Journal of Neonatal Screening | 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 |
| Education (Basel) | 23.0 | 3 | 0 |
| Q11219 | 18.3 | 2 | 0 |
| Schneider Electric | 18.3 | 2 | 0 |
| SPSS | 18.3 | 2 | 0 |
| Nvidia | 18.3 | 2 | 0 |
| DirectX | 18.3 | 2 | 0 |
| Robert Bosch | 18.3 | 2 | 0 |
| IFS AB | 18.3 | 2 | 0 |
| Microsoft Paint | 18.3 | 2 | 0 |
| Microsoft Digital Image | 18.3 | 2 | 0 |
| Deloitte | 18.3 | 2 | 0 |
| Software AG | 18.3 | 2 | 0 |
| Autodesk | 18.3 | 2 | 0 |
| Azure | 18.3 | 2 | 0 |
| Honeywell | 18.3 | 2 | 0 |
| Microsoft AutoRoute | 18.3 | 2 | 0 |
| Salesforce | 18.3 | 2 | 0 |
| IBM Informix | 18.3 | 2 | 0 |
| CICS | 18.3 | 2 | 0 |
| IBM Rational DOORS | 18.3 | 2 | 0 |
| Microsoft Virtual Server | 18.3 | 2 | 0 |
| MIT Media Lab | 18.3 | 2 | 0 |
| TIBCO Spotfire Analytics | 18.3 | 2 | 0 |
| Splunk Inc. | 18.3 | 2 | 0 |
| Rational Rhapsody | 18.3 | 2 | 0 |
| Oracle Application Server | 18.3 | 2 | 0 |
| IBM Power Systems | 18.3 | 2 | 0 |
| Siebel Systems | 18.3 | 2 | 0 |
| CPLEX | 18.3 | 2 | 0 |
| Microsoft Student | 18.3 | 2 | 0 |
| Vantive | 18.3 | 2 | 0 |
| AutoCAD Architecture | 18.3 | 2 | 0 |
| Entropy | 18.3 | 2 | 0 |
| Microsoft Layer for Unicode | 18.3 | 2 | 0 |
| Autodesk MotionBuilder | 18.3 | 2 | 0 |
| IBM Configuration Management Version Control | 18.3 | 2 | 0 |
| Yahoo! Finance | 18.3 | 2 | 0 |
| Energies | 18.3 | 2 | 0 |
| Genes | 18.3 | 2 | 0 |
| IntelliType | 18.3 | 2 | 0 |
| International Journal of Environmental Research and Public Health | 18.3 | 2 | 0 |
| Java BluePrints | 18.3 | 2 | 0 |
| Machine Design | 18.3 | 2 | 0 |
| Materials | 18.3 | 2 | 0 |
| Microsoft Japan | 18.3 | 2 | 0 |
| Microsoft Search Server | 18.3 | 2 | 0 |
| Nimble Storage | 18.3 | 2 | 0 |
| Nutrients | 18.3 | 2 | 0 |
| Oracle Property Manager | 18.3 | 2 | 0 |
| Scientia Pharmaceutica | 18.3 | 2 | 0 |
| Viruses | 18.3 | 2 | 0 |
| Microsoft Pinyin IME | 18.3 | 2 | 0 |
| Toxins | 18.3 | 2 | 0 |
| Pharmaceuticals | 18.3 | 2 | 0 |
| Sustainability | 18.3 | 2 | 0 |
| Water | 18.3 | 2 | 0 |
| Microsoft Movies & TV | 18.3 | 2 | 0 |
| Microsoft Mobile | 18.3 | 2 | 0 |
| Remote Sensing | 18.3 | 2 | 0 |
| Life | 18.3 | 2 | 0 |
| Biology | 18.3 | 2 | 0 |
| Q18146823 | 18.3 | 2 | 0 |
| Q18168774 | 18.3 | 2 | 0 |
| Performance Analyzer | 18.3 | 2 | 0 |
| Oracle BlueKai Data Management Platform | 18.3 | 2 | 0 |
| Insects | 18.3 | 2 | 0 |
| Microsoft Lumia 950 XL | 18.3 | 2 | 0 |
| SAP S/4HANA | 18.3 | 2 | 0 |
| Biomolecules | 18.3 | 2 | 0 |
