Relevant bibliographies by topics / Generative AI

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Generative AI'

Author: Grafiati
Published: 14 September 2024
Last updated: 25 July 2025

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Journal articles on the topic "Generative AI"

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Andreia, Gabriela Andrei, Mațcu-Zaharia Mara, and Florentin Mariciuc Dragoș. "Ready to Grip AI's Potential? Insights from an Exploratory Study on Perceptions of Human-AI Collaboration." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 15, no. 2 (2024): 01–22. https://doi.org/10.18662/brain/15.2/560.

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One of the emerging technologies arising with Industry 4.0 is generative artificial intelligence (AI). Despite its disruptive nature and controversies, the effective and ethical use of AI is increasingly preoccupying organizations of all sizes as well as their employees. Focusing on generative AI, this paper presents findings from a qualitative study that provides insights into how Generation Z, the newest workforce, perceives human-AI collaboration. Based on in-depth interviews and a micro-meso-macro approach, the study reveals a dual perspective. Participants recognized the advantages AI bri
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Sai, Kalyana Pranitha Buddiga. "Understanding Generative AI Architectures: A comprehensive Overview and Classification." European Journal of Advances in Engineering and Technology 10, no. 12 (2023): 21–23. https://doi.org/10.5281/zenodo.11213709.

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Generative Artificial Intelligence (Generative AI) encompasses a diverse set of architectures and models that enable machines to create new data samples that resemble real-world examples. This white paper provides a comprehensive overview of Generative AI architectures, categorizing them into distinct classes based on their underlying principles, structures, and applications. By exploring the various Generative AI architectures, readers will gain insights into the state-of-the-art techniques and their potential applications across different domains.
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Annaji, Kuthe, Shangarwar Sandesh, Kamble Yegnesh, Sakhale Prajwal, Bhende Raj, and Thakre Jidnyasa. "Portfolio Generator using Generative AI in Web Development." International Journal of Mathematics and Computer Research 12, no. 05 (2024): 4162–65. https://doi.org/10.5281/zenodo.11100803.

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The rise of generative intelligence (AI) has led to significant transformations in various industries, notably web development. This paper introduces a unique strategy for creating personalized portfolios for web developers through the utilization of generative AI techniques. By exploiting generative AI models, developers can automate the process of generating distinct portfolio designs customized to individual preferences, resulting in saving time and effort. This paper delves into the methodology, execution, and potential advantages of utilizing generative AI in portfolio generation for web
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Koda, Masahide. "Generative AI." Okayama Igakkai Zasshi (Journal of Okayama Medical Association) 136, no. 3 (2024): 139–40. https://doi.org/10.4044/joma.136.139.

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Krishna, Guntamukkala Gopi. "Generative AI." International Journal of Advanced Engineering and Nano Technology 10, no. 8 (2023): 1–3. http://dx.doi.org/10.35940/ijaent.g0474.0810823.

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Recent advancements in generative artificial intelligence (AI) have made it possible for machines to independently produce a variety of creative content. In the context of producing creative content, this essay examines the developments, difficulties, and ethical issues relating to generative AI. It looks into how generative models, such Generative Adversarial Networks (GANs) and Variational Auto encoders (VAEs), can produce realistic artwork like music, literature, and visuals. However, it is frequently discovered that GAN training is extremely unstable and frequently experiences non-converge
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Euchner, Jim. "Generative AI." Research-Technology Management 66, no. 3 (2023): 71–74. http://dx.doi.org/10.1080/08956308.2023.2188861.

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Guntamukkala, Gopi Krishna. "Generative AI." International Journal of Advanced Engineering and Nano Technology (IJAENT) 10, no. 8 (2023): 1–3. https://doi.org/10.35940/ijaent.G0474.0810823.

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<strong>Abstract: </strong>Recent advancements in generative artificial intelligence (AI) have made it possible for machines to independently produce avariety of creative content. In the context of producing creative content, this essay examines the developments, difficulties, and ethical issues relating to generative AI. It looks into how generative models, such Generative Adversarial Networks (GANs) and Variational Auto encoders (VAEs), can produce realistic artwork like music, literature, and visuals. However, it is frequently discovered that GAN training is extremely unstable and frequentl
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Swaroopa, Pethota. "StoryCraft AI: Exploring Generative Approaches to Story Narration through AI." International Scientific Journal of Engineering and Management 03, no. 04 (2024): 1–9. http://dx.doi.org/10.55041/isjem01633.

