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  • Open Access

    ARTICLE

    Deep Neural Network Based Detection and Segmentation of Ships for Maritime Surveillance

    Kyamelia Roy1, Sheli Sinha Chaudhuri1, Sayan Pramanik2, Soumen Banerjee2,*

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 647-662, 2023, DOI:10.32604/csse.2023.024997

    Abstract In recent years, computer vision finds wide applications in maritime surveillance with its sophisticated algorithms and advanced architecture. Automatic ship detection with computer vision techniques provide an efficient means to monitor as well as track ships in water bodies. Waterways being an important medium of transport require continuous monitoring for protection of national security. The remote sensing satellite images of ships in harbours and water bodies are the image data that aid the neural network models to localize ships and to facilitate early identification of possible threats at sea. This paper proposes a deep learning… More >

  • Open Access

    ARTICLE

    Deep Learning Based Intrusion Detection in Cloud Services for Resilience Management

    S. Sreenivasa Chakravarthi1,*, R. Jagadeesh Kannan2, V. Anantha Natarajan3, Xiao-Zhi Gao4

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5117-5133, 2022, DOI:10.32604/cmc.2022.022351

    Abstract In the global scenario one of the important goals for sustainable development in industrial field is innovate new technology, and invest in building infrastructure. All the developed and developing countries focus on building resilient infrastructure and promote sustainable developments by fostering innovation. At this juncture the cloud computing has become an important information and communication technologies model influencing sustainable development of the industries in the developing countries. As part of the innovations happening in the industrial sector, a new concept termed as ‘smart manufacturing’ has emerged, which employs the benefits of emerging technologies like internet… More >

  • Open Access

    ARTICLE

    Building Graduate Salary Grading Prediction Model Based on Deep Learning

    Jong-Yih Kuo*, Hui-Chi Lin, Chien-Hung Liu

    Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 53-68, 2021, DOI:10.32604/iasc.2021.014437

    Abstract Predicting salary trends of students after employment is vital for helping students to develop their career plans. Particularly, salary is not only considered employment information for students to pursue jobs, but also serves as an important indicator for measuring employability and competitiveness of graduates. This paper considers salary prediction as an ordinal regression problem and uses deep learning techniques to build a salary prediction model for determining the relative ordering between different salary grades. Specifically, to solve this problem, the model uses students’ personal information, grades, and family data as input features and employs a More >

  • Open Access

    ARTICLE

    Deep Learning-Based Intrusion System for Vehicular Ad Hoc Networks

    Fei Li1, *, Jiayan Zhang1, Edward Szczerbicki2, Jiaqi Song1, Ruxiang Li 1, Renhong Diao1

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 653-681, 2020, DOI:10.32604/cmc.2020.011264

    Abstract The increasing use of the Internet with vehicles has made travel more convenient. However, hackers can attack intelligent vehicles through various technical loopholes, resulting in a range of security issues. Due to these security issues, the safety protection technology of the in-vehicle system has become a focus of research. Using the advanced autoencoder network and recurrent neural network in deep learning, we investigated the intrusion detection system based on the in-vehicle system. We combined two algorithms to realize the efficient learning of the vehicle’s boundary behavior and the detection of intrusive behavior. In order to More >

  • Open Access

    ARTICLE

    Outlier Detection for Water Supply Data Based on Joint Auto-Encoder

    Shu Fang1, Lei Huang1, Yi Wan2, Weize Sun1, *, Jingxin Xu3

    CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 541-555, 2020, DOI:10.32604/cmc.2020.010066

    Abstract With the development of science and technology, the status of the water environment has received more and more attention. In this paper, we propose a deep learning model, named a Joint Auto-Encoder network, to solve the problem of outlier detection in water supply data. The Joint Auto-Encoder network first expands the size of training data and extracts the useful features from the input data, and then reconstructs the input data effectively into an output. The outliers are detected based on the network’s reconstruction errors, with a larger reconstruction error indicating a higher rate to be… More >

  • Open Access

    ARTICLE

    A Convolution-Based System for Malicious URLs Detection

    Chaochao Luo1, Shen Su2, *, Yanbin Sun2, Qingji Tan3, Meng Han4, Zhihong Tian2, *

    CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 399-411, 2020, DOI:10.32604/cmc.2020.06507

    Abstract Since the web service is essential in daily lives, cyber security becomes more and more important in this digital world. Malicious Uniform Resource Locator (URL) is a common and serious threat to cybersecurity. It hosts unsolicited content and lure unsuspecting users to become victim of scams, such as theft of private information, monetary loss, and malware installation. Thus, it is imperative to detect such threats. However, traditional approaches for malicious URLs detection that based on the blacklists are easy to be bypassed and lack the ability to detect newly generated malicious URLs. In this paper, More >

  • Open Access

    ARTICLE

    Fault Diagnosis of Motor in Frequency Domain Signal by Stacked De-noising Auto-encoder

    Xiaoping Zhao1, Jiaxin Wu1,*, Yonghong Zhang2, Yunqing Shi3, Lihua Wang2

    CMC-Computers, Materials & Continua, Vol.57, No.2, pp. 223-242, 2018, DOI:10.32604/cmc.2018.02490

    Abstract With the rapid development of mechanical equipment, mechanical health monitoring field has entered the era of big data. Deep learning has made a great achievement in the processing of large data of image and speech due to the powerful modeling capabilities, this also brings influence to the mechanical fault diagnosis field. Therefore, according to the characteristics of motor vibration signals (nonstationary and difficult to deal with) and mechanical ‘big data’, combined with deep learning, a motor fault diagnosis method based on stacked de-noising auto-encoder is proposed. The frequency domain signals obtained by the Fourier transform More >

  • Open Access

    ARTICLE

    Snow Cover Mapping for Mountainous Areas by Fusion of MODIS L1B and Geographic Data Based on Stacked Denoising Auto-Encoders

    Xi Kan1, Yonghong Zhang2,*, Linglong Zhu2, Liming Xiao2, Jiangeng Wang3, Wei Tian4, Haowen Tan5

    CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 49-68, 2018, DOI:10.32604/cmc.2018.02376

    Abstract Snow cover plays an important role in meteorological and hydrological researches. However, the accuracies of currently available snow cover products are significantly lower in mountainous areas than in plains, due to the serious snow/cloud confusion problem caused by high altitude and complex topography. Aiming at this problem, an improved snow cover mapping approach for mountainous areas was proposed and applied in Qinghai-Tibetan Plateau. In this work, a deep learning framework named Stacked Denoising Auto-Encoders (SDAE) was employed to fuse the MODIS multispectral images and various geographic datasets, which are then classified into three categories: Snow,… More >

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