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  • SA 4270  (10)
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  • Mathematics  (10)
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  • SA 4270  (10)
  • 1
    Online Resource
    Online Resource
    AGCNN: Adaptive Gabor Convolutional Neural Networks with Receptive Fields for Vein Biometric Recognition
    Wiley ; 2022
    In:  Concurrency and Computation: Practice and Experience Vol. 34, No. 12 ( 2022-05-30)
    Details
    In: Concurrency and Computation: Practice and Experience, Wiley, Vol. 34, No. 12 ( 2022-05-30)
    Abstract: In recent years, finger vein recognition has attracted more attention and research as a secure method of identification. Convolutional neural networks have achieved great success in the field of finger vein recognition, yet they suffer from high computational complexity, large parameters, and other challenges. To solve these problems, we propose a Gabor convolutional neural network with receptive fields. We use Gabor filters with receptive field properties to design Gabor convolutional layers. Then we replace the conventional convolutional layer with the Gabor convolutional layer; analyze the influence of different loss functions, convolution kernel size, and feature size on the network model; and choose the most suitable model parameters and loss function. Finally, we systematically investigate comparative performance using AGCNN and CNNs in different finger vein databases. Experimental results show that the parameter complexity of AGCNN is significantly less than that of CNNs with a slight performance decrease.
    Type of Medium: Online Resource
    ISSN: 1532-0626 , 1532-0634
    URL: Issue
    URL: Article
    DOI: 10.1002/cpe.v34.12
    DOI: 10.1002/cpe.5697
    RVK:
    SA 4270
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 2052606-4
    SSG: 11
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Retracted: Complex image denoising framework with CNN‐wavelet under concurrency scenarios for informatics systems
    Wiley ; 2021
    In:  Concurrency and Computation: Practice and Experience Vol. 33, No. 12 ( 2021-06-25)
    Details
    In: Concurrency and Computation: Practice and Experience, Wiley, Vol. 33, No. 12 ( 2021-06-25)
    Abstract: Image denoising based on convolutional neural networks and wavelet transform is a novel approach for the applications. In the image acquisition process, images are often contaminated by noise, which affects the image quality; therefore, it is necessary to eliminate noise before analyzing and using images. Wavelet analysis is a local analysis method with multi‐resolution characteristics, which is developed on the basis of short‐time Fourier transform. It can be used for the multi‐scale analysis of signals by means of expansion, translation and other operations, and extracting effective information from signals, which is a powerful tool for analyzing non‐stationary signals. Wavelet has good time‐frequency local characteristics, low entropy, and decorrelation. In this paper, we propose MRI image denoising framework based on convolutional neural networks and wavelet transform, and the experiment results show that the proposed method can keep the edge and curvature structure better while denoising. Compared with the other novel methodologies, the proposed algorithm can provide the higher robustness. In the future research, we will try the implementations of the methodologies.
    Type of Medium: Online Resource
    ISSN: 1532-0626 , 1532-0634
    URL: Issue
    URL: Article
    DOI: 10.1002/cpe.v33.12
    DOI: 10.1002/cpe.5059
    RVK:
    SA 4270
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 2052606-4
    SSG: 11
    Location Call Number Limitation Availability
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    Online Resource
  • 3
    Online Resource
    Online Resource
    Blind image quality assessment based on the multiscale and dual‐domains features fusion
    Wiley ; 2023
    In:  Concurrency and Computation: Practice and Experience Vol. 35, No. 18 ( 2023-08-15)
    Details
    In: Concurrency and Computation: Practice and Experience, Wiley, Vol. 35, No. 18 ( 2023-08-15)
    Abstract: Image quality assessment is to simulate subjective human visual perception and realize image quality inference automatically. Although deep neural networks have achieved great success, the majority of them do not fully consider perception characteristics. Therefore, according to the human visual scale characteristics, we proposed an image quality assessment algorithm based on multiscale and dual domains fusion. Firstly, the original image and its phase congruency respectively input into two branches, feature pyramid and channel attention mechanism are adopted to extract multiscale features. After that, bilinear pool is used to aggregate the spatial and frequency domain characteristics of the corresponding scales, and allows arbitrary scale input to ensure that the features are extracted from the inherent quality images. Finally, the single quality score is obtained through learned weights of each scale. Comparative experiments between our approach and state‐of‐the‐art are conducted on five public databases, the results demonstrate that the proposed algorithm is not only robust to different types and across database, but also sensitive to scale.
