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Acoustic holography (AH) is a technique with significant potential in realms, such as biomedicine, industry, and augmented reality. The implementation of acoustic holograms can be realized by a passive approach or active ones. Although the passive approach (by a 3D printer) can achieve high-quality acoustic field generation, it is constrained by high manufacturing costs and static field control. On the contrary, the active approach with a phased transducer array (PTA) as the latest technique stands out since it supports dynamic, flexible, and reconfigurable acoustic field generation. However, current PTA-based AH techniques face the drawback of inferior acoustic field fineness due to the Spatial Bandwidth Product (SBP) limit of PTA, which hinders the application of PTA in precise tasks, such as neural electrodes and microfluidics control. To address this issue, we propose a super-resolution acoustic holography (SRAH) method inspired by the concept of super-resolution in ultrasonic imaging and computer vision, by which we can generate acoustic fields reaching the physical diffraction limit of acoustic waves regardless SBP of PTA. In other words, this method enables high-SBP acoustic field generation with low-SBP PTA. The method is based on self-supervised learning, integrating a generative adversarial network and a physical model of acoustic wave propagation, specifically the linear accumulation method. Both simulation and experimental results demonstrate that the proposed method can generate high-fidelity acoustic fields suitable for intricate tasks with low-SBP PTA. Moreover, the performance of the algorithm improves as the target SBP increases. Therefore, the proposed SRAH method shows great potential for applications requiring elaborate manipulation. [ABSTRACT FROM AUTHOR]

An accurate estimation of three-dimensional (3D) temperature fields in channel flows is challenging but critical for many important applications such as heat exchangers, radiation energy collectors, and enhanced geothermal systems. In this paper, we demonstrate the possibility of inferring temperature fields from concentration fields for laminar convection flows in a 3D channel using a machine learning (ML) approach. The study involves generation of data using 3D numerical simulations, application of deep learning methodology using conditional generative adversarial networks (cGANs), and analysis of how dataset selection affects model performance. The model is also tested for applicability in different convection scenarios. Results show that cGANs can successfully infer temperature fields from concentration fields, and the reconstruction accuracy is sensitive to the training dataset selected. In this study, we demonstrate how ML can be used to overcome the limitations of traditional heat and mass analogy functions widely used in heat transfer research. [ABSTRACT FROM AUTHOR]

Laser-based powder bed fusion (L-PBF) has become the de facto choice for metal additive manufacturing (AM) processes. Even after considerable research investments, components manufactured using L-PBF lack consistency in their quality. Realizing the crucial role of the melt pool in controlling the final build quality, we predict the morphology of the melt pool directly from the build commands in an L-PBF process. We leverage machine learning techniques to predict quantitative attributes like the size as well as qualitative attributes like the shape of the melt pool. The area of the melt pool is predicted using an LSTM network. The outlined LSTM-based approach estimates the area with $ 90.7\% $ 90.7 % accuracy. The shape is inferred by synthesising the images of the melt pool by using a Melt Pool Generative Adversarial Network (MP-GAN). The synthetic images attain a structural similarity score of 0.91. The precision and accuracy of the results showcase the efficacy of the outlined approach and pave the way for real-time monitoring and control of the melt pool to build products with consistently better quality. [ABSTRACT FROM AUTHOR]

4D MRI with high spatiotemporal resolution is desired for image-guided liver radiotherapy. Acquiring densely sampled k-space data is time-consuming. Accelerated acquisition with sparse sampling is desirable but often causes degraded image quality or long reconstruction time. We propose the Reconstruct Paired Conditional Generative Adversarial Network (Re-Con-GAN) for shortening the 4D MRI reconstruction time while maintaining the reconstruction quality. Methods: Patients underwent free-breathing liver 4D MRI were included in the study. Fully- and retrospectively under-sampled data at 3, 6 and 10 times (3x, 6x and 10x) were first reconstructed using the nuFFT algorithm. Re-Con-GAN then trained input and output in pairs. Three types of networks, ResNet9, UNet and reconstruction swin transformer, were explored as generators. PatchGAN was selected as the discriminator. Re-Con-GAN processed the data (3D+t) as temporal slices (2D+t). A total of 48 patients with 12332 temporal slices were split into training (37 patients with 10721 slices) and test (11 patients with 1611 slices). Compressed sensing (CS) reconstruction with spatiotemporal sparsity constraint was used as a benchmark. Reconstructed image quality was further evaluated with a liver gross tumor volume (GTV) localization task using Mask-RCNN trained from a separate 3D static liver MRI dataset (70 patients; 103 GTV contours).Results: Re-Con-GAN consistently achieved comparable/better PSNR, SSIM, and RMSE scores compared to CS/UNet models. The inference time of Re-Con-GAN, UNet and CS are 0.15s, 0.16s, and 120s. The GTV detection task showed that Re-Con-GAN and CS, compared to UNet, better improved the dice score (3x Re-Con-GAN 80.98%; 3x CS 80.74%; 3x UNet 79.88%) of unprocessed under-sampled images (3x 69.61%). Conclusion: A generative network with adversarial training is proposed with promising and efficient reconstruction results demonstrated on an in-house dataset. The rapid and qualitative reconstruction of 4D liver MR has the potential to facilitate online adaptive MR-guided radiotherapy for liver cancer.

The demand for specialized hardware to train AI models has increased in tandem with the increase in the model complexity over the recent years. Graphics processing unit (GPU) is one such hardware that is capable of parallelizing operations performed on a large chunk of data. Companies like Nvidia, AMD, and Google have been constantly scaling-up the hardware performance as fast as they can. Nevertheless, there is still a gap between the required processing power and processing capacity of the hardware. To increase the hardware utilization, the software has to be optimized too. In this paper, we present some general GPU optimization techniques we used to efficiently train the optiGAN model, a Generative Adversarial Network that is capable of generating multidimensional probability distributions of optical photons at the photodetector face in radiation detectors, on an 8GB Nvidia Quadro RTX 4000 GPU. We analyze and compare the performances of all the optimizations based on the execution time and the memory consumed using the Nvidia Nsight Systems profiler tool. The optimizations gave approximately a 4.5x increase in the runtime performance when compared to a naive training on the GPU, without compromising the model performance. Finally we discuss optiGANs future work and how we are planning to scale the model on GPUs.

