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GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management in Agriculture
In the rapidly evolving field of artificial intelligence (AI), the application of large language models (LLMs) in agriculture, particularly in pest management, remains nascent.
Spatio-Temporal Similarity Measure based Multi-Task Learning for Predicting Alzheimer's Disease Progression using MRI Data
The experimental results show that compared with directly ROI based learning, our proposed method is more effective in predicting disease progression.
Embedding-based Retrieval with LLM for Effective Agriculture Information Extracting from Unstructured Data
Pest identification is a crucial aspect of pest control in agriculture.
Closing the Gap between Client and Global Model Performance in Heterogeneous Federated Learning
The heterogeneity of hardware and data is a well-known and studied problem in the community of Federated Learning (FL) as running under heterogeneous settings.
MFL_COVID19: Quantifying Country-based Factors affecting Case Fatality Rate in Early Phase of COVID-19 Epidemic via Regularised Multi-task Feature Learning
The fused sparse group Lasso (FSGL) method allows the simultaneous selection of a common set of country-based factors for multiple time points of COVID-19 epidemic and also enables incorporating temporal smoothness of each factor over the whole early phase period.
