Papers Hybrid Machine Learning
“Hybrid Machine Learning” 태그가 달린 논문 103편 · 필터 해제
Uncertainty in Supply Chain Digital Twins: A Quantum-Classical Hybrid Approach
This study investigates uncertainty quantification (UQ) using quantum-classical hybrid machine learning (ML) models for applications in complex and dynamic fields, such as attaining resiliency in supply chain digital twi…
Decision MakingHybrid Machine LearningOutlier DetectionUncertainty QuantificationAccident Impact Prediction based on a deep convolutional and recurrent neural network model
Traffic accidents pose a significant threat to public safety, resulting in numerous fatalities, injuries, and a substantial economic burden each year. The development of predictive models capable of real-time forecasting…
Hybrid Machine LearningCarbon price fluctuation prediction using blockchain information A new hybrid machine learning approach
In this study, the novel hybrid machine learning approach is proposed in carbon price fluctuation prediction. Specifically, a research framework integrating DILATED Convolutional Neural Networks (CNN) and Long Short-Term…
Hybrid Machine LearningL2 RegularizationReconstructing dynamics from sparse observations with no training on target system
In applications, an anticipated situation is where the system of interest has never been encountered before and sparse observations can be made only once. Can the dynamics be faithfully reconstructed from the limited obs…
Hybrid Machine LearningTime Series AnalysisExploring the impact of virtual reality user engagement on tourist behavioral response integrated an environment concern of touristic travel perspective: A new hybrid machine learning approach
Due to the impact of the COVID-19 pandemic, new attractions ways are tended to be adapted by compelling sites to provide tours product and services, such as virtual reality (VR) to visitors. Based on a systematic human-c…
Hybrid Machine LearningRemaining Useful Life Prediction for Batteries Utilizing an Explainable AI Approach with a Predictive Application for Decision-Making
Accurately estimating the Remaining Useful Life (RUL) of a battery is essential for determining its lifespan and recharge requirements. In this work, we develop machine learning-based models to predict and classify batte…
Decision Makingenergy managementEnsemble LearningHybrid Machine LearningFlight Delay Prediction using Hybrid Machine Learning Approach: A Case Study of Major Airlines in the United States
The aviation industry has experienced constant growth in air traffic since the deregulation of the U.S. airline industry in 1978. As a result, flight delays have become a major concern for airlines and passengers, leadin…
Hybrid Machine LearningShort-term Wind Speed Forecasting for Power Integration in Smart Grids based on Hybrid LSSVM-SVMD Method
Owing to its minimal pollution and efficient energy use, wind energy has become one of the most widely exploited renewable energy resources. The successful integration of wind power into the grid system is contingent upo…
Hybrid Machine LearningHybrid Machine Learning Approach For Real-Time Malicious Url Detection Using Som-Rmo And Rbfn With Tabu Search Optimization
The proliferation of malicious URLs has become a significant threat to internet security, encompassing SPAM, phishing, malware, and defacement attacks. Traditional detection methods struggle to keep pace with the evolvin…
Hybrid Machine LearningPlant Doctor: A hybrid machine learning and image segmentation software to quantify plant damage in video footage
Artificial intelligence has significantly advanced the automation of diagnostic processes, benefiting various fields including agriculture. This study introduces an AI-based system for the automatic diagnosis of urban st…
Computational EfficiencyDiagnosticHybrid Machine LearningImage Segmentation+1Hybrid Machine Learning Approach for Cyberattack Mitigation of Parallel Converters in a DC Microgrid
Cyberattack susceptibilities are introduced as the communication requirement increases with the incorporation of more renewable energy sources into DC microgrids. Parallel DC-DC converters are utilized to provide high cu…
Hybrid Machine LearningA novel method for identifying rice seed purity based on hybrid machine learning algorithms
In the grain industry, the identification of seed purity is a crucial task as it is an important factor in evaluating the quality of seeds. For rice seeds, this property allows for the reduction of unexpected influences …
Hybrid Machine LearningPrediction Beyond the Medium Range with an Atmosphere-Ocean Model that Combines Physics-based Modeling and Machine Learning
This paper explores the potential of a hybrid modeling approach that combines machine learning (ML) with conventional physics-based modeling for weather prediction beyond the medium range. It extends the work of Arcomano…
Hybrid Machine LearningPredicting Ship Responses in Different Seaways using a Generalizable Force Correcting Machine Learning Method
A machine learning (ML) method is generalizable if it can make predictions on inputs which differ from the training dataset. For predictions of wave-induced ship responses, generalizability is an important consideration …
Hybrid Machine LearningGeneralization capabilities and robustness of hybrid models grounded in physics compared to purely deep learning models
This study investigates the generalization capabilities and robustness of purely deep learning (DL) models and hybrid models based on physical principles in fluid dynamics applications, specifically focusing on iterative…
Deep LearningHybrid Machine LearningTime Series ForecastingOn-board classification of underwater images using hybrid classical-quantum CNN based method
Underwater images taken from autonomous underwater vehicles (AUV's) often suffer from low light, high turbidity, poor contrast, motion-blur and excessive light scattering and hence require image enhancement techniques fo…
Autonomous VehiclesGPUHybrid Machine Learningimage-classification+5A Novel Algorithm for Digital Lithological Mapping-Case Studies in Sri Lanka's Mineral Exploration
Conventional manual lithological mapping (MLM) through field surveys are resource-extensive and time-consuming. Digital lithological mapping (DLM), harnessing remotely sensed spectral imaging techniques, provides an effe…
Hybrid Machine LearningX-Ray Diffraction (XRD)Stochastic parameter reduced-order model based on hybrid machine learning approaches
Establishing appropriate mathematical models for complex systems in natural phenomena not only helps deepen our understanding of nature but can also be used for state estimation and prediction. However, the extreme compl…
Computational EfficiencyHybrid Machine LearningState EstimationEnhancing Traffic Incident Management with Large Language Models: A Hybrid Machine Learning Approach for Severity Classification
This research showcases the innovative integration of Large Language Models into machine learning workflows for traffic incident management, focusing on the classification of incident severity using accident reports. By …
ClassificationFeature EngineeringHybrid Machine LearningLanguage Modelling+1Hybrid Machine Learning techniques in the management of harmful algal blooms impact
Harmful algal blooms (HABs) are episodes of high concentrations of algae that are potentially toxic for human consumption. Mollusc farming can be affected by HABs because, as filter feeders, they can accumulate high conc…
Hybrid Machine LearningManagement