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Journal articles on the topic 'Generative AI'

Author: Grafiati

Published: 14 September 2024

Last updated: 25 July 2025

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1

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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Chandra, Sekhar Veluru. "The Impact of Generative AI on Content Curation and Content Advancements in Education and Training." European Journal of Advances in Engineering and Technology 11, no. 4 (2024): 121–30. https://doi.org/10.5281/zenodo.12737572.

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Generative AI, a branch of artificial intelligence, has gained significant attention and interest for its potential applications in various fields, including education and training. Its integration in educational settings has sparked curiosity and enthusiasm as it holds the potential to revolutionize instructional strategies and transform the learning experience. Generative AI is the utilization of machine learning algorithms to generate fresh content by drawing inspiration from pre-existing material. At the same time, many online education portals are making use of the open-ended resource (OE

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Luttrell, Regina, and Nicholas Bowman. "Generating Deep Discussion Around Generative AI." Newhouse Impact Journal 1, no. 1 (2024): 1–2. http://dx.doi.org/10.14305/jn.29960819.2024.1.1.10.

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Kars, Muhammet Emir. "Generative AI in Education." London Journal of Social Sciences, no. 6 (September 20, 2023): 144–51. http://dx.doi.org/10.31039/ljss.2023.6.114.

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Recently, the field of artificial intelligence (AI) has advanced significantly, with generativeAI rising to the top of the tech industry's most-discussed subjects list. Education like manyother fields could be transformed by generative AI like ChatGPT, Bard, DALL-E,Midjourney, and DeepMind, which all have the ability to revolutionize a number of industrieswith all the benefits and drawbacks they entail.The paper begins by providing an overview of generative AI, highlighting its capacity togenerate human-like text, images, and even interactive simulations. It delves into theunderlying principle

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Prabu, Vivek Prasanna. "How Generative AI Can Improve Enterprise Data Management." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem46621.

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Generative AI is reshaping the enterprise technology landscape, offering intelligent automation, insight generation, and contextual understanding capabilities that redefine how businesses handle data. Enterprise data management (EDM) - once constrained by rigid architectures, manual processing, and fragmented governance - can now evolve into a dynamic, self-improving ecosystem through the integration of generative AI. With organizations generating petabytes of data from operations, customer interactions, supply chains, and IoT devices, the need for scalable and intelligent data handling system

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Ali, Safinah, Prerna Ravi, Randi Williams, Daniella DiPaola, and Cynthia Breazeal. "Constructing Dreams Using Generative AI." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23268–75. http://dx.doi.org/10.1609/aaai.v38i21.30374.

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Generative AI tools introduce new and accessible forms of media creation for youth. They also raise ethical concerns about the generation of fake media, data protection, privacy and ownership of AI-generated art. Since generative AI is already being used in products used by youth, it is critical that they understand how these tools work and how they can be used or misused. In this work, we facilitated students’ generative AI learning through expression of their imagined future identities. We designed a learning workshop - Dreaming with AI - where students learned about the inner workings of ge

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Agrawal, Vishakha. "Systems for Generative AI: Challenges and Opportunities." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–7. https://doi.org/10.55041/ijsrem30153.

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Abstract—The emergence of large-scale generative AI models, such as those powering text, image, and code generation, has introduced unprecedented systems challenges distinct from tradi- tional AI applications. This paper examines the unique demands of generative AI systems, focusing on the specific computational patterns of generative models, their distinctive inference re- quirements, and the specialized infrastructure needed for token generation, prompt processing, and context management. We analyze the particular challenges and opportunities in building systems optimized for generative work

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Johnson, Russell. "Guiding generative AI." Nature Chemical Biology 21, no. 3 (2025): 311. https://doi.org/10.1038/s41589-025-01854-y.

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KIM, YOUN-SUNG. "A Study on how to Secure Competitiveness through Case Analysis of Media and Contents using Generative AI." International Journal of Religion 5, no. 12 (2024): 1795–803. https://doi.org/10.61707/9r4wdc33.

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Due to the ChatGPT craze, all industries around the world have a hot Generative AI of interest. In particular, since Open AI announced AI Sora, which makes text into video on February 15, 2024, the media and content industries, both at home and abroad, have expressed expectations and a sense of crisis. By learning a large amount of Hyper-scale Data with artificial intelligence technology that actively generates results according to the specific needs of live Generative AI users, it is looking beyond the realm of creation, which can be called the human domain[1][2][3]. Although, unlike image-ge

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Kamlakar Pangavhane, Ankita, and Hemangi Kulkarni. "Generative AI Based PCB Tester." International Journal of Science and Research (IJSR) 14, no. 4 (2025): 1599–605. https://doi.org/10.21275/sr25419222745.