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In this project, we demonstrate a Storytelling AI system, which can generate short stories and complementary illustrated images with minimal input from the user. The system makes use of a text generation model, a text-to-image synthesis network, and a neural style transfer model. The final project is deployed into a website where users can build their stories. The field of AI has made significant changes in various domains, including Natural Language Processing and Generative models. One captivating application of these advancements is in the realm of storytelling. This project introduces a no
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Rathod, Rohan. "Renaissance on Generative AI." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (2024): 1354–59. http://dx.doi.org/10.22214/ijraset.2024.63324.

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Abstract: Generative Artificial Intelligence (Generative AI) stands at the forefront of innovation, promising to revolutionize creative content generation across various domains. This paper delves into the multifaceted implications of Generative AI in reshaping the landscape of artistic expression. Through an extensive literature survey and analysis, we explore the applications, advancements, and challenges of Generative AI in text generation, visual arts, and music composition. From state-of-the-art models like OpenAI's GPT series to cutting-edge techniques such as Generative Adversarial Netw
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Khanam, Aesha Tarannum. "Role of Generative AI in Enhancing Library Management Software." International Journal of Sciences and Innovation Engineering 1, no. 2 (2024): 1–10. https://doi.org/10.70849/IJSCI27934.

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Enhancing Library Management Software (LMS) through work automation, more efficient cataloging, and customized user experiences are all made possible by generative AI. In order to better understand how generative AI fits into library systems, this paper looks at how it may provide accurate information, optimize operations, and provide tailored suggestions depending on user preferences. AI-driven systems reduce the human labor necessary for categorization and resource management while boosting user engagement through targeted offerings. Notwithstanding these advantages, problems like prejudice
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Dissertations / Theses on the topic "Generative AI"

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Lacan, Alice. "Transcriptomics data generation with deep generative models." Electronic Thesis or Diss., université Paris-Saclay, 2025. http://www.theses.fr/2025UPASG010.

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Cette thèse explore l'utilisation de modèles génératifs profonds pour améliorer la génération de données transcriptomiques, répondant aux défis de rareté des données dans la classification de phénotypes de cancers. Nous évaluons la capacité des Autoencodeurs Variationnels (VAEs), des Réseaux Antagonistes Génératifs (GANs) et des modèles de diffusion (DDPM/DDIM) à équilibrer réalisme et diversité sur des données tabulaires de haute dimension. Nous avons d'abord adapté des métriques d'évaluation, supervisées et non supervisées. Nous avons ensuite intégré un moduled'auto-attention basé sur les conn
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TOMA, ANDREA. "PHY-layer Security in Cognitive Radio Networks through Learning Deep Generative Models: an AI-based approach." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/1003576.

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Recently, Cognitive Radio (CR) has been intended as an intelligent radio endowed with cognition which can be developed by implementing Artificial Intelligence (AI) techniques. Specifically, data-driven Self-Awareness (SA) functionalities, such as detection of spectrum abnormalities, can be effectively implemented as shown by the proposed research. One important application is PHY-layer security since it is essential to establish secure wireless communications against external jamming attacks. In this framework, signals are non-stationary and features from such kind of dynamic spectrum, with m
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Misino, Eleonora. "Deep Generative Models with Probabilistic Logic Priors." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24058/.

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Many different extensions of the VAE framework have been introduced in the past. How­ ever, the vast majority of them focused on pure sub­-symbolic approaches that are not sufficient for solving generative tasks that require a form of reasoning. In this thesis, we propose the probabilistic logic VAE (PLVAE), a neuro-­symbolic deep generative model that combines the representational power of VAEs with the reasoning ability of probabilistic ­logic programming. The strength of PLVAE resides in its probabilistic ­logic prior, which provides an interpretable structure to the latent space that can b
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Mennborg, Alexander. "AI-Driven Image Manipulation : Image Outpainting Applied on Fashion Images." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-85148.

