Advancements in Radar Performance through Generative AI: A Sector-Wide Survey

Generative artificial intelligence, especially with regard to the generative adversarial network (GAN), is an important research area in radiology as evidenced by a number of literature reviews on the role of GAN in radiology published in the last few years. However, no review article about GAN in pediatric radiology has been published yet. The purpose of this paper is to systematically review applications of GAN in pediatric radiology, their performances, and methods for their performance evaluation. Electronic databases were used for a literature search on 6 April 2023. Thirty-seven papers met the selection criteria and were included. This review reveals that the GAN can be applied to magnetic resonance imaging, X-ray, computed tomography, ultrasound and positron emission tomography for image translation, segmentation, reconstruction, quality assessment, synthesis and data augmentation, and disease diagnosis. About 80% of the included studies compared their GAN model performances with those of other approaches and indicated that their GAN models outperformed the others by 0.1–158.6%. However, these study findings should be used with caution because of a number of methodological weaknesses. For future GAN studies, more robust methods will be essential for addressing these issues. Otherwise, this would affect the clinical adoption of the GAN-based applications in pediatric radiology and the potential advantages of GAN could not be realized widely.

The general objective of the research is to determine the advances related to Generative Artificial Intelligence. Methodology, in this research, 47 documents have been selected, carried out in the period 2014 - 2023; including: scientific articles, review articles and information from websites of recognized organizations. Results, Generative Artificial Intelligence is demonstrating its importance in various human activities, making it necessary to use it ethically and responsibly. Conclusions, the general objective of the research is to determine the advances related to Generative Artificial Intelligence. Artificial intelligence has evolved from predictive to generative. Key Techniques: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Autoregressive Models. Countries are establishing standards for the ethical use of AI, while respecting human rights. Currently, AI has many applications in human activity, but the ethical use of AI is necessary. Various countries are establishing regulations in this regard. Generative Artificial Intelligence is demonstrating its importance in various human activities, making it necessary to use it ethically and responsibly. The specific objectives of the research are to identify the applications and the software of Generative Artificial Intelligence. Applications: Generating realistic images, creating natural language text, composing music. Generative artificial intelligence (AI) tools, such as Bard, ChatGPT, and GitHub CoPilot.

Generative artificial intelligence (GAI), a specialized branch of artificial intelligence, has developed as a dynamic discipline that drives innovation and creativity across several domains. It is concerned with creating models that can autonomously produce novel text, images, videos, music, 3D, code, and more. GAI is distinguished by its capacity to acquire knowledge from extensive datasets, identify recurring patterns, capture distributions, and produce novel content demonstrating similar features. This review presents a comprehensive historical overview of the development and progression of GAI techniques over the years. Various essential methodologies employed in developing GAI are also discussed, including Generative Adversarial Networks (GANs), Variational AutoEncoders (VAEs), transformers, and diffusion models. Moreover, a detailed overview of the technologies used in GAI is provided for generating images, videos, music, code, and text, such as ChatGPT, DALL-E, Midjourney, Claude, Bard, GitHub Copilot, and others. The research subsequently introduces the different datasets used to train the GAI models and the evaluation metrics for evaluating their performances. Ultimately, the research investigates the diverse applications of GAI across various domains, challenges, and ethical implications.

The lack of the sufficient and diverse training data is one of the main challenges limiting performances of the machine learning enabled applications in optical networks. Here, we propose a deep learning based sequential data augmentation technique for the aggregate traffic data augmentation for diverse optical network scenarios. A generative adversarial network (GAN) model is trained with the experimental traffic data to automatically extract the substantial characteristics of the experimental traffic data through the zero-sum game theory and then augment the traffic data adaptively. The statistical evaluation parameters of the augmented traffic are mean, variance and Hurst exponent. To add comparisons, two other classical generative models including the statistical parameter configuration (SPC) model and the variational autoencoder (VAE) model are also adopted to generate the traffic data that are similar to the actual traffic data. The comprehensive comparisons among the proposed GAN, the SPC and VAE show that the performances of the GAN exceed those of the SPC and the VAE obviously. The mean and the variance of the augmented traffic data from the GAN are almost equal to those of the experimental traffic data, where the average deviations are both within 2%. The Hurst exponent of the augmented traffic data from the GAN is respectively near 90% and 96% of those of the experimental traffic data in the access network and the core network. To estimate the similarity intuitively, the well-known k-mean algorithm is used to cluster the augmented traffic data according to the centroids determined by the corresponding experimental traffic data and the clustering accuracies are all higher than 95% for 6 kinds of typical traffic types in the optical networks. These results demonstrate that the proposed GAN is able to effectively generate the traffic data that is very close to the experimental traffic data and is difficult to be distinguished for diverse traffic types. Moreover, a relatively small dataset with a few hundred pieces of experimental traffic data is required and the amount of the augmented traffic data from the GAN is unlimited in theory, which can be augmented as much as we need. The proposed traffic data augmentation technique also has the potential to be utilized in other sequential data augmentation applications for the optical networks.

