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Relevant bibliographies by topics / Cascade neural networks

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Journal articles

Academic literature on the topic 'Cascade neural networks'

Author: Grafiati

Published: 30 May 2022

Last updated: 11 June 2025

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Journal articles on the topic "Cascade neural networks"

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Shpinareva, Irina M., Anastasia A. Yakushina, Lyudmila A. Voloshchuk, and Nikolay D. Rudnichenko. "Detection and classification of network attacks using the deep neural network cascade." Herald of Advanced Information Technology 4, no. 3 (2021): 244–54. http://dx.doi.org/10.15276/hait.03.2021.4.

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This article shows the relevance of developing a cascade of deep neural networks for detecting and classifying network attacks based on an analysis of the practical use of network intrusion detection systems to protect local computer networks. A cascade of deep neural networks consists of two elements. The first network is a hybrid deep neural network that contains convolutional neural network layers and long short-term memory layers to detect attacks. The second network is a CNN convolutional neural network for classifying the most popular classes of network attacks such as Fuzzers, Analysis, Backdoors, DoS, Exploits, Generic, Reconnais-sance, Shellcode, and Worms. At the stage of tuning and training the cascade of deep neural networks, the selection of hyperparame-ters was carried out, which made it possible to improve the quality of the model. Among the available public datasets, one ofthe current UNSW-NB15 datasets was selected, taking into account modern traffic. For the data set under consideration, a data prepro-cessing technology has been developed. The cascade of deep neural networks was trained, tested, and validated on the UNSW-NB15 dataset. The cascade of deep neural networks was tested on real network traffic, which showed its ability to detect and classify at-tacks in a computer network. The use of a cascade of deep neural networks, consisting of a hybrid neural network CNN + LSTM and a neural network CNNhas improved the accuracy of detecting and classifying attacks in computer networks and reduced the fre-quency of false alarms in detecting network attacks

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Pedrycz, W., M. Reformat, and C. W. Han. "Cascade Architectures of Fuzzy Neural Networks." Fuzzy Optimization and Decision Making 3, no. 1 (2004): 5–37. http://dx.doi.org/10.1023/b:fodm.0000013070.26870.e6.

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Konarev, D. I., and A. A. Gulamov. "Synthesis of Neural Network Architecture for Recognition of Sea-Going Ship Images." Proceedings of the Southwest State University 24, no. 1 (2020): 130–43. http://dx.doi.org/10.21869/2223-1560-2020-24-1-130-143.

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Purpose of research. The current task is to monitor ships using video surveillance cameras installed along the canal. It is important for information communication support for navigation of the Moscow Canal. The main subtask is direct recognition of ships in an image or video. Implementation of a neural network is perspectively.Methods. Various neural network are described. images of ships are an input data for the network. The learning sample uses CIFAR-10 dataset. The network is built and trained by using Keras and TensorFlow machine learning libraries.Results. Implementation of curving artificial neural networks for problems of image recognition is described. Advantages of such architecture when working with images are also described. The selection of Python language for neural network implementation is justified. The main used libraries of machine learning, such as TensorFlow and Keras are described. An experiment has been conducted to train swirl neural networks with different architectures based on Google collaboratoty service. The effectiveness of different architectures was evaluated as a percentage of correct pattern recognition in the test sample. Conclusions have been drawn about parameters influence of screwing neural network on showing its effectiveness.Conclusion. The network with a single curl layer in each cascade showed insufficient results, so three-stage curls with two and three curl layers in each cascade were used. Feature map extension has the greatest impact on the accuracy of image recognition. The increase in cascades' number has less noticeable effect and the increase in the number of screwdriver layers in each cascade does not always have an increase in the accuracy of the neural network. During the study, a three-frame network with two buckling layers in each cascade and 128 feature maps is defined as an optimal architecture of neural network under described conditions. operability checking of architecture's part under consideration on random images of ships confirmed the correctness of optimal architecture choosing.

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Duan, Shuo, Shuai Xu, Xiao Meng Xu, Xin Zhang, and Chang Li Zhou. "Simultaneous Determination of p-Nitrochlorobenzene and o-Nitrophenol in Mixture by Single-Sweep Oscillopolarography Based on Cascade Neural Network." Advanced Materials Research 217-218 (March 2011): 1469–74. http://dx.doi.org/10.4028/www.scientific.net/amr.217-218.1469.

