Search Results for author: Keqin Li

Found 13 papers, 1 papers with code

Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network

no code implementations19 Jan 2021 Jianguo Chen, Kenli Li, Keqin Li, Philip S. Yu, Zeng Zeng

The BSDP system contains four modules: bicycle drop-off location clustering, bicycle-station graph modeling, bicycle-station location prediction, and bicycle-station layout recommendation.

Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems using Multi-objective Reinforcement Learning

no code implementations19 Jan 2021 Jianguo Chen, Kenli Li, Keqin Li, Philip S. Yu, Zeng Zeng

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.

Stochastic Client Selection for Federated Learning with Volatile Clients

no code implementations17 Nov 2020 Tiansheng Huang, Weiwei Lin, Li Shen, Keqin Li, Albert Y. Zomaya

Federated Learning (FL), arising as a novel secure learning paradigm, has received notable attention from the public.

Fairness Federated Learning

An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee

no code implementations3 Nov 2020 Tiansheng Huang, Weiwei Lin, Wentai Wu, Ligang He, Keqin Li, Albert Y. Zomaya

The client selection policy is critical to an FL process in terms of training efficiency, the final model's quality as well as fairness.

Distributed Computing Fairness +1

A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19

no code implementations4 Jul 2020 Jianguo Chen, Kenli Li, Zhaolei Zhang, Keqin Li, Philip S. Yu

The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak.

Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing

no code implementations12 Apr 2019 Jianguo Chen, Kenli Li, Qingying Deng, Keqin Li, Philip S. Yu

We implement the proposed DIVS system and address the problems of parallel training, model synchronization, and workload balancing.

Edge-computing

Gradient Scheduling with Global Momentum for Non-IID Data Distributed Asynchronous Training

no code implementations21 Feb 2019 Chengjie Li, Ruixuan Li, Haozhao Wang, Yuhua Li, Pan Zhou, Song Guo, Keqin Li

Distributed asynchronous offline training has received widespread attention in recent years because of its high performance on large-scale data and complex models.

A High-Performance CNN Method for Offline Handwritten Chinese Character Recognition and Visualization

1 code implementation30 Dec 2018 Pavlo Melnyk, Zhiqiang You, Keqin Li

Recent researches introduced fast, compact and efficient convolutional neural networks (CNNs) for offline handwritten Chinese character recognition (HCCR).

Offline Handwritten Chinese Character Recognition

A Bi-layered Parallel Training Architecture for Large-scale Convolutional Neural Networks

no code implementations17 Oct 2018 Jianguo Chen, Kenli Li, Kashif Bilal, Xu Zhou, Keqin Li, Philip S. Yu

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.

Distributed Computing

A Disease Diagnosis and Treatment Recommendation System Based on Big Data Mining and Cloud Computing

no code implementations17 Oct 2018 Jianguo Chen, Kenli Li, Huigui Rong, Kashif Bilal, Nan Yang, Keqin Li

It is crucial to provide compatible treatment schemes for a disease according to various symptoms at different stages.

A Periodicity-based Parallel Time Series Prediction Algorithm in Cloud Computing Environments

no code implementations17 Oct 2018 Jianguo Chen, Kenli Li, Huigui Rong, Kashif Bilal, Keqin Li, Philip S. Yu

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.

Distributed Computing Time Series +1

A novel graph structure for salient object detection based on divergence background and compact foreground

no code implementations30 Nov 2017 Chenxing Xia, Hanling Zhang, Keqin Li

Different from prior methods, we calculate the saliency value of each node based on the relationship between the corresponding node and the virtual node.

RGB Salient Object Detection Saliency Detection +1

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