AIMC Topic: Models, Theoretical

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Evaluation of coseismic landslide susceptibility by combining Newmark model and XGBoost algorithm.

PloS one Aug 11, 2025
Coseismic landslides are among the most perilous geological disasters in hilly places after earthquakes. Precise assessment of coseismic landslide susceptibility is crucial for forecasting the effects of landslides and alleviating subsequent tragedie...
Models, Theoretical Monte Carlo Method Machine Learning Algorithms Landslides Disasters Earthquakes Boosting Machine Learning Algorithms
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Digital Twins for Personalized Medicine Require Epidemiological Data and Mathematical Modeling: Viewpoint.

Journal of medical Internet research Aug 5, 2025
Digital twin (DT) technology is revolutionizing clinical practice by integrating diverse epidemiological data sources to create dynamic, patient-specific simulations. By leveraging data from genomics, proteomics, imaging, sociodemographics, and real-...
Machine Learning Precision Medicine Models, Theoretical Bayes Theorem Humans Artificial Intelligence
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Zero-shot performance analysis of large language models in sumrate maximization.

PloS one Aug 4, 2025
Large language models have revolutionized the field of natural language processing and are now becoming a one-stop solution to various tasks. In the field of Networking, LLMs can also play a major role when it comes to resource optimization and shari...
Language Models, Theoretical Humans Algorithms Natural Language Processing Large Language Models
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Using machine learning to forecast conflict events for use in forced migration models.

Scientific reports Aug 2, 2025
Forecasting the movement of populations during conflict outbreaks remains a significant challenge in contemporary humanitarian efforts. Accurate predictions of displacement patterns are crucial for improving the delivery of aid to refugees and other ...
Emigration and Immigration Refugees Models, Theoretical Armed Conflicts Humans Machine Learning Forecasting
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Dual prompt personalized federated learning in foundation models.

Scientific reports Jul 31, 2025
Personalized federated learning (PFL) has garnered significant attention for its ability to address heterogeneous client data distributions while preserving data privacy. However, when local client data is limited, deep learning models often suffer f...
Models, Theoretical Federated Learning Machine Learning Humans Deep Learning
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A bearing fault diagnosis method based on hybrid artificial intelligence models.

PloS one Jul 31, 2025
The working state of rolling bearing severely affects the performance of industrial equipment. Addressing the issue of that the difficulty of incipient weak signals feature extraction influences the rolling bearing diagnosis accuracy, an efficient be...
Models, Theoretical Algorithms Artificial Intelligence Deep Learning Humans Neural Networks, Computer
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Evaluating crop yield prediction models in illinois using aquacrop, semi-physical model and artificial neural networks.

Scientific reports Jul 28, 2025
Crop yield is important for agricultural productivity and the country's economy. While crop yield estimation is an essential aspect of modern agriculture, it continues to be one of the most challenging tasks to manage effectively. Corn and soybean ar...
Neural Networks, Computer Models, Theoretical Illinois Agriculture Crops, Agricultural Zea mays Glycine max
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Multi-modal deep learning for intelligent landscape design generation: A novel CBS3-LandGen model.

PloS one Jul 24, 2025
With the acceleration of the global urbanization process, landscape design is facing increasingly complex challenges. Traditional manual design methods are gradually unable to meet the needs for efficiency, precision, and sustainability. To address t...
Urbanization Models, Theoretical Conservation of Natural Resources Humans Deep Learning
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Federated fault diagnosis method for collaborative self-diagnosis and cross-robot peer diagnosis.

PloS one Jul 23, 2025
In multi-robot collaboration, individual failures can propagate to other robots due to the topological coupling between them. Existing fault diagnosis models are designed for single robots and fail to meet the practical requirements of multi-robot sc...
Models, Theoretical Robotics Cooperative Behavior Machine Learning Algorithms Humans
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Physically-constrained evapotranspiration models with machine learning parameterization outperform pure machine learning: Critical role of domain knowledge.

PloS one Jul 23, 2025
Physics-informed machine learning techniques have emerged to tackle challenges inherent in pure machine learning (ML) approaches. One such technique, the hybrid approach, has been introduced to estimate terrestrial evapotranspiration (ET), a crucial ...
Models, Theoretical Plant Transpiration Algorithms Machine Learning Water
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