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The e-commerce industry frequently has to deal with displaying product images in a website where the images are provided by the selling partners. The images in question can have drastically different aspect ratios and resolutions which makes it harder to present them while maintaining a coherent user experience. Manipulating images by cropping can sometimes result in parts of the foreground (i.e. product or person within the image) to be cut off. Image outpainting is a technique that allows images to be extended past its boundaries and can be used to alter the aspect ratio of images. Together
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Alabdallah, Abdallah. "Human Understandable Interpretation of Deep Neural Networks Decisions Using Generative Models." Thesis, Högskolan i Halmstad, Halmstad Embedded and Intelligent Systems Research (EIS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-41035.

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Deep Neural Networks have long been considered black box systems, where their interpretability is a concern when applied in safety critical systems. In this work, a novel approach of interpreting the decisions of DNNs is proposed. The approach depends on exploiting generative models and the interpretability of their latent space. Three methods for ranking features are explored, two of which depend on sensitivity analysis, and the third one depends on Random Forest model. The Random Forest model was the most successful to rank the features, given its accuracy and inherent interpretability.
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PANFILO, DANIELE. "Generating Privacy-Compliant, Utility-Preserving Synthetic Tabular and Relational Datasets Through Deep Learning." Doctoral thesis, Università degli Studi di Trieste, 2022. http://hdl.handle.net/11368/3030920.

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Due tendenze hanno rapidamente ridefinito il panorama dell'intelligenza artificiale (IA) negli ultimi decenni. La prima è il rapido sviluppo tecnologico che rende possibile un'intelligenza artificiale sempre più sofisticata. Dal punto di vista dell'hardware, ciò include una maggiore potenza di calcolo ed una sempre crescente efficienza di archiviazione dei dati. Da un punto di vista concettuale e algoritmico, campi come l'apprendimento automatico hanno subito un'impennata e le sinergie tra l'IA e le altre discipline hanno portato a sviluppi considerevoli. La seconda tendenza è la crescente co
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Gopinathan, Muraleekrishna. "Toward embodied navigation through vision and language." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2025. https://ro.ecu.edu.au/theses/2894.

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Embodied AI is a challenging but exciting field in which a robot learns to interact with human-living spaces to perform various tasks. This thesis studies the embodied navigation problem in which a robotic agent navigates in a previously unseen indoor environment based on a challenging task. In particular, the Vision-and-Language Navigation (VLN) task requires a robot to navigate based on a descriptive human-language instruction. This thesis aims to improve VLN agents on four key aspects - their understanding of the environment, training via additional data, correcting navigational errors, and
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Haidar, Ahmad. "Responsible Artificial Intelligence : Designing Frameworks for Ethical, Sustainable, and Risk-Aware Practices." Electronic Thesis or Diss., université Paris-Saclay, 2024. https://www.biblio.univ-evry.fr/theses/2024/interne/2024UPASI008.pdf.

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L'intelligence artificielle (IA) transforme rapidement le monde, redéfinissant les relations entre technologie et société. Cette thèse explore le besoin essentiel de développer, de gouverner et d'utiliser l'IA et l'IA générative (IAG) de manière responsable et durable. Elle traite des risques éthiques, des lacunes réglementaires et des défis associés aux systèmes d'IA, tout en proposant des cadres concrets pour promouvoir une Intelligence Artificielle Responsable (IAR) et une Innovation Numérique Responsable (INR).La thèse commence par une analyse approfondie de 27 déclarations éthiques mondia
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Hagström, Adrian, and Rustam Stanikzai. "Writer identification using semi-supervised GAN and LSR method on offline block characters." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-43316.

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Block characters are often used when filling out forms, for example when writing ones personal number. The question of whether or not there is recoverable, biometric (identity related) information within individual digits of hand written personal numbers is then relevant. This thesis investigates the question by using both handcrafted features and extracting features via Deep learning (DL) models, and successively limiting the amount of available training samples. Some recent works using DL have presented semi-supervised methods using Generative adveserial network (GAN) generated data together
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Boutet, de Monvel Violaine. "Du feedback vidéo à l'IA générative : sur la récursivité dans les arts et médias." Electronic Thesis or Diss., Paris 3, 2025. http://www.theses.fr/2025PA030009.

