Xiaofeng Li
International University, Cambodia
16 Papers
20 Citations
Xiaofeng Li is an academic researcher from International University, Cambodia. The author has contributed to research in topics: Computer science & Convolutional neural network. The author has an hindex of 5, co-authored 15 publications.
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Papers
An Improved Collaborative Filtering Recommendation Algorithm and Recommendation Strategy
Xiaofeng Li,Dong Li +1 more
TL;DR: An improved collaborative filtering algorithm is proposed, the community detection algorithm is investigated, and two overlapping community detection algorithms based on the central node and k-based faction are proposed, which effectively mine the community in the network.
Edge detection algorithm of cancer image based on deep learning.
TL;DR: The experimental results show that the three-dimensional reconstruction accuracy of the proposed algorithm is about 95%, the fitness of the optimization coefficient is high, the algorithm has a strong edge information detection ability, and the output result smoothness and the accuracy of edge feature detection are high, which can effectively realize the detection of cancer image edge.
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Intelligent medical heterogeneous big data set balanced clustering using deep learning
TL;DR: The results show that the proposed algorithm has the advantages of small data cluster center offset distance, short clustering time, low energy consumption, high Macro-F1 value and NMI value, and the accuracy of clustering can be as high as 95%, the calculational cost is low, which has certain advantages.
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Gesture Recognition Algorithm of Human Motion Target Based on Deep Neural Network
TL;DR: In this article, a gesture recognition algorithm of human motion based on deep neural network was proposed, where the Kinect interface equipment was used to collect the coordinate information of human skeleton joints, extract the characteristics of motion gesture nodes, and construct the whole structure of key node network by using deep neural networks.
Automatic Annotation Algorithm of Medical Radiological Images using Convolutional Neural Network
TL;DR: The results show that the image segmentation effect of the proposed algorithm is good, the number of feature points is accurate, and the accuracy of multi-resolution feature extraction is as high as 98.7%.
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