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Physiological-signal-based mental workload estimation via transfer dynamical autoencoders in a deep learning framework

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NEUROCOMPUTING 2019年 347卷 212-229页

作者： Yin, Zhong Zhao, Mengyuan Zhang, Wei Wang, Yongxiong Wang, Yagang Zhang, Jianhua Univ Shanghai Sci & Technol Engn Res Ctr Opt Instrument & Syst Shanghai Key Lab Modern Opt Syst Minist Educ Shanghai 200093 Peoples R China Univ Shanghai Sci & Technol Sch Marxism Shanghai 200093 Peoples R China Oslo Metropolitan Univ Dept Comp Sci OsloMet Artificial Intelligence Lab N-0130 Oslo Norway

Evaluating operator mental workload (MW) in human-machine systems via neurophysiological signals is crucial for preventing unpredicted operator performance degradation. However, the feature of physiological signals is associated with the historical values at the previous time steps and its statistical properties vary across individuals and types of mental tasks. In this study, we propose a new transfer dynamical autoencoder (TDAE) to capture the dynamical properties of electroencephalograph (EEG) features and the individual differences. The TDAE consists of three consecutively-connected modules, which are termed as feature filter, abstraction filter, and transferred MW classifier. The feature and abstraction filters introduce dynamical deep network to abstract the EEG features across adjacent time steps to salient MW indicators. Transferred MW classifier exploits large volume EEG data from an source-domain EEG database recorded under emotional stimuli to improve the model training stability. We tested our algorithms on two target EEG databases. The classification performance shows TDAE significantly outperforms existing shallow and deep MW classification models. We also investigated how to select TDAE hyper-parameters and found its superiority in accuracy can be achieved with proper filter orders. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Mental workload Electroencephalograph Deep learning stacked autoencoder Operator functional state

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Multi-stage intrusion detection system aided by grey wolf optimization algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第3期27卷 3819-3836页

作者： Chatterjee, Somnath Shaw, Vaibhav Das, Ranit Future Inst Engn & Management Comp Sci & Engn Dept Sonarpur Stn Rd Kolkata 700150 West Bengal India

A Network Intrusion Detection System (NIDS) is frequently used for monitoring and detecting malicious activities in network traffic. A typical NIDS has four stages: a data source, data pre-processing, a decision-making technique, and a defense reaction. We have utilized both anomaly and signature based techniques to build a framework which is resilient to identifying both known and unknown attack. The incoming data packet is fed into the stacked autoencoder to identify whether it is a benign or malicious. If found to be malicious we extract the most relevant features from the network packet using grey wolf optimization algorithm. Then these attribute are provided to RandomForest classifier to determine if this malign attack is present in our knowledge base. If it is present we progress to identify the attack type using LightGBM classifier. If not, we term it as zero-day attack. To evaluate the usability of the proposed framework we have assessed it using two publicly available datasets namely UNSW-NB15 and CIC-IDS-2017 dataset. We have obtained an accuracy of 90.94% and 99.67% on the datasets respectively.

关键词： Computer networks Intrusion detection system stacked autoencoder Decison trees

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Modified genetic algorithm-based feature selection combined with pre-trained deep neural network for demand forecasting in outpatient department

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 82卷 216-230页

作者： Jiang, Shancheng Chin, Kwai-Sang Wang, Long Qu, Gang Tsui, Kwok L. City Univ Hong Kong Dept Syst Engn & Engn Management 83 Tat Chee Ave Kowloon Tong Hong Kong Peoples R China Dalian Univ Affiliated Xinhua Hosp 146 Wansui St Dalian Liaoning Peoples R China

A well-performed demand forecasting can provide outpatient department (OPD) managers with essential information for staff scheduling and rostering, considering the non-reservation policy of OPD in China. Based on the results reported by relevant studies, most approaches have focused on forecasting the overall amount of patient flow and ignored the demand for other key resources in OPD or similar department. Moreover, few studies have conducted feature selection before training a forecast model, which is a significant pre-processing operation of data mining and widely applied for knowledge discovery in expert and intelligent system. This study develops a novel hybrid methodology to forecast the patients' demand for different key resources in OPD, by combining a new feature selection method and a deep learning approach. A modified version of genetic algorithm (MGA) is proposed for feature selection. The key operators of normal genetic algorithm are redesigned to utilize useful information provided by filter-based feature selection and feature combinations. A feedforward deep neural network is introduced as the forecast model, and the initial parameter set is generated from a stacked autoencoder-based pre-training process to overcome the optimization challenges in constructing deep architectures. In order to evaluate the performance of our methodology, it is applied to an OPD located at Northeast China. The results are compared with those obtained from combinations of other feature selection methods and demand forecasting models. Compared with GA and PCA, MGA improves the quality and efficiency of feature selection, with less selected features to get higher forecast accuracy. Pre-trained DNN optimally strengthens the advantage of MGA, compared with MLR, ARIMAX and SANN. The combination of MGA and pre-trained DNN possesses strongest predictive power among all involved combinations. Furthermore, the results of proposed methodology are crucial prerequisites for staff sche

关键词： Deep learning Feature selection Modified genetic algorithm stacked autoencoder Demand forecasting in hospital

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

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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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