| Microsoft Entra ID | 18.3 | 2 | 0 |
| Azure Cognitive Search | 18.3 | 2 | 0 |
| Medicina | 18.3 | 2 | 0 |
| Microsoft Dynamics 365 | 18.3 | 2 | 0 |
| Diversity | 18.3 | 2 | 0 |
| Cancers | 18.3 | 2 | 0 |
| Pharmaceutics | 18.3 | 2 | 0 |
| Atmosphere | 18.3 | 2 | 0 |
| Journal of Functional Biomaterials | 18.3 | 2 | 0 |
| Behavioral Sciences | 18.3 | 2 | 0 |
| Membranes | 18.3 | 2 | 0 |
| Metabolites | 18.3 | 2 | 0 |
| Symmetry | 18.3 | 2 | 0 |
| Antibodies | 18.3 | 2 | 0 |
| Plants | 18.3 | 2 | 0 |
| Pathogens | 18.3 | 2 | 0 |
| Brain Sciences | 18.3 | 2 | 0 |
| Metals | 18.3 | 2 | 0 |
| Journal of Marine Science and Engineering | 18.3 | 2 | 0 |
| Cells | 18.3 | 2 | 0 |
| Journal of Personalized Medicine | 18.3 | 2 | 0 |
| Journal of Clinical Medicine | 18.3 | 2 | 0 |
| Biosensors | 18.3 | 2 | 0 |
| Nanomaterials | 18.3 | 2 | 0 |
| Geosciences | 18.3 | 2 | 0 |
| Journal of Developmental Biology | 18.3 | 2 | 0 |
| Proteomes | 18.3 | 2 | 0 |
| Religions | 18.3 | 2 | 0 |
| International Journal of Geo Information | 18.3 | 2 | 0 |
| Administrative Sciences | 18.3 | 2 | 0 |
| Microorganisms | 18.3 | 2 | 0 |
| Photonics | 18.3 | 2 | 0 |
| Medical Sciences | 18.3 | 2 | 0 |
| Electronics | 18.3 | 2 | 0 |
| Vaccines | 18.3 | 2 | 0 |
| Societies | 18.3 | 2 | 0 |
| Applied Sciences | 18.3 | 2 | 0 |
| Land | 18.3 | 2 | 0 |
| Animals | 18.3 | 2 | 0 |
| Journal of Intelligence | 18.3 | 2 | 0 |
| Actuators | 18.3 | 2 | 0 |
| Diseases | 18.3 | 2 | 0 |
| Antibiotics | 18.3 | 2 | 0 |
| Toxics | 18.3 | 2 | 0 |
| Social Sciences | 18.3 | 2 | 0 |
| Micromachines | 18.3 | 2 | 0 |
| Children | 18.3 | 2 | 0 |
| Journal of Cardiovascular Development and Disease | 18.3 | 2 | 0 |
| Non-Coding RNA | 18.3 | 2 | 0 |
| Processes | 18.3 | 2 | 0 |
| Diagnostics | 18.3 | 2 | 0 |
| Forests | 18.3 | 2 | 0 |
| Laws | 18.3 | 2 | 0 |
| Healthcare | 18.3 | 2 | 0 |
| Minerals | 18.3 | 2 | 0 |
| Antioxidants | 18.3 | 2 | 0 |
| Foods | 18.3 | 2 | 0 |
| Medicines | 18.3 | 2 | 0 |
| Agronomy | 18.3 | 2 | 0 |
| Crystals | 18.3 | 2 | 0 |
| Informatics | 18.3 | 2 | 0 |
| Bioengineering | 18.3 | 2 | 0 |
| Fibers | 18.3 | 2 | 0 |
| Chemosensors | 18.3 | 2 | 0 |
| Pharmacy | 18.3 | 2 | 0 |
| Veterinary Sciences | 18.3 | 2 | 0 |
| Atoms | 18.3 | 2 | 0 |
| Cosmetics | 18.3 | 2 | 0 |
| Separations | 18.3 | 2 | 0 |
| Technologies | 18.3 | 2 | 0 |
| Future Internet | 18.3 | 2 | 0 |
| Data | 18.3 | 2 | 0 |
| Risks | 18.3 | 2 | 0 |
| MPDV Mikrolab (Germany) | 18.3 | 2 | 0 |
| MDPI | 18.3 | 2 | 0 |
