An Enhanced Virtual Fitting Room Using Deep Neural Network

Computational visual perception, also known as computer vision, is a field of artificial intelligence that enables computers to process digital images and videos in a similar way as biological vision does. It involves methods to be developed to replicate the capabilities of biological vision. The computer vision’s goal is to surpass the capabilities of biological vision in extracting useful information from visual data. The massive data generated today is one of the driving factors for the tremendous growth of computer vision. This survey incorporates an overview of existing applications of deep learning in computational visual perception. The survey explores various deep learning techniques adapted to solve computer vision problems using deep convolutional neural networks and deep generative adversarial networks. The pitfalls of deep learning and their solutions are briefly discussed. The solutions discussed were dropout and augmentation. The results show that there is a significant improvement in the accuracy using dropout and data augmentation. Deep convolutional neural networks’ applications, namely, image classification, localization and detection, document analysis, and speech recognition, are discussed in detail. In‐depth analysis of deep generative adversarial network applications, namely, image‐to‐image translation, image denoising, face aging, and facial attribute editing, is done. The deep generative adversarial network is unsupervised learning, but adding a certain number of labels in practical applications can improve its generating ability. However, it is challenging to acquire many data labels, but a small number of data labels can be acquired. Therefore, combining semisupervised learning and generative adversarial networks is one of the future directions. This article surveys the recent developments in this direction and provides a critical review of the related significant aspects, investigates the current opportunities and future challenges in all the emerging domains, and discusses the current opportunities in many emerging fields such as handwriting recognition, semantic mapping, webcam‐based eye trackers, lumen center detection, query‐by‐string word, intermittently closed and open lakes and lagoons, and landslides.

As data visibility in factories has increased with the deployment of sensors, data-driven maintenance has become popular in industries. Machine learning has been a promising tool for fault detection, but the problem is that the amount of fault data is much less than that of normal data which causes a data imbalance. In this study, we designed a deep neural network for fault detection and diagnosis, and compared the oversampling by a generative adversarial network to standard oversampling techniques. Simulation results indicate that oversampling by the generative adversarial network performs well under the given condition and the deep neural network designed is capable of classifying the faults of an induction motor with high accuracy.

The farming industry widely requires automatic detection and analysis of rice diseases to avoid wasting financial and other resources, reduce yield loss, improve processing efficiency, and obtain healthy crop yields. The proposed Deep Spectral Generative Adversarial Neural Network (DSGAN2) method is used for detecting rice plant leaf disease. Initially, fed into the input of healthy and non-healthy leaves from the collected dataset. Then, apply an Improved Threshold Neural Network (ITNN) method to enhance the image quality. Next, it uses a Segmentation using a Segment Multiscale Neural Slicing (SMNS) algorithm to identify the support-intensive color saturation based on the enhanced image. After that, the Spectral Scaled Absolute Feature Selection (S2AFS) method is applied to select optimal features and the closest weight from segmented rice plant leaves. Social Spider Optimization will select the feature using the Closest Weight (S2O-FCW) algorithm to analyze the feature weight values. Finally, the proposed Soft-Max Logistic Activation Function with Deep Spectral Generative Adversarial Neural Network (DSGAN2) algorithm detects rice plant disease based on selected features. With an accuracy of 97 %, the model helps farmers identify and identify Rice Plant diseases. The proposed system Deep Spectral Generative Adversarial Neural Network (DSGAN2) produces a decreasing false rate compared to the existing system of ACPSOSVM-Dual Channels Convolutional Neural Network (APS-DCCNN) is 55.2 %, Alex Net is 50.4 %, and Convolutional Neural Network (CNN) is 49.5 %.

In a previous paper, we introduced a deep learning neural network that should be able to detect the existence of very shallow periodic planetary transits in the presence of red noise. The network in that feasibility study would not provide any further details about the detected transits. The current paper completes this missing part. We present a neural network that tags samples that were obtained during transits. This is essentially similar to the task of identifying the semantic context of each pixel in an image—an important task in computer vision, called “semantic segmentation,” which is often performed by deep neural networks. The neural network we present makes use of novel deep learning concepts such as U-Nets, Generative Adversarial Networks, and adversarial loss. The resulting segmentation should allow further studies of the light curves that are tagged as containing transits. This approach toward the detection and study of very shallow transits is bound to play a significant role in future space-based transit surveys such as PLATO, which are specifically aimed to detect those extremely difficult cases of long-period shallow transits. Our segmentation network also adds to the growing toolbox of deep learning approaches that are being increasingly used in the study of exoplanets; but, so far mainly for vetting transits, rather than their initial detection.