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Gabriel, Sonja. "Generative AI in Writing Workshops: A Path to AI Literacy." International Conference on AI Research 4, no. 1 (2024): 126–32. https://doi.org/10.34190/icair.4.1.3022.

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The widespread use of generative AI tools which can support or even take over several part of the writing process has sparked many discussions about integrity, AI literacy and changes to academic writing processes. This paper explores the impact of generative artificial intelligence (AI) tools on the academic writing pro-cess, drawing on data from a writing workshop and interviews with university students of a university teacher college in Austria. Despite the widespread assumption that generative AI, such as ChatGPT, is widely used by students to support their academic tasks, initial findings

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Idrisov, Baskhad, and Tim Schlippe. "Program Code Generation with Generative AIs." Algorithms 17, no. 2 (2024): 62. http://dx.doi.org/10.3390/a17020062.

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Our paper compares the correctness, efficiency, and maintainability of human-generated and AI-generated program code. For that, we analyzed the computational resources of AI- and human-generated program code using metrics such as time and space complexity as well as runtime and memory usage. Additionally, we evaluated the maintainability using metrics such as lines of code, cyclomatic complexity, Halstead complexity and maintainability index. For our experiments, we had generative AIs produce program code in Java, Python, and C++ that solves problems defined on the competition coding website l

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Duan, Rumeng, and Manman Li. "A Study of High School English Multimodal Discourse Teaching Based on Generative Artificial Intelligence." Development of Humanities and Social Sciences 1, no. 1 (2025): 1000077. https://doi.org/10.71204/7qs5md53.

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This article focuses on the application of generative artificial intelligence (GAI) in multimodal discourse teaching in high school English, deeply analyzes the limitations of traditional high school English discourse teaching, and discusses how generative artificial intelligence can optimize teaching effectiveness through technological empowerment. First, the article explains the core value and key role of generative AI in multimodal discourse teaching, highlighting its importance in enhancing teaching interactivity, providing personalized feedback, generating teaching resources, and dynamica

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Mohit, Thodupunuri. "NLP vs. Generative AI: A Comparative Study with Insights into Other Leading AI Technologies." Journal of Scientific and Engineering Research 8, no. 3 (2021): 285–93. https://doi.org/10.5281/zenodo.15223036.

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Natural Language Processing (NLP) and Generative AI are two key fields in artificial intelligence. NLP decodes language using tokenization and semantic parsing. It uses deep learning and neural networks to understand text. Meanwhile, Generative AI creates new content with transformers and autoregressive models. It produces text, code, and multimedia. Both use advanced machine learning techniques. Moreover, NLP boosts customer service and data analysis. Likewise, Generative AI drives creative writing and code generation. However, both face challenges. NLP struggles with contextual ambiguity. Ge

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Park, Jisook. "A Study on the Art Education Model Using Generative AI to Enhance Creativity and Convergent Competency." Korean Society for Creativity Education 25, no. 1 (2025): 1–16. https://doi.org/10.36358/jce.2025.25.1.1.

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This study examines the role of Generative AI in school art education, focusing on enhancing Creativity and Convergence Competency. It explores a Generative AI-based art education model and proposes an Art Game as a practical tool. Effective Generative AI integration strategies were developed based on school environments and student needs. In elementary education, Generative AI-supported visual exploration and creative idea generation were emphasized. Middle and high school programs focused on critical thinking and art appreciation through historical and analytical approaches. At the universit

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Moon, Su-Ji. "Effects of Perception of Potential Risk in Generative AI on Attitudes and Intention to Use." International Journal on Advanced Science, Engineering and Information Technology 14, no. 5 (2024): 1748–55. http://dx.doi.org/10.18517/ijaseit.14.5.20445.

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Generative artificial intelligence (AI) is rapidly advancing, offering numerous benefits to society while presenting unforeseen potential risks. This study aims to identify these potential risks through a comprehensive literature review and investigate how user’s perceptions of risk factors influence their attitudes and intentions to use generative AI technologies. Specifically, we examined the impact of four key risk factors: fake news generation, trust, bias, and privacy concerns. Our analysis of data collected from experienced generative AI users yielded several significant findings: First,

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Rahul Modak. "Generative AI for automated business report generation and analysis." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 894–901. https://doi.org/10.30574/wjaets.2025.15.2.0610.

