A New Parallel Collision Detection Algorithm Based on Particle Swarm Optimization

COVID-19 has been spread around the world and has caused a huge number of deaths. Early detection of this disease is the most efficient way to prevent its rapid spread. Due to the development of internet technology and edge intelligence, developing an early detection system for COVID-19 in the medical environment of the Internet of Things (IoT) can effectively alleviate the spread of the disease. In this paper, a detection algorithm is developed, which can detect COVID-19 effectively by utilizing the features from Chest X-ray (CXR) images. First, a pre-trained model (ResNet18) is adopted for feature extraction. Then, a discrete social learning particle swarm optimization algorithm (DSLPSO) is proposed for feature selection. By filtering redundant and irrelevant features, the dimensionality of the feature vector is reduced. Finally, the images are classified by a Support Vector Machine (SVM) for COVID-19 detection. Experimental results show that the proposed algorithm can achieve competitive performance with fewer features, which is suitable for edge computing devices with lower computation power.

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Collision detection for virtual environment using particle swarm optimization with adaptive cauchy mutation

Cluster Computing ◽

10.1007/s10586-017-0815-6 ◽

2017 ◽

Vol 20 (2) ◽

pp. 1765-1774 ◽

Cited By ~ 7

Author(s):

Yanni Zou ◽

Peter X. Liu ◽

Chunsheng Yang ◽

Chunquan Li ◽

Qiangqiang Cheng

Keyword(s):

Particle Swarm Optimization ◽

Virtual Environment ◽

Collision Detection ◽

Particle Swarm ◽

Swarm Optimization ◽

Cauchy Mutation

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A Shot Boundary Detection Method Based on PSO-SVM

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.130-134.3821 ◽

2011 ◽

Vol 130-134 ◽

pp. 3821-3825 ◽

Cited By ~ 1

Author(s):

Long Zhao ◽

Xue Mei Sun ◽

Ming Wei Zhang

Keyword(s):

Particle Swarm Optimization ◽

Video Retrieval ◽

Particle Swarm ◽

Boundary Detection ◽

Detection Algorithm ◽

Video Data ◽

Shot Boundary Detection ◽

Support Vector ◽

Swarm Optimization ◽

Shot Boundary

Shot boundary detection (SBD) is the first step which segments video data into elementary shots for content-based video retrieval. In this paper, a shot boundary detection algorithm based on support vector machine (SVM) and particle swarm optimization (PSO) is proposed. First of all, the extracted features of pixel domain and compressed domain are combined to form a multi-dimension feature vector by using the scheme of sliding window. Next, particle swarm optimization with global search capacity is adopted to seek the approximately optimal parameters of radial basis function of SVM. Finally the model trained by the parameters obtained is applied to judge and categorize the frames into cut transitions, gradual transitions and non-transitions. The experimental results on the TREC video set 2001 demonstrate our algorithm is efficient and robust, and it solves the difficulty in parameter selection of SVM well.

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