Adversarial attacks are a big problem for deep neural networks (DNNs), especially when it comes to image classification tasks where even small changes can lead to big mistakes. This weakness makes deep convolutional neural networks (DCNNs), generative adversarial networks (GANs), and new architectures like Vision Transformers less reliable. In the last few years, there have been a lot of new ways to attack, from gradient-based methods like FGSM and PGD to optimization-based, patch, and backdoor attacks. As a result, defense mechanisms have developed in many areas, such as adversarial training, input transformations, detection frameworks, and generative purification methods. Even with these improvements, no one defense has been shown to be strong enough to work in all situations. Most methods have to choose between accuracy, scalability, and resistance to adaptive adversaries. To solve this problem, researchers are looking into optimization-driven methods to make systems more robust and improve training efficiency. These methods include biologically inspired metaheuristics like Sparrow Search and Cuckoo Optimization, as well as modern deep learning optimizers. Benchmark datasets like MNIST, Fashion-MNIST, and CIFAR-10 have become very important for testing both attacks and defenses. They give us a standard way to measure accuracy, precision, recall, and sensitivity when things go wrong. This review brings together changes from 2018 to 2023, with a focus on optimization-enhanced training and the different types of adversarial strategies and defense paradigms. Moreover, we suggest a hybrid defense framework that combines adversarial training, metaheuristic optimization, and lightweight input filtering to protect against both white-box and black-box attacks at multiple levels. The goal is to get a full picture of the adversarial landscape, find research gaps that keep coming up, and find ways to make deep learning models for image classification that are more resistant to attacks.

Tourism is one of the most important sectors of the global economy, generating billions of euros in revenue and supporting millions of jobs. However, the tourism industry also faces many challenges, such as changing consumer preferences, environmental impacts and competitive pressures. In this paper, we explore how specialized and generative AI, a branch of artificial intelligence that can create novel content, such as text, images, or audio, can support hotels and other tourist accommodation companies in enhancing their products and services, improving their marketing and communication strategies and optimizing their operations and management. We review the existing literature on AI and its applications in tourism and propose potential use cases of generative AI for tourist companies. We also present some examples of generative AI tools that can be used for these purposes and discuss their benefits and limitations. Finally, we offer some conclusions and future implications for research and practice in this emerging field. Theoretical implications of the present paper suggest that the incorporation of generative AI in the tourism sector can significantly advance the current understanding of AI applications in service industries. By leveraging machine learning techniques, such as deep neural networks and generative adversarial networks, the tourism industry can create novel content and optimize various operations. This study enriches existing literature by identifying new use cases for generative AI, thus providing a foundation for further academic inquiry. Practically, our findings reveal several actionable insights for tourism companies. Generative AI tools can be harnessed to personalize customer experiences, enhance marketing efforts, and streamline operational management.

Recovering images from highly undersampled measurements has a wide range of applications across imaging sciences. State-of-the-art analytics however are not aware of the image perceptual quality, and demand iterative algorithms that incur significant computational overhead. To sidestep these hurdles, this paper brings forth a novel compressive imaging framework using deep neural networks that approximates a low-dimensional manifold of images using generative adversarial networks. To ensure the images are consistent with the measurements a recurrent GAN (RGAN) architecture is deployed that consists of multiple alternative blocks of generator networks and affine projection, which is then followed by a discriminator network to score the perceptual quality of the generated images. A deep residual network with skip connections is used for the generator, while the discriminator is a multilayer perceptron. Experiments performed with real-world contrast enhanced MRI data corroborate the diagnostic quality of the retrieved images relative to state-of-the-art CS schemes. In addition, it achieves about two-orders of magnitude faster reconstruction.

In recent years, deep neural networks have demonstrated high efficiency in solving the problems associated with image processing and analysis. The latest studies in generative adversarial neural networks open up broad prospects for solving problems in image style transfer, cross-domain adaptation, and the generation of new data for a target probability distribution. The ability of generative networks to implement complex and weakly formalized transformations allows the information systems based on generative adversarial technologies to solve problems in machine learning in a manner similar to human creative thinking. This paper presents the results of an experimental study on the design, training and deployment of an intelligent control system for a CNC laser engraver based on the generative adversarial network. In order to train deep models, large amounts of data are required. Therefore, one of the main challenges in this experimental study is a lack of labelled training dataset. The proposed control system for the laser engraver based on the deep generative model allows decreasing the time of the manufacturing process.