Recent advancements in generative artificial intelligence (AI) have introduced novel opportunities for the application of AI within the financial sector. This chapter examines the various ways in which generative AI is contributing to the rapidly evolving landscape of finance. While traditional machine learning techniques have long been applied to financial tasks such as customer service, text summarization, risk prediction, algorithmic trading, and document processing, generative AI offers enhanced capabilities aimed at improving operational efficiency, elevating customer experience, and enabling responsible growth. Applications of generative AI in finance now extend to areas including decision-making, risk assessment, portfolio optimization, personalized financial advising, fraud detection, and predictive analytics. The field encompasses a diverse set of models, including generative adversarial networks (GANs), variational autoencoders (VAEs), temporal point processes (TPPs), transformers, and large language models (LLMs). This chapter explores how these models are applied in financial contexts, highlighting both theoretical foundations and practical implementations. A dedicated case study is presented, summarizing recent literature on the use of LLMs in financial tasks, including approaches such as zero-shot and few-shot learning with pretrained models, domain-specific fine-tuning, and training models from the ground up. Finally, the chapter discusses key opportunities and challenges associated with generative AI in FinTech, including the balance between innovation and responsibility, and the need for enhanced interpretability, robustness, and ethical compliance in financial applications, hence aiding towards goals of Industry 5.0.

Generative AI has revolutionized computer vision by enabling machines to synthesize and enhance visual data. Advances in deep learning, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models, have led to high-quality image generation with applications in medical imaging, data augmentation, and autonomous systems. Despite its potential, challenges like ethical concerns, dataset biases, and computational costs remain critical for future research and implementation. This paper explores the evolution of generative AI, its methodologies, applications, and the ethical and computational challenges it presents.

In recent years, the manufacturing industry has undergone transformative changes spurred by the rapid evolution of artificial intelligence (AI), particularly Generative AI (GenAI). As a subset of AI capable of creating new data from learned patterns, GenAI is poised to reshape manufacturing processes by enhancing productivity, product customization, quality assurance, and operational efficiency. This review synthesizes key findings from scholarly articles published in the first half of 2024, with a primary focus on three GenAI models: Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-Based Architectures. The paper critically analyzes the roles and capabilities of GenAI technologies in improving predictive maintenance, supply chain optimization, and sustainable production. It also sheds light on the prevailing challenges and potential future advancements of GenAI integration in industrial environments.

Among the broad area of artificial intelligence (AI), generative AI algorithms have emerged as a revolutionary technology able to produce highly realistic ‘synthetic’ data, akin to standard simulation but with fewer contraints. The main focus of generative AI has been on phenotypes, but here we argue it can serve as well for generating synthetic environments and genotypes. This data-driven technology may be able to overcome some of the limitations that standard simulations have, such as strong assumptions on the underlying genotype to phenotype map. We discuss key features of popular generative models including autoregressive models, generative adversarial networks, variational autoencoders, diffusion and flow-based models. Several of these methods utilize a latent space, often of lower dimensionality than the raw data, that can help making the models interpretable and can be a link between simulation and generative algorithms. Augmenting data as realistically as possible with genAI can improve inference and predictive performance of genomic prediction models, but symbolic simulation will continue to play a fundamental role in predictive breeding. A hybrid tool that implements both approaches can be extremely powerful to evaluate predictive breeding strategies in silico. One promising direction is to simulate novel genotypes using conventional methods, then apply generative models to produce realistic phenotypes conditional on genotype and environment.

The semiconductor industry plays a vital role in driving technological advancements, and the incorporation of AI (Artificial Intelligence) can greatly enhance its efficiency and productivity. Through optimizing material usage and reducing defects, AI can significantly reduce costs and enhance production efficiency and product quality. However, despite the increasing interest in AI applications in the semiconductor industry, comprehensive reviews are lacking to systematically analyze existing research and identify the challenges and opportunities in this field. This review aims to bridge this gap by providing a thorough overview of AI-driven techniques in optimizing semiconductor manufacturing and offering valuable insights for future research directions. The integration of Artificial Intelligence (AI) into chip design marks a transformative phase for the semiconductor industry. Traditional design methodologies, often labor-intensive and time-consuming, are increasingly constrained by human expertise and iterative processes. Generative AI, utilizing advanced machine learning models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), offers innovative approaches to automate and optimize various stages of chip design. This paper examines how generative AI can revolutionize chip design by automating complex tasks, including architecture exploration, circuit optimization, and layout generation. Through case studies, we demonstrate significant improvements in design efficiency, performance optimization, and reduced time-to-market. Additionally, we address challenges such as data availability, model interpretability, and the integration of AI-generated designs into existing verification workflows. The findings highlight the potential of generative AI to enhance design capabilities, reduce development costs, and accelerate innovation in semiconductor technology.