| SAS Institute | 18.3 | 2 | 0 |
| Oracle Cloud | 18.3 | 2 | 0 |
| Catalysts | 18.3 | 2 | 0 |
| CaosDB - Research Data Management for Complex, Changing, and Automated Research Workflows | 18.3 | 2 | 0 |
| Philosophies | 18.3 | 2 | 0 |
| Magnetochemistry | 18.3 | 2 | 0 |
| Mathematical and Computational Applications | 18.3 | 2 | 0 |
| Lubricants | 18.3 | 2 | 0 |
| Galaxies | 18.3 | 2 | 0 |
| Sports | 18.3 | 2 | 0 |
| Buildings | 18.3 | 2 | 0 |
| Arts | 18.3 | 2 | 0 |
| Humanities | 18.3 | 2 | 0 |
| Environments | 18.3 | 2 | 0 |
| Coatings | 18.3 | 2 | 0 |
| Economies | 18.3 | 2 | 0 |
| Machines | 18.3 | 2 | 0 |
| Axioms | 18.3 | 2 | 0 |
| Games | 18.3 | 2 | 0 |
| Computers | 18.3 | 2 | 0 |
| Resources | 18.3 | 2 | 0 |
| Journal of Low Power Electronics and Applications | 18.3 | 2 | 0 |
| Systems | 18.3 | 2 | 0 |
| European Journal of Investigation in Health, Psychology and Education | 18.3 | 2 | 0 |
| Robotics | 18.3 | 2 | 0 |
| Climate | 18.3 | 2 | 0 |
| Education Sciences | 18.3 | 2 | 0 |
| International Journal of Financial Studies | 18.3 | 2 | 0 |
| Dentistry Journal | 18.3 | 2 | 0 |
| Journal of Manufacturing and Materials Processing | 18.3 | 2 | 0 |
| Computation | 18.3 | 2 | 0 |
| Challenges | 18.3 | 2 | 0 |
| Agriculture | 18.3 | 2 | 0 |
| Epigenomes | 18.3 | 2 | 0 |
| Journal of Open Innovation: Technology, Market and Complexity | 18.3 | 2 | 0 |
| Universe | 18.3 | 2 | 0 |
| Biomedicines | 18.3 | 2 | 0 |
| Mathematics | 18.3 | 2 | 0 |
| Languages | 18.3 | 2 | 0 |
| Aerospace | 18.3 | 2 | 0 |
| Econometrics | 18.3 | 2 | 0 |
| Journal of Sensor and Actuator Networks | 18.3 | 2 | 0 |
| Horticulturae | 18.3 | 2 | 0 |
| Fermentation | 18.3 | 2 | 0 |
| C | 18.3 | 2 | 0 |
| Fluids | 18.3 | 2 | 0 |
| Gels | 18.3 | 2 | 0 |
| Geriatrics | 18.3 | 2 | 0 |
| Beverages | 18.3 | 2 | 0 |
| Hydrology | 18.3 | 2 | 0 |
| ChemEngineering | 18.3 | 2 | 0 |
| Logistics | 18.3 | 2 | 0 |
| Inorganics | 18.3 | 2 | 0 |
| Biomimetics | 18.3 | 2 | 0 |
| Genealogy | 18.3 | 2 | 0 |
| Safety | 18.3 | 2 | 0 |
| Journal of Imaging | 18.3 | 2 | 0 |
| Recycling | 18.3 | 2 | 0 |
| Batteries | 18.3 | 2 | 0 |
| Instruments | 18.3 | 2 | 0 |
| Condensed Matter | 18.3 | 2 | 0 |
| Fishes | 18.3 | 2 | 0 |
| Cryptography | 18.3 | 2 | 0 |
| Quantum Beam Science | 18.3 | 2 | 0 |
| Infrastructures | 18.3 | 2 | 0 |
| Vision | 18.3 | 2 | 0 |
| Journal of Functional Morphology and Kinesiology | 18.3 | 2 | 0 |
| Inventions | 18.3 | 2 | 0 |
| Urban Science | 18.3 | 2 | 0 |