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This research paper explores the application of generative artificial intelligence (AI) in automating business report generation and analysis. The study investigates the potential of various AI models, including natural language processing (NLP) and machine learning (ML) techniques, to streamline the process of creating comprehensive business reports. We examine the effectiveness of these AI-driven approaches in extracting relevant information from diverse data sources, generating insightful analyses, and presenting findings in a coherent and user-friendly manner. The research also addresses t

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Yang, Yue. "The Study of Copyright Infringement Liability of Generative Artificial Intelligence." Lecture Notes in Education Psychology and Public Media 34, no. 1 (2024): 88–95. http://dx.doi.org/10.54254/2753-7048/34/20231893.

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As AI continues to produce content at an unprecedented rate, it is essential to establish a robust legal framework to protect the rights of creators and appropriately assign liability in cases of copyright infringement. Generative AI technology has the characteristics of high data demand, strong human-computer interaction, weak interpretability, and weak stability. The main civil subjects in generative AI services are generative AI service providers and users. In the pre-training stage, if generative AI uses copyrighted works without authorization, it should be recognized as copyright infringe

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Alexander, Kuznetsov. "THE ANALYSIS OF THE EFFICIENCY OF GENERATIVE AI ALGORITHMS FOR CREATING A NATURAL DIALOGUE." American Journal of Interdisciplinary Innovations and Research 6, no. 6 (2024): 26–34. http://dx.doi.org/10.37547/tajiir/volume06issue06-08.

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In the modern world, artificial intelligence (AI) plays an increasingly important role in various fields of human activity. One of the most promising areas of AI application is the generation of natural dialogue. The purpose of this work is to analyze the efficiency of generative AI algorithms for creating natural dialogue. The relevance of this topic is due to the growing interest in the use of AI to create dialogue systems capable of interacting with people in a natural way. The results of the study can be useful for developers of dialogue systems, researchers in the field of AI, as well as

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Nahla, Febri, and Anis Masruri. "Analysis of the Impact of Artificial Intelligence on Information-Seeking Behavior." JPUA: Jurnal Perpustakaan Universitas Airlangga: Media Informasi dan Komunikasi Kepustakawanan 14, no. 2 (2024): 69–75. https://doi.org/10.20473/jpua.v14i2.2024.69-75.

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Background of the study: The need for information in the era of globalization is rapidly growing, causing users to strategize to obtain information effectively. One of the strategies used in information retrieval is generative AI applications. Purpose: This study aims to discuss generative AI applications used in information retrieval and the impact of generative AI usage on information-seeking behavior. Methods: Library research is used to collect data from various relevant literature sources. Finding: The use of generative AI in information retrieval provides positive impacts, such as speedi

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Tivari, Gaurav D., Dr Satvik Khara, Dr Jay A. Dave, and Vishwa Patel. "Enhancing Reality: Exploring the Potential of Generative Artificial Intelligence." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem36378.

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This paper delves into the realm of recent advancements in artificial intelligence, with a particular focus on Generative AI. Generative AI, an emerging field within AI, leverages machine learning algorithms and neural networks to generate original content across various mediums such as images, music, speech, and text. Its potential to revolutionize industries like advertising, gaming, and healthcare through personalized content creation, task automation, and enhanced accuracy in complex endeavors like drug discovery and medical diagnosis is profound. We explore different models of Generative

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Journal, IJSREM. "Generative AI for Healthcare: Applications, Challenges, and Ethical Considerations." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–6. https://doi.org/10.55041/ijsrem39600.

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Generative Artificial Intelligence (AI) is rapidly transforming the healthcare sector, offering novel approaches to medical imaging, drug discovery, personalized medicine, and data privacy through the generation of synthetic datasets. This paper explores the applications, challenges, and ethical considerations surrounding the use of generative AI in healthcare. Key applications of this technology include enhancing diagnostic capabilities by generating high-quality medical images, accelerating the drug discovery process by simulating chemical compounds, and tailoring treatment plans through per

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Hu, Fangfei, Shiyuan Liu, Xinrui Cheng, Pengyou Guo, and Mengqi Yu. "Risks of Generative Artificial Intelligence and Multi-Tool Governance." Academic Journal of Management and Social Sciences 9, no. 2 (2024): 88–93. http://dx.doi.org/10.54097/stvem930.

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While generative AI, represented by ChatGPT, brings a technological revolution and convenience to life, