Trust‐aware generative adversarial network with recurrent neural network for recommender systems

2020 ◽  
Author(s):  
Honglong Chen ◽  
Shuai Wang ◽  
Nan Jiang ◽  
Zhe Li ◽  
Na Yan ◽  
...  
Keyword(s):  
Neural Network ◽  
Recommender Systems ◽  
Recurrent Neural Network ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 4953
Author(s):  
Sara Al-Emadi ◽  
Abdulla Al-Ali ◽  
Abdulaziz Al-Ali
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Learning Algorithms ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Networks ◽  
Detection And Identification ◽  
Learning Techniques ◽  
The Impact

Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming concern in regard to the physical infrastructure security, safety and privacy has arisen due to the potential of their use in malicious activities. To address this problem, we propose a novel solution that automates the drone detection and identification processes using a drone’s acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. In this paper, we aim to fill this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio samples using a state-of-the-art deep learning technique known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with different deep learning algorithms, namely, the Convolutional Neural Network, the Recurrent Neural Network and the Convolutional Recurrent Neural Network in drone detection and identification. Moreover, we investigate the impact of our proposed hybrid dataset in drone detection. Our findings prove the advantage of using deep learning techniques for drone detection and identification while confirming our hypothesis on the benefits of using the Generative Adversarial Networks to generate real-like drone audio clips with an aim of enhancing the detection of new and unfamiliar drones.

AN AI-BASED APPROACH TO ENHANCED FRACTURE RESOLUTION IN IMAGE LOGS

2021 ◽  
Author(s):  
James Howard ◽  
◽  
Joe Tracey ◽  
Mike Shen ◽  
Shawn Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Nearest Neighbor ◽  
Rock Fracture ◽  
Short Interval ◽  
Acoustic Properties ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Deep Learning Neural Network ◽  
Borehole Image

Borehole image logs are used to identify the presence and orientation of fractures, both natural and induced, found in reservoir intervals. The contrast in electrical or acoustic properties of the rock matrix and fluid-filled fractures is sufficiently large enough that sub-resolution features can be detected by these image logging tools. The resolution of these image logs is based on the design and operation of the tools, and generally is in the millimeter per pixel range. Hence the quantitative measurement of actual width remains problematic. An artificial intelligence (AI) -based workflow combines the statistical information obtained from a Machine-Learning (ML) segmentation process with a multiple-layer neural network that defines a Deep Learning process that enhances fractures in a borehole image. These new images allow for a more robust analysis of fracture widths, especially those that are sub-resolution. The images from a BHTV log were first segmented into rock and fluid-filled fractures using a ML-segmentation tool that applied multiple image processing filters that captured information to describe patterns in fracture-rock distribution based on nearest-neighbor behavior. The robust ML analysis was trained by users to identify these two components over a short interval in the well, and then the regression model-based coefficients applied to the remaining log. Based on the training, each pixel was assigned a probability value between 1.0 (being a fracture) and 0.0 (pure rock), with most of the pixels assigned one of these two values. Intermediate probabilities represented pixels on the edge of rock-fracture interface or the presence of one or more sub-resolution fractures within the rock. The probability matrix produced a map or image of the distribution of probabilities that determined whether a given pixel in the image was a fracture or partially filled with a fracture. The Deep Learning neural network was based on a Conditional Generative Adversarial Network (cGAN) approach where the probability map was first encoded and combined with a noise vector that acted as a seed for diverse feature generation. This combination was used to generate new images that represented the BHTV response. The second layer of the neural network, the adversarial or discriminator portion, determined whether the generated images were representative of the actual BHTV by comparing the generated images with actual images from the log and producing an output probability of whether it was real or fake. This probability was then used to train the generator and discriminator models that were then applied to the entire log. Several scenarios were run with different probability maps. The enhanced BHTV images brought out fractures observed in the core photos that were less obvious in the original BTHV log through enhanced continuity and improved resolution on fracture widths.

scholarly journals Convolutional Neural Network Audio Classifier for Alarm Sound Detection

2019 ◽  
Vol 8 (6) ◽  
pp. 4554-4557
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Sound Recognition ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Differential Network ◽  
Sound Detection ◽  
Long Short Term Memory ◽  
Lstm Network

Neural Networks (ANN) has evolved through many stages in the last three decades with many researchers contributing in this challenging field. With the power of math complex problems can also be solved by ANNs. ANNs like Convolutional Neural Network (CNN), Deep Neural network, Generative Adversarial Network (GAN), Long Short Term Memory (LSTM) network, Recurrent Neural Network (RNN), Ordinary Differential Network etc., are playing promising roles in many MNCs and IT industries for their predictions and accuracy. In this paper, Convolutional Neural Network is used for prediction of Beep sounds in high noise levels. Based on Supervised Learning, the research is developed the best CNN architecture for Beep sound recognition in noisy situations. The proposed method gives better results with an accuracy of 96%. The prototype is tested with few architectures for the training and test data out of which a two layer CNN classifier predictions were the best.

