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  • Open Access


    Broad Federated Meta-Learning of Damaged Objects in Aerial Videos

    Zekai Li1, Wenfeng Wang2,3,4,5,6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2881-2899, 2023, DOI:10.32604/cmes.2023.028670

    Abstract We advanced an emerging federated learning technology in city intelligentization for tackling a real challenge— to learn damaged objects in aerial videos. A meta-learning system was integrated with the fuzzy broad learning system to further develop the theory of federated learning. Both the mixed picture set of aerial video segmentation and the 3D-reconstructed mixed-reality data were employed in the performance of the broad federated meta-learning system. The study results indicated that the object classification accuracy is up to 90% and the average time cost in damage detection is only 0.277 s. Consequently, the broad federated meta-learning system is efficient and… More >

  • Open Access


    Crop Disease Recognition Based on Improved Model-Agnostic Meta-Learning

    Xiuli Si1, Biao Hong1, Yuanhui Hu1, Lidong Chu2,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6101-6118, 2023, DOI:10.32604/cmc.2023.036829

    Abstract Currently, one of the most severe problems in the agricultural industry is the effect of diseases and pests on global crop production and economic development. Therefore, further research in the field of crop disease and pest detection is necessary to address the mentioned problem. Aiming to identify the diseased crops and insect pests timely and accurately and perform appropriate prevention measures to reduce the associated losses, this article proposes a Model-Agnostic Meta-Learning (MAML) attention model based on the meta-learning paradigm. The proposed model combines meta-learning with basic learning and adopts an Efficient Channel Attention (ECA) module. The module follows the… More >

  • Open Access


    Meta-Learning Multi-Scale Radiology Medical Image Super-Resolution

    Liwei Deng1, Yuanzhi Zhang1, Xin Yang2,*, Sijuan Huang2, Jing Wang3,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2671-2684, 2023, DOI:10.32604/cmc.2023.036642

    Abstract High-resolution medical images have important medical value, but are difficult to obtain directly. Limited by hardware equipment and patient’s physical condition, the resolution of directly acquired medical images is often not high. Therefore, many researchers have thought of using super-resolution algorithms for secondary processing to obtain high-resolution medical images. However, current super-resolution algorithms only work on a single scale, and multiple networks need to be trained when super-resolution images of different scales are needed. This definitely raises the cost of acquiring high-resolution medical images. Thus, we propose a multi-scale super-resolution algorithm using meta-learning. The algorithm combines a meta-learning approach with… More >

  • Open Access


    Multi-Task Deep Learning with Task Attention for Post-Click Conversion Rate Prediction

    Hongxin Luo, Xiaobing Zhou*, Haiyan Ding, Liqing Wang

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3583-3593, 2023, DOI:10.32604/iasc.2023.036622

    Abstract Online advertising has gained much attention on various platforms as a hugely lucrative market. In promoting content and advertisements in real life, the acquisition of user target actions is usually a multi-step process, such as impression→click→conversion, which means the process from the delivery of the recommended item to the user’s click to the final conversion. Due to data sparsity or sample selection bias, it is difficult for the trained model to achieve the business goal of the target campaign. Multi-task learning, a classical solution to this problem, aims to generalize better on the original task given several related tasks by… More >

  • Open Access


    SW-Net: A novel few-shot learning approach for disease subtype prediction


    BIOCELL, Vol.47, No.3, pp. 569-579, 2023, DOI:10.32604/biocell.2023.025865

    Abstract Few-shot learning is becoming more and more popular in many fields, especially in the computer vision field. This inspires us to introduce few-shot learning to the genomic field, which faces a typical few-shot problem because some tasks only have a limited number of samples with high-dimensions. The goal of this study was to investigate the few-shot disease sub-type prediction problem and identify patient subgroups through training on small data. Accurate disease sub-type classification allows clinicians to efficiently deliver investigations and interventions in clinical practice. We propose the SW-Net, which simulates the clinical process of extracting the shared knowledge from a… More >

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