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Cette thèse érige, sous le prisme du feedback, un pont entre l’art vidéo pionnier des années 1960 à 1980 et les pratiques en lien avec l’IA générative, que les avancées phénoménales de l’apprentissage profond ont précipitées depuis le milieu des années 2010. La rétroaction renvoie en cybernétique à l’autorégulation par la boucle de systèmes naturels et technologiques. Appliqué à des dispositifs analogiques, numériques ou hybrides en circuit fermé, ce processus automatisé qualifie aussi les effets contingents qui en résultent à l'écran. La première partie revient sur l’influence colossale que l
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Books on the topic "Generative AI"

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Taulli, Tom. Generative AI. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9367-6.

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Huang, Ken, Yang Wang, Ben Goertzel, Yale Li, Sean Wright, and Jyoti Ponnapalli, eds. Generative AI Security. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54252-7.

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Singh, Akansha, and Krishna Kant Singh, eds. Multimodal Generative AI. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2355-6.

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Lyu, Zhihan, ed. Applications of Generative AI. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-46238-2.

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Narciso, Paolo. Generative AI in Education. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0844-9.

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Pratschke, B. Mairéad. Generative AI and Education. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-67991-9.

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Okadome, Takeshi. Essentials of Generative AI. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-0029-8.

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Bouzid, Ahmed, Paolo Narciso, and Weiye Ma. Generative AI For Executives. Apress, 2024. https://doi.org/10.1007/979-8-8688-0950-7.

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Field, Anne. Generative AI and Employment. CQ Press, 2025. https://doi.org/10.4135/cqresrre20250509.

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Adirim, Terry, ed. Digital Health, AI and Generative AI in Healthcare. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-83526-1.

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Book chapters on the topic "Generative AI"

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Bala, Ram, Natarajan Balasubramanian, and Amit Joshi. "The Generative Generation." In The AI-Centered Enterprise. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003541561-2.

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Taulli, Tom. "AI Fundamentals." In Generative AI. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9367-6_3.

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Das, Ravindra. "Introduction to Generative AI, Natural Language Processing, and the Digital Person." In Generative AI. CRC Press, 2024. http://dx.doi.org/10.1201/9781003503781-3.

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Das, Ravindra. "Introduction to Phishing." In Generative AI. CRC Press, 2024. http://dx.doi.org/10.1201/9781003503781-1.

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Das, Ravindra. "Overview of Artificial Intelligence, Neural Networks, and Machine Learning." In Generative AI. CRC Press, 2024. http://dx.doi.org/10.1201/9781003503781-2.

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Das, Ravindra. "Review of Cybersecurity Metrics." In Generative AI. CRC Press, 2024. http://dx.doi.org/10.1201/9781003503781-4.

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Das, Ravindra. "Conclusions." In Generative AI. CRC Press, 2024. http://dx.doi.org/10.1201/9781003503781-5.

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Taulli, Tom. "The Impact on Major Industries." In Generative AI. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9367-6_8.

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Taulli, Tom. "Introduction to Generative AI." In Generative AI. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9367-6_1.

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Taulli, Tom. "Core Generative AI Technology." In Generative AI. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9367-6_4.

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Conference papers on the topic "Generative AI"

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Yang, Yue, Yueyue Hu, Hanchi Ren, Yunying Wang, Jingjing Deng, and Xianghua Xie. "Spatial layout generation via generative adversarial networks." In International Conference on AI-generated Content (AIGC 2024), edited by Duoqian Miao and Feng Zhao. SPIE, 2025. https://doi.org/10.1117/12.3065211.

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Sarasan, Ashish P., Anuvind M, and Sreelekshmi S. "Explainable Generative AI: Enhancing Stable Diffusion with Machine Learning and Generative AI." In 2025 5th International Conference on Pervasive Computing and Social Networking (ICPCSN). IEEE, 2025. https://doi.org/10.1109/icpcsn65854.2025.11035339.

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Vinothkumar, S., S. Varadhaganapathy, R. Shanthakumari, S. Dhanushya, S. Guhan, and P. Krisvanth. "Utilizing Generative AI for Text-to-Image Generation." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725454.

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Thakkar, Karan, Kunaal Vadgama, Krish Ranawat, Richa Sharma, and Monika Mangla. "Generative AI based Interior Designing." In 2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT). IEEE, 2024. http://dx.doi.org/10.1109/iceect61758.2024.10739260.