Crop yield estimation and prediction constitutes a key issue in agricultural management, particularly under the context of demographic pressure and climate change. Currently, the main challenge in estimating crop yields based on remotely sensed data and data-driven methods is how to cope with small datasets and the limited amount of annotated samples. In order to cope with small datasets and the limited amount of annotated samples and improve the accuracy of winter wheat yield estimation in the Guanzhong Plain, PR China, this study proposed a method of combining generative adversarial networks (GANs) and convolutional neural network (CNN) for comprehensive growth monitoring of winter wheat, in which the remotely sensed leaf area index (LAI), vegetation temperature condition index (VTCI) and meteorological data at four growth stages of winter wheat during 2012–2017 were generated as the inputs of multi-layer convolutional neural networks (CNNs), and GAN was employed to artificially increase the number of training samples. Then, a linear regression model between the simulated comprehensive growth monitoring (I) and the measured yields was established to estimate yields of winter wheat in the Guanzhong Plain pixel by pixel. The final results showed when GAN was used to double the size of the training samples, and the simulation values obtained by CNN based on augmented samples using GAN provided a better training (R2 = 0.95, RMSE = 0.05), validation (R2 = 0.54, RMSE = 0.16) and testing (R2 = 0.50, RMSE = 0.14) performance than that just using the original samples. The achieved best pixel-scale yield estimation accuracy of winter wheat (R2 = 0.50, RMSE = 591.46 kg/ha) in the Guanzhong Plain. These results showed that small samples can be enlarged by GAN, thus, more important features for reflecting the growth conditions and yields of winter wheat from the remotely sensed indices and meteorological indices can be extracted, and indicated that CNN accompanied with GAN could contribute a lot to the comprehensive growth monitoring and yield estimation of winter wheat and data augmentation methods are extremely useful for the application of small samples in deep learning.

Glaucoma is the second leading cause of blindness in the world. Around 76 million people were affected by this disease in 2020. Due to the irreversible glaucoma nature, several studies in the literature presented early diagnosis methods to assist in decisionmaking specialists. Some of these studies employ machine learning methods despite the slow process of collecting data present in many medical fields. This work presents an analysis of generative adversarial networks in the generation of synthetic RNFL (retinal nerve fiber layer) thickness maps for use in the training of classifiers for the diagnosis of glaucoma. Mainly classifiers that use deep neural networks and require a large amount of data for training. Synthetic RNFL thickness maps were created using a generative adversarial network, creating 11 mixed datasets with real data and synthetic data in different proportions. These datasets were used in the training of 8 deep neural networks to aid in glaucoma diagnosis. Classifiers' performance was measured using AROC values. Increasing the dataset by one hundred times gave the best AROC values, where 50 % of the data were synthetic, and 50 % were real. The best AROC value was 0.865, obtained by the VGG16 network. Such results indicate that the use of synthetic RNFL thickness maps generated by generative adversarial networks contributes to improving the performance of deep neural networks for the diagnosis of glaucoma.

With the continuous development of wireless communication and artificial intelligence technology, Internet of Things (IoT) technology has made great progress. Deep learning methods are currently used in IoT technology, but deep neural networks (DNNs) are notoriously susceptible to adversarial examples, and subtle pixel changes to images can result in incorrect recognition results from DNNs. In the real‐world application, the patches generated by the recent physical attack methods are larger or less realistic and easily detectable. To address this problem, a Generative Adversarial Network based on Visual attention model and Style transfer network (GAN‐VS) is proposed, which reduces the patch area and makes the patch more natural and less noticeable. A visual attention model combined with generative adversarial network is introduced to detect the critical regions of image recognition, and only generate patches within the critical regions to reduce patch area and improve attack efficiency. For any type of seed patch, an adversarial patch can be generated with a high degree of stylistic and content similarity to the attacked image by generative adversarial network and style transfer network. Experimental evaluation shows that the proposed GAN‐VS has good camouflage and outperforms state‐of‐the‐art adversarial patch attack methods.

