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In this paper, we present a distributed computing framework for image classification towards the current challenge of image big data due to enormous streaming image data sources, such as image sharing over online social network and massive video surveillance streams from ubiquitous cameras all over our daily life. The proposed framework consists of four modules aiming at feature extraction, dimension...
Investigating the root causes of abnormal events is a crucial task for an industrial process. When process faults are detected, isolating the faulty variables provides additional information for investigating the root causes of the faults. Numerous data-driven approaches require the datasets of known faults, which may not exist for some industrial processes, to isolate the faulty variables. The contribution...
Sparse Representation-based Classification (SRC) is a newly introduced algorithm for face recognition, notable for its robust performance to occlusions and corruptions. Local Binary Patterns (LBP) is a very powerful method to describe the texture and shape of images. In this paper, we propose a novel method for facial expression recognition based on sparse representation of LBP features. Extensive...
Sparse representation in compressed sensing is a hot topic in signal processing and artificial intelligence due to its success in various applications. A general classification algorithm based on sparse representation theory named Sparse Representation Classification (SRC) was successfully applied in face recognition. In this paper, we research the issue of facial expression recognition (FER) via...
Codon usage preference and the highly expressed genes have strong correlations occur in many organisms. Codon usage preference of viruses may evolve much similar with its infected host to increase the fitness. In this study we investigated differences in codon usage preferences among influenza A H1N1 viruses which infected avian, swine and human, and may cause major pandemic around world. The relative...
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