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Graph-Based Semisupervised Learning With Weighted Features for Hyperspectral Remote Sensing Image Classification

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 6356-6370页

作者： Wang, Qingyan Zhang, Qi Zhang, Junping Kang, Shouqiang Wang, Yujing Harbin Univ Sci & Technol Sch Measurement Control & Commun Engn Harbin 150080 Peoples R China Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China

Graph neural network has an excellent performance in obtaining the similarity relationship of samples, so it has been widely used in computer vision. But the hyperspectral remote sensing image (HSI) has some problems, such as data redundancy, noise, lack of labeled samples, and insufficient utilization of spatial information. These problems affect the accuracy of HSI classification using graph neural networks. To solve the aforementioned problems, this article proposes graph-based semisupervised learning with weighted features for HSI classification. The method proposed in this article first uses the stacked autoencoder network to extract features, which is used to remove the redundancy of HSI data. Then, the similarity attenuation coefficient is introduced to improve the original feature weighting scheme. In this way, the contribution difference of adjacent pixels to the center pixel is reflected. Finally, to obtain more generalized spectral features, a shallow feature extraction mechanism is added to the stacked autoencoder network. And features that have good generalization can solve the problem of the lack of labeled samples. The experiment on three different types of datasets demonstrates that the proposed method in this article can get better classification performance in the case of the scarcity of labeled samples than other classification methods.

关键词： Feature extraction Merging Data mining Convolution Hyperspectral imaging Generative adversarial networks Decoding Convolutional neural network (CNN) graph with weighted features hyperspectral image classification stacked autoencoder

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Abnormal Driving Detection With Normalized Driving Behavior Data: A Deep Learning Approach

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2020年第7期69卷 6943-6951页

作者： Hu, Jie Zhang, Xiaoqin Maybank, Steve Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China Wenzhou Univ Inst Big Data & Informat Technol Wenzhou 325035 Peoples R China Birkbeck Coll Sch Comp Sci & Informat Syst London WC1E 7HX England

Abnormal driving may cause serious danger to both the driver and the public. Existing detectors of abnormal driving behavior are mainly based on shallow models, which require large quantities of labeled data. The acquisition and labelling of abnormal driving data are, however, difficult, labor-intensive and time-consuming. This situation inspires us to rethink the abnormal driving detection problem and to apply deep architecture models. In this study, we establish a novel deep-learning-based model for abnormal driving detection. A stacked sparse autoencoders model is used to learn generic driving behavior features. The model is trained in a greedy layer-wise fashion. As far as the authors know, this is the first time that a deep learning approach is applied using autoencoders as building blocks to represent driving features for abnormal driving detection. In addition, a method for denoising is added to the algorithm to increase the robustness of feature expression. The dropout technology is introduced into the entire training process to avoid overfitting. Experiments carried out on our self-created driving behavior dataset demonstrate that the proposed scheme achieves a superior performance for abnormal driving detection compared to the state-of-the-art.

关键词： Vehicles Machine learning Feature extraction Data models Adaptation models Biological system modeling Acceleration Abnormal driving detection deep learning stacked autoencoder

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Integrating pixels and segments: A deep-learning method inspired by the informational diversity of the visual pathways

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NEUROCOMPUTING 2020年 396卷 314-323页

作者： Tang, Xue-song Wei, Hui Hao, Kuangrong Zhao, Mingbo Li, Dawei Donghua Univ Coll Informat Sci & Technol Engn Res Ctr Digitized Text Apparel Technol Minist Educ Shanghai 201620 Peoples R China Fudan Univ Sch Comp Sci Lab Cognit Model & Algorithm 825 Zhangheng Rd Shanghai 201203 Peoples R China

