检索结果-内蒙古大学图书馆

Optimized Deep autoencoder Model for Internet of Things Intruder Detection

IEEE ACCESS 2022年 10卷 8434-8448页

作者： Lahasan, Badr Samma, Hussein Univ Shabwah Fac Comp & Informat Technol Shabwah Yemen Univ Teknol Malaysia UTM Fac Engn Sch Comp Johor Baharu 81310 Johor Malaysia

The development of an optimized deep learning intruder detection model that could be executed on IoT devices with limited hardware support has several advantages, such as the reduction of communication energy, lowering latency, and protecting data privacy. Motivated by these benefits, this research aims to design a lightweight autoencoder deep model that has a shallow architecture with a small number of input features and a few hidden neurons. To achieve this objective, an efficient two-layer optimizer is used to evolve a lightweight deep autoencoder model by performing simultaneous selection for the input features, the training instances, and the number of hidden neurons. The optimized deep model is constructed guided by both the accuracy of a K-nearest neighbor (KNN) classifier and the complexity of the autoencoder model. To evaluate the performance of the proposed optimized model, it has been applied for the N-baiot intrusion detection dataset. Reported results showed that the proposed model achieved anomaly detection accuracy of 99% with a lightweight autoencoder model with on average input features around 30 and output hidden neurons of 2 only. In addition, the proposed two-layers optimizer was able to outperform several optimizers such as Arithmetic Optimization Algorithm (AOA), Particle Swarm Optimization (PSO), and Reinforcement Learning-based Memetic Particle Swarm Optimization (RLMPSO).

关键词： Convolutional neural networks Floods Deep learning Data models Optimization Botnet Anomaly detection Deep learning autoencoder IoT anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Novel Deep Learning-Enabled LSTM autoencoder Architecture for Discovering Anomalous Events From Intelligent Transportation Systems

引用

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第7期22卷 4507-4518页

作者： Ashraf, Javed Bakhshi, Asim D. Moustafa, Nour Khurshid, Hasnat Javed, Abdullah Beheshti, Amin Natl Sci & Technol NUST Dept Comp Software Engn Islamabad 44000 Pakistan Univ New South Wales ADFA Sch Engn & Informat Technol Canberra ACT 2612 Australia Sir Syed Ctr Adv Studies Engn CASE Inst Technol Islamabad 44000 Pakistan Macquarie Univ Dept Comp Sydney NSW 2109 Australia

Intelligent Transportation Systems (ITS), especially Autonomous Vehicles (AVs), are vulnerable to security and safety issues that threaten the lives of the people. Unlike manual vehicles, the security of communications and computing components of AVs can be compromised using advanced hacking techniques, thus barring AVs from the effective use in our routine lives. Once manual vehicles are connected to the Internet, called the Internet of Vehicles (IoVs), it would be exploited by cyber-attacks, like denial of service, sniffing, distributed denial of service, spoofing and replay attacks. In this article, we present a deep learning-based Intrusion Detection System (IDS) for ITS, in particular, to discover suspicious network activity of In-Vehicles Networks (IVN), vehicles to vehicles (V2V) communications and vehicles to infrastructure (V2I) networks. A Deep Learning architecture-based Long-Short Term Memory (LSTM) autoencoder algorithm is designed to recognize intrusive events from the central network gateways of AVs. The proposed IDS is evaluated using two benchmark datasets, i.e., the car hacking dataset for in-vehicle communications and the UNSW-NB15 dataset for external network communications. The experimental results demonstrated that our proposed system achieved over a 99% accuracy for detecting all types of attacks on the car hacking dataset and a 98% accuracy on the UNSW-NB15 dataset, outperforming other eight intrusion detection techniques.

关键词： Intelligent transport systems CAN bus internet of vehicles autonomous vehicles intrusion detection system deep learning LSTM autoencoder

来源：评论

学校读者我要写书评

暂无评论

Automated eye blink artefact removal from EEG using support vector machine and autoencoder

引用

IET SIGNAL PROCESSING 2019年第2期13卷 141-148页

作者： Ghosh, Rajdeep Sinha, Nidul Biswas, Saroj Kumar NIT Silchar Dept Comp Sci & Engn Silchar Assam India NIT Silchar Dept Elect Engn Silchar Assam India

Electroencephalogram (EEG) is a highly sensitive instrument and is frequently corrupted with eye blinks. Methods based on adaptive noise cancellation (ANC) and discrete wavelet transform (DWT) have been used as a standard technique for removal of eye blink artefacts. However, these methods often require visual inspection and appropriate thresholding for identifying and removing artefactual components from the EEG signal. The proposed work describes an automated windowed method with a window size of 0.45 s that is slid forward and fed to a support vector machine (SVM) classifier for identification of artefacts, after the identification of artefacts, it is fed to an autoencoder for correction of artefacts. The proposed method is evaluated on the data collected from the project entitled 'Analysis of Brain Waves and Development of Intelligent Model for Silent Speech Recognition'. From the results it is observed that the proposed method performs better in identifying and removing artefactual components from EEG data than existing wavelet and ANC based methods. The proposed method does not require the application of independent component analysis (ICA) before processing and can be applied to multiple channels in parallel.