    Type of Medium: Online Resource
    ISSN: 1532-0626 , 1532-0634
    URL: Issue
    URL: Article
    DOI: 10.1002/cpe.v35.18
    DOI: 10.1002/cpe.6177
    RVK:
    SA 4270
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 2052606-4
    SSG: 11
    Location Call Number Limitation Availability
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    Online Resource
  • 4
    Online Resource
    Online Resource
    Survey on compressed sensing reconstruction method for 3D data
    Wiley ; 2023
    In:  Concurrency and Computation: Practice and Experience Vol. 35, No. 2 ( 2023-01-25)
    Details
    In: Concurrency and Computation: Practice and Experience, Wiley, Vol. 35, No. 2 ( 2023-01-25)
    Abstract: The information society has higher and higher requirements for the collection, transmission and storage of digital signals, and signal utilization efficiency has become an increasingly important part of the digital signal processing process. The traditional digital signal processing needs to satisfy the Nyquist sampling theorem to ensure the restoration of the signal, while the digital signal processing method based on compressed sensing can sample and reconstruct the signal under the conditions that much lower than the Nyquist sampling theorem. Undoubtedly, the utilization efficiency of digital signals has been greatly accelerated. In this article, taking hyperspectral images and videos as examples, we review the basic theory, reconstruction model, and reconstruction algorithm of 3D data compressed sensing. We analyze, summarize, and discuss the existing literature. Finally, the research status of compressive sensing reconstruction methods for hyperspectral images and videos is compared, and several important research directions for compressive sensing reconstruction of 3D data in the future are proposed.
    Type of Medium: Online Resource
    ISSN: 1532-0626 , 1532-0634
    URL: Issue
    URL: Article
    DOI: 10.1002/cpe.v35.2
    DOI: 10.1002/cpe.7479
    RVK:
    SA 4270
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 2052606-4
    SSG: 11
    Location Call Number Limitation Availability
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  • 5
    Online Resource
    Online Resource
    Malicious code detection based on many‐objective transfer model
    Wiley ; 2023
    In:  Concurrency and Computation: Practice and Experience Vol. 35, No. 22 ( 2023-10-10)
    Details
    In: Concurrency and Computation: Practice and Experience, Wiley, Vol. 35, No. 22 ( 2023-10-10)
    Abstract: With the rapid growth of malicious codes, personal privacy, and Internet security are seriously threatened. Existing transfer learning‐based malicious code detection improves detection accuracy by transferring pre‐trained neural networks. However, it cannot efficiently tune the structure and parameters of the neural networks. Here, we first propose a novel many‐objective transfer model. It mainly focuses on the detection accuracy and the total number of parameters of the neural network model. The optimal structure and parameters are captured from the pre‐trained neural network by many‐objective optimization algorithm. Second, the partitioned crossover‐mutation vector angle‐based evolutionary algorithm for unconstrained many‐objective optimization is proposed to solve the model. The algorithm performs crossover mutation operations in different ways on different regions of the candidate solution to improve population diversity. The simulation results show that the model can reduce the pre‐trained neural network structure by 49% while maintaining the accuracy in malicious code detection.
    Type of Medium: Online Resource
    ISSN: 1532-0626 , 1532-0634
    URL: Issue
    URL: Article
    DOI: 10.1002/cpe.v35.22
    DOI: 10.1002/cpe.7728
    RVK:
    SA 4270
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 2052606-4
    SSG: 11
    Location Call Number Limitation Availability
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    Online Resource
  • 6
    Online Resource
    Online Resource
    Multi‐view frontal face image generation: A survey
    Wiley ; 2023
    In:  Concurrency and Computation: Practice and Experience Vol. 35, No. 18 ( 2023-08-15)
    Details
    In: Concurrency and Computation: Practice and Experience, Wiley, Vol. 35, No. 18 ( 2023-08-15)
    Abstract: Face images from different perspectives reduce the accuracy of face recognition, and the generation of frontal face images is an important research topic in the field of face recognition. To understand the development of frontal face generation models and grasp the current research hotspots and trends, existing methods based on 3D models, deep learning, and hybrid models are summarized, and the current commonly used face generation methods are introduced. Dataset, and compare the performance of existing models through experiments. The purpose of this paper is to fundamentally understand the advantages of existing frontal face generation, sort out the key issues of such generation, and look toward future development trends.
    Type of Medium: Online Resource
    ISSN: 1532-0626 , 1532-0634
    URL: Issue
    URL: Article
    DOI: 10.1002/cpe.v35.18
    DOI: 10.1002/cpe.6147
    RVK:
    SA 4270
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 2052606-4
    SSG: 11
    Location Call Number Limitation Availability
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    Online Resource
  • 7
    Online Resource
    Online Resource
    SDCCP: Control the network using software‐defined networking and end‐to‐end congestion control
    Wiley ; 2022
    In:  Concurrency and Computation: Practice and Experience Vol. 34, No. 16 ( 2022-07-25)
    Details
    In: Concurrency and Computation: Practice and Experience, Wiley, Vol. 34, No. 16 ( 2022-07-25)
    Abstract: The Internet of Things is becoming widely popular in the past decade, which comes with huge amount of data. These magnanimous data, stored in data centers, put forward the new demand for the efficient management of the network. In this article, we propose Software‐Defined Congestion Control Plane (SDCCP), a hybrid network control architecture that aims to fully utilize the network while avoiding congestion. SDCCP is based on Software‐Defined Networking and CCP, in which the controller collects the network statistics and specifies the behavior of the end‐to‐end hosts by sending feedback or modifying their transport layer parameters directly. It can also be used to mitigate Distributed Denial of Service attacks and other security problems. In addition, we propose FCA, a Feedback‐based Congestion Avoidance algorithm running on SDCCP, which adapts the congestion window based on the feedback from the remote controller. We evaluate SDCCP and FCA in Mininet and the result shows that FCA can achieve high network utilization while keeping the queue length of the routers in a low level. Also, FCA is robust to noncongestion loss, and outperforms other algorithms at high loss rate.