Control over the two-dimensional electron gas (2DEG) in AlGaN/GaN heterostructures is crucial for their practical applications in current semiconducting devices. However, the oxide surface structures inducing 2DEG are still ambiguous because oxide-stoichiometry (OS) matching structures possess occupied surface donor states at 1.0–1.8 eV below the conduction band minimum of bulk but are usually not available in energy than electron counting (EC) rule structures. In this work, a global optimization algorithm was introduced to explore the possible oxidation structures on GaN (0001) and AlN (0001) surfaces; the method was demonstrated to be available due to the fact that the reported oxidized structures were reproduced at each stoichiometry. Interestingly, the two similar oxide structures with close energy were found in each oxide-bilayer, which can be used to clarify the experimental observations of disordered surface oxide layers below 550 °C. Additionally, new stable oxidation structures with low surface energy were proposed. Interestingly, the new OS matching structures are proposed with remarkably lower energy than EC rule structures under cation-rich and oxygen-poor conditions, which is caused by the large formation enthalpy of Al2O3 and Ga2O3. Further electronic structure calculations demonstrate that the new OS structures possess highest occupied states above the half of the gap and are the origin of 2DEG in AlGaN/GaN heterostructures. [ABSTRACT FROM AUTHOR]

Generative Adversarial Networks (GANs) have shown immense potential in fields such as text and image generation. Only very recently attempts to exploit GANs to statistical-mechanics models have been reported. Here we quantitatively test this approach by applying it to a prototypical stochastic process on a lattice. By suitably adding noise to the original data we succeed in bringing both the Generator and the Discriminator loss functions close to their ideal value. Importantly, the discreteness of the model is retained despite the noise. As typical for adversarial approaches, oscillations around the convergence limit persist also at large epochs. This undermines model selection and the quality of the generated trajectories. We demonstrate that a simple multi-model procedure where stochastic trajectories are advanced at each step upon randomly selecting a Generator leads to a remarkable increase in accuracy. This is illustrated by quantitative analysis of both the predicted equilibrium probability distribution and of the escape-time distribution. Based on the reported findings, we believe that GANs are a promising tool to tackle complex statistical dynamics by machine learning techniques. [ABSTRACT FROM AUTHOR]

Summary: Anomaly event recognition and identification has a crucial part in several areas, particularly in night vision environments. Conventional techniques of event recognition are hugely based upon data extracted from certain images for classification purposes. This needs users to select suitable features to establish the feature depictions for actual images per definite situations. Manual feature selection is laborious as well as heuristic tasks and the features obtained in this manner generally have worse robustness. Here, a Faster Region‐based Convolutional fused Social Generative Adversarial Network (FRC‐SGAN) is designed for anomaly event recognition in a night vision environment. At the cloud, key frame extraction, pre‐processing, feature extraction, human detection (HD) and anomalous event recognition are carried out. Initially, input video from the database is subjected to perform pre‐processing. The visibility enhancement is utilized for pre‐processing. Thereafter, features like ResNet features, texture features and statistical features are extracted. Then, HD is accomplished by DeepJoint segmentation with chord distance. Finally, anomalous detection is done by FRC‐SGAN that is the incorporation of Fast Regional Convolutional Neural Network (FR‐CNN) and Social Generative Adversarial Network (SGAN). In addition, FRC‐SGAN acquired 90.8% of accuracy, 89.7% of precision, and 89.2% of recall. [ABSTRACT FROM AUTHOR]

Summary: Wireless body sensor network (WBSN) is essential for monitoring patients' health problems and offers a low‐cost option for various healthcare applications. In this manuscript, a Novel Health Monitoring Approach for WBSNs (DIWGAN‐WBSN) is proposed, which uses Dual Interactive Wasserstein Generative Adversarial Network (DIWGAN) optimized with War Strategy Optimization Algorithm (WSOA). After sensing the aforementioned attribute information, it is the responsibility of WBSN nodes to transfer the sensed data to the sink node. The Volcano Eruption Algorithm (VEA) is applied to select the optimum cluster heads in WBSN. The results from VEA are fed to the target node; it consists of DIWGAN to classify the health records and to portray the patient's health status. Generally, DIWGAN does not adopt any optimization methods for measuring the ideal parameters and guaranteeing accurate health monitoring and risk assessment. So the proposed WSOA is considered to enhance the DIWGAN. The proposed method is activated in MATLAB; its efficacy is estimated under performance metrics, like precision, specificity, accuracy, and energy utilization. The proposed approach attains 23.9%, 21.34%, and 51.09% higher accuracy; 21.45%, 13.94%, and 20.6% higher precision; 31.32%, 29.61%, and 11.03% higher specificity; and 20.9%, 19.87%, and 24.6% lower energy utilization for HD classification using the Cleveland database than the existing methods like back propagation neural network‐based risk detection in WBSN for health monitoring, random forest algorithm–based health monitoring in WBSN, and ensemble deep learning and feature fusion for health monitoring using WBSN methods, respectively. [ABSTRACT FROM AUTHOR]

Semi-supervised learning techniques utilize both labeled and unlabeled images to enhance classification performance in scenarios where labeled images are limited. However, challenges such as integrating unlabeled images with incorrect pseudo-labels, determining appropriate thresholds for the pseudo-labels, and label prediction fluctuations on low-confidence unlabeled images, hinder the effectiveness of existing methods. This research introduces a novel framework named Interpolation Consistency for Bad Generative Adversarial Networks (IC-BGAN) that utilizes a new loss function. The proposed model combines bad adversarial training, fusion techniques, and regularization to address the limitations of semi-supervised learning. IC-BGAN creates three types of image augmentations and label consistency regularization in interpolation of bad fake images, real and bad fake images, and unlabeled images. It demonstrates linear interpolation behavior, reducing fluctuations in predictions, improving stability, and facilitating the identification of decision boundaries in low-density areas. The regularization techniques boost the discriminative capability of the classifier and discriminator, and send a better signal to the bad generator. This improves the generalization and the generation of diverse inter-class fake images as support vectors with information near the true decision boundary, which helps to correct the pseudo-labeling of unlabeled images. The proposed approach achieves notable improvements in error rate from 2.87 to 1.47 on the Modified National Institute of Standards and Technology (MNIST) dataset, 3.59 to 3.13 on the Street View House Numbers (SVHN) dataset, and 12.13 to 9.59 on the Canadian Institute for Advanced Research, 10 classes (CIFAR-10) dataset using 1000 labeled training images. Additionally, it reduces the error rate from 22.11 to 18.40 on the CINIC-10 dataset when using 700 labeled images per class. The experiments demonstrate the IC-BGAN framework outperforms existing semi-supervised methods, providing a more accurate classification solution with smoother class label estimates, especially for low-confidence unlabeled images. [ABSTRACT FROM AUTHOR]

