A Novel Biometric Image Enhancement Approach With the Hybridization of Undecimated Wavelet Transform and Deep Autoencoder

Sasirekha, K.; Thangavel, K.

doi:10.4018/978-1-5225-9611-0.ch012

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…While constructing a deep autoencoder, each layer receives its input from previous layer. In particularly, the autoencoder is trained to convert the raw input into some latent or more abstract representation and the output is reconstructed from that compressed representation [25][26][27][28][29]. The encoder receives the raw data as input I(x) ∈ R d and maps into a latent representation H(x) ∈ R � with the function as, Here, sigmoid is used as the learning function.…”

Section: Deep Autoencodermentioning

confidence: 99%

A novel GPU based intrusion detection system using deep autoencoder with Fruitfly optimization

et al. 2021

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Intrusion Detection Systems (IDSs) have received more attention to safeguarding the vital information in a network system of an organization. Generally, the hackers are easily entering into a secured network through loopholes and smart attacks. In such situation, predicting attacks from normal packets is tedious, much challenging, time consuming and highly technical. As a result, different algorithms with varying learning and training capacity have been explored in the literature. However, the existing Intrusion Detection methods could not meet the desired performance requirements. Hence, this work proposes a new Intrusion Detection technique using Deep Autoencoder with Fruitfly Optimization. Initially, missing values in the dataset have been imputed with the Fuzzy C-Means Rough Parameter (FCMRP) algorithm which handles the imprecision in datasets with the exploit of fuzzy and rough sets while preserving crucial information. Then, robust features are extracted from Autoencoder with multiple hidden layers. Finally, the obtained features are fed to Back Propagation Neural Network (BPN) to classify the attacks. Furthermore, the neurons in the hidden layers of Deep Autoencoder are optimized with population based Fruitfly Optimization algorithm. Experiments have been conducted on NSL_KDD and UNSW-NB15 dataset. The computational results of the proposed intrusion detection system using deep autoencoder with BPN are compared with Naive Bayes, Support Vector Machine (SVM), Radial Basis Function Network (RBFN), BPN, and Autoencoder with Softmax. Article Highlights A hybridized model using Deep Autoencoder with Fruitfly Optimization is introduced to classify the attacks. Missing values have been imputed with the Fuzzy C-Means Rough Parameter method. The discriminate features are extracted using Deep Autoencoder with more hidden layers.

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Section: Deep Autoencodermentioning

confidence: 99%

A novel GPU based intrusion detection system using deep autoencoder with Fruitfly optimization

et al. 2021

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“…10,11 Recent studies have also demonstrated that when dealing with signals, their transformation into images could provide competitive results in supervised setups. [12][13][14] In these works, the authors show that encoding time series to images can help to emphasize, capture, or condense local patterns that would otherwise be spread over time. In the literature, we identified four promising encoding algorithms: the Gramian Angular Field, the Markov Transition Field, 15 the recurrence plot, 16 and grey scale encoding.…”

Section: Introductionmentioning

confidence: 99%

Temporal signals to images: Monitoring the condition of industrial assets with deep learning image processing algorithms

Garcia

Michau

Ducoffe

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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The ability to detect anomalies in time series is considered highly valuable in numerous application domains. The sequential nature of time series objects is responsible for an additional feature complexity, ultimately requiring specialized approaches in order to solve the task. Essential characteristics of time series, situated outside the time domain, are often difficult to capture with state-of-the-art anomaly detection methods when no transformations have been applied to the time series. Inspired by the success of deep learning methods in computer vision, several studies have proposed transforming time series into image-like representations, used as inputs for deep learning models, and have led to very promising results in classification tasks. In this paper, we first review the signal to image encoding approaches found in the literature. Second, we propose modifications to some of their original formulations to make them more robust to the variability in large datasets. Third, we compare them on the basis of a common unsupervised task to demonstrate how the choice of the encoding can impact the results when used in the same deep learning architecture. We thus provide a comparison between six encoding algorithms with and without the proposed modifications. The selected encoding methods are Gramian Angular Field, Markov Transition Field, recurrence plot, grey scale encoding, spectrogram, and scalogram. We also compare the results achieved with the raw signal used as input for another deep learning model. We demonstrate that some encodings have a competitive advantage and might be worth considering within a deep learning framework. The comparison is performed on a dataset collected and released by Airbus SAS, containing highly complex vibration measurements from real helicopter flight tests. The different encodings provide competitive results for anomaly detection.

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Stress detection with encoding physiological signals and convolutional neural network

Quadrini,

Capuccio,

Falcone

et al. 2024

Mach Learn

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A Novel Biometric Image Enhancement Approach With the Hybridization of Undecimated Wavelet Transform and Deep Autoencoder

Cited by 5 publications

References 30 publications

A novel GPU based intrusion detection system using deep autoencoder with Fruitfly optimization

A novel GPU based intrusion detection system using deep autoencoder with Fruitfly optimization

Temporal signals to images: Monitoring the condition of industrial assets with deep learning image processing algorithms

Stress detection with encoding physiological signals and convolutional neural network

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