Visual cortex is able to process information in multiple pathways and integrate various forms of representations. This paper proposed a bio-inspired method that utilizes the line-segment-based representation to perform a dedicated channel for the geometric feature learning process. The extracted geometric information can be integrated with the original pixel-based information and implemented on both the convolutional neural networks (SegCNN) and the stacked autoencoders (SegSAE). Segment-based operations such as segConvolve and segPooling are designed to further process the extracted geometric features. The proposed models are verified on the MNIST dataset, Caltech 101 dataset and QuickDraw dataset for image classification. According to the experimental results, the proposed models can facilitate the classification accuracies especially when the sizes of the training set are limited. Particularly, the method based on multiple representations is found to be effective for classifying the hand-drawn sketches. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Deep neural networks Visual cortex modelling Segment-based representation Convolutional neural network stacked autoencoder

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A Learning-Based Multimodel Integrated Framework for Dynamic Traffic Flow Forecasting

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NEURAL PROCESSING LETTERS 2019年第1期49卷 407-430页

作者： Zhou, Teng Han, Guoqiang Xu, Xuemiao Han, Chu Huang, Yuchang Qin, Jing Shantou Univ Dept Comp Sci Coll Engn Shantou Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou Guangdong Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Sha Tin Hong Kong Peoples R China South China Agr Univ Coll Math & Informat Guangzhou Guangdong Peoples R China Hong Kong Polytech Univ Ctr Smart Hlth Sch Nursing Kowloon Hong Kong Peoples R China

Accurate and timely traffic flow forecasting is essential for many intelligent transportation systems. However, it is quite challenging to develop an efficient and robust forecasting model due to the inherent randomness and large variations of traffic flow. Over the past two decades, a variety of traffic flow forecasting models have been proposed. While each model has its merits and can achieve satisfactory forecasting results under certain traffic conditions, it is difficult for a single model to deal with various conditions well. In this paper, we proposed a novel deep learning-based multimodel integration framework in order to overcome the limitations of previous methods in dealing with large variations and uncertainties of traffic flow and hence improve the forecasting accuracy. Our framework can dynamically choose an optimal model or an optimal subset of models from a set of candidate models to forecast the future traffic flow conditions according to current input data. We employ stacked autoencoder (SAE), a simple yet efficient deep learning architecture, to extract the implicit relationships hidden in the traffic flow data and employed labeled data to fine tune the parameters of the architecture. Compared with the hand-crafted features and explicable dependence relations leveraged in previous models, the features learning from SAE are more representative and hence have more powerful forecasting capability. In addition, we propose a model-driven scheme to automatically label the training data and develop three strategies to integrate multiple models. Extensive experiments performed on three typical traffic flow datasets demonstrate the proposed framework outperforms state-of-the-art models and achieves much more accurate forecasting results under large and sudden variations.

关键词： Traffic flow forecasting stacked autoencoder Multimodel integration Variation and uncertainty Deep learning

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A deep learning-based intrusion detection approach for mobile Ad-hoc network

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SOFT COMPUTING 2023年第14期27卷 9425-9439页

作者： Meddeb, Rahma Jemili, Farah Triki, Bayrem Korbaa, Ouajdi Univ Sousse MARS Res Lab ISITCom LR17ES05 Hammam Sousse 4011 Tunisia

The goal of the paper is to present a stacked autoencoder approach for enhancing Intrusion Detection Systems (IDSs) in Mobile Ad-Hoc Networks (MANETs). The paper proposes a stacked autoencoder-based approach for MANET (stacked AE-IDS) to reduce correlation and model relevant features with high-level representation. This method reproduces the input with a reduced correlation, and the output of the autoencoder is used as the input of the Deep Neural Network (DNN) classifier (DNN-IDS). The proposed Deep Learning-based IDS focuses on Denial of Services (DoS) attacks within labeled datasets, which are available for intrusion detection, and employs the most potential attacks that impact routing services in Mobile Networks. The proposed stacked AE-IDS method enhances the effectiveness of IDSs in detecting attacks in MANETs by reducing the correlation and modeling high-level representations of relevant features. The focus on DoS attacks and their impact on routing services in Mobile Networks makes the proposed approach particularly relevant for MANET security. The proposed stacked AE-IDS approach has potential applications in enhancing the security of MANETs by improving the effectiveness of IDSs. This approach can be used to detect different types of attacks, particularly DoS attacks, and their impact on routing services in Mobile Networks.