Recently, several studies have used deep convolutional neural networks (DCNN) for plant disease classification based on leaf symptoms images. Most of these studies use the PlantVillage dataset, which contains simple images with simple backgrounds. This has led to classifiers achieving high accuracy in academic settings but underperforming in practical scenarios where images taken by farmers are much more complex. In this paper, we propose a data augmentation (DA) method that transforms simple images from the PlantVillage dataset into more complex images, thereby bridging the gap between academic results and practical performance. Our technique focuses on making the images more realistic by generating images containing multiple leaves and complex backgrounds, such as soil. To evaluate the impact of our proposed method, we trained DCNN models using various augmentation methods and tested them on a dataset containing highly complex images comparable to those taken by farmers, posing a substantial challenge. The experimental results showed that models trained on images generated by our method outperformed traditional data augmentation methods, such as geometric transformations. Moreover, our method is competitive with Generative Adversarial Networks (GANs) without requiring any training phase. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) technology has numerous applications in various fields such as health, entertainment and gaming, transportation, advertising and marketing, education, and many more. The recognition system based on seven basic expressions of emotion cannot satisfy the requirement for compound expression recognition as compound expressions have complex features due to a combination of basic emotions. Deep learning-based Recognition of compound emotions has recently emerged as a significant subject of academic research. Employing deep learning models to compound emotion recognition is facing imbalanced images in the dataset. To overcome the problem of imbalanced images in the dataset, the deep convolutional generative adversarial network (DCGAN) architecture has been utilized to balance the imbalanced dataset. The study proposes a novel approach for compound emotion recognition based on deep convolutional generative adversarial network (DCGAN) and convolutional neural network (CNN), which has been validated using the Compound Facial Expression Emotions (CFEE) and Real-world Affective Faces Database (RAFDB) datasets. Through utilizing the proposed approach, it achieved a significantly high accuracy of 72.0% for the CFEE dataset and 65.4% for RAF-DB for compound facial expressions of emotions. The AU-ROC/ AU-PR curves and ablation experiments also validated the performance of the proposed approach. The performance of the proposed model is compared with the state-of-the-art advanced models on the balanced datasets, and for both the CFEE and RAF-DB datasets, the accuracy of compound emotion recognition improved considerably. [ABSTRACT FROM AUTHOR]

Deepfakes represent a class of synthetic media crafted with the aid of advanced deep learning techniques that exhibit an unparalleled degree of authenticity. The rapid advancement in Artificial Intelligence (AI) has contributed to an increase in the prevalence of deepfakes on the internet, consequently amplifying the spread of misinformation among the public. Consequently, the detection of deepfakes has become a pressing concern. In this context, we put forth a comprehensive framework for deepfake video detection, which is built upon three distinct layers. The first layer, termed as the RGB features extraction layer, is designed to identify potential signs of forgery within the spatial domain of analogous video frames. The second layer, known as the GAN features extraction layer, focuses on the extraction of forgery fingerprints in the high-frequency region. This layer is specifically engineered to detect the fingerprints left by the Generative Adversarial Network (GAN) process in fake videos and the traces of the imaging process in genuine videos. The third and final layer, referred to as the facial region intra-frame inconsistency feature extraction layer, is dedicated to uncovering the anomalies associated with the manipulation process. This is achieved by extracting features from both the inner and outer regions of the manipulated portion of a frame. The extensive experimental evaluations have underscored the superior performance of proposed approach in comparison to existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Cancer involves abnormal cell growth, with types like intestinal and oesophageal cancer often diagnosed in advanced stages, making them hard to cure. Symptoms are like burning sensations in the stomach and swallowing difficulties are specified as colorectal cancer. Deep learning significantly impacts the medical image processing and diagnosis, offering potential improvements in accuracy and efficiency. The Kruskal Szekeres Generative Adversarial Network Augmented Deep Autoencoder (KSGANA-DA) is introduced for early colorectal cancer detection and it comprises two stages; Initial stage, data augmentation uses Affine Transform via Random Horizontal Rotation and Geometric Transform via Kruskal–Szekeres that coordinates to improve the training dataset diversity, boosting detection performance. The second stage, a Deep Autoencoder Anatomical Landmark-based Image Segmentation preserves edge pixel spatial locations, improving precision and recall for early boundary detection. Experiments validate KSGANA-DA performance and different existing methods are implemented into Python. The results of KSGANA-DA are to provide higher precision by 41%, recall by 7%, and lesser training time by 46% than compared to conventional methods. [ABSTRACT FROM AUTHOR]

Modern technology like Artificial Intelligence (AI) must be used in the agricultural sector if sustainable agricultural output is to be achieved. One of the most convenient strategies for resolving current and future issues is data-driven agriculture. For this, disease prediction is a major task for precise farming. For predictive analysis and precise agriculture monitoring systems, with the application of AI, Machine Learning (ML) and Deep Learning (DL) play vital roles in building a more robust system. In this work, we will design a DL-integrated rice disease prediction system to be implemented for precise farming. Improvisation of the developed model to detect rice plant diseases & pest attacks with a high level of precision. In this work, the Progressive Loss-Aware Fine-Tuning Stepwise Learning (PLAFTSL) model is proposed for disease detection. For step-wise learning fine-tuned ResNet50 model is used with the introduction of freezing and unfreezing layers. This reduces the training parameters and thus computational complexity. The introduction of the step-wise and progressive loss-aware layer will result in fast convergence and improved training efficiency during information exchange among layers respectively. Our proposed work uses a dataset from two sources. The result analysis is presented with an ablation study. Additionally, the baseline model, ResNet50, is used to display the outcomes of the ablation. The results demonstrate that the fine-tuned model results in better performance as compared to the transfer learning model. The Conditional Generative Adversarial Network (cGAN) augmentation is also added to the designed model which will improve detection effectiveness and can also manage the imbalance in input data. The model has achieved approx. 98% accuracy and outperforms better with comparative state-of-art models. [ABSTRACT FROM AUTHOR]

