IEEE

Adaptive Task Scheduling in Fog Computing Using Federated DQN and K-Means Clustering

Abstract: Task scheduling in distributed cloud and fog computing applications must be efficient to optimize resource utilization, minimize latency, and comply with strict service level agreements. The ...
GitHub

STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...
Journal of Medical Internet Research

Large Language Model–Driven Knowledge Graph Construction in Sepsis Care Using Multicenter Clinical Databases: Development and Usability Study

Conclusions: This study represents a pioneering effort in using LLMs, particularly GPT-4.0, to construct a comprehensive sepsis knowledge graph. The innovative application of prompt engineering, ...
IEEE

Smooth Multiple Kernel k-Means via Underlying Graph Filtering

Abstract: Clustering has attracted more and more attention as one of the most fundamental techniques in the field of unsupervised learning. To deal with nonlinear problems, clustering methods have ...
GitHub

Data Structures and Algorithms Final Project

Dijkstra Shortest Path: Compute shortest paths on a weighted graph using a binary heap (priority queue). Binary Heap Visualization: Convert an array-based binary heap into a binary tree structure and ...