Deep Sparse Autoencoder for Feature Extraction and Diagnosis of Locomotive Adhesion Status

Zhang, Changfan; Cheng, Xiang; Liu, Jianhua; He, Jing; Liu, Guangwei

doi:10.1155/2018/8676387

Cited by 38 publications

(25 citation statements)

References 30 publications

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“…where S 2 is the neuron count in the internal layer and β is the weight of sparse penalty term [53]. Fig 4 represents the heatmap of the candidate features selected by Adaptive WSO.…”

Section: E Sparse Autoencoder Neural Networkmentioning

confidence: 99%

Deep Feature Extraction From the Vocal Vectors Using Sparse Autoencoders for Parkinson’s Classification

Xiong

Yao-hua

2020

IEEE Access

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Parkinson's disease (PD) is a chronic, neurodegenerative disorder that mainly affects the motor system of the body. The progression rate is very slow but, as the condition worsens it stimulates the non-motor characteristics, especially mood disorders, hallucinations, delusions, and other observable cognitive changes. Genetic and environmental factors greatly influence the risk of Parkinson's development. The early diagnosis of PD improves the condition through proper treatment. During the initial stage of PD, vocal impairments become more common among the affected individuals. Advanced studies on vocal disorders give more assistance for precise PD detection. In this paper, the vocal features of PD affected individuals are analyzed with sophisticated computational models. Initially, the samples are pre-processed, as it contains more missing values. Then the predictor candidate subset is identified from the processed vocal features using the Adaptive Grey Wolf Optimization Algorithm, a meta-heuristic global search optimization technique. Furthermore, the latent representation of the candidate features is extracted through sparse autoencoders for effective discrimination between the PD affected and control cases. For classification, six supervised machine learning algorithms were employed. The proposed model is trained with the data, divided through 10-fold cross-validation scheme and the performance is evaluated based on validation metrics. The PD dataset is accessed from University of California (UCI), Irvine Machine Learning repository to conduct the experimental analysis. The result shows that the proposed algorithm outperformed the benchmarked models, exhibits its efficacy in distinguishing the affected and healthy samples of PD. The outcome of this study emphasizes the significance of intelligent learning models in complex disease diagnosis.

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“…where S 2 is the neuron count in the internal layer and β is the weight of sparse penalty term [53]. Fig 4 represents the heatmap of the candidate features selected by Adaptive WSO.…”

Section: E Sparse Autoencoder Neural Networkmentioning

confidence: 99%

Deep Feature Extraction From the Vocal Vectors Using Sparse Autoencoders for Parkinson’s Classification

Xiong

Yao-hua

2020

IEEE Access

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“…However, whether there were differences between each kind of behavior signal requires more study on characteristic distribution of each kind of sample signal generated by our network. The unidimensional Convolutional Auto-Encoder (CAE) can effectively show the deep characteristic differences between different samples [ 26 , 27 , 28 ]. Therefore, the output of the auto-encoder was set as a two-dimensional vector.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Unidimensional ACGAN Applied to Link Establishment Behaviors Recognition of a Short-Wave Radio Station

Chen

Lei

2020

Sensors

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It is difficult to obtain many labeled Link Establishment (LE) behavior signals sent by non-cooperative short-wave radio stations. We propose a novel unidimensional Auxiliary Classifier Generative Adversarial Network (ACGAN) to get more signals and then use unidimensional DenseNet to recognize LE behaviors. Firstly, a few real samples were randomly selected from many real signals as the training set of unidimensional ACGAN. Then, the new training set was formed by combining real samples with fake samples generated by the trained ACGAN. In addition, the unidimensional convolutional auto-coder was proposed to describe the reliability of these generated samples. Finally, different LE behaviors could be recognized without the communication protocol standard by using the new training set to train unidimensional DenseNet. Experimental results revealed that unidimensional ACGAN effectively augmented the training set, thus improving the performance of recognition algorithm. When the number of original training samples was 400, 700, 1000, or 1300, the recognition accuracy of unidimensional ACGAN+DenseNet was 1.92, 6.16, 4.63, and 3.06% higher, respectively, than that of unidimensional DenseNet.

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“…The autoencoder can learn the latent representation of features in a reduced space in an unsupervised setting. The DSAE builds on the concept of an autoencoder adding the sparse penalty term, which hinders feature learning to achieve a concise representation of the input vector [ 25 , 26 ]. Furthermore, an autoencoder using non-linear activation functions and multiple layers possesses the ability to obtain non-linear relationships, unlike Principal Component Analysis (PCA).…”

Section: Proposed Methodologymentioning

confidence: 99%

“…Following [ 25 ], the input vector x defines sets of NetFlow data

, which are later reconstructed into an

dataset; therefore,

. These NetFlow data are used as an input matrix X initially condensed to a lower dimension that is expressed by a set of one or more hidden layers

.…”

Section: Proposed Methodologymentioning

confidence: 99%

A Deep Learning Ensemble for Network Anomaly and Cyber-Attack Detection

Dutta

Choraś

Pawlicki

et al. 2020

Sensors

111

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Currently, expert systems and applied machine learning algorithms are widely used to automate network intrusion detection. In critical infrastructure applications of communication technologies, the interaction among various industrial control systems and the Internet environment intrinsic to the IoT technology makes them susceptible to cyber-attacks. Given the existence of the enormous network traffic in critical Cyber-Physical Systems (CPSs), traditional methods of machine learning implemented in network anomaly detection are inefficient. Therefore, recently developed machine learning techniques, with the emphasis on deep learning, are finding their successful implementations in the detection and classification of anomalies at both the network and host levels. This paper presents an ensemble method that leverages deep models such as the Deep Neural Network (DNN) and Long Short-Term Memory (LSTM) and a meta-classifier (i.e., logistic regression) following the principle of stacked generalization. To enhance the capabilities of the proposed approach, the method utilizes a two-step process for the apprehension of network anomalies. In the first stage, data pre-processing, a Deep Sparse AutoEncoder (DSAE) is employed for the feature engineering problem. In the second phase, a stacking ensemble learning approach is utilized for classification. The efficiency of the method disclosed in this work is tested on heterogeneous datasets, including data gathered in the IoT environment, namely IoT-23, LITNET-2020, and NetML-2020. The results of the evaluation of the proposed approach are discussed. Statistical significance is tested and compared to the state-of-the-art approaches in network anomaly detection.

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Deep Sparse Autoencoder for Feature Extraction and Diagnosis of Locomotive Adhesion Status

Cited by 38 publications

References 30 publications

Deep Feature Extraction From the Vocal Vectors Using Sparse Autoencoders for Parkinson’s Classification

Deep Feature Extraction From the Vocal Vectors Using Sparse Autoencoders for Parkinson’s Classification

Unidimensional ACGAN Applied to Link Establishment Behaviors Recognition of a Short-Wave Radio Station

A Deep Learning Ensemble for Network Anomaly and Cyber-Attack Detection

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