Constructing a prior-dependent graph for data clustering and dimension reduction in the edge of AIoT

Guo, Tan; Yu, Keping; Aloqaily, Moayad; Wan, Shaohua

doi:10.1016/j.future.2021.09.044

Cited by 93 publications

(33 citation statements)

References 48 publications

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“…Although the above method solves the chromosome problem to some extent, there are still some limitations [ 27 , 28 , 29 , 30 ]. First, the image quality of the private dataset used in their method is very high.…”

Section: Related Workmentioning

confidence: 99%

SRAS‐net: Low‐resolution chromosome image classification based on deep learning

Liu

Lin

et al. 2022

IET Systems Biology

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Prenatal karyotype diagnosis is important to determine if the foetus has genetic diseases and some congenital diseases. Chromosome classification is an important part of karyotype analysis, and the task is tedious and lengthy. Chromosome classification methods based on deep learning have achieved good results, but if the quality of the chromosome image is not high, these methods cannot learn image features well, resulting in unsatisfactory classification results. Moreover, the existing methods generally have a poor effect on sex chromosome classification. Therefore, in this work, the authors propose to use a super‐resolution network, Self‐Attention Negative Feedback Network, and combine it with traditional neural networks to obtain an efficient chromosome classification method called SRAS‐net. The method first inputs the low‐resolution chromosome images into the super‐resolution network to generate high‐resolution chromosome images and then uses the traditional deep learning model to classify the chromosomes. To solve the problem of inaccurate sex chromosome classification, the authors also propose to use the SMOTE algorithm to generate a small number of sex chromosome samples to ensure a balanced number of samples while allowing the model to learn more sex chromosome features. Experimental results show that our method achieves 97.55% accuracy and is better than state‐of‐the‐art methods.

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Section: Related Workmentioning

confidence: 99%

SRAS‐net: Low‐resolution chromosome image classification based on deep learning

Liu

Lin

et al. 2022

IET Systems Biology

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“… 2013 ; Guo et al. 2022 ). For classification of brain tumors we construct the CNN networks with 4 alternating convolutional layers and max-pooling layers and a dropout layer after each Conv/pooling pair.…”

Section: Methodsmentioning

confidence: 99%

MCNN: a multi-level CNN model for the classification of brain tumors in IoT-healthcare system

Haq

Kumar

et al. 2022

J Ambient Intell Human Comput

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The classification of brain tumors is significantly important for diagnosing and treating brain tumors in IoT healthcare systems. In this work, we have proposed a robust classification model for brain tumors employing deep learning techniques. In the design of the proposed method, an improved Convolutional neural network is used to classify Meningioma, Glioma, and Pituitary types of brain tumors. To test the multi-level convolutional neural network model, brain magnetic resonance image data is utilized. The MCNN model classification results were improved using data augmentation and transfer learning methods. In addition, hold-out and performance evaluation metrics have been employed in the proposed MCNN model. The experimental results show that the proposed model obtained higher outcomes than the state-of-the-art techniques and achieved 99.89% classification accuracy. Due to the higher results of the proposed approach, we recommend it for the identification of brain cancer in IoT-healthcare systems.

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“…The sparse linear representation model based on an over-complete dictionary of primitives or signal atoms is a powerful tool to analyze the data with sparseness property [28][29][30][31]. A sufficiently sparse representation is informative for reasoning and decision-making.…”

Section: Sparse Representation Theory-based Hsi Target Detectionmentioning

confidence: 99%

Meta-Pixel-Driven Embeddable Discriminative Target and Background Dictionary Pair Learning for Hyperspectral Target Detection

et al. 2022

Self Cite

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In hyperspectral target detection, the spectral high-dimensionality, variability, and heterogeneity will pose great challenges to the accurate characterizations of the target and background. To alleviate the problems, we propose a Meta-pixel-driven Embeddable Discriminative target and background Dictionary Pair (MEDDP) learning model by combining low-dimensional embeddable subspace projection and the discriminative target and background dictionary pair learning. In MEDDP, the meta-pixel set is built by taking the merits of homogeneous superpixel segmentation and the local manifold affinity structures, which can significantly reduce the influence of spectral variability and find the most typical and informative prototype spectral signature. Afterward, an embeddable discriminative dictionary pair learning model is established to learn a target and background dictionary pair based on the structural incoherent constraint with embeddable subspace projection. The proposed joint learning strategy can reduce the high-dimensional redundant information and simultaneously enhance the discrimination and compactness of the target and background dictionaries. The proposed MEDDP model is solved by an iterative and alternate optimization algorithm and applied with the meta-pixel-level target detection method. Experimental results on four benchmark HSI datasets indicate that the proposed method can consistently yield promising performance in comparison with some state-of-the-art target detectors.

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Constructing a prior-dependent graph for data clustering and dimension reduction in the edge of AIoT

Cited by 93 publications

References 48 publications

SRAS‐net: Low‐resolution chromosome image classification based on deep learning

SRAS‐net: Low‐resolution chromosome image classification based on deep learning

MCNN: a multi-level CNN model for the classification of brain tumors in IoT-healthcare system

Meta-Pixel-Driven Embeddable Discriminative Target and Background Dictionary Pair Learning for Hyperspectral Target Detection

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