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  • Open Access


    Sea-Land Segmentation of Remote Sensing Images Based on SDW-UNet

    Tianyu Liu1,3,4, Pengyu Liu1,2,3,4,*, Xiaowei Jia5, Shanji Chen2, Ying Ma2, Qian Gao1,3,4

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1033-1045, 2023, DOI:10.32604/csse.2023.028225

    Abstract Image segmentation of sea-land remote sensing images is of great importance for downstream applications including shoreline extraction, the monitoring of near-shore marine environment, and near-shore target recognition. To mitigate large number of parameters and improve the segmentation accuracy, we propose a new Squeeze-Depth-Wise UNet (SDW-UNet) deep learning model for sea-land remote sensing image segmentation. The proposed SDW-UNet model leverages the squeeze-excitation and depth-wise separable convolution to construct new convolution modules, which enhance the model capacity in combining multiple channels and reduces the model parameters. We further explore the effect of position-encoded information in NLP (Natural Language Processing) domain on sea-land… More >

  • Open Access


    Log Anomaly Detection Based on Hierarchical Graph Neural Network and Label Contrastive Coding

    Yong Fang, Zhiying Zhao, Yijia Xu*, Zhonglin Liu

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4099-4118, 2023, DOI:10.32604/cmc.2023.033124

    Abstract System logs are essential for detecting anomalies, querying faults, and tracing attacks. Because of the time-consuming and labor-intensive nature of manual system troubleshooting and anomaly detection, it cannot meet the actual needs. The implementation of automated log anomaly detection is a topic that demands urgent research. However, the prior work on processing log data is mainly one-dimensional and cannot profoundly learn the complex associations in log data. Meanwhile, there is a lack of attention to the utilization of log labels and usually relies on a large number of labels for detection. This paper proposes a novel and practical detection model… More >

  • Open Access


    Activation Functions Effect on Fractal Coding Using Neural Networks

    Rashad A. Al-Jawfi*

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 957-965, 2023, DOI:10.32604/iasc.2023.031700

    Abstract Activation functions play an essential role in converting the output of the artificial neural network into nonlinear results, since without this nonlinearity, the results of the network will be less accurate. Nonlinearity is the mission of all nonlinear functions, except for polynomials. The activation function must be differentiable for backpropagation learning. This study’s objective is to determine the best activation functions for the approximation of each fractal image. Different results have been attained using Matlab and Visual Basic programs, which indicate that the bounded function is more helpful than other functions. The non-linearity of the activation function is important when… More >

  • Open Access


    A double-edged sword: The HBV-induced non-coding RNAs alterations in hepatocellular carcinoma


    BIOCELL, Vol.47, No.1, pp. 27-32, 2023, DOI:10.32604/biocell.2022.023568

    Abstract Non-coding RNAs are speculated to exert important regulatory functions at the level of gene expression, oncogenesis, and many other pathologies. Hepatitis B virus (HBV) infection is a leading cause of hepatocellular carcinoma (HCC), and some studies have shown that the expression of non-coding RNAs has an assignable effect on the development of HBV-induced HCC. In this context, the functions and molecular mechanisms of the HBVinduced non-coding RNA expression in the development of hepatoma have attracted increasing attention. This review covers the progress in the exploration of the relationship between HBV-induced hepatoma and non-coding RNA expression, cataloging the recent reports about… More >

  • Open Access


    Multiway Relay Based Framework for Network Coding in Multi-Hop WSNs

    Vinod Kumar Menaria1, Anand Nayyar2, Sandeep Kumar3, Ketan Kotecha4,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1199-1216, 2023, DOI:10.32604/cmc.2023.032162

    Abstract In today’s information technology (IT) world, the multi-hop wireless sensor networks (MHWSNs) are considered the building block for the Internet of Things (IoT) enabled communication systems for controlling everyday tasks of organizations and industry to provide quality of service (QoS) in a stipulated time slot to end-user over the Internet. Smart city (SC) is an example of one such application which can automate a group of civil services like automatic control of traffic lights, weather prediction, surveillance, etc., in our daily life. These IoT-based networks with multi-hop communication and multiple sink nodes provide efficient communication in terms of performance parameters… More >

  • Open Access


    Fault Diagnosis of Wind Turbine Generator with Stacked Noise Reduction Autoencoder Based on Group Normalization

