L
Lichuan Gu
Researcher at Anhui Agricultural University
Publications - 27
Citations - 661
Lichuan Gu is an academic researcher from Anhui Agricultural University. The author has contributed to research in topics: Computer science & Pattern recognition (psychology). The author has an hindex of 5, co-authored 17 publications receiving 262 citations. Previous affiliations of Lichuan Gu include University of North Texas.
Papers
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Journal ArticleDOI
Feature selection based on artificial bee colony and gradient boosting decision tree
Haidi Rao,Xianzhang Shi,Ahoussou Kouassi Rodrigue,Juanjuan Feng,Yingchun Xia,Mohamed Elhoseny,Xiaohui Yuan,Lichuan Gu +7 more
TL;DR: Experimental results demonstrate that the proposed feature selection method effectively reduces the dimensions of the dataset and achieves superior classification accuracy using the selected features.
Journal ArticleDOI
A review of deep learning methods for semantic segmentation of remote sensing imagery
TL;DR: A summary of the fundamental deep neural network architectures and the most recent developments of deep learning methods for semantic segmentation of remote sensing imagery including non-conventional data such as hyperspectral images and point clouds are reviewed.
Journal ArticleDOI
Module overlapping structure detection in PPI using an improved link similarity-based Markov clustering algorithm
TL;DR: A Markov clustering algorithm based on link similarity (MLS) is proposed, which cannot only accurately identify the functional modules, but also outperform the original MCL algorithm and the F-measure value improved 5–10% compared with it.
Proceedings ArticleDOI
A Fast and Accurate Retina Image Verification Method Based on Structure Similarity
TL;DR: This paper presents a method based on the Structural Similarity (SSIM) that includes three main phases: a preprocessing phase to enhance the quality of the retinal images, a registration phase to align images for comparison, and an application phase to measure the similarity for verification.
Journal ArticleDOI
A collective entity linking algorithm with parallel computing on large-scale knowledge base
TL;DR: A collective entity linking algorithm based on topic model and graph that has 5.2% improvement in F 1 -measure than AGDISTIS on NLPCC2014 and demonstrates the validity of the proposed method.