In recent years, the growth of social media has significantly increased in people's daily lives. Social media platforms offer various benefits to users. Still, they also come with vulnerabilities, including spam, posing threats to media owners and users, leading to user fatigue, financial and security damages, sensitive information disclosure, and media credibility decline. Due to the high volume of spam, manual detection is challenging. However, employing machine and deep learning methods makes it possible to identify and block them effectively. Also, class imbalances in real-world datasets are prevalent, with more non-spam instances. This poses challenges for models in effectively identifying samples with limited data. In this regard, we propose a neural network model to detect spam based on its textual content. Furthermore, using a code-based Generative Adversarial Networks, the model balances the number of spam and non-spam samples. We used the Wasserstein distance with penalty regularization to measure the convergence between the actual and generated data distributions to address the mode collapse and vanishing gradient issues. Our evaluation of experimental results from two benchmark datasets demonstrates a significant improvement in spam classification and addresses the issue of imbalanced datasets. We compared our model with three other spam detection models, one of which employed data augmentation. In our evaluation, the proposed model achieved an accuracy of 98.2% and an F1-score of 93.0%. In this study, we also assessed the quality of text generated by our proposed model using the perplexity metric to evaluate the fluency and accuracy of the spam content. [ABSTRACT FROM AUTHOR]

Artificial intelligence models have been increasingly used in the analysis of tumor histology to perform tasks ranging from routine classification to identification of molecular features. These approaches distill cancer histologic images into high-level features, which are used in predictions, but understanding the biologic meaning of such features remains challenging. We present and validate a custom generative adversarial network--HistoXGAN--capable of reconstructing representative histology using feature vectors produced by common feature extractors. We evaluate HistoXGAN across 29 cancer subtypes and demonstrate that reconstructed images retain information regarding tumor grade, histologic subtype, and gene expression patterns. We leverage HistoXGAN to illustrate the underlying histologic features for deep learning models for actionable mutations, identify model reliance on histologic batch effect in predictions, and demonstrate accurate reconstruction of tumor histology from radiographic imaging for a "virtual biopsy.". [ABSTRACT FROM AUTHOR]

This paper explores a novel emotional voice conversion (EVC) method based on the style generative adversarial network combined with dynamic fundamental frequency difference compensation. On the one hand, in terms of spectrum mapping, based on the method of EVC using star generative adversarial network, we propose to replace one-hot vectors with the emotion style features extracted from spectrum to represent emotional information. It enables the conversion not only to fully learn emotion style from the target speech, but also to transfer unseen emotion styles to a new utterance, that is, one-shot EVC. On the other hand, given that the traditional logarithm Gaussian normalization transformation and its some variants for prosody transfer are not enough to reflect the fine-grained fundamental frequency distribution difference between the different emotional speech, we propose the improved strategy of dynamic fundamental frequency difference compensation. The experimental results show that our proposed method can accomplish high quality one-shot EVC, significantly outperforming the baseline in terms of speech quality and emotional saturation in both objective and subjective evaluations. [ABSTRACT FROM AUTHOR]

Accurate and timely bearing fault detection is imperative for optimal system functioning and the implementation of preventative maintenance measures. Deep learning models provide viable solutions to these malfunctions, however, the lack of labelled data makes the training both expensive and cumbersome. To remedy this, various semi-supervised approaches have surfaced in the last decade, significantly mitigating the need for extensive labelled data but with added computational cost. This study proposes one such approach by leveraging generative adversarial networks (GAN) trained on a time-frequency based representation. The proposed Parallel Convolutions Semi-Supervised GAN, namely PC-SSGAN, uses bottleneck parallel convolutions blocks to capture multi-scale features in both local and global contexts, lacing both the generator and discriminator with enhanced feature extraction capabilities and simultaneously reducing the parameters and training time. The Proposed framework is evaluated on two distinct open-source datasets. The classification accuracy for both models exceeded 99.50%. Moreover, the proposed parallel convolutions-based architecture spent approximately 33% less time on training than the normal convolutional layers. It has been foreseen that the proposed fault detection system can be integrated into the motor fault tolerant control system to produce a unified framework that can make informed decisions to handle the bearing faults effectively. [ABSTRACT FROM AUTHOR]

Background and Aim Methods Results Conclusion Computed tomography of the abdomen exhibits subtle and complex features of liver lesions, subjectively interpreted by physicians. We developed a deep learning‐based localization and classification (DLLC) system for focal liver lesions (FLLs) in computed tomography imaging that could assist physicians in more robust clinical decision‐making.We conducted a retrospective study (approval no. EMRP‐109‐058) on 1589 patients with 17 335 slices with 3195 FLLs using data from January 2004 to December 2020. The training set included 1272 patients (male: 776, mean age 62 ± 10.9), and the test set included 317 patients (male: 228, mean age 57 ± 11.8). The slices were annotated by annotators with different experience levels, and the DLLC system was developed using generative adversarial networks for data augmentation. A comparative analysis was performed for the DLLC system versus physicians using external data.Our DLLC system demonstrated mean average precision at 0.81 for localization. The system's overall accuracy for multiclass classifications was 0.97 (95% confidence interval [CI]: 0.95–0.99). Considering FLLs ≤ 3 cm, the system achieved an accuracy of 0.83 (95% CI: 0.68–0.98), and for size > 3 cm, the accuracy was 0.87 (95% CI: 0.77–0.97) for localization. Furthermore, during classification, the accuracy was 0.95 (95% CI: 0.92–0.98) for FLLs ≤ 3 cm and 0.97 (95% CI: 0.94–1.00) for FLLs > 3 cm.This system can provide an accurate and non‐invasive method for diagnosing liver conditions, making it a valuable tool for hepatologists and radiologists. [ABSTRACT FROM AUTHOR]