    Sihua Wang1,2, Wenhui Zhang1,2,*, Gaofei Zheng1,2, Xujie Li1,2, Yougeng Zhao1,2

    Energy Engineering, Vol.119, No.6, pp. 2431-2445, 2022, DOI:10.32604/ee.2022.020779

    Abstract In order to improve the condition monitoring and fault diagnosis of wind turbines, a stacked noise reduction autoencoding network based on group normalization is proposed in this paper. The network is based on SCADA data of wind turbine operation, firstly, the group normalization (GN) algorithm is added to solve the problems of stack noise reduction autoencoding network training and slow convergence speed, and the RMSProp algorithm is used to update the weight and the bias of the autoenccoder, which further optimizes the problem that the loss function swings too much during the update process. Finally, in the last layer of… More >

  • Open Access


    Refined Sparse Representation Based Similar Category Image Retrieval

    Xin Wang, Zhilin Zhu, Zhen Hua*

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 893-908, 2023, DOI:10.32604/cmes.2022.021287

    Abstract Given one specific image, it would be quite significant if humanity could simply retrieve all those pictures that fall into a similar category of images. However, traditional methods are inclined to achieve high-quality retrieval by utilizing adequate learning instances, ignoring the extraction of the image’s essential information which leads to difficulty in the retrieval of similar category images just using one reference image. Aiming to solve this problem above, we proposed in this paper one refined sparse representation based similar category image retrieval model. On the one hand, saliency detection and multi-level decomposition could contribute to taking salient and spatial… More >

  • Open Access


    Long noncoding RNA PPP1R14B-AS1 imitates microRNA-134-3p to facilitate breast cancer progression by upregulating LIM and SH3 protein 1


    Oncology Research, Vol.29, No.4, pp. 251-262, 2021, DOI:10.32604/or.2022.03582

    Abstract Long noncoding RNA PPP1R14B antisense RNA 1 (PPP1R14B-AS1) has emerged as a critical modulator of liver cancer and lung adenocarcinoma progression. However, the functional importance and biological relevance of PPP1R14B-AS1 in breast cancer remain unclear. Therefore, this study was designed to detect PPP1R14B-AS1 levels in breast cancer cells using qRT–PCR and elucidate the influence of PPP1R14B-AS1 on aggressive phenotypes. Furthermore, molecular events mediating the action of PPP1R14B-AS1 were characterized in detail. Functional experiments addressed the impacts of PPP1R14B-AS1 knockdown on breast cancer cells. In this study, PPP1R14B-AS1 was found to be overexpressed in breast cancer, exhibiting a close correlation with… More >

  • Open Access


    Long noncoding RNA TMEM147-AS1 serves as a microRNA-326 sponge to aggravate the malignancy of gastric cancer by upregulating SMAD5


    Oncology Research, Vol.29, No.4, pp. 263-273, 2021, DOI:10.32604/or.2022.03568

    Abstract The abnormal expression of long noncoding RNAs (lncRNAs) is frequently observed in gastric cancer (GC) and considered an important driving force in GC progression. However, little is known regarding the involvement of TMEM147-AS1 in GC. Therefore, we examined TMEM147-AS1 expression in GC and determined its prognostic value. In addition, TMEM147-AS1 expression was depleted to identify the functional changes in response to TMEM147-AS1 deficiency. Using the cancer genome atlas dataset and our own cohort, we identified a strong expression of TMEM147-AS1 in GC. Increased TMEM147-AS1 levels in GC showed a significant association with poor prognosis. TMEM147-AS1 interference resulted in the inhibition… More >

  • Open Access


    Long noncoding RNA LINC02568 sequesters microRNA-874-3p to facilitate malignancy in breast cancer cells via cyclin E1 overexpression


    Oncology Research, Vol.29, No.4, pp. 291-303, 2021, DOI:10.32604/or.2022.025172

    Abstract Increasing numbers of long noncoding RNAs (lncRNAs) are implicated in breast cancer oncogenicity. However, the contribution of LINC02568 toward breast cancer progression remains unclear and requires further investigation. Herein, we evaluated LINC02568 expression in breast cancer and clarified its effect on disease malignancy. We also investigated the mechanisms underlying the pro-oncogenic role of LINC02568. Consequently, LINC02568 was upregulated in breast cancer samples, with a notable association with worse overall survival. Functionally, depleted LINC02568 suppressed cell proliferation, colony formation, and metastasis, whereas LINC02568 overexpression exerted the opposite effects. Our mechanistic investigations suggested that LINC02568 was physically bound to and sequestered microRNA-874-3p… More >

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