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Found 12 papers, 2 papers with code
Graph Neural Networks for Protein-Protein Interactions - A Short Survey
Protein-protein interactions (PPIs) play key roles in a broad range of biological processes.
Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions
We explore why blockchain is applicable to FL, how it can be implemented, and the challenges and existing solutions for its integration.
Online Parameter Identification of Generalized Non-cooperative Game
This work studies the parameter identification problem of a generalized non-cooperative game, where each player's cost function is influenced by an observable signal and some unknown parameters.
MMTSA: Multimodal Temporal Segment Attention Network for Efficient Human Activity Recognition
The efficiency evaluation on an edge device showed that MMTSA achieved significantly better accuracy, lower computational load, and lower inference latency than SOTA methods.
Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems using Multi-objective Reinforcement Learning
We model the DL-PBS system from the perspective of CPS and use deep learning to predict the layout of bicycle parking spots and the dynamic demand of bicycle dispatching.
Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network
The BSDP system contains four modules: bicycle drop-off location clustering, bicycle-station graph modeling, bicycle-station location prediction, and bicycle-station layout recommendation.
A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19
The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak.
A Domain Adaptive Density Clustering Algorithm for Data with Varying Density Distribution
However, clustering algorithms based on density peak have limited clustering effect on data with varying density distribution (VDD), equilibrium distribution (ED), and multiple domain-density maximums (MDDM), leading to the problems of sparse cluster loss and cluster fragmentation.
Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing
We implement the proposed DIVS system and address the problems of parallel training, model synchronization, and workload balancing.
A Periodicity-based Parallel Time Series Prediction Algorithm in Cloud Computing Environments
In this paper, a Periodicity-based Parallel Time Series Prediction (PPTSP) algorithm for large-scale time-series data is proposed and implemented in the Apache Spark cloud computing environment.
A Disease Diagnosis and Treatment Recommendation System Based on Big Data Mining and Cloud Computing
It is crucial to provide compatible treatment schemes for a disease according to various symptoms at different stages.
A Bi-layered Parallel Training Architecture for Large-scale Convolutional Neural Networks
In this paper, we focus on the time-consuming training process of large-scale CNNs and propose a Bi-layered Parallel Training (BPT-CNN) architecture in distributed computing environments.
