AIMC Topic: Models, Theoretical

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Zero-shot performance analysis of large language models in sumrate maximization.

PloS one
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...

Using machine learning to forecast conflict events for use in forced migration models.

Scientific reports
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 ...

Dual prompt personalized federated learning in foundation models.

Scientific reports
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...

A bearing fault diagnosis method based on hybrid artificial intelligence models.

PloS one
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...

Evaluating crop yield prediction models in illinois using aquacrop, semi-physical model and artificial neural networks.

Scientific reports
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...

Multi-modal deep learning for intelligent landscape design generation: A novel CBS3-LandGen model.

PloS one
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...

Federated fault diagnosis method for collaborative self-diagnosis and cross-robot peer diagnosis.

PloS one
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...

Physically-constrained evapotranspiration models with machine learning parameterization outperform pure machine learning: Critical role of domain knowledge.

PloS one
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 ...

Optimizing models for the prediction of one step ahead extreme flows to wastewater treatment plants using different synthetic sampling methods.

Journal of environmental management
High-flow events that significantly impact Water Resource Recovery Facility (WRRF) operations are rare, but accurately predicting these flows could improve treatment operations. Data-driven modeling approaches could be used; however, high flow events...

Spatio-Temporal SIR Model of Pandemic Spread During Warfare with Optimal Dual-use Health Care System Administration using Deep Reinforcement Learning.

Disaster medicine and public health preparedness
OBJECTIVES: Large-scale crises, including wars and pandemics, have repeatedly shaped human history, and their simultaneous occurrence presents profound challenges to societies. Understanding the dynamics of epidemic spread during warfare is essential...