Image-based 3D shape reconstruction is essential for 3D modeling in recognition, virtual reality, generation of video games, 3D animation, and in the creation of 3D virtual fitting rooms - the sought-after application in this dissertation. In this work, we propose a novel reconstruction method that uses a generic 3D model that roughly controls the overall reconstruction shape which in turn, reduces the number of 2D images needed to just two (frontal and profile) for obtaining a 3D point model. The generic model consists of a massive set of 3D points that lack saliency, topological meaning, and relational interconnections. This is also true about points in the two canonical images. In this work, we propose finding a small set of interconnected ordered intrinsic salient points (control points) residing on the silhouette of the projections of the generic model onto the two image spaces, as well as finding those for the canonical images. Points on the generic model and the images are automatically given saliency with order and interconnections through loop-subdivision operating on those control points and their equivalent points on the canonical images. The transformation of the generic model into the specific object is done through morphing the points on the generic model to follow and be consistent with their equivalent points on the canonical images. The control points on the canonical images are automatically obtained using an active shape model (ASM) that modifies an average trained control point set attained using an offline training, to account for class shape variability and boundary regularization guaranteeing the control points to reside on the silhouette (boundary) of the individual. The reconstructed 3D model is furtherly refined through smoothing the reconstructed surface, while fixing local holes using a principle component analysis (PCA) decomposition. This reconstruction method is convenient, simple, efficient, and results in an average geometric error of less than 0.5% over 700 diverse body models from the CAESAR dataset. We then implement the contour-based articulated model to the personalized 3D model for pose recovery. The evolution of the model through the articulation process is captured in a video clip. 3D transformations are found and applied to the 3D model on its parts so that the projections of the 3D model 'match' those observed in the video sequence at corresponding frames. This is done by minimizing the error between the frontal projection body region points and the target points from the image for each independent moving part, with sub-resolution recovery errors. Once the personalized 3D model is obtained and the articulated model is implemented, the algorithm simulates how garments virtually appear on the reconstructed person under different poses. Different types of clothes are classified as tight or loose, where for the first type of garments, the garment is mapped onto the person's body, whereas for the second type, the mapping adjusts to accommodate the loose nature of the garment, and its fitting on the body of the reconstructed person model. This creates a virtual in-home fitting room for garment fitting, which is timely and is a needed application that would go hand in hand with online clothes shopping, allowing the trying of garments at home with changing poses before ordering them, and can be updated as often as necessary to accommodate changes in physique, weight, aging, etc. This is also done with minimum requirements from the user aside from taking two images and a video of pose articulations using their cam or smart phone, and stands as a useful, needed, and attractive App.

Portable ultrasound devices with less numbers of transducers become popularly spread soon or later. Accordingly, this work explores how to make quality of ultrasonic B-mode images from 32 channels approaching to that from 128 channels by using Deep Neural Networks (DNNs). Based on the concepts of auto-encoder, context encoder, semi-supervised latent-class learning, and context-conditional Generative Adversarial Network (GAN), we develop the Context Encoder Reconstruction GAN (CER-GAN)., which adopts the contour learning mechanism to establish the relationship between 32-channel and 128-channel Point Spread Functions (PSF) via the mini-batch training. In our experiments, 20 pairs of 32 and 128-channel ultrasound images were used where the way of “leave one person out” was employed for cross-validation. Our CER-GAN automatically encodes 32-channel PSF blocks, and generates pseudo 128-channel PSF ones which are matched with real 128-channel ones to obtain discrimination and amendment from the adversarial mechanism. Finally, these pseudo blocks are used to yield an improved image. The experimental results reveal that the proposed CER-GAN outperforms the conventional DNNs. In Full Width at Half Maximum (FWHM), the minimum distinguishable pixel lengths from the 32-channel, CER-GAN, and 128-channel images are 13.34, 11.15, and 8.62, respectively. In Contrast Noise Ratio (CNR)/PICMUS CNR, the scores of the 32-channel, CER-GAN, and 128-channel images are 0.939/2.381, 1.508/6.502, and 1.422/6.002, respectively. The reason of the favorable scores from CER-GAN is that the background speckle noise is lowered to some extent. Therefore, our CER-GAN can effectively ameliorate the quality of 32-channel ultrasound imaging, close to that of 128-channel one.

Characterization of hydration and dry shrinkage behavior of cement emulsified asphalt composites using deep learning

In this article, we present an ultra-low-power multi-modal signal processing system on chip (SoC) [audio and image multi-modal intelligence (AIMMI)] that integrates a versatile deep neural network (DNN) engine with audio and image signal processing accelerators for multi-modal Internet-of-Things (IoT) intelligence. In order to get high energy efficiency under resource-constrained IoT scenarios, AIMMI features three efficiency-boosting techniques: 1) 2-MB on-chip non-volatile magnetoresistive RAM (MRAM) to store all DNN weights with MRAM-cache microarchitecture that incorporates dynamic power gating to reduce both leakage and dynamic power consumption; 2) a deliberate power management scheme that enables optimized power modes under different operating situations; and 3) a novel reconfigurable neural engine (NE) with energy-efficient dataflow for comprehensive DNN instructions. Fabricated in TSMC 22-nm ultra-low leakage (ULL) technology with MRAM, AIMMI achieves up to 3–10-TOPS/W peak energy efficiency and consumes only 0.25–3.84 mW. It demonstrates convolutional neural network (CNN), generative adversarial network (GAN), and back-propagation (BP) operations on a single accelerator SoC for multi-modal fusion, outperforming state-of-the-art DNN processors by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.4 \times -4.5 \times$ </tex-math></inline-formula> in energy efficiency.