Juggler-ResNet: A Flexible and High-Speed ResNet Optimization Method for Intrusion Detection System in Software-Defined Industrial Networks

Zhu, Zongwei; Zhai, Wenjie; Liu, Huanghe; Geng, Jiawie; Ji, Cheng; Zhou, Mingliang; Jia, Gangyong

doi:10.1109/tii.2021.3121783

Cited by 26 publications

(9 citation statements)

References 19 publications

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“…ResNet is a type of deep-learning algorithm that has been used in various applications, especially in the healthcare sector for monitoring patients through data acquisition and data analysis [ 45 ]. This work essentially utilized the enhanced residual convolutional neural network to identify epileptic seizure episodes exploiting EEG sensor data.…”

Section: Methodsmentioning

confidence: 99%

Random Neural Network Based Epileptic Seizure Episode Detection Exploiting Electroencephalogram Signals

Shah

Larijani

Gibson

et al. 2022

Sensors

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Epileptic seizures are caused by abnormal electrical activity in the brain that manifests itself in a variety of ways, including confusion and loss of awareness. Correct identification of epileptic seizures is critical in the treatment and management of patients with epileptic disorders. One in four patients present resistance against seizures episodes and are in dire need of detecting these critical events through continuous treatment in order to manage the specific disease. Epileptic seizures can be identified by reliably and accurately monitoring the patients’ neuro and muscle activities, cardiac activity, and oxygen saturation level using state-of-the-art sensing techniques including electroencephalograms (EEGs), electromyography (EMG), electrocardiograms (ECGs), and motion or audio/video recording that focuses on the human head and body. EEG analysis provides a prominent solution to distinguish between the signals associated with epileptic episodes and normal signals; therefore, this work aims to leverage on the latest EEG dataset using cutting-edge deep learning algorithms such as random neural network (RNN), convolutional neural network (CNN), extremely random tree (ERT), and residual neural network (ResNet) to classify multiple variants of epileptic seizures from non-seizures. The results obtained highlighted that RNN outperformed all other algorithms used and provided an overall accuracy of 97%, which was slightly improved after cross validation.

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Section: Methodsmentioning

confidence: 99%

Random Neural Network Based Epileptic Seizure Episode Detection Exploiting Electroencephalogram Signals

Shah

Larijani

Gibson

et al. 2022

Sensors

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“…Multi-scale Gated model [32]: They develop an attentiongating module for the top-down adaptive fusion as opposed to the typical method of feature concatenation from several layers or multi-scale inputs. It directs the second level's learning through the gating operation, the fusion process begins.…”

Section: B Multi-scale Gated Resnetmentioning

confidence: 99%

Meta Heuristic Fusion Model for Classification with Modified U-Net-based Segmentation

Kuna¹,

Prasad²,

Bulla³

2023

IJACSA

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General cause of diabetes mellitus is Diabetic Retinopathy (DR), which outcomes in lesions on the retinas that impair vision. If it is not detected in time, the result is severe blindness issues. Regrettably, there is no treatment for DR. Early diagnosis and treatment of DR can greatly lower the risk of visual loss. In contrast to computer-aided diagnosis methods, the manual diagnosis of DR using retina fundus images is more timeconsuming effort, and high cost as well, as it is highly prone to error. Deep learning has emerged as one of the most popular methods for improving performance, particularly in the classification and analysis of medical images. Therefore, a deep structure-based DR detection and severity classification has been demonstrated for treating the DR with the usage of fundus images. The major aim of this developed technique is to classify the severity level of the retinal region of the human eye from the fundus images. At first, the required retinal fundus images are collected from the standard benchmark data sources. Secondly, image enhancement techniques are applied to the collected fundus images to improve the quality of images. Thirdly, the abnormality segmentations are carried out by using the optic disc removal process using active contouring model and then, the regional segmentation is done via the Modified U-Net method. Finally, the segmented image is subjected to the hybrid classifier network named a Hybrid Soft Attention-based DenseNet with Multi-Scale Gated ResNet (HSADMGR Net) for classifying the retinal fundus images and finding the severity level of the retinal images with higher accuracy. Furthermore, the parameters present inside the hybrid classifier network are optimized with the help of implemented Multi-Armed Bandits Groundwater Flow Algorithm (MABGFA). The test results regarding the developed deep structure-based DR model are validated with the existing DR detection and classification approaches by using different performance measures.

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“…In the past few years, deep learning has shown its effect in computer vision [ 23 , 24 ] and natural language processing [ 25 ]. Recently, various deep learning network frameworks have proven to have great potential in computer vision, such as convolution neural networks [ 26 ], residual networks (ResNets) [ 27 ], generation countermeasure networks [ 28 ], and recurrent neural networks [ 29 ]. Moreover, deep learning methods have been fully developed in the field of emitter recognition, which can be divided into two categories.…”

Section: Introductionmentioning

confidence: 99%

A Complex-Valued Self-Supervised Learning-Based Method for Specific Emitter Identification

Zhao

Yang

Liu

et al. 2022

Entropy

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Specific emitter identification (SEI) refers to distinguishing emitters using individual features extracted from wireless signals. The current SEI methods have proven to be accurate in tackling large labeled data sets at a high signal-to-noise ratio (SNR). However, their performance declines dramatically in the presence of small samples and a significant noise environment. To address this issue, we propose a complex self-supervised learning scheme to fully exploit the unlabeled samples, comprised of a pretext task adopting the contrastive learning concept and a downstream task. In the former task, we design an optimized data augmentation method based on communication signals to serve the contrastive conception. Then, we embed a complex-valued network in the learning to improve the robustness to noise. The proposed scheme demonstrates the generality of handling the small and sufficient samples cases across a wide range from 10 to 400 being labeled in each group. The experiment also shows a promising accuracy and robustness where the recognition results increase at 10–16% from 10–15 SNR.

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Juggler-ResNet: A Flexible and High-Speed ResNet Optimization Method for Intrusion Detection System in Software-Defined Industrial Networks

Cited by 26 publications

References 19 publications

Random Neural Network Based Epileptic Seizure Episode Detection Exploiting Electroencephalogram Signals

Random Neural Network Based Epileptic Seizure Episode Detection Exploiting Electroencephalogram Signals

Meta Heuristic Fusion Model for Classification with Modified U-Net-based Segmentation

A Complex-Valued Self-Supervised Learning-Based Method for Specific Emitter Identification

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