    Type of Medium: Online Resource
    ISSN: 1532-0626 , 1532-0634
    URL: Issue
    URL: Article
    DOI: 10.1002/cpe.v34.16
    DOI: 10.1002/cpe.5716
    RVK:
    SA 4270
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 2052606-4
    SSG: 11
    Location Call Number Limitation Availability
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    Online Resource
  • 8
    Online Resource
    Online Resource
    A many‐objective integrated evolutionary algorithm for feature selection in anomaly detection
    Wiley ; 2020
    In:  Concurrency and Computation: Practice and Experience Vol. 32, No. 22 ( 2020-11-25)
    Details
    In: Concurrency and Computation: Practice and Experience, Wiley, Vol. 32, No. 22 ( 2020-11-25)
    Abstract: At present, irrelevant or redundant features in network traffic data occupy a lot of storage and computing resources, reducing the accuracy of network anomaly detection. Aiming at this problem, a many‐objective feature selection model is proposed in this article. The model takes the number of selected feature, false alarm rate, detection rate, precision and accuracy as the optimization objectives, and characterizes the performance of the feature selection method from different perspectives. At the same time, a many‐objective integration optimization algorithm (IN‐MaOEA) is designed to solve this model. First, the algorithm will build the evolution strategy pool and the dominance strategy pool, then a random probability strategy selection mechanism is designed to improve the algorithm's convergence and diversity. At the same time, an anomaly detection simulation was performed using the NSL‐KDD dataset. Experimental results show that the IN‐MaOEA algorithm can effectively improve the performance of detection.
    Type of Medium: Online Resource
    ISSN: 1532-0626 , 1532-0634
    URL: Issue
    URL: Article
    DOI: 10.1002/cpe.v32.22
    DOI: 10.1002/cpe.5861
    RVK:
    SA 4270
    Language: English
    Publisher: Wiley
    Publication Date: 2020
    detail.hit.zdb_id: 2052606-4
    SSG: 11
    Location Call Number Limitation Availability
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  • 9
    Online Resource
    Online Resource
    Extraction‐and‐excitation deep neural network for pansharpening
    Wiley ; 2023
    In:  Concurrency and Computation: Practice and Experience Vol. 35, No. 18 ( 2023-08-15)
    Details
    In: Concurrency and Computation: Practice and Experience, Wiley, Vol. 35, No. 18 ( 2023-08-15)
    Abstract: With the recent advances achieved by deep neural networks in image processing applications, researchers have begun exploring deep learning in pansharpening and obtained remarkable results. However, the existing methods are generally limited by their weak feature representation ability, often leading to spectral distortion or spatial blur. To generate high‐quality pansharpened images, this article proposes a novel neural network for pansharpening that includes both feature extraction and excitation mechanisms to consider important features. The neural network is modified with domain knowledge in pansharpening to fully extract spectral and spatial structures, and the proposed method outperforms traditional methods.
    Type of Medium: Online Resource
    ISSN: 1532-0626 , 1532-0634
    URL: Issue
    URL: Article
    DOI: 10.1002/cpe.v35.18
    DOI: 10.1002/cpe.6098
    RVK:
    SA 4270
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 2052606-4
    SSG: 11
    Location Call Number Limitation Availability
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    Online Resource
  • 10
    Online Resource
    Online Resource
    A review of research on co‐training
    Wiley ; 2023
    In:  Concurrency and Computation: Practice and Experience Vol. 35, No. 18 ( 2023-08-15)
    Details
    In: Concurrency and Computation: Practice and Experience, Wiley, Vol. 35, No. 18 ( 2023-08-15)
    Abstract: Co‐training algorithm is one of the main methods of semi‐supervised learning in machine learning, which explores the effective information in unlabeled data by multi‐learner collaboration. Based on the development of co‐training algorithm, the research work in recent years was further summarized in this article. In particular, three main steps of relevant co‐training algorithms are introduced: view acquisition, learners' differentiation, and label confidence estimation. Finally, we summarized the problems existing in the current co‐training methods, gave some suggestions for improvement, and looked forward to the future development direction of the co‐training algorithm.
    Type of Medium: Online Resource
    ISSN: 1532-0626 , 1532-0634
    URL: Issue
    URL: Article
    DOI: 10.1002/cpe.v35.18
    DOI: 10.1002/cpe.6276
    RVK:
    SA 4270
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 2052606-4
    SSG: 11
    Location Call Number Limitation Availability
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    Online Resource
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