关键词： electroencephalography discrete wavelet transforms medical signal processing support vector machines signal denoising eye signal classification visual inspection automated windowed method autoencoder automated eye blink artefact removal support vector machine highly sensitive instrument discrete wavelet transform adaptive noise cancellation EEG signal electroencephalogram EEG data

来源：评论

学校读者我要写书评

暂无评论

Impact of autoencoder based compact representation on emotion detection from audio

引用

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022年第2期13卷 867-885页

作者： Patel, Nivedita Patel, Shireen Mankad, Sapan H. Nirma Univ Inst Technol CSE Dept Ahmadabad Gujarat India

Emotion recognition from speech has its fair share of applications and consequently extensive research has been done over the past few years in this interesting field. However, many of the existing solutions aren't yet ready for real time applications. In this work, we propose a compact representation of audio using conventional autoencoders for dimensionality reduction, and test the approach on two benchmark publicly available datasets. Such compact and simple classification systems where the computing cost is low and memory is managed efficiently may be more useful for real time application. System is evaluated on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and the Toronto Emotional Speech Set (TESS). Three classifiers, namely, support vector machines (SVM), decision tree classifier, and convolutional neural networks (CNN) have been implemented to judge the impact of the approach. The results obtained by attempting classification with Alexnet and Resnet50 are also reported. Observations proved that this introduction of autoencoders indeed can improve the classification accuracy of the emotion in the input audio files. It can be concluded that in emotion recognition from speech, the choice and application of dimensionality reduction of audio features impacts the results that are achieved and therefore, by working on this aspect of the general speech emotion recognition model, it may be possible to make great improvements in the future.

关键词： Audio Emotion RAVDESS TESS autoencoder

来源：评论

学校读者我要写书评

暂无评论

DSVD-autoencoder: A scalable distributed privacy-preserving method for one-class classification

引用

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2021年第1期36卷 177-199页

作者： Fontenla-Romero, Oscar Perez-Sanchez, Beatriz Guijarro-Berdinas, Bertha Univ A Coruna CITIC Fac Informat Dept Comp Sci & Informat Technol Campus Elvina S-N La Coruna 15071 Spain

One-class classification has gained interest as a solution to certain kinds of problems typical in a wide variety of real environments like anomaly or novelty detection. autoencoder is the type of neural network that has been widely applied in these one-class problems. In the Big Data era, new challenges have arisen, mainly related with the data volume. Another main concern derives from Privacy issues when data is distributed and cannot be shared among locations. These two conditions make many of the classic and brilliant methods not applicable. In this paper, we present distributed singular value decomposition (DSVD-autoencoder), a method for autoencoders that allows learning in distributed scenarios without sharing raw data. Additionally, to guarantee privacy, it is noniterative and hyperparameter-free, two interesting characteristics when dealing with Big Data. In comparison with the state of the art, results demonstrate that DSVD-autoencoder provides a highly competitive solution to deal with very large data sets by reducing training from several hours to seconds while maintaining good accuracy.

关键词： autoencoder big data distributed learning neural network one-class classification privacy-preserving singular value decomposition

来源：评论

学校读者我要写书评

暂无评论

Residual-recursive autoencoder for accelerated evolution in savonius wind turbines optimization

引用

NEUROCOMPUTING 2022年第0期500卷 909-920页

作者： Zhou, Qianwei Li, Baoqing Tao, Peng Xu, Zhang Zhou, Chen Wu, Yanzhuang Hu, Haigen Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou Peoples R China Key Lab Visual Media Intelligent Proc Technol Zhej Hangzhou Peoples R China Zhejiang Univ Technol Coll Mech Engn Hangzhou Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Beijing Peoples R China

Recent studies verified that a genetic algorithm can discover efficient and innovative wind turbines by using image encoding and decoding techniques. To accelerate the optimization, in this work, ResidualRecursion autoencoder (RRAE) is proposed to extract low-dimensional latent codes from rotors' crosssection images while maintaining reconstruction accuracy as high as possible. As a kind of neural network framework, the advantages of using RRAE are threefold: 1) RRAE can wrap over different kinds of autoencoders and improve their performance;2) RRAE is compatible with different kinds of loss functions and works well with very low-dimensional latent codes;3) RRAE is easy to use and efficient in decoding latent codes which is important to the rapid convergence of the genetic algorithm. The experiment results has shown that the reconstruction loss has decreased by 30.56% on a recursive autoencoder, 11.40% to 29.34% on different feedforward autoencoders. Two RRAE-accelerated optimizations have been carried out in this work. One has used only 14% of the calculation required by the baseline method without any deterioration in rotor performance. The other one has used 52.33% and increased the rotor performance by 7.59%.

