With the rapid development of network technology, there are more and more application scenarios of software defined networking (SDN), such as big data, cloud computing, internet of things, etc. However, the facilities...
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With the rapid development of network technology, there are more and more application scenarios of software defined networking (SDN), such as big data, cloud computing, internet of things, etc. However, the facilities in the SDN network face security issues such as DDoS attacks, network monitoring, and privacy. In addition, the SDN controller is also the main target of the attacker. This paper makes a simple analysis of the security risks in SDN and proposes a machine learning-based intrusion detection system for SDN (ML-SDNIDS). According to the characteristics of SDN, ML-SDNIDS uses autoencoder and one-class support vector machine algorithm to train intrusion detection model in the control plane, and uses P4 programming language combined with machine learning algorithm to realise real-time intrusion detection function in the data plane. And compared with the traditional SVM and OCSVM intrusion detection models in the latest intrusion detection dataset CIC-DDoS2019, the experimental results show that the scheme proposed in this paper has greatly improved the detection accuracy and the execution efficiency of the model. In addition, this experimental scheme can make the intrusion detection accuracy of data plane P4 switch as high as 97%, and its packet transmission efficiency is still millisecond.
the limitation of conventional singlescene image denoising algorithms in filtering mixed environmental disturbances, and recognizing the drawbacks of cascaded image enhancement algorithms, which have poor realtime per...
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the limitation of conventional singlescene image denoising algorithms in filtering mixed environmental disturbances, and recognizing the drawbacks of cascaded image enhancement algorithms, which have poor realtime performance and high computational demands, The composite weather adaptive denoising network (CWADN) is proposed. A Cascade Hourglass Feature Extraction Network is constructed with a visual attention mechanism to extract characteristics of rain, fog, and low-light noise from authentic natural images. These features are then transferred from their original real distribution domain to a synthetic distribution domain using a deep residual convolutional neural network. The generator and style encoder of the adversarial network work together to adaptively remove the transferred noise through a combination of supervised and unsupervised training, this approach achieves adaptive denoising capabilities tailored to complex natural environmental noise. Experimental results demonstrate that the proposed denoising network yields a high signal-to-noise ratio while maintaining excellent image fidelity. It effectively prevents image distortion, particularly in critical target areas. Additionally, it adapts to various types of mixed noise, making it a valuable tool for preprocessing images in advanced machine vision algorithms such as target recognition and tracking.
Computerized myocardial infarction (MI) detection and localization can be useful for early prevention of its aggravation and related cardiac health complications. However, the published research either focuses on bina...
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Computerized myocardial infarction (MI) detection and localization can be useful for early prevention of its aggravation and related cardiac health complications. However, the published research either focuses on binary classification or implements complex classifiers for localization to achieve good accuracy. In this letter, the objective is to implement an 11-class MI localization system on resource-constrained hardware with low complexity and latency. A simple and optimized autoencoder-k-NN classifier has been used to achieve accuracy and F1-score of 99.74% and 99.20%, respectively, while evaluating single lead Electrocardiogram (ECG) features from the PTB-Diagnostic ECG database. A standalone hardware implementation with an ARM-v6-based controller resulted in a latency and runtime memory engagement of 0.48 s and 4.31 MB, respectively, to process 5 s ECG data. The present research can be useful for quick screening of MI for portable healthcare applications.
Traditional portfolio theory divides stocks into different categories using indicators such as industry, market value, and liquidity, and then selects representative stocks according to them. In this paper, we propose...
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Traditional portfolio theory divides stocks into different categories using indicators such as industry, market value, and liquidity, and then selects representative stocks according to them. In this paper, we propose a novel portfolio learning approach based on deep learning and apply it to China's stock market. Specifically, this method is based on the similarity of deep features extracted from candlestick charts. First, we obtained whole stock information from Tushare, a professional financial data interface. These raw time series data are then plotted into candlestick charts to make an image dataset for studying the stock market. Next, the method extracts high-dimensional features from candlestick charts through an autoencoder. After that, K-means is used to cluster these high-dimensional features. Finally, we choose one stock from each category according to the Sharpe ratio and a low-risk, high-return portfolio is obtained. Extensive experiments are conducted on stocks in the Chinese stock market for evaluation. The results demonstrate that the proposed portfolio outperforms the market's leading funds and the Shanghai Stock Exchange Composite Index (SSE Index) in a number of metrics.
Background: Sepsis is a heterogenous syndrome and individualized management strategy is the key to successful treatment. Genome wide expression profiling has been utilized for identifying subclasses of sepsis, but the...