The current quantitative retrieval of Aerosol Optical Depth (AOD) typically uses Top-of-atmosphere (TOA) reflectance data obtained by the scale and offset terms of radiometric calibration. Errors can be introduced during the conversion of Digital Number (DN) values to TOA reflectance, restraining the accurate retrieval of AOD. Particularly when surface reflectance is high, conversion errors can significantly distort the retrieval of Aerosol Optical Depth (AOD), given the minimal impact of aerosols on the radiation detected by satellite sensors. To counteract this, we introduce an innovative retrieval algorithm that harnesses a Generative Adversarial Network (GANN), a deep learning (DL) model, to accurately derive AOD from the scaled digital number (DN) values of multi-spectral satellite imagery. The DL technology enables the use of scaled DN values for AOD retrieval, bypassing the conversion errors associated with TOA reflectance since it relies on a sample learning approach instead of radiative transfer calculations. To ensure the number and representativeness of training samples, a total of 37,103 samples from different underlying surfaces from 2014 to 2018 were obtained using a space-time matching strategy. Our K-cross validation demonstrates that employing DN values for AOD retrieval promises to yield higher accuracy compared to using TOA reflectance. Independent 2019 data were used to estimate GANN, MCD19A2 and MOD04_3K aerosol products. Our model showed superior performance compared to the other products in terms of a higher correlation (R = 0.8184) with AERONET AOD and obtaining more reliable retrievals (7601). The sensitivity experiment suggests that GANN exhibits limitations in areas characterized by high aerosol loads and heterogeneity. Our study can give a novel insight into AOD retrieval based on multi-spectral satellite and DL models. [ABSTRACT FROM AUTHOR]

Advancements in infrastructure management have significantly benefited from automatic pavement crack detection systems, relying on image processing enhanced by high‐resolution imaging and machine learning. However, image and motion blur substantially challenge the accuracy of crack detection and analysis. Nevertheless, research on mitigating motion blur remains sparse. This study introduces an effective image processing system adept at deblurring and segmentation, employing a generative adversarial network (GAN) with UNet as the generator and Wasserstein GAN with Gradient Penalty (WGAN‐gp) as the loss function. This approach performs exceptionally in deblurring pavement crack images and improves segmentation accuracy. Models were trained with sharp and artificially blurred images, with WGAN‐gp surpassing other loss functions in effectiveness. This research innovatively suggests assessing deblurring quality through segmentation accuracy in addition to peak signal‐to‐noise ratio (PSNR) and structural similarity (SSIM), revealing that PSNR and SSIM may not fully capture deblurring effectiveness for pavement crack images. An extensive evaluation of various generators, including UNet, lightweight UNet, TransUNet, DeblurGAN, DeblurGAN‐v2, and MIMO‐UNet, identifies the superior performance of UNet on simulated motion blur. Validation with actual motion‐blurred images confirms the effectiveness of the proposed model. These findings demonstrate that GAN‐based models have great potential in overcoming motion blur challenges in pavement crack detection systems, marking a notable advancement in the field. [ABSTRACT FROM AUTHOR]

This study proposes a synthetic data generation model to create a classification framework for cerebellar ataxia patients using trajectory data from the visuomotor adaptation task. The classification objectives include patients with cerebellar ataxia, age-matched normal individuals, and young healthy subjects. Synthetic data for the three classes is generated based on class conditions and random noise by leveraging a combination of conditional adversarial generative neural networks and reconstruction networks. This synthetic data, alongside real data, is utilized as training data for the patient classification model to enhance classification accuracy. The fidelity of the synthetic data is assessed visually to measure the validity and diversity of the generated data qualitatively while quantitatively evaluating distribution similarity to real data. Furthermore, the clinical efficacy of the patient classification model employing synthetic data is demonstrated by showcasing improved classification accuracy through a comparative analysis between results obtained using solely real data and those obtained when both real and synthetic data are utilized. This methodological approach holds promise in addressing data insufficiency in the digital healthcare domain, employing deep learning methodologies, and developing early disease diagnosis tools. [ABSTRACT FROM AUTHOR]

Cyber-physical system is one of the essential components of wireless sensor network (WSN). WSN conjointly senses, collects, analyzes and communicates data on recognized items at service part before transmitting this data to the owner of the network. Blackhole, Grayhole, Flooding, Scheduling are the common WSN attacks that rapidly damage the system. Intrusion detection systems, such as WSN have drawbacks, like less detection rates, high computation costs and high percentage of false alarms, because sensor nodes have limited resources. Therefore, a Self Attention Generative Adversarial Capsule Network as an intrusion detection scheme for WSN (SAGACN-ID-WSN) is proposed in this paper to address the aforementioned issues. Initially, the data are amassed from WSN-DS dataset. Then, the data are sent for pre-processing. The input data are preprocessed using the Guided Box Filtering (GBF) method. Then, the preprocessed data are provided to the Atomic Orbital Search (AOS), Tasmanian Devil Optimization (TDO) and Ebola Optimization Search (EOS) algorithms for feature selection. The attacks are divided into four categories: normal, Grayhole attack, Black hole attack, flooding attack, and scheduling attack. The proposed ID-WSN is implemented in Network simulator 2 (NS-2) utilizing the dataset of WSN-DS. The metrics, like accuracy, precision, recall, F-measure, ROC and computational time are analyzed. The proposed SAGACN-ID-WSN technique attains 25.5%, 20.12% and 20.7% high accuracy, 51.136%, 59.04% and 32.81%; higher precision, 2.292%, 1.51% and 3.915% lower error rate compared to the existing models, like Dataset for Intrusion Detection Systems in WSN (SLGBM-ID-WSN), multi-layer machine learning-based ID-WSN (NB-RF-ID-WSN), deep learning and entity embedding-base intrusion detection systems in WSN, respectively. [ABSTRACT FROM AUTHOR]

The surface quality of aluminium alloy castings is crucial to quality control. Aiming to address the challenges of limited samples and extensive computation in deep learning‐based surface defect detection for aluminium alloy castings, this paper proposes a surface defect detection method based on data enhancement and the Casting Real‐Time DEtection TRansformer. First, to tackle the issue of small sample sizes and uneven distribution in surface defect data sets of aluminium alloy castings, ECA‐MetaAconC Deep Convolution Generative Adversarial Networks is proposed for generating defects with fewer samples and employ the image augmentation (IMGAUG) library for sample enhancement. Second, building upon the Real‐Time DEtection TRansformer (RT‐DETR), a lightweight partial‐rep convolution is designed to decrease the network's parameter count. Simultaneously, the Deformable attention module and the DRBC3 module are introduced to enhance the neck network, thereby improving the model's capability to capture information and enhancing its detection performance. Compared to RT‐DETR, this method reduces the number of model parameters by 38.7%, increases mAP by 1.5%, and achieves a frame rate that is 1.58 times higher than the original model. The experimental results demonstrate that this method can effectively and accurately detect surface defects in aluminium alloy castings, satisfying industrial requirements. [ABSTRACT FROM AUTHOR]

Insulators, as typical equipment for distribution networks, provide good electrical insulation between live conductors and earth. Timely and accurate detection is essential for insulator detection issues. However, as the complexity of neural networks increases, the detection efficiency is often lower. Therefore, this paper proposes a fast insulator positioning and defect detection method. Firstly, for insulator target localization, the SqueezeNet network is improved using ECA attention mechanism. In addition, to address the issue of low defect detection accuracy, a joint algorithm has been proposed. The integration of convolutional variational autoencoder (CVAE) and generative adversarial network (GAN) solve their own shortcomings due to different image focus angles. The target localization accuracy reaches 94.30%, and the defect detection accuracy reaches 89.60%. It solves the problems of difficulty in locating small targets in a large field of view and inaccurate detection due to a small number of abnormal samples. This method has been tried and tested in practical distribution network systems. [ABSTRACT FROM AUTHOR]

Background: Bioactive peptides are important bioactive molecules composed of short-chain amino acids that play various crucial roles in the body, such as regulating physiological processes and promoting immune responses and antibacterial effects. Due to their significance, bioactive peptides have broad application potential in drug development, food science, and biotechnology. Among them, understanding their biological mechanisms will contribute to new ideas for drug discovery and disease treatment. Results: This study employs generative adversarial capsule networks (CapsuleGAN), gated recurrent units (GRU), and convolutional neural networks (CNN) as base classifiers to achieve ensemble learning through voting methods, which not only obtains high-precision prediction results on the angiotensin-converting enzyme (ACE) inhibitory peptides dataset and the anticancer peptides (ACP) dataset but also demonstrates effective model performance. For this method, we first utilized the protein language model—evolutionary scale modeling (ESM-2)—to extract relevant features for the ACE inhibitory peptides and ACP datasets. Following feature extraction, we trained three deep learning models—CapsuleGAN, GRU, and CNN—while continuously adjusting the model parameters throughout the training process. Finally, during the voting stage, different weights were assigned to the models based on their prediction accuracy, allowing full utilization of the model's performance. Experimental results show that on the ACE inhibitory peptide dataset, the balanced accuracy is 0.926, the Matthews correlation coefficient (MCC) is 0.831, and the area under the curve is 0.966; on the ACP dataset, the accuracy (ACC) is 0.779, and the MCC is 0.558. The experimental results on both datasets are superior to existing methods, demonstrating the effectiveness of the experimental approach. Conclusion: In this study, CapsuleGAN, GRU, and CNN were successfully employed as base classifiers to implement ensemble learning, which not only achieved good results in the prediction of two datasets but also surpassed existing methods. The ability to predict peptides with strong ACE inhibitory activity and ACPs more accurately and quickly is significant, and this work provides valuable insights for predicting other functional peptides. The source code and dataset for this experiment are publicly available at https://github.com/Zhou-Jianren/bioactive-peptides. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusion Automatic primary gross tumor volume (GTVp) segmentation for nasopharyngeal carcinoma (NPC) is a quite challenging task because of the existence of similar visual characteristics between tumors and their surroundings, especially on computed tomography (CT) images with severe low contrast resolution. Therefore, most recently proposed methods based on radiomics or deep learning (DL) is difficult to achieve good results on CT datasets.A peritumoral radiomics‐guided generative adversarial network (PRG‐GAN) was proposed to address this challenge.A total of 157 NPC patients with CT images was collected and divided into training, validation, and testing cohorts of 108, 9, and 30 patients, respectively. The proposed model was based on a standard GAN consisting of a generator network and a discriminator network. Morphological dilation on the initial segmentation results from GAN was first conducted to delineate annular peritumoral region, in which radiomics features were extracted as priori guide knowledge. Then, radiomics features were fused with semantic features by the discriminator's fully connected layer to achieve the voxel‐level classification and segmentation. The dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and average symmetric surface distance (ASSD) were used to evaluate the segmentation performance using a paired samples t‐test with Bonferroni correction and Cohen's d (d) effect sizes. A two‐sided p‐value of less than 0.05 was considered statistically significant.The model‐generated predictions had a high overlap ratio with the ground truth. The average DSC, HD95, and ASSD were significantly improved from 0.80 ± 0.12, 4.65 ± 4.71  mm, and 1.35 ± 1.15  mm of GAN to 0.85 ± 0.18 (p = 0.001, d = 0.71), 4.15 ± 7.56 mm (p = 0.002, d = 0.67), and 1.11 ± 1.65 mm (p < 0.001, d = 0.46) of PRG‐GAN, respectively.Integrating radiomics features into GAN is promising to solve unclear border limitations and increase the delineation accuracy of GTVp for patients with NPC. [ABSTRACT FROM AUTHOR]

ABSTRACT The rapid expansion of Internet of Things (IoT) applications presents unprecedented challenges for antenna design, demanding solutions that are versatile, efficient, and capable of operating across multiple frequency bands. This paper addresses these challenges through the development of a design of a multiband antenna using Auxiliary Classifier Wasserstein Generative Adversarial Network for IoT applications (DMA‐ACWGAN‐IoT). Here, the Auxiliary Classifier Wasserstein Generative Adversarial Network (ACWGAN) is employed to generate synthetic data representing electromagnetic field distributions and antenna characteristics across various frequencies. The proposed metamaterial‐based Multiple‐Input Multiple‐Output (MIMO) antenna contains four discrete elements, each provided by a micro strip feed. The structures overall width and length are 60 and 52 mm. The metamaterial is printed on a patch and dispersed from the fields with the most effective coupling. The proposed structures strong impedance bandwidth works at 8.3 GHz, 10.8 GHz, 12.3 GHz, 13.7 GHz, 16.1 GHz, and 18.1 GHz, fabricating the proposed antenna prototype using a substrate made of FR‐4 material. The proposed DMA‐ACWGAN‐IoT design provides 6.5 dB maximum gain and 25.40%, 21.60%, and 20.05% higher efficiency compared to existing dual band antenna design with resonance frequency prediction under machine learning models (DBA‐PRF‐ML), machine learning verification depending on a distinctive SWB multiple slotted four‐port high isolated MIMO antenna loaded with metasurface for the applications of IoT (SWB‐MIMO‐IOT‐ML), and dual‐band miniaturized composite right–left‐handed transmission line ZOR antenna along machine learning method for microwave communication (DB‐ZORA‐MC‐ML). [ABSTRACT FROM AUTHOR]

A novel damage detection model, which utilizes the spatiotemporal characteristics of the acceleration data, is proposed to assess the structural integrity of railway bridges. For this, the measured acceleration data are decomposed into several intrinsic mode functions (IMFs) using the sparse random mode decomposition model. The generated IMFs are subsequently integrated into the enhanced time series conditional generative adversarial network model to identify possible damage in bridges across various frequency bands. The influence of environmental and operational variables (EOVs), particularly temperature fluctuations, was also investigated. The proposed model was verified using both numerical and experimental data from a plate girder bridge. Further validation was conducted using the Z24 bridge dataset, and damage cases under the influence of EOVs were successfully predicted. Throughout the validation process, various anomaly metrics were introduced to establish a threshold value, and a covariance‐based time domain metric was proven to be the most effective in our cases. [ABSTRACT FROM AUTHOR]

The availability of comprehensive datasets is a crucial challenge for developing artificial intelligence (AI) models in various applications and fields. The lack of large and diverse public fabric defect datasets forms a major obstacle to properly and accurately developing and training AI models for detecting and classifying fabric defects in real-life applications. Models trained on limited datasets struggle to identify underrepresented defects, reducing their practicality. To address these issues, this study suggests using a conditional generative adversarial network (cGAN) for fabric defect data augmentation. The proposed image-to-image translator GAN features a conditional U-Net generator and a 6-layered PatchGAN discriminator. The conditional U-Network (U-Net) generator can produce highly realistic synthetic defective samples and offers the ability to control various characteristics of the generated samples by taking two input images: a segmented defect mask and a clean fabric image. The segmented defect mask provides information about various aspects of the defects to be added to the clean fabric sample, including their type, shape, size, and location. By augmenting the training dataset with diverse and realistic synthetic samples, the AI models can learn to identify a broader range of defects more accurately. This technique helps overcome the limitations of small or unvaried datasets, leading to improved defect detection accuracy and generalizability. Moreover, this proposed augmentation method can find applications in other challenging fields, such as generating synthetic samples for medical imaging datasets related to brain and lung tumors. [ABSTRACT FROM AUTHOR]

This study addresses the challenges of data scarcity and class imbalance in structural health monitoring (SHM) of composite structures. Data-driven SHM techniques that benefit from non-destructive evaluation (NDE) are used in various composite structures. However, the lack of damaged state data causes data scarcity and class imbalance problems that prevent robust diagnostics of composite structures. This study introduces a novel data-driven multi-class data augmentation method for composite structures, employing a multi-class generative adversarial network (MC-GAN) for the first time to generate synthetic data for multiple classes without the need for excessive experimentation or simulation. Additionally, the MC-GAN model is integrated with the convolutional neural network (CNN) to develop the MC-GAN-CNN model for autonomous data augmentation and delamination detection. The approach has been validated using experimentally obtained vibrational data for laminated composites. The damage detection using manual feature extraction showed overfitting and very high standard deviation during 10-fold cross-validation for various machine learning models. However, the proposed method suggested a more rigorous assessment with a mean accuracy of 99.72 ± 0.08 %. In addition, the proposed framework assists in handling the delamination detection problem autonomously without requiring hand-crafted statistical features with a good generalization capability. • An intelligent data-driven framework for autonomous delamination detection. • Multi-class GAN model to overcome multi-class imbalance and scarce data. • Qualitative and quantitative assessment of synthetic data. • Delamination detection in laminated composites using CNN-based deep learning model. • The augmented dataset provided high accuracy, precision, recall and f1-score. [ABSTRACT FROM AUTHOR]

Piezoelectric needle selectors, as key weaving components in the jacquard knitting process of knitting machinery, are widely used in textile equipment. Accurate diagnostic procedures for needle selector drive faults are crucial to ensure the normal operation of the equipment. However, traditional vibration diagnosis methods cannot detect weak periodic signals, resulting in low accuracy of monitoring results. To address this issue, this paper proposes an adaptive stochastic resonance (SR) method based on an improved generative adversarial network (IGAN). Firstly, in order to solve the problem of difficulty in obtaining stochastic resonance parameters, SR is combined with IGAN, and IGAN provides the optimal SR parameters. Secondly, a soft threshold residual attention mechanism and residual network were introduced in the GAN network, and multiple generators were utilized to alleviate the problem of model collapse, in order to adapt to the actual working environment. In addition, due to the large amount of data, it is recommended to use feature parameters for training to improve the efficiency of model training. Finally, through a typical vibration data, the influence of different parameter quantities on the training accuracy of the model was studied, and the superiority of the proposed model compared to other models and the stability of the model under different environmental influences were explored. The results show that this method can effectively detect weak periodic signals of the needle selection driver, effectively alleviate the problem of model collapse in different environments, and is superior to existing methods in terms of accuracy and stability. [ABSTRACT FROM AUTHOR]

ABSTRACT In practical industrial applications, equipment usually operates normally and failures are relatively rare, resulting in serious imbalances in the collected data. This imbalance leads to issues such as overfitting, instability, and poor robustness, significantly reducing the accuracy and stability of fault diagnosis system. To address these challenges, this research proposes a method for imbalanced data augmentation and industrial process fault diagnosis based on improved Generative Adversarial Network (GAN). The method adopts Wasserstein distance with gradient penalty and integrates residual connections into the architecture of the generator. This innovation not only helps improve gradient transfer in the generator, but also significantly enhances the data generation capabilities of the generative model through improving the stability of training. Limited industrial process data is used by a generative model to produce synthetic samples with high similarity and diversity. These high‐quality samples improve fault diagnosis by enriching the imbalanced dataset. Experimental results on two industrial datasets confirm the method's effectiveness in enhancing fault diagnosis performance with limited data. [ABSTRACT FROM AUTHOR]

Viral oncoproteins play crucial roles in transforming normal cells into cancer cells, representing a significant factor in the etiology of various cancers. Traditionally, identifying these oncoproteins is both time-consuming and costly. With advancements in computational biology, bioinformatics tools based on machine learning have emerged as effective methods for predicting biological activities. Here, for the first time, we propose an innovative approach that combines Generative Adversarial Networks (GANs) with supervised learning methods to enhance the accuracy and generalizability of viral oncoprotein prediction. Our methodology evaluated multiple machine learning models, including Random Forest, Multilayer Perceptron, Light Gradient Boosting Machine, eXtreme Gradient Boosting, and Support Vector Machine. In ten-fold cross-validation on our training dataset, the GAN-enhanced Random Forest model demonstrated superior performance metrics: 0.976 accuracy, 0.976 F1 score, 0.977 precision, 0.976 sensitivity, and 1.0 AUC. During independent testing, this model achieved 0.982 accuracy, 0.982 F1 score, 0.982 precision, 0.982 sensitivity, and 1.0 AUC. These results establish our new tool, VirOncoTarget, accessible via a web application. We anticipate that VirOncoTarget will be a valuable resource for researchers, enabling rapid and reliable viral oncoprotein prediction and advancing our understanding of their role in cancer biology. [ABSTRACT FROM AUTHOR]

Our study develops a generative adversarial network (GAN)-based method that generates faithful synthetic image data of human cardiomyocytes at varying stages in their maturation process, as a tool to significantly enhance the classification accuracy of cells and ultimately assist the throughput of computational analysis of cellular structure and functions. Human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) were cultured on micropatterned collagen coated hydrogels of physiological stiffnesses to facilitate maturation and optical measurements were performed for their structural and functional analyses. Control groups were cultured on collagen coated glass well plates. These image recordings were used as the real data to train the GAN model. The results show the GAN approach is able to replicate true features from the real data, and inclusion of such synthetic data significantly improves the classification accuracy compared to usage of only real experimental data that is often limited in scale and diversity. The proposed model outperformed four conventional machine learning algorithms with respect to improved data generalization ability and data classification by incorporating synthetic data. This work demonstrates the importance of integrating synthetic data in situations where there are limited sample sizes and thus, effectively addresses the challenges imposed by data availability. [ABSTRACT FROM AUTHOR]

The floor plan layout of museum exhibition spaces is the skeleton network of the museum, which determines the internal circulation and spatial form of the museum. This paper studies the method and practice of using artificial intelligence technology to assist in the space design of exhibition halls in urban cultural museums. First, it introduces the limitations of traditional space design methods for exhibition halls in urban cultural museums and the superiority and application prospects of the CGAN (conditional generative adversarial network) model in space design. Second, the principle and training process of the CGAN model are explained in detail, and the experimental results and analysis are given. By learning 100 floor plans of exhibition halls of urban culture museums, the CGAN model can generate a new floor plan design for an exhibition hall, which provides a new idea and innovative method for this design task. Finally, the limitations and future research directions of the CGAN model in the space design of urban cultural museum exhibition halls are discussed. The study shows that using the CGAN model to learn the floor plans of exhibition halls of urban cultural museums can effectively improve the innovation and practicability of space design and has the following advantages: (1) It can quickly generate a large number of exhibition hall floor plans, shorten the design cycle, and improve design efficiency. (2) The generated floor plan designs of the exhibition hall are diverse and personalized, meeting the design requirements of different scenarios and needs. (3) The method promotes the deep integration of space design and artificial intelligence technology and provides new possibilities and ideas for space design. These conclusions provide new ideas and methods for the space design of exhibition halls of urban cultural museums and provide a reference and inspiration for space design and intelligent applications in other fields, such as office space design, home decoration space design, landscape space design, and historical arcade and building renovation design. [ABSTRACT FROM AUTHOR]

In image dehazing, non-uniform haze leads to issues such as image blurring, distortion, low contrast, and surface detail loss. Existing dehazing techniques struggle with the uneven distribution of haze, especially in scenes with varying depth, making it difficult to restore high-quality images. This research introduces the Central Difference Embedding Convoluted Generative Adversarial Network (CDECGAN), which enhances local contrast variations to address haze-induced blur and low-contrast areas. To further improve adaptability, the Updated Human Memory Algorithm (UHMA) is integrated to optimise hyperparameters and model performance. The approach also combines image denoising and dehazing, using the Generic Edge Attention (GAE) architecture to denoise while preserving edge sharpness. The Weighted Median Channel Prior (WMCP) model helps focus on high-frequency details and estimate scene depth, solving issues related to fixed-size local patches. Performance metrics confirm the effectiveness of CDECGAN, achieving an average peak signal-to-noise ratio of 25.58 dB, a structural similarity index of 0.884, a natural image quality evaluator score of 11.18, and other notable results. The CDECGAN method outperforms existing techniques by successfully handling non-uniform haze and depth-related challenges in image dehazing. [ABSTRACT FROM AUTHOR]