关键词： Mobile Ad-Hoc networks Intrusion detection systems Semi-supervised learning stacked autoencoder Deep neural network Denial of service attack

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Global-Local Consistency Constrained Deep Embedded Clustering for Hyperspectral Band Selection

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IEEE ACCESS 2023年 11卷 129709-129721页

作者： Ning, Shangfeng Wang, Wenhong Liaocheng Univ Coll Comp Sci Liaocheng 252059 Peoples R China

Hyperspectral band selection plays a key role for overcoming the curse of dimensionality in the classification of hyperspectral remote sensing images (HSIs). Recently, clustering-based band selection methods have demonstrated great potential to select informative and representative bands for hyperspectral classification tasks. However, most clustering-based methods perform clustering directly on the original high-dimensional data, which reduces their performance. To address this problem, a novel band selection method called global-local consistency constrained deep embedded clustering (GLC-DEC) is proposed in this paper. In GLC-DEC, to simultaneously learn the low-dimensional embedded representation and cluster assignments of all bands in an HSI, the stacked autoencoder is integrated with the K-means method. In addition, to reduce the adverse impact of a limited number of training samples available in HSIs, local and global consistency constraints are imposed on the embedded representation so that discriminatively consistent representation of all bands is learned. Specifically, local graph regularization and global graph regularization are introduced into the GLC-DEC model, by which the strong correlation between neighboring bands and the manifold structure of all bands are fully exploited. Based on the clustering results provided by GLC-DEC, a group of representative bands are selected by using the minimum noise method. Experimental results on two real datasets demonstrate that the proposed GLC-DEC outperformed several state-of-the-art methods.

关键词： Hyperspectral band selection deep embedded clustering stacked autoencoder representation learning graph regularization

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A novel scalable intrusion detection system based on deep learning

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INTERNATIONAL JOURNAL OF INFORMATION SECURITY 2021年第3期20卷 387-403页

作者： Mighan, Soosan Naderi Kahani, Mohsen Ferdowsi Univ Mashhad Fac Engn Comp Dept Mashhad Razavi Khorasan Iran

This paper successfully tackles the problem of processing a vast amount of security related data for the task of network intrusion detection. It employs Apache Spark, as a big data processing tool, for processing a large size of network traffic data. Also, we propose a hybrid scheme that combines the advantages of deep network and machine learning methods. Initially, stacked autoencoder network is used for latent feature extraction, which is followed by several classification-based intrusion detection methods, such as support vector machine, random forest, decision trees, and naive Bayes which are used for fast and efficient detection of intrusion in massive network traffic data. A real time UNB ISCX 2012 dataset is used to validate our proposed method and the performance is evaluated in terms of accuracy, f-measure, sensitivity, precision and time.

关键词： Apache Spark stacked autoencoder Latent Accuracy ISCX Intrusion detection

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An Efficient Deep Learning Approach To IoT Intrusion Detection

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COMPUTER JOURNAL 2022年第11期65卷 2870-2879页

作者： Cao, Jin Lin, Liwei Ma, Ruhui Guan, Haibing Tian, Mengke Wang, Yong Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China FuJian Univ Technol Sch Comp Sci & Math Fuzhou 350028 Fujian Peoples R China Peking Univ Sch Integrated Circuits Beijing 100871 Peoples R China Beijing Microelect Technol Inst Beijing 100076 Peoples R China

With the rapid development of the Internet of Things (IoT), network security challenges are becoming more and more complex, and the scale of intrusion attacks against the network is gradually increasing. Therefore, researchers have proposed Intrusion Detection Systems and constantly designed more effective systems to defend against attacks. One issue to consider is using limited computing power to process complex network data efficiently. In this paper, we take the AWID dataset as an example, propose an efficient data processing method to mitigate the interference caused by redundant data and design a lightweight deep learning-based model to analyze and predict the data category. Finally, we achieve an overall accuracy of 99.77% and an accuracy of 97.95% for attacks on the AWID dataset, with a detection rate of 99.98% for the injection attack. Our model has low computational overhead and a fast response time after training, ensuring the feasibility of applying to edge nodes with weak computational power in the IoT.

