Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (9)
  • Open Access


    Numerical Simulation of Multiphase Flow in Subsurface Reservoirs: Existing Challenges and New Treatments

    Shuyu Sun1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.2, pp. 1-2, 2023, DOI:10.32604/icces.2023.09671

    Abstract Two or multiple phases commonly occur as fluid mixture in petroleum industry, where oil, gas and water are often produced and transported together. As a result, petroleum reservoir engineers spent great efforts in the development and production of oil and gas reservoirs by conducting and interpolating the simulation of multiphase flows in porous geological formation. Meanwhile, environmental scientists use subsurface flow and transport models to investigate and compare for example various schemes to inject and store CO2 in subsurface geological formations, such as depleted reservoirs and deep saline aquifers. In this work, we first present an introduction of numerical simulation… More >

  • Open Access


    Emotion Analysis: Bimodal Fusion of Facial Expressions and EEG

    Huiping Jiang1,*, Rui Jiao1, Demeng Wu1, Wenbo Wu2

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2315-2327, 2021, DOI:10.32604/cmc.2021.016832

    Abstract With the rapid development of deep learning and artificial intelligence, affective computing, as a branch field, has attracted increasing research attention. Human emotions are diverse and are directly expressed via non-physiological indicators, such as electroencephalogram (EEG) signals. However, whether emotion-based or EEG-based, these remain single-modes of emotion recognition. Multi-mode fusion emotion recognition can improve accuracy by utilizing feature diversity and correlation. Therefore, three different models have been established: the single-mode-based EEG-long and short-term memory (LSTM) model, the Facial-LSTM model based on facial expressions processing EEG data, and the multi-mode LSTM-convolutional neural network (CNN) model that combines expressions and EEG. Their… More >

  • Open Access


    VGG-CovidNet: Bi-Branched Dilated Convolutional Neural Network for Chest X-Ray-Based COVID-19 Predictions

    Muhammed Binsawad1,*, Marwan Albahar2, Abdullah Bin Sawad1

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2791-2806, 2021, DOI:10.32604/cmc.2021.016141

    Abstract The coronavirus disease 2019 (COVID-19) pandemic has had a devastating impact on the health and welfare of the global population. A key measure to combat COVID-19 has been the effective screening of infected patients. A vital screening process is the chest radiograph. Initial studies have shown irregularities in the chest radiographs of COVID-19 patients. The use of the chest X-ray (CXR), a leading diagnostic technique, has been encouraged and driven by several ongoing projects to combat this disease because of its historical effectiveness in providing clinical insights on lung diseases. This study introduces a dilated bi-branched convoluted neural network (CNN)… More >

  • Open Access


    A Learning Framework for Intelligent Selection of Software Verification Algorithms

    Weipeng Cao1, Zhongwu Xie1, Xiaofei Zhou2, Zhiwu Xu1, Cong Zhou1, Georgios Theodoropoulos3, Qiang Wang3,*

    Journal on Artificial Intelligence, Vol.2, No.4, pp. 177-187, 2020, DOI:10.32604/jai.2020.014829

    Abstract Software verification is a key technique to ensure the correctness of software. Although numerous verification algorithms and tools have been developed in the past decades, it is still a great challenge for engineers to accurately and quickly choose the appropriate verification techniques for the software at hand. In this work, we propose a general learning framework for the intelligent selection of software verification algorithms, and instantiate the framework with two state-of-the-art learning algorithms: Broad learning (BL) and deep learning (DL). The experimental evaluation shows that the training efficiency of the BL-based model is much higher than the DL-based models and… More >

  • Open Access


    A Survey of GAN-Generated Fake Faces Detection Method Based on Deep Learning

    Xin Liu*, Xiao Chen

    Journal of Information Hiding and Privacy Protection, Vol.2, No.2, pp. 87-94, 2020, DOI:10.32604/jihpp.2020.09839

    Abstract In recent years, with the rapid growth of generative adversarial networks (GANs), a photo-realistic face can be easily generated from a random vector. Moreover, the faces generated by advanced GANs are very realistic. It is reasonable to acknowledge that even a well-trained viewer has difficulties to distinguish artificial from real faces. Therefore, detecting the face generated by GANs is a necessary work. This paper mainly introduces some methods to detect GAN-generated fake faces, and analyzes the advantages and disadvantages of these models based on the network structure and evaluation indexes, and the results obtained in the respective data sets. On… More >

  • Open Access


    Stacked Attention Networks for Referring Expressions Comprehension

    Yugang Li1, *, Haibo Sun1, Zhe Chen1, Yudan Ding1, Siqi Zhou2

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2529-2541, 2020, DOI:10.32604/cmc.2020.011886

    Abstract Referring expressions comprehension is the task of locating the image region described by a natural language expression, which refer to the properties of the region or the relationships with other regions. Most previous work handles this problem by selecting the most relevant regions from a set of candidate regions, when there are many candidate regions in the set these methods are inefficient. Inspired by recent success of image captioning by using deep learning methods, in this paper we proposed a framework to understand the referring expressions by multiple steps of reasoning. We present a model for referring expressions comprehension by… More >

  • Open Access


    An Attention-Based Friend Recommendation Model in Social Network

    Chongchao Cai1, 2, Huahu Xu1, *, Jie Wan2, Baiqing Zhou2, Xiongwei Xie3

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2475-2488, 2020, DOI:10.32604/cmc.2020.011693

    Abstract In social networks, user attention affects the user’s decision-making, resulting in a performance alteration of the recommendation systems. Existing systems make recommendations mainly according to users’ preferences with a particular focus on items. However, the significance of users’ attention and the difference in the influence of different users and items are often ignored. Thus, this paper proposes an attention-based multi-layer friend recommendation model to mitigate information overload in social networks. We first constructed the basic user and item matrix via convolutional neural networks (CNN). Then, we obtained user preferences by using the relationships between users and items, which were later… More >

  • Open Access


    Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks

    Omer Berat Sezer*, Ahmet Murat Ozbayoglu

    Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 323-334, 2020, DOI:10.31209/2018.100000065

    Abstract Even though computational intelligence techniques have been extensively utilized in financial trading systems, almost all developed models use the time series data for price prediction or identifying buy-sell points. However, in this study we decided to use 2-D stock bar chart images directly without introducing any additional time series associated with the underlying stock. We propose a novel algorithmic trading model CNN-BI (Convolutional Neural Network with Bar Images) using a 2-D Convolutional Neural Network. We generated 2-D images of sliding windows of 30-day bar charts for Dow 30 stocks and trained a deep Convolutional Neural Network (CNN) model for our… More >

  • Open Access


    Image Super-Resolution Based on Generative Adversarial Networks: A Brief Review

    Kui Fu1, Jiansheng Peng1, 2, *, Hanxiao Zhang2, Xiaoliang Wang3, Frank Jiang4

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1977-1997, 2020, DOI:10.32604/cmc.2020.09882

    Abstract Single image super resolution (SISR) is an important research content in the field of computer vision and image processing. With the rapid development of deep neural networks, different image super-resolution models have emerged. Compared to some traditional SISR methods, deep learning-based methods can complete the superresolution tasks through a single image. In addition, compared with the SISR methods using traditional convolutional neural networks, SISR based on generative adversarial networks (GAN) has achieved the most advanced visual performance. In this review, we first explore the challenges faced by SISR and introduce some common datasets and evaluation metrics. Then, we review the… More >

Displaying 1-10 on page 1 of 9. Per Page