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By combining the improved wavelet neural network and BP neural network, a new structure based on mixed cascade neural network was established. The novel cascade neural network has been used to the oscillopolargriphic signals analysis. By the figure fitting and parameters extracting, we realized the prediction of the simulation samples.The training speed and the predication accuracy can be enhanced by optimizing the network structure and parameters. The result of concentration prediction is satisfied . The method has been applied to the simultaneous determination of p- Nitrochlorobenzene (p-NCB) and o-Nitrophenol (o-NP) in simulation samples with satisfactory results. The Relative error and Recovery of p-NCB、o-NP were 3.76%、96.2%； 4.05%、96.0%, respectively. This novel cascade neural network combines the advantage of wavelet neural networks and BP neural networks, and performs its own functions respectively. It has shown a unique advantage in the overlap peak analyze.

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KWAK, Keun-Chang. "A Development of Cascade Granular Neural Networks." IEICE Transactions on Information and Systems E94-D, no. 7 (2011): 1515–18. http://dx.doi.org/10.1587/transinf.e94.d.1515.

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Choi, S., and A. Cichocki. "Cascade neural networks for multichannel blind deconvolution." Electronics Letters 34, no. 12 (1998): 1186. http://dx.doi.org/10.1049/el:19980856.

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Smith, H. Allison, and J. Geoffrey Chase. "Identification of Structural System Parameters Using the Cascade-Correlation Neural Network." Journal of Dynamic Systems, Measurement, and Control 116, no. 4 (1994): 790–92. http://dx.doi.org/10.1115/1.2899280.

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The use of neural networks for structural system identification is receiving an increasing amount of attention through the research focused on structural control and intelligent systems. These systems require continuous monitoring and controlling of structural response; thus, on-line identification techniques are needed to provide real-time information about structural parameters. The Cascade-Correlation (Cascor) neural network is applied here to the structural system identification problem. The Cascor network utilizes a dynamic network architecture and a variable error threshold mechanism which facilitates training and can increase the network’s ability to generalize.

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Patan, Krzysztof. "Local stability conditions for discrete-time cascade locally recurrent neural networks." International Journal of Applied Mathematics and Computer Science 20, no. 1 (2010): 23–34. http://dx.doi.org/10.2478/v10006-010-0002-x.

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Local stability conditions for discrete-time cascade locally recurrent neural networksThe paper deals with a specific kind of discrete-time recurrent neural network designed with dynamic neuron models. Dynamics are reproduced within each single neuron, hence the network considered is a locally recurrent globally feedforward. A crucial problem with neural networks of the dynamic type is stability as well as stabilization in learning problems. The paper formulates local stability conditions for the analysed class of neural networks using Lyapunov's first method. Moreover, a stabilization problem is defined and solved as a constrained optimization task. In order to tackle this problem, a gradient projection method is adopted. The efficiency and usefulness of the proposed approach are justified by using a number of experiments.

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Shuqi Zhang, Shuqi Zhang. "Cascade Attention-based Spatial-temporal Convolutional Neural Network for Motion Image Posture Recognition." 電腦學刊 33, no. 1 (2022): 021–30. http://dx.doi.org/10.53106/199115992022023301003.

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<p>The traditional motion posture recognition methods cannot capture the temporal relationship in a video sequence, which leads to the problem that the recognition effect of time-dependent behaviors is not ideal. Therefore, this paper proposes a cascade attention-based spatial-temporal convolutional neural network for motion posture recognition. Firstly, the convolutional neural network is used to model the time sequence relationship in the video, so as to capture the spatial-temporal information in the video efficiently. At the same time, the cascade attention mechanism is used to improve the low learning ability of spatial features caused by channel information moving on the time axis. Meanwhile, a new spatial-temporal network structure is constructed, which includes the spatial-temporal appearance information flow and spatial-temporal motion information flow. Finally, the weighted average method is used to fuse the two spatial-temporal networks to obtain the final recognition result. Experiments are conducted on UCF101 and HMDB51 datasets, respectively, and the recognition accuracy is 96.8% and 79.6%. Experiment results show that compared with the state-of-the-art network methods, the recognition accuracy with the proposed method has better effect and robustness.</p> <p>&nbsp;</p>

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Smit, Mohammad, and Abdel-Nasser Al-Assimi. "Cascade Deep Neural Networks Classifiers for Phonemes Recognition." Journal of Engineering and Applied Sciences 15, no. 7 (2020): 1664–70. http://dx.doi.org/10.36478/jeasci.2020.1664.1670.

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