关键词： IoT security intrusion detection system attack classification stacked autoencoder

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A data-driven computational framework for non-intrusive reduced-order modelling of turbulent flows passing around bridge piers

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OCEAN ENGINEERING 2024年 308卷

作者： Zhu, Chuanhua Xiao, Dunhui Fu, Jinlong Feng, Yuntian Fu, Rui Wang, Jinsheng Anhui Jianzhu Univ Sch Environm & Energy Engn Hefei 230601 Peoples R China Swansea Univ Zienkiewicz Inst Modelling Data & AI Fac Sci & Engn Swansea SA1 8EN Wales Tongji Univ Sch Math Sci Key Lab Intelligent Comp & Applicat Minist Educ Shanghai 200092 Peoples R China Queen Mary Univ London Fac Sci & Engn Dept Mech Engn London E1 4NS England Tongji Univ State Key Lab Disaster Reduct Civil Engn Shanghai 200092 Peoples R China Univ Birmingham Sch Civil Engn Birmingham B15 2TT England

Repetitively conducting high-fidelity numerical simulations under varying conditions is often a crucial requirement in the optimisation design of offshore bridges and structures. Reduced-order modelling (ROM) provides an efficient approach to quickly and reliably obtain solutions by extracting low-dimensional representations from full-order numerical systems. This paper presents a novel data-driven computational framework for non- intrusive ROM of turbulent/unsteady flows passing around bridge piers, consisting of two interconnected components: the stacked autoencoder (SAE) and the Dynamic Mode Decomposition (DMD). The novelty lies in utilising SAE to achieve nonlinear dimensionality reduction by projecting the full-order dynamical system onto a low-dimensional latent space, followed by constructing reduced-order models through data-driven DMD to represent fluid dynamics in the latent feature space. This new SAE-DMD-based method is applied to develop reduced-order models for two unsteady flow problems, and it is also compared with classical DMD and highfidelity numerical simulations in terms of modelling accuracy, forecasting efficiency and memory requirements. The results demonstrate that the proposed method can rapidly offer reliable predictions while significantly reducing memory usage and it exhibits excellent extrapolation capability by accurately preserving primary nonlinear characteristics of fluid dynamics. This new method shows potential to overcome computational challenges associated with high-resolution numerical modelling for complex large-scale flow problems.

关键词： Model order reduction Unsteady/turbulent flow Computational fluid dynamics stacked autoencoder Dynamic mode decomposition Non-intrusive

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An integrated feature learning approach using deep learning for travel time prediction

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 139卷 112864-000页

作者： Abdollahi, Mohammad Khaleghi, Tannaz Yang, Kai Wayne State Univ Dept Ind & Syst Engn Detroit MI 48202 USA

Travel time data is a vital factor for numbers of performance measures in transportation systems. Travel time prediction is both a challenging and interesting problem in ITS, because of the underlying traffic and events' hidden patterns. In this study, we propose a multi-step deep-learning-based algorithm for predicting travel time. Our algorithm starts with data pre-processing. Then, the data is augmented by incorporating external datasets. Moreover, extensive feature learning and engineering such as spatiotemporal feature analysis, feature extraction, and clustering algorithms is applied to improve the feature space. Furthermore, for representing features we used a deep stacked autoencoder with dropout layer as regularizer. Finally, a deep multi-layer perceptron is trained to predict travel times. For testing our predictive accuracy, we used a 5-fold cross validation to test the generalization of our predictive model. As we observed, the performance of the proposed algorithm is on average 4 min better than applying the deep neural network to the initial feature space. Furthermore, we have noticed that representation learning using stacked autoencoders makes our learner robust to overfitting. Moreover, our algorithm is capable of capturing the general dynamics of the traffic, however further works need to be done for some rare events which impact travel time prediction significantly. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Deep learning stacked autoencoder Transportation Travel time prediction Deep neural network Spatiotemporal analysis

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