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Metwally, Mariam Alaa, and Milad Ghantous. "Detecting Generative AI in Images." In 2024 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC). IEEE, 2024. https://doi.org/10.1109/miucc62295.2024.10783626.

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Menon, Gokulraj, Amal P. Peter, Ebin Jomon, Joe Varghese, and M. Anly Antony. "Apparel Customization Using Generative AI." In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10717142.

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Pushkala, Sriharsha Anand. "Generative AI in battling Fraud." In 2024 IEEE 4th International Conference on ICT in Business Industry & Government (ICTBIG). IEEE, 2024. https://doi.org/10.1109/ictbig64922.2024.10911802.

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Kshirsagar, Vanita G., Digvijay G. Bhosale, Shubhangi Suryawanshi, et al. "Generative AI Powered Forensic Device." In 2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA). IEEE, 2024. https://doi.org/10.1109/iccubea61740.2024.10774908.

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Yahia, Imen Grida Ben, and Young Jung. "Generative AI for Network Operations." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.m3a.2.

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Zhou, Larry. "Generative AI Applications in Telecom." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.m3a.1.

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This presentation explores GenAI's transformation role in telecom, enhancing predictive maintenance, automated troubleshooting, network modeling, and AI-driven service assurance. By leveraging multimodal AI and edge computing, we enable smarter, more efficient, and resilient network operations. Full-text article not available; see video presentation
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Reports on the topic "Generative AI"

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Cevallos, Adrian, Lucia Latorre, Gianfranco Alicandro, et al. Tech Report Generative AI. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0005105.

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Generative AI is an emerging sub-domain of AI that is revolutionizing the use of technology as we know it. Its ability to generate new and unique content has great potential as a knowledge assistant, although it is still in the exploration phase. Instead of simply classifying, analyzing, or processing existing data, Generative AI attempts to generate new data that resembles the original and is indistinguishable from that created by humans.
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Latorre, Lucía, Eduardo Riego, Ignacio Cerrato, Jose Daniel Zarate, and Lorenzo De Leo. Tech Report: Generative AI. Inter-American Development Bank, 2025. https://doi.org/10.18235/0013473.

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This report aims to provide a comprehensive overview of how Generative AI works and will provide an update to the information presented on the September 2023 IDB Report. Further sections will delve into the wide-ranging applications of Generative AI, showcasing examples across different fields and discussing the challenges and risks associated with its deployment, including ethical considerations, its security, and privacy concerns. Lastly, the report will highlight best practices for the safe and responsible use of Generative AI tools to equip readers with essential insights into responsible
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Brynjolfsson, Erik, Danielle Li, and Lindsey Raymond. Generative AI at Work. National Bureau of Economic Research, 2023. http://dx.doi.org/10.3386/w31161.

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Eisfeldt, Andrea, Gregor Schubert, and Miao Ben Zhang. Generative AI and Firm Values. National Bureau of Economic Research, 2023. http://dx.doi.org/10.3386/w31222.

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Beck, Sedefka, and Donka Brodersen. Generative AI in Economics: Teaching Economics and AI Literacy. The Economics Network, 2024. http://dx.doi.org/10.53593/n4121a.

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Bown, Oliver. The challenges ahead for generative AI. Edited by Lachlan Guselli. Monash University, 2023. http://dx.doi.org/10.54377/aac6-c1f6.

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Silver, Christina. Using Generative AI for Qualitative Analysis. Instats Inc., 2023. http://dx.doi.org/10.61700/qs6nzkddi20lw469.

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This comprehensive workshop is aimed at equipping researchers with the skills to utilize AI appropriately in their qualitative analysis. Covering a range of topics from understanding AI software tools to ethical considerations and troubleshooting, this seminar provides a unique opportunity to enhance research capabilities and gain a competitive edge in the academic field.
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Gans, Joshua. How will Generative AI impact Communication? National Bureau of Economic Research, 2024. http://dx.doi.org/10.3386/w32690.

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Bick, Alexander, Adam Blandin, and David J. Deming. The Rapid Adoption of Generative AI. Federal Reserve Bank of St. Louis, 2024. http://dx.doi.org/10.20955/wp.2024.027.

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Bick, Alexander, Adam Blandin, and David Deming. The Rapid Adoption of Generative AI. National Bureau of Economic Research, 2024. http://dx.doi.org/10.3386/w32966.

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