scholarly journals Automatic Target Recognition for Synthetic Aperture Radar Images Based on Super-Resolution Generative Adversarial Network and Deep Convolutional Neural Network

2019 ◽  
Vol 11 (2) ◽  
pp. 135 ◽  
Author(s):  
Xiaoran Shi ◽  
Feng Zhou ◽  
Shuang Yang ◽  
Zijing Zhang ◽  
Tao Su
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Target Recognition ◽  
Super Resolution ◽  
Automatic Target Recognition ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Feature Characterization

Aiming at the problem of the difficulty of high-resolution synthetic aperture radar (SAR) image acquisition and poor feature characterization ability of low-resolution SAR image, this paper proposes a method of an automatic target recognition method for SAR images based on a super-resolution generative adversarial network (SRGAN) and deep convolutional neural network (DCNN). First, the threshold segmentation is utilized to eliminate the SAR image background clutter and speckle noise and accurately extract target area of interest. Second, the low-resolution SAR image is enhanced through SRGAN to improve the visual resolution and the feature characterization ability of target in the SAR image. Third, the automatic classification and recognition for SAR image is realized by using DCNN with good generalization performance. Finally, the open data set, moving and stationary target acquisition and recognition, is utilized and good recognition results are obtained under standard operating condition and extended operating conditions, which verify the effectiveness, robustness, and good generalization performance of the proposed method.

scholarly journals Design of Painting Art Style Rendering System Based on Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xingyu Xie ◽  
Bin Lv
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Generative Adversarial Network ◽  
Convolutional Network ◽  
Data Set ◽  
Adversarial Network ◽  
Paired Samples ◽  
Network Generator ◽  
The Stability ◽  
Art Style

Convolutional Neural Network- (CNN-) based GAN models mainly suffer from problems such as data set limitation and rendering efficiency in the segmentation and rendering of painting art. In order to solve these problems, this paper uses the improved cycle generative adversarial network (CycleGAN) to render the current image style. This method replaces the deep residual network (ResNet) of the original network generator with a dense connected convolutional network (DenseNet) and uses the perceptual loss function for adversarial training. The painting art style rendering system built in this paper is based on perceptual adversarial network (PAN) for the improved CycleGAN that suppresses the limitation of the network model on paired samples. The proposed method also improves the quality of the image generated by the artistic style of painting and further improves the stability and speeds up the network convergence speed. Experiments were conducted on the painting art style rendering system based on the proposed model. Experimental results have shown that the image style rendering method based on the perceptual adversarial error to improve the CycleGAN + PAN model can achieve better results. The PSNR value of the generated image is increased by 6.27% on average, and the SSIM values are all increased by about 10%. Therefore, the improved CycleGAN + PAN image painting art style rendering method produces better painting art style images, which has strong application value.

scholarly journals Comparison of Algebraic Reconstruction Technique Methods and Generative Adversarial Network in Image Reconstruction of Magnetic Induction Tomography (MIT)

2021 ◽  
Vol 2071 (1) ◽  
pp. 012044
Author(s):  
A J Lubis ◽  
N F Mohd Nasir ◽  
Z Zakaria ◽  
M Jusoh ◽  
M M Azizan ◽  
...  
Keyword(s):  
Neural Network ◽  
Regularization Method ◽  
Tikhonov Regularization Method ◽  
Reconstruction Technique ◽  
Algebraic Reconstruction Technique ◽  
Training Process ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Non Linear ◽  
Magnetic Induction Tomography

Abstract Magnetic induction tomography (MIT) is a technique used for imaging electromagnetic properties of objects using eddy current effects. The non-linear characteristics had led to more difficulties with its solution especially in dealing with low conductivity imaging materials such as biological tissues. Two methods that could be applied for MIT image processing which is the Generative Adversarial Network (GAN) and the Algebraic Reconstruction Technique (ART). ART is widely used in the industry due to its ability to improve the quality of the reconstructed image at a high scanning speed. GAN is an intelligent method which would be able to carry out the training process. In the GAN method, the MIT principle is used to find the optimum global conductivity distribution and it is described as a training process and later, reconstructed by a generator. The output is an approximate reconstruction of the distribution’s internal conductivity image. Then, the results were compared with the previous traditional algorithm, namely the regularization algorithm of BPNN and Tikhonov Regularization method. It turned out that GAN had able to adjust the non-linear relationship between input and output. GAN was also able to solve non-linear problems that cannot be solved in the previous traditional algorithms, namely Back Propagation Neural Network (BPNN) and Tikhonov Regularization method. There are several other intelligent algorithms such as CNN (Convolution Neural Network) and K-NN (K-Nearest Neighbor), but such algorithms have not been able to produce the expected image quality. Thus, further study is still needed for the improvement of the image quality. The expected result in this study is the comparison of these two techniques, namely ART and GAN to get the best results on the image reconstruction using MIT. Thus, it is shown that GAN is a better candidate for this purpose.

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