Showing 241 - 260 results of 510 for search '"deep neural network"', query time: 0.07s Refine Results
  1. 241

    Intelligent anti-jamming decision algorithm based on proximal policy optimization by MA Song, LI Li, LI Wei, HUANG Wei, WANG Jun

    Published 2024-08-01
    “…The existing intelligent anti-jamming methods based on deep reinforcement learning are applied to space-ground TT&C and communication links, in which the deep neural network used for decision-making has a complex structure, and the resources of satellites and other vehicles are limited, making it difficult to independently complete the timely training of complex neural network under the constraints of limited complexity, and the decision-making of anti-jamming cannot converge. …”
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  2. 242

    Development of Deep Convolutional Neural Network with Adaptive Batch Normalization Algorithm for Bearing Fault Diagnosis by Chao Fu, Qing Lv, Hsiung-Cheng Lin

    Published 2020-01-01
    “…The performance results verify that the proposed model is superior to Support Vector Machine with Fast Fourier Transform (FFT-SVM) and Multilayer Perceptron with Fast Fourier Transform (FFT-MLP) models and Deep Neural Network with Fast Fourier Transform (FFT-DNN).…”
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  3. 243

    Investigating the Quality of DermaMNIST and Fitzpatrick17k Dermatological Image Datasets by Kumar Abhishek, Aditi Jain, Ghassan Hamarneh

    Published 2025-02-01
    “…However, while large datasets play a crucial role in the development of reliable deep neural network models, the quality of data therein and their correct usage are of paramount importance. …”
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  4. 244

    Optimization of Photonic Nanocrystals for Invisibility Using Artificial Intelligence by Z. Dorrani

    Published 2024-12-01
    “…Therefore, this paper employs the deep neural network architecture ResNet to optimize photonic crystals. …”
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  5. 245

    Neural processing of naturalistic audiovisual events in space and time by Yu Hu, Yalda Mohsenzadeh

    Published 2025-01-01
    “…Comparing neural representations to a two-branch deep neural network model highlighted the necessity of early cross-modal connections to build a biologically plausible model of audiovisual perception. …”
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  6. 246

    Metric-based learning approach to botnet detection with small samples by Honggang LIN, Junjing ZHU, Lin CHEN

    Published 2023-10-01
    “…Botnets pose a great threat to the Internet, and early detection is crucial for maintaining cybersecurity.However, in the early stages of botnet discovery, obtaining a small number of labeled samples restricts the training of current detection models based on deep learning, leading to poor detection results.To address this issue, a botnet detection method called BT-RN, based on metric learning, was proposed for small sample backgrounds.The task-based meta-learning training strategy was used to optimize the model.The verification set was introduced into the task and the similarity between the verification sample and the training sample feature representation was measured to quickly accumulate experience, thereby reducing the model’s dependence on the labeled sample space.The feature-level attention mechanism was introduced.By calculating the attention coefficients of each dimension in the feature, the feature representation was re-integrated and the importance attention was assigned to optimize the feature representation, thereby reducing the feature sparseness of the deep neural network in small samples.The residual network design pattern was introduced, and the skip link was used to avoid the risk of model degradation and gradient disappearance caused by the deeper network after increasing the feature-level attention mechanism module.…”
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  7. 247

    Comment on “Prediction of the SYM‐H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification” by Abduallah et al. (2024) by Armando Collado‐Villaverde, Pablo Muñoz, Consuelo Cid

    Published 2024-08-01
    “…Abstract Abduallah et al. (2024b, https://doi.org/10.1029/2023sw003824) proposed a novel approach using a deep neural network model, which includes a graph neural network and a bidirectional LSTM layer, named SYMHnet, to forecast the SYM‐H index one and 2 hr in advance. …”
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  8. 248

    DDoS attack detection and defense based on hybrid deep learning model in SDN by Chuanhuang LI, Yan WU, Zhengzhe QIAN, Zhengjun SUN, Weiming WANG

    Published 2018-07-01
    “…Software defined network (SDN) is a new kind of network technology,and the security problems are the hot topics in SDN field,such as SDN control channel security,forged service deployment and external distributed denial of service (DDoS) attacks.Aiming at DDoS attack problem of security in SDN,a DDoS attack detection method called DCNN-DSAE based on deep learning hybrid model in SDN was proposed.In this method,when a deep learning model was constructed,the input feature included 21 different types of fields extracted from the data plane and 5 extra self-designed features of distinguishing flow types.The experimental results show that the method has high accuracy,it’s better than the traditional support vector machine (SVM) and deep neural network (DNN) and other machine learning methods.At the same time,the proposed method can also shorten the processing time of classification detection.The detection model is deployed in SDN controller,and the new security policy is sent to the OpenFlow switch to achieve the defense against specific DDoS attack.…”
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  9. 249

    SCRD-Net: A Deep Convolutional Neural Network Model for Glaucoma Detection in Retina Tomography by Hua Wang, Jingfei Hu, Jicong Zhang

    Published 2021-01-01
    “…Early and accurate diagnosis of glaucoma is critical for avoiding human vision deterioration and preventing blindness. A deep-neural-network model has been developed for the diagnosis of glaucoma based on Heidelberg retina tomography (HRT), called “Seeking Common Features and Reserving Differences Net” (SCRD-Net) to make full use of the HRT data. …”
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  10. 250