关键词： Recursive neural network autoencoder Deep learning Geometry optimization Savonius rotor

来源：评论

学校读者我要写书评

暂无评论

An Integral Projection-Based Semantic autoencoder for Zero-Shot Learning

引用

IEEE ACCESS 2023年 11卷 85351-85360页

作者： Heyden, William Ullah, Habib Siddiqui, Muhammad Salman Al-Machot, Fadi Norwegian Univ Life Sci NMBU Fac Sci & Technol REALTEK N-1430 As Norway

Zero-shot Learning (ZSL) classification categorizes or predicts classes (labels) that are not included in the training set (unseen classes). Recent works proposed different semantic autoencoder (SAE) models where the encoder embeds a visual feature vector space into the semantic space and the decoder reconstructs the original visual feature space. The objective is to learn the embedding by leveraging a source data distribution, which can be applied effectively to a different but related target data distribution. Such embedding-based methods are prone to domain shift problems and are vulnerable to biases. We propose an integral projection-based semantic autoencoder (IP-SAE) where an encoder projects a visual feature space concatenated with the semantic space into a latent representation space. We force the decoder to reconstruct the visual-semantic data space. Due to this constraint, the visual-semantic projection function preserves the discriminatory data included inside the original visual feature space. The enriched projection forces a more precise reconstitution of the visual feature space invariant to the domain manifold. Consequently, the learned projection function is less domain-specific and alleviates the domain shift problem. Our proposed IP-SAE model consolidates a symmetric transformation function for embedding and projection, and thus, it provides transparency for interpreting generative applications in ZSL. Therefore, in addition to outperforming state-of-the-art methods considering four benchmark datasets, our analytical approach allows us to investigate distinct characteristics of generative-based methods in the unique context of zero-shot inference.

关键词： autoencoder generative modelling generative regularisation latent space linear transfor-mation semantic embedding visual projection zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

Comparing autoencoder Variants for Real-Time Denoising of Hyperspectral X-Ray

引用

IEEE SENSORS JOURNAL 2022年第18期22卷 17997-18007页

作者： Bonettini, Nicolo Gonano, Carlo Andrea Bestagini, Paolo Marcon, Marco Garavelli, Bruno Tubaro, Stefano Politecn Milan Comp Sci & Engn Milan Italy Politecn Milan Elect Engn Milan Italy Politecn Milan Informat Technol Milan Italy Informat & Commun Technol ICT Trieste Italy

Hyperspectral X ray analysis is used in many industrial pipelines, from quality control to detection of low-density contaminants in food. Unfortunately, the signal acquired by X-ray sensors is often affected by a great amount of noise. This hinders the performance of most of the applications building on top of these acquisitions (e.g., detection of food contaminants). Therefore, a good denoising pipeline is necessary. This article proposes a comparison between three different autoencoder variants: the Variational autoencoder, the Augmented autoencoder, and a plain vanilla autoencoder. All the networks are trained in an unsupervised fashion to denoise a given noisy spectrum. Focusing on the specific application of recognizing possible food contaminants, we force the latent space of the networks to have just two parameters, as suggested by the physical law of Lambert- Beer. We validate our experiments on a synthetic dataset composed of roughly 15 million spectra. Results suggest that the Augmented autoencoder is the best network configuration for this task, showing excellent performance without suffering from the nondeterministic behavior of the Variational autoencoder.

关键词： autoencoder denoising hyperspectral X-ray

来源：评论

学校读者我要写书评

暂无评论

An autoencoder-Based I/Q Channel Interaction Enhancement Method for Automatic Modulation Recognition

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第7期72卷 9620-9625页

作者： Zhang, Fuxin Luo, Chunbo Xu, Jialang Luo, Yang Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

This article proposes an autoencoder-based method to enhance the information interaction between in-phase/quadrature (I/Q) channels of the input data for automatic modulation recognition (AMR). The proposed method utilizes an autoencoder built by fully-connected layers to correlate the features of I/Q data and obtain the interaction feature from the intermediate layer, which is concatenated together with the original I/Q data as model inputs. To accommodate the new data dimensions, a modification scheme for the existing representative deep learning based AMR (DL-AMR) models is presented. Experimental results show that our method can improve the recognition accuracy of the state-of-the-art baseline models, and has a smaller time overhead compared with complex-valued neural networks.

关键词： Automatic modulation recognition in-phase quadrature signal deep learning autoencoder

来源：评论

学校读者我要写书评

暂无评论

An end-to-end multi-scale network based on autoencoder for infrared and visible image fusion

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第13期82卷 20139-20156页

作者： Liu, Hongzhe Yan, Hua Sichuan Univ Coll Elect & Informat Engn Chengdu 610065 Sichuan Peoples R China

Infrared and visible image fusion aims to obtain a more informative fusion image by merging the infrared and visible images. However, the existing methods have some shortcomings, such as detail information loss, unclear boundaries, and not being end-to-end. In this paper, we propose an end-to-end network architecture for infrared and visible image fusion task. Our network contains three essential parts: encoders, residual fusion module, and decoder. First, we input infrared and visible images to two encoders to extract shallow features, respectively. Subsequently, the two sets of features are concatenated and fed to the residual fusion module to extract multi-scale features and fuse them adequately. Finally, the fused image is obtained by the decoder. We conduct objective and subjective experiments on two public datasets. The comparison results with the state-of-art methods prove that the fusion results of the proposed method have better objective metrics and contain more detail information and more explicit boundary.

关键词： autoencoder Image fusion Infrared image Visible image

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：