| Tropical Medicine and Infectious Disease | 18.3 | 2 | 0 |
| Multimodal Technologies and Interaction | 18.3 | 2 | 0 |
| Journal of Composites Science | 18.3 | 2 | 0 |
| Big Data and Cognitive Computing | 18.3 | 2 | 0 |
| International Journal of Turbomachinery, Propulsion and Power | 18.3 | 2 | 0 |
| Polymers | 18.3 | 2 | 0 |
| Microsoft Academic Graph | 18.3 | 2 | 0 |
| Ceramics | 18.3 | 2 | 0 |
| Particles | 18.3 | 2 | 0 |
| Heritage | 18.3 | 2 | 0 |
| Drones | 18.3 | 2 | 0 |
| J | 18.3 | 2 | 0 |
| Gastrointestinal Disorders | 18.3 | 2 | 0 |
| Quaternary | 18.3 | 2 | 0 |
| Stats | 18.3 | 2 | 0 |
| Reports | 18.3 | 2 | 0 |
| Clean Technologies | 18.3 | 2 | 0 |
| Vibration | 18.3 | 2 | 0 |
| Acoustics | 18.3 | 2 | 0 |
| Fire | 18.3 | 2 | 0 |
| Colloids and Interfaces | 18.3 | 2 | 0 |
| High-Throughput | 18.3 | 2 | 0 |
| Applied System Innovation | 18.3 | 2 | 0 |
| World Electric Vehicle Journal | 18.3 | 2 | 0 |
| Plasma | 18.3 | 2 | 0 |
| Surfaces | 18.3 | 2 | 0 |
| Methods and Protocols | 18.3 | 2 | 0 |
| AgriEngineering | 18.3 | 2 | 0 |
| Clocks & Sleep | 18.3 | 2 | 0 |
| Designs | 18.3 | 2 | 0 |
| Soil Systems | 18.3 | 2 | 0 |
| Fractal and Fractional | 18.3 | 2 | 0 |
| monday.com | 18.3 | 2 | 0 |
| Microsoft Saudi | 18.3 | 2 | 0 |
| Sinusitis | 18.3 | 2 | 0 |
| Eureka - Engineering Materials and Design | 18.3 | 2 | 0 |
| IBM Cloud | 18.3 | 2 | 0 |
| Forecasting | 18.3 | 2 | 0 |
| Dairy | 18.3 | 2 | 0 |
| Automation | 18.3 | 2 | 0 |
| Journal of Cybersecurity and Privacy | 18.3 | 2 | 0 |
| Reactions | 18.3 | 2 | 0 |
| Journal of Nanotheranostics | 18.3 | 2 | 0 |
| Nitrogen | 18.3 | 2 | 0 |
| Signals | 18.3 | 2 | 0 |
| Quantum Reports | 18.3 | 2 | 0 |
| Plejd | 18.3 | 2 | 0 |
| Journal of Respiration | 18.3 | 2 | 0 |
| Journal of Zoological and Botanical Gardens | 18.3 | 2 | 0 |
| Psychiatry International | 18.3 | 2 | 0 |
| Microsoft Mesh | 18.3 | 2 | 0 |
| IBM Cloud Object Storage | 18.3 | 2 | 0 |
| Salesforce | 18.3 | 2 | 0 |
| Microsoft Lists | 18.3 | 2 | 0 |
| Microsoft Berlin | 18.3 | 2 | 0 |
| GE Vernova | 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 |
| Microsoft Typography | 18.3 | 2 | 0 |
| Honeywell Indoor Air Quality Laboratory | 18.3 | 2 | 0 |
| NVIDIA Project DIGITS | 18.3 | 2 | 0 |
| Nvidia RTX Pro 6000 Blackwell Workstation Edition | 18.3 | 2 | 0 |
| Microsoft Security Copilot | 18.3 | 2 | 0 |
| machine learning | 17.2 | 0 | 1 |
| Nashville | 17.2 | 0 | 1 |
| David Haussler | 17.2 | 0 | 1 |
| Q92894 | 17.2 | 0 | 1 |