    R&D of the EM Calorimeter Energy Calibration with Machine Learning based on the low-level features of the Cluster by Morimasa Suzuna, Iwasaki Masako, Suehara Taikan, Tanaka Junichi, Saito Masahiko, Nagahara Hajime, Nakashima Yuta, Takemura Noriko, Nakano Takashi

    Published 2024-01-01
    “…In this method, we use a deep neural network (DNN) for a regression to determine the energy of incident EM particles, improving the energy calibration resolution of the ECAL. …”
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  11. 251

    Deep learning based demodulation method for multiple access of visible light communication in 6G by SHAO Xinyu, YAO Yao, WANG Liang

    Published 2024-08-01
    “…In view of this problem, by utilizing the correlation among received signals of the visible communication system, a multiple user detection and signal recovery method of multiple users for multiple access based on deep neural network was proposed. The transmitter model and the receiver model of the visible light communication system were presented based on sparse code multiple access, the temporal convolutional network was adopted to learn the inter-signal temporal correlation of the long sequence, the output sequence was delivered to dense layer to learn the spatial mapping relationship of the signal sequence, in the end, signals of all users were recovered in the receiver of the visible light communication system. …”
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  12. 252

    Acoustic Model with Multiple Lexicon Types for Indonesian Speech Recognition by Taufik Fuadi Abidin, Alim Misbullah, Ridha Ferdhiana, Laina Farsiah, Muammar Zikri Aksana, Hammam Riza

    Published 2022-01-01
    “…The quality of the dataset was evaluated using a deep neural network. The time delay neural network (TDNN) was used to build the acoustic model by applying the alignment data from the Gaussian mixture model-hidden Markov model (GMM-HMM). …”
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  13. 253

    Performance Prediction for Higher Education Students Using Deep Learning by Shuping Li, Taotang Liu

    Published 2021-01-01
    “…The proposed method used deep neural network in prediction by extracting informative data as a feature with corresponding weights. …”
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  14. 254

    A graph backdoor detection method for data collection scenarios by Xiaogang Xing, Ming Xu, Yujing Bai, Dongdong Yang

    Published 2025-01-01
    “…Different from most existing backdoor detection methods of Neural Network (NN) models, especially the Deep Neural Network (DNN) models, the difference in predictions of backdoor samples in clean and backdoor models is exploited for backdoor detection in CGBD. …”
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  15. 255

    Image Reconstruction for High-Performance Electrical Capacitance Tomography System Using Deep Learning by Yanpeng Zhang, Deyun Chen

    Published 2021-01-01
    “…But the ECT system still requires improvements in the quality of image reconstruction given its importance of great significance to obtain the reliability and usefulness of measurement results. The deep neural network is used in this study to extract new features and to update the number of nodes and hidden layers in the system. …”
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  16. 256

    Quantitative Detection of Financial Fraud Based on Deep Learning with Combination of E-Commerce Big Data by Jian Liu, Xin Gu, Chao Shang

    Published 2020-01-01
    “…This method builds a deep neural network model with multiple hidden layers, learns the characteristic expression of the data, and fully depicts the rich internal information of the data, thereby improving the accuracy of financial fraud detection. …”
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  17. 257

    Exploiting Interslice Correlation for MRI Prostate Image Segmentation, from Recursive Neural Networks Aspect by Qikui Zhu, Bo Du, Baris Turkbey, Peter Choyke, Pingkun Yan

    Published 2018-01-01
    “…To tackle this problem, in this paper, we propose a deep neural network with bidirectional convolutional recurrent layers for MRI prostate image segmentation. …”
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  18. 258

    Construction of advanced persistent threat attack detection model based on provenance graph and attention mechanism by Yuancheng LI, Hao LUO, Xinyu WANG, Jiexuan YUAN

    Published 2024-03-01
    “…In response to the difficulty of existing attack detection methods in dealing with advanced persistent threat (APT) with longer durations, complex and covert attack methods, a model for APT attack detection based on attention mechanisms and provenance graphs was proposed.Firstly, provenance graphs that described system behavior based on system audit logs were constructed.Then, an optimization algorithm was designed to reduce the scale of provenance graphs without sacrificing key semantics.Afterward, a deep neural network (DNN) was utilized to convert the original attack sequence into a semantically enhanced feature vector sequence.Finally, an APT attack detection model named DAGCN was designed.An attention mechanism was applied to the traceback graph sequence.By allocating different weights to different positions in the input sequence and performing weight calculations, sequence feature information of sustained attacks could be extracted over a longer period of time, which effectively identified malicious nodes and reconstructs the attack process.The proposed model outperforms existing models in terms of recognition accuracy and other metrics.Experimental results on public APT attack datasets show that, compared with existing APT attack detection models, the accuracy of the proposed model in APT attack detection reaches 93.18%.…”
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  19. 259

    Research and DSP Implementation of Speech Enhancement Technology Based on Dynamic Mixed Features and Adaptive Mask by Jie Yang, Yachun Tang

    Published 2022-01-01
    “…Then, an improved deep neural network model is designed to effectively improve the speech enhancement performance. …”
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  20. 260

    Regressions on quantum neural networks at maximal expressivity by Iván Panadero, Yue Ban, Hilario Espinós, Ricardo Puebla, Jorge Casanova, Erik Torrontegui

    Published 2024-12-01
    “…Abstract Considering a universal deep neural network organized as a series of nested qubit rotations, accomplished by adjustable data re-uploads we analyze its expressivity. …”
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