THE USE OF ARTIFICIAL INTELLIGENCE IN THE THERAPEUTIC MANAGEMENT OF PAPILLARY THYROID MICROCARCINOMA: A RANDOMIZED CONTROLLED TRIAL PROTOCOL by Ramona Elena Teiu, Tudor Florin Ursuleanu, Hlescu Cristian Stefan, Roxana Grigorovici, Andreea Roxana Luca, Maria Paula Comanescu, Alina Ionela Calin, Alexandru Grigorovici

“…Materials and methods The optimization of the method will consist in the development and training of artificial intelligence models, using AI algorithms in the diagnosis of papillary thyroid cancererol by accurately identifying pathological lesions and adenopathy and generating 3D images from 2D CT images. use the transfer function for opacity and color, grayscale from DICOM images projected in a three-dimensional space. [1]; [2] We also use artificial intelligence (AI), through the Generative Adversarial Networks (GAN) technique, which has proven to be effective in representing complex data distributions [2], as we do in this study. …”
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Attention-Based Image-to-Video Translation for Synthesizing Facial Expression Using GAN by Kidist Alemayehu, Worku Jifara, Demissie Jobir

“…With the development of generative adversarial networks (GANs), great progress has been made in image generation tasks which can be used for facial expression synthesis. …”
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Vision-Based Pick and Place Control System for Industrial Robots Using an Eye-in-Hand Camera by van-Truong Nguyen, Phu-Tuan Nguyen, Shun-Feng Su, Phan Xuan Tan, Thanh-Lam Bui

“…We propose a novel approach that combines the YOLOv7 (You Only Look Once V7) deep learning network with GAN (Generative Adversarial Networks) to achieve fast and accurate object recognition. …”
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Lung Diseases Diagnosis-Based Deep Learning Methods: A Review by Shahad A. Salih, Sadik Kamel Gharghan, Jinan F. Mahdi, Inas Jawad Kadhim

“…This review discusses the various DL methods that have been developed for lung disease diagnosis, including convolutional neural networks (CNNs), deep neural networks (DNNs), and generative adversarial networks (GANs). The advantages and limitations of each method are discussed, along with the types of medical imaging techniques used, such as X-ray and computed tomography (CT). …”
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Innovative approaches for skin disease identification in machine learning: A comprehensive study by Kuldeep Vayadande, Amol A. Bhosle, Rajendra G. Pawar, Deepali J. Joshi, Preeti A. Bailke, Om Lohade

“…Investigate the effectiveness and performance of several algorithms, such as the flexible k-nearest neighbor, the sturdy support vector machine (SVM), and the complex convolutional neural networks (CNNs), advanced techniques for automated skin disease detection encompass deep learning methods such as recurrent neural networks (RNNs) for sequential data processing, generative adversarial networks (GANs) for generating synthetic data, and attention mechanisms for focusing on relevant image regions by means of a thorough examination of the most recent studies. …”
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Classification of cervical cancer using Dense CapsNet with Seg-UNet and denoising autoencoders by Hui Yang, Walid Aydi, Nisreen Innab, Mohamed E. Ghoneim, Massimiliano Ferrara

“…Contrast maximization is performed in the pre-processing phase, and the images are augmented using Multi-modal Generative Adversarial Networks (m-GAN) in the second phase. In the third phase, cervical cancer images are segmented using the Seg-UNet model, which is forwarded to the feature extraction phase that employs denoising autoencoders. …”
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An imbalanced deep learning framework for pre-fault flexible multi-zone dynamic security assessment via transfer learning based graph convolutional network by Sasan Azad, Mohammad Taghi Ameli

“…This paper addresses these two challenges with the help of transfer learning (TL) and conditional tabular generative adversarial networks (CTGAN). This paper first generates synthetic data using CTGAN, which helps create a balanced and representative training database to combat the negative effects of an imbalanced database. …”
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Identifying Tomato Growth Stages in Protected Agriculture with StyleGAN3–Synthetic Images and Vision Transformer by Yao Huo, Yongbo Liu, Peng He, Liang Hu, Wenbo Gao, Le Gu

“…This paper proposes an innovative solution combining generative adversarial networks (GANs) and deep learning techniques to address these challenges. …”
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Combined Input Deep Learning Pipeline for Embryo Selection for In Vitro Fertilization Using Light Microscopic Images and Additional Features by Krittapat Onthuam, Norrawee Charnpinyo, Kornrapee Suthicharoenpanich, Supphaset Engphaiboon, Punnarai Siricharoen, Ronnapee Chaichaowarat, Chanakarn Suebthawinkul

“…In addition, a custom weight was trained using a self-supervised learning framework known as the Simple Framework for Contrastive Learning of Visual Representations (SimCLR) in cooperation with generated images using generative adversarial networks (GANs). The best model was developed from the EfficientNet-B0 model using preprocessed images combined with pseudo-features generated using separate EfficientNet-B0 models, and optimized by Optuna to tune the hyperparameters of the models. …”
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One‐Day Forecasting of Global TEC Using a Novel Deep Learning Model by Sujin Lee, Eun‐Young Ji, Yong‐Jae Moon, Eunsu Park