| Corinna Cortes | 17.2 | 0 | 1 |
| Donald Michie | 17.2 | 0 | 1 |
| Yoav Freund | 17.2 | 0 | 1 |
| Leslie Valiant | 17.2 | 0 | 1 |
| International Federation of Robotics | 17.2 | 0 | 1 |
| Weka | 17.2 | 0 | 1 |
| emerging technology | 17.2 | 0 | 1 |
| lazy learning | 17.2 | 0 | 1 |
| explanation-based learning | 17.2 | 0 | 1 |
| Zentrum Zahnärztliche Qualität | 17.2 | 0 | 1 |
| EtherCAT | 17.2 | 0 | 1 |
| palletizer | 17.2 | 0 | 1 |
| artificial neural network | 17.2 | 0 | 1 |
| deep learning | 17.2 | 0 | 1 |
| ISO 9000 family | 17.2 | 0 | 1 |
| Six Sigma | 17.2 | 0 | 1 |
| ensemble learning | 17.2 | 0 | 1 |
| AQUA-Institut | 17.2 | 0 | 1 |
| Jensen Huang | 17.2 | 0 | 1 |
| Talib Kweli | 17.2 | 0 | 1 |
| sales planning | 17.2 | 0 | 1 |
| process optimization | 17.2 | 0 | 1 |
| supervised learning | 17.2 | 0 | 1 |
| computer-aided quality assurance | 17.2 | 0 | 1 |
| Java | 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 |
| Q11278 | 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 |
| Falcon 2000 | 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 |
| Hitachi | 11.5 | 1 | 0 |
| IBM Lotus Sametime | 11.5 | 1 | 0 |
| Siemens | 11.5 | 1 | 0 |
| IBM WebSphere Application Server | 11.5 | 1 | 0 |
| Windows 95 | 11.5 | 1 | 0 |
| AutoCAD | 11.5 | 1 | 0 |
| Oracle WebLogic Server | 11.5 | 1 | 0 |
| Larry Ellison | 11.5 | 1 | 0 |
| Simon Peyton Jones | 11.5 | 1 | 0 |
| arXiv | 11.5 | 1 | 0 |
| IBM Lotus SmartSuite | 11.5 | 1 | 0 |
| Molecules | 11.5 | 1 | 0 |
| Bing Maps Platform | 11.5 | 1 | 0 |
| Windows Glyph List 4 | 11.5 | 1 | 0 |
| Jakarta Transactions | 11.5 | 1 | 0 |
| MATLAB | 11.5 | 1 | 0 |
| Gartner | 11.5 | 1 | 0 |
| Windows Installer | 11.5 | 1 | 0 |
| Jakarta Server Pages | 11.5 | 1 | 0 |
| Dassault Rafale | 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 |
| Autodesk 3ds Max | 11.5 | 1 | 0 |
| PL/I | 11.5 | 1 | 0 |
| Q223653 | 11.5 | 1 | 0 |
| Tecnomatix | 11.5 | 1 | 0 |
| Microsoft Defender Antivirus | 11.5 | 1 | 0 |
| Windows XP Professional x64 Edition | 11.5 | 1 | 0 |
| Group Policy | 11.5 | 1 | 0 |
| MSX BASIC | 11.5 | 1 | 0 |
| IBM AIX | 11.5 | 1 | 0 |
| Autodesk Maya | 11.5 | 1 | 0 |
| Notepad | 11.5 | 1 | 0 |
| Oracle Financial Services Software | 11.5 | 1 | 0 |
| webMethods | 11.5 | 1 | 0 |
| VSE | 11.5 | 1 | 0 |
| Alten | 11.5 | 1 | 0 |
| IQVIA Holdings, Inc. | 11.5 | 1 | 0 |
| TopLink | 11.5 | 1 | 0 |
| ActiveSync | 11.5 | 1 | 0 |
| ADABAS | 11.5 | 1 | 0 |
| Q368338 | 11.5 | 1 | 0 |
| Connect:Direct | 11.5 | 1 | 0 |