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      Analysis of the Relevance Environment between Marxist Philosophy and System Theory Based on Deep Learning

      research-article
      Journal of Environmental and Public Health
      Hindawi

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          Abstract

          In social science and natural science, MP (Marxist Philosophy) has played an active role in promoting its development, and MP also guides people's practice and understanding. There is an inevitable connection with system theory MP. In a sense, both system theory and PMbelong to methodology and both contain the viewpoints of movement and development. In this paper, various text features in natural scenes are discussed in detail, and the original vector is studied by using CNN (Convective Neural Network) of DL (Deep Learning), so as to construct a one-dimensional text vector and realize the mutual influence and continuous optimization of feature extraction and text clustering. The experimental results show that under the condition of calculating the current cosine similarity measure, the accuracy rate is the highest, reaching 93.67%. This algorithm can effectively improve its performance in text classification tasks on large data sets.

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          Most cited references23

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          Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm

          We tested the use of a deep learning algorithm to classify the clinical images of 12 skin diseases-basal cell carcinoma, squamous cell carcinoma, intraepithelial carcinoma, actinic keratosis, seborrheic keratosis, malignant melanoma, melanocytic nevus, lentigo, pyogenic granuloma, hemangioma, dermatofibroma, and wart. The convolutional neural network (Microsoft ResNet-152 model; Microsoft Research Asia, Beijing, China) was fine-tuned with images from the training portion of the Asan dataset, MED-NODE dataset, and atlas site images (19,398 images in total). The trained model was validated with the testing portion of the Asan, Hallym and Edinburgh datasets. With the Asan dataset, the area under the curve for the diagnosis of basal cell carcinoma, squamous cell carcinoma, intraepithelial carcinoma, and melanoma was 0.96 ± 0.01, 0.83 ± 0.01, 0.82 ± 0.02, and 0.96 ± 0.00, respectively. With the Edinburgh dataset, the area under the curve for the corresponding diseases was 0.90 ± 0.01, 0.91 ± 0.01, 0.83 ± 0.01, and 0.88 ± 0.01, respectively. With the Hallym dataset, the sensitivity for basal cell carcinoma diagnosis was 87.1% ± 6.0%. The tested algorithm performance with 480 Asan and Edinburgh images was comparable to that of 16 dermatologists. To improve the performance of convolutional neural network, additional images with a broader range of ages and ethnicities should be collected.
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            Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing

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              Is Open Access

              Deep hybrid: Multi-graph neural network collaboration for hyperspectral image classification

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                Author and article information

                Contributors
                Journal
                J Environ Public Health
                J Environ Public Health
                jeph
                Journal of Environmental and Public Health
                Hindawi
                1687-9805
                1687-9813
                2022
                31 July 2022
                : 2022
                : 6322272
                Affiliations
                Mudanjiang Normal University, Mudanjiang 157011, China
                Author notes

                Academic Editor: Zhao Kaifa

                Author information
                https://orcid.org/0000-0003-4931-6092
                Article
                10.1155/2022/6322272
                9357689
                2398e1d3-355e-43cb-b656-f0560594be37
                Copyright © 2022 Xiaoming Jiang.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 1 June 2022
                : 26 June 2022
                : 28 June 2022
                Categories
                Research Article

                Public health
                Public health

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