Today, with the involvement of artificial intelligence (AI), where sensitive information is being collected massively to analyse data for various purposes, privacy-preserving data analysis has gained much importance in the data-driven world. Traditionally, using normal data analysis techniques, privacy-preserving methods often faced several security challenges to ensure the privacy of individuals and organizations where the shared data resources have been used for collaboration or decision-making. Hence, generative artificial intelligence has offered numerous solutions that can ensure privacy to such security concerns by enabling privacy-preserving analysis while getting fruitful knowledge from data. Therefore, in this chapter, the authors aim to provide an overview of privacy-preserving data analysis techniques using AI-based approaches such as differential privacy, federated learning (FL), generative adversarial network (GAN), and other variational auto-encoders (VAEs). While providing individuals protection in privacy, they have discussed how generative AI can be used to preserve statistical properties of plain data using synthetic data analysis. Moreover, some real-world applications have also been part of the discussion to illustrate the effectiveness of such techniques in various domains. Finally, they have also provided some future challenges, limitations, and ethical considerations for implementing these techniques effectively and have provided future directions in the rapidly emerging domain.

Sudan's post-conflict transition presents a critical opportunity to reimagine state institutions through inclusive, resilient, and ethically grounded reform.This paper explores the potential for generative artificial intelligence (AI) specifically Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformer-based models to support this complex process of institutional reconstruction.Moving beyond technocratic solutions, the study examines the theoretical foundations of these models and maps their operational mechanisms onto key domains of reform, including constitutional design, budget simulation, civic engagement, and transitional justice.It argues that generative AI, when locally adapted and ethically governed, can serve as a catalyst for reimagining governance, amplifying civic agency, and embedding pluralistic narratives into the fabric of institutional renewal.The paper concludes with ethical reflections and comparative insights from Rwanda, Colombia, and Tunisia, outlining a framework for contextually sensitive implementation.

A de-ambiguous condition monitoring scheme for wind turbines using least squares generative adversarial networks

Malware detection plays a significant role in the realm of cybersecurity, yet traditional methods frequently struggle to identify novel and polymorphic threats. It investigates the growing role of Generative Artificial Intelligence (GenAI), including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based models, in enhancing malware detection. We systematically reviewed and analyzed over 25 peer-reviewed studies and technical reports published between 2017 and 2024, including recent surveys and case studies on GenAI-based cybersecurity solutions. Four benchmark datasets-Malimg, UNSW-NB15, CIC-IoT23, and EMBER-were evaluated across the literature for their suitability in GenAI-based malware detection. The techniques are compared based on performance metrics such as classification accuracy, anomaly detection precision, F1-score, adversarial robustness, and computational efficiency. The paper also identifies key limitations, including mode collapse, low interpretability, and vulnerability to evasion and poisoning attacks. Finally, we identify various possibilities for future research, including real-time AI defenses, hybrid GenAI-discriminative approaches, and explainable AI integration. This study serves as a foundational reference for advancing GenAI-powered malware detection frameworks in cybersecurity. To provide a more reliable and precise malware detection pipeline, the suggested hybrid framework combines Transformer-based sequence modeling, VAE-driven latent feature encoding, and GAN-based data augmentation. Furthermore, the proposed hybrid model combining GAN-based augmentation, VAE-driven latent representation learning, and Transformerbased sequence modeling demonstrates improved robustness and detection accuracy, offering a unified approach to address malware diversity, imbalance, and evolving threat behaviors. The proposed hybrid model delivers the highest accuracy <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(96.4 \%)$</tex>.

Generative Artificial Intelligence (GenAI) techniques have received massive and widespread industry-disrupting attention because they can create synthetic data, images, text, and other types of content that look very close to real-world examples. Unlike traditional AI that mainly focuses on classification or prediction, GenAI models learn and copy data distributions. This allows new applications such as content creation, data augmentation, and training of machine learning models. In this survey, we organise the discussion around two major themes: general strategies in GenAI and domain-specific adaptation. We develop a mapping approach to help researchers and practitioners find suitable GenAI techniques for different domains. For each domain, we provide detailed discussions about data considerations, real-world case studies, the use of domain-related tools and frameworks, and practical guidance. We also review domain-specific challenges and opportunities to support future research and application work. Our findings give a full view of the changing GenAI field and suggest ways to build more effective, flexible, and strong generative solutions.

The gas chromatography analysis method for chemical substances enables accurate analysis to precisely distinguish the components of a mixture. This paper presents a technique for augmenting time-series data of chemicals measured by gas chromatography instruments with artificial intelligence techniques such as generative adversarial networks (GAN). We propose a novel GAN algorithm called GCGAN for gas chromatography data, a unified model of autoencoder (AE) and GAN for effective time-series data learning with an attention mechanism. The proposed GCGAN utilizes AE to learn a limited number of data more effectively. We also build a layer of high-performance generative adversarial neural networks based on the analysis of the features of data measured by gas chromatography instruments. Then, based on the proposed learning, we synthesize the features embedded in the gas chromatography data into a feature distribution that extracts the temporal variability. GCGAN synthesizes the features embedded in the gas chromatography data into a feature distribution that extracts the temporal variability of the data over time. We have fully implemented the proposed GCGAN and experimentally verified that the data augmented by the GCGAN have the characteristic properties of the original gas chromatography data. The augmented data demonstrate high quality with the Pearson correlation coefficient, Spearman correlation coefficient, and cosine similarity all exceeding 0.9, significantly enhancing the performance of AI classification models by 40%. This research can be effectively applied to various small dataset domains other than gas chromatography data, where data samples are limited and difficult to obtain.