A Systematic Review of Deep Dream

doi:10.33103/uot.ijccce.23.2.15

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…These techniques are divided into two categories: the Layers-CNN-model, tailored to specific requirements, and the pre-trained-CNN-model. Which leverages learning transfer using established frameworks like ResNet, VGGNet, GoogleNet, VGG-16, and AlexNet [20,21]. Consistently, Lakovidis et al implemented a twostep approach comprising a Deep Saliency Detection (DSD) algorithm for salient points detection and a Weakly Supervised Convolutional Neural Network (WCNN) for classification [22].…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

A Robust Approach for Ulcer Classification/Detection in WCE Images

Dahmouni,

Ait Abdelouahad,

Aderghal

et al. 2024

Int. J. Onl. Eng.

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Wireless Capsule Endoscopy (WCE) is a medical diagnostic technique recognized for its minimally invasive and painless nature for the patients. It uses remote imaging techniques to explore various segments of the gastrointestinal (GI) tract, particularly the hard-to-reach small intestine, making it an effective alternative to traditional endoscopic techniques. However, physicians face a significant challenge when it comes to analyzing a large number of endoscopic images due to the effort and time required. It is therefore imperative to implement aided-diagnostic systems capable of automatically detecting suspicious areas for subsequent medical assessment. In this paper, we present a novel approach to identify gastrointestinal tract abnormalities from WCE images, with a particular focus on ulcerated areas. Our approach involves the use of the Median Robust Extended Local Binary Pattern (MRELBP) descriptor, which effectively overcomes the challenges faced when WCE image acquisition, such as variations in illumination and contrast, rotation, and noise. Using machine learning algorithms, we conducted experiments on the extensive Kvasir-Capsule dataset, and subsequently compared our results with recent relevant studies. Noteworthy is the fact that our approach achieved an accuracy of 97.04% with the SVM (RBF) classifier and 96.77% with the RF classifier.

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Section: Deep Learning-based Methodsmentioning

confidence: 99%

A Robust Approach for Ulcer Classification/Detection in WCE Images

Dahmouni,

Ait Abdelouahad,

Aderghal

et al. 2024

Int. J. Onl. Eng.

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Prediction of physical realizations of the coordinated universal time with gated recurrent unit

Rivera-Ruiz,

López-Romero,

Mendez-Vazquez

2024

Review of Scientific Instruments

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Coordinated Universal Time (UTC), produced by the Bureau International des Poids et Mesures (BIPM), is the official worldwide time reference. Given that there is no physical signal associated with UTC, physical realizations of the UTC, called UTC(k), are very important for demanding applications such as global navigation satellite systems, communication networks, and national defense and security, among others. Therefore, the prediction of the time differences UTC-UTC(k) is important to maintain the accuracy and stability of the UTC(k) timescales. In this paper, we report for the first time the use of a deep learning (DL) technique called Gated Recurrent Unit (GRU) to predict a sequence of H futures values of the time differences UTC-UTC(k) for ten different UTC(k) timescales. UTC-UTC(k) time differences published on the monthly Circular T document of the BIPM are used as training samples. We utilize a multiple-input, multiple-output prediction strategy. After a training process where about 300 past values of the difference UTC-UTC(k) are used, H (H = 6) values of the Circular T can be predicted using p (typically p = 6) past values. The model has been tested with data from ten different UTC(k) timescales. When comparing GRU results with other standard DL algorithms, we found that the GRU approximation has a good performance in predicting UTC(k) timescales. According to our results, the GRU error in predicting UTC-UTC(k) values is typically 1 ns. The frequency instability of the UTC(k) timescale is the main limitation in reducing the GRU error in the time difference prediction.

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Deep network fault diagnosis for imbalanced small-sized samples via a coupled adversarial autoencoder based on the Bayesian method

Zhang,

Wang,

Zhou

et al. 2024

Review of Scientific Instruments

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Deep network fault diagnosis methods heavily rely on abundant labeled data for effective model training. However, small-sized samples and imbalanced samples often lead to insufficient features, resulting in accuracy degradation and even instability in the diagnosis model. To address this challenge, this paper introduces a coupled adversarial autoencoder (CoAAE) based on the Bayesian method. This model aims to solve the issue of insufficient samples by generating fake samples and integrating them with the original ones. Within the CoAAE framework, the probability density distribution of the original data is captured using an encoder and fake samples are generated by random sampling from this distribution and decoding them. This process is the adversarial interaction between the encoder and a classifier to obtain the prior distribution of the encoder’s parameters. The encoder’s parameters are updated through the decoder’s reconstruction process, leading to the posterior distribution. Concurrently, the decoder is trained to enhance its ability to reconstruct samples accurately. To address the imbalance in the original samples, a parallel coupled network is employed. This network shares the weights of the extraction layer in the encoder, enabling it to learn the joint distribution between fault-related and normal samples. To evaluate the effectiveness of the proposed data augmentation method, experiments were conducted on a bearing database from Case Western Reserve University using ResNet18 as the deep learning diagnosis model representative. The results demonstrate that CoAAE can effectively augment imbalanced datasets and outperform other advanced methods.

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A Systematic Review of Deep Dream

Cited by 3 publications

References 23 publications

A Robust Approach for Ulcer Classification/Detection in WCE Images

A Robust Approach for Ulcer Classification/Detection in WCE Images

Prediction of physical realizations of the coordinated universal time with gated recurrent unit

Deep network fault diagnosis for imbalanced small-sized samples via a coupled adversarial autoencoder based on the Bayesian method

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