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Background: Sepsis is a heterogenous syndrome and individualized management strategy is the key to successful treatment. Genome wide expression profiling has been utilized for identifying subclasses of sepsis, but the clinical utility of these subclasses was limited because of the classification instability, and the lack of a robust class prediction model with extensive external validation. The study aimed to develop a parsimonious class model for the prediction of class membership and validate the model for its prognostic and predictive capability in external datasets. Methods: The Gene Expression Omnibus (GEO) and ArrayExpress databases were searched from inception to April 2020. Datasets containing whole blood gene expression profiling in adult sepsis patients were included. autoencoder was used to extract representative features for k-means clustering. Genetic algorithms (GA) were employed to derive a parsimonious 5-gene class prediction model. The class model was then applied to external datasets (n = 780) to evaluate its prognostic and predictive performance. Findings: A total of 12 datasets involving 1613 patients were included. Two classes were identified in the discovery cohort (n = 685). Class 1 was characterized by immunosuppression with higher mortality than class 2 (21.8% [70/321] vs. 12.1% [44/364];p < 0.01 for Chi-square test). A 5-gene class model (C14orf159, AKNA, PILRA, STOM and USP4) was developed with GA. In external validation cohorts, the 5-gene class model (AUC: 0.707;95% CI: 0.664 - 0.750) performed better in predicting mortality than sepsis response signature (SRS) endotypes (AUC: 0.610;95% CI: 0.521 - 0.700), and performed equivalently to the APACHE II score (AUC: 0.681;95% CI: 0.595 - 0.767). In the dataset E-MTAB-7581, the use of hydrocortisone was associated with increased risk of mortality (OR: 3.15 [1.13, 8.82];p = 0.029) in class 2. The effect was not statistically significant in class 1 (OR: 1.88 [0.70, 5.09];p = 0.211). Interpretati
Context: We study the benefits of using a large public neuroimaging database composed of functional magnetic resonance imaging (fMRI) statistic maps, in a self-taught learning framework, for improving brain decoding o...
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Context: We study the benefits of using a large public neuroimaging database composed of functional magnetic resonance imaging (fMRI) statistic maps, in a self-taught learning framework, for improving brain decoding on new tasks. First, we leverage the NeuroVault database to train, on a selection of relevant statistic maps, a convolutional autoencoder to reconstruct these maps. Then, we use this trained encoder to initialize a supervised convolutional neural network to classify tasks or cognitive processes of unseen statistic maps from large collections of the NeuroVault database. Results: We show that such a self-taught learning process always improves the performance of the classifiers, but the magnitude of the benefits strongly depends on the number of samples available both for pretraining and fine-tuning the models and on the complexity of the targeted downstream task. Conclusion: The pretrained model improves the classification performance and displays more generalizable features, less sensitive to individual differences.
A Wireless Intrusion Detection System is an important part of any system or company connected to the internet and has a wireless connection inside it because of the increasing number of internal or external attacks on...
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A Wireless Intrusion Detection System is an important part of any system or company connected to the internet and has a wireless connection inside it because of the increasing number of internal or external attacks on the network. These WIDS systems are used to predict and detect wireless network attacks such as flooding, DoS attack, and evil- twin that badly affect system availability. Artificial intelligence (Machine Learning, Deep Learning) are popular techniques used as a good solution to build effective network intrusion detection. That's because of the ability of these algorithms to learn complicated behaviors and then use the learned system for discovering and detecting network attacks. In this work, we have performed an autoencoder with a DNN deep algorithm for protecting the companies by detecting intrusion and attacks in 5G wireless networks. We used the Aegean Wi-Fi Intrusion dataset (AWID). Our WIDS resulted in a very good performance with an accuracy of 99% for the dataset attack types: Flooding, Impersonation, and Injection.
Inferring gene regulatory networks through deep learning and causal inference methods is a crucial task in the field of computational biology and bioinformatics. This study presents a novel approach that uses a Graph ...
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Inferring gene regulatory networks through deep learning and causal inference methods is a crucial task in the field of computational biology and bioinformatics. This study presents a novel approach that uses a Graph Convolutional Network (GCN) guided by causal information to infer Gene Regulatory Networks (GRN). The transfer entropy and reconstruction layer are utilized to achieve causal feature reconstruction, mitigating the information loss problem caused by multiple rounds of neighbor aggregation in GCN, resulting in a causal and integrated representation of node features. Separable features are extracted from gene expression data by the Gaussian-kernel autoencoder to improve computational efficiency. Experimental results on the DREAM5 and the mDC dataset demonstrate that our method exhibits superior performance compared to existing algorithms, as indicated by the higher values of the AUPRC metrics. Furthermore, the incorporation of causal feature reconstruction enhances the inferred GRN, rendering them more reasonable, accurate, and reliable.
A fundamental hindrance to building data-driven reduced-order models (ROMs) is the poor topological quality of a low-dimensional data projection. This includes behavior such as overlapping, twisting, or large curvatur...
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A fundamental hindrance to building data-driven reduced-order models (ROMs) is the poor topological quality of a low-dimensional data projection. This includes behavior such as overlapping, twisting, or large curvatures or uneven data density that can generate nonuniqueness and steep gradients in quantities of interest (QoIs). Here, we employ an encoder-decoder neural network architecture for dimensionality reduction. We find that nonlinear decoding of projection-dependent QoIs, when embedded in a dimensionality reduction technique, promotes improved low-dimensional representations of complex multiscale and multiphysics datasets. When data projection (encoding) is affected by forcing accurate nonlinear reconstruction of the QoIs (decoding), we minimize nonuniqueness and gradients in representing QoIs on a projection. This in turn leads to enhanced predictive accuracy of a ROM. Our findings are relevant to a variety of disciplines that develop data-driven ROMs of dynamical systems such as reacting flows, plasma physics, atmospheric physics, or computational neuroscience.
In this work, a machine learning model is trained on the basis of an autoencoder. The aim of the model is to recognise faulty cuts during laser cutting, as faulty cuts lead to high reject rates. The literature shows t...
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