“…Abstract In this study, we make a global total electron content (TEC) forecasting using a novel deep learning method, which is based on conditional generative adversarial networks. For training, we use the International GNSS Service (IGS) TEC maps from 2003 to 2012 with 2‐h time cadence. …”
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Semi-Supervised Change Detection with Data Augmentation and Adaptive Thresholding for High-Resolution Remote Sensing Images by Wuxia Zhang, Xinlong Shu, Siyuan Wu, Songtao Ding

“…These approaches typically employ strategies such as consistency regularization, pseudo-labeling, and generative adversarial networks. However, they usually face the problems of insufficient data augmentation and unbalanced quality and quantity of pseudo-labeling. …”
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A Deep-Learning Approach to a Volumetric Radio Environment Map Construction for UAV-Assisted Networks by Bethelhem S. Shawel, Dereje H. Woldegebreal, Sofie Pollin

“…Specifically, the approach uses two deep learning-based models: volume-to-volume (Vol2Vol) VREM with 3D-generative adversarial networks and sliced VREM with altitude-aware spider-UNets. …”
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A novel prediction method for low wind output processes under very few samples based on improved W‐DCGAN by Shihua Liu, Han Wang, Weiye Song, Shuang Han, Jie Yan, Yongqian Liu

“…Therefore, a novel prediction method for LWOP under very few samples based on improved Wasserstein deep convolutional generative adversarial networks (W‐DCGAN) is proposed here. Firstly, a multi‐dimensional identification method is proposed to accurately identify historical LWOP. …”
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Deep learning-based encryption scheme for medical images using DCGAN and virtual planet domain by Manish Kumar, Aneesh Sreevallabh Chivukula, Gunjan Barua

“…This paper presents a novel encryption technique that integrates the Deep Convolutional Generative Adversarial Networks (DCGAN) and Virtual Planet Domain (VPD) approach to enhance the protection of medical images. …”
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Exploring the subtle and novel renal pathological changes in diabetic nephropathy using clustering analysis with deep learning by Tomohisa Yabe, Yuko Tsuruyama, Kazutoshi Nomura, Ai Fujii, Yuto Matsuda, Keiichiro Okada, Shogo Yamakoshi, Yuya Hamabe, Shogo Omote, Akihiro Shioya, Norifumi Hayashi, Keiji Fujimoto, Yuki Todo, Tatsuro Tanaka, Sohsuke Yamada, Akira Shimizu, Katsuhito Miyazawa, Hitoshi Yokoyama, Kengo Furuichi

“…We also used visualizing techniques (gradient-weighted class activation mapping (Grad-CAM) and generative adversarial networks (GAN)) to identify the novel and early pathological changes on light microscopy in diabetic nephropathy. …”
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Feasibility of generating sagittal radiographs from coronal views using GAN-based deep learning framework in adolescent idiopathic scoliosis by Tito Bassani, Andrea Cina, Fabio Galbusera, Andrea Cazzato, Maria Elena Pellegrino, Domenico Albano, Luca Maria Sconfienza

“…Abstract Background Minimizing radiation exposure is crucial in monitoring adolescent idiopathic scoliosis (AIS). Generative adversarial networks (GANs) have emerged as valuable tools being able to generate high-quality synthetic images. …”
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UAV Hyperspectral Remote Sensing Image Classification: A Systematic Review by Zhen Zhang, Lehao Huang, Qingwang Wang, Linhuan Jiang, Yemao Qi, Shunyuan Wang, Tao Shen, Bo-Hui Tang, Yanfeng Gu

“…This article provides an in-depth and systematic review of UAV HSI classification techniques, systematically examining the evolution from traditional machine learning approaches, such as sparse coding, compressed sensing, and kernel methods, to cutting-edge deep learning frameworks, including convolutional neural networks, Transformer models, recurrent neural networks, graph convolutional networks, generative adversarial networks, and hybrid models. Although traditional methods demonstrate effectiveness in certain scenarios, their limitations become increasingly apparent when dealing with high-dimensional, nonlinear spectral data. …”
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Time-Frequency Analysis and Target Recognition of HRRP Based on CN-LSGAN, STFT, and CNN by Jianghua Nie, Yongsheng Xiao, Lizhen Huang, Feng Lv

“…Combining the Least-Squares Generative Adversarial Network (LSGAN) with the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), the CN-LSGAN is presented and applied to the HRRP denoise. …”
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A review on inverse analysis models in steel material design by Yoshitaka Adachi, Ta‐Te Chen, Fei Sun, Daichi Maruyama, Kengo Sawai, Yoshihito Fukatsu, Zhi‐Lei Wang

“…Key models discussed include the convolutional neural network–artificial neural network‐coupled model, which employs convolutional neural networks for feature extraction; the Bayesian‐optimized generative adversarial network–conditional generative adversarial network model, which generates diverse virtual microstructures; the multi‐objective optimization model, which concentrates on process–property relationships; and the microstructure–process parallelization model, which correlates microstructural features with process conditions. …”
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A Novel FS-GAN-Based Anomaly Detection Approach for Smart Manufacturing by Tae-yong Kim, Jieun Lee, Seokhyun Gong, Jaehoon Lim, Dowan Kim, Jongpil Jeong

“…In this study, we present the few-shot generative adversarial network (FS-GAN) model, which integrates few-shot learning and a generative adversarial network with an unsupervised learning approach (AnoGAN) to address the challenges of anomaly detection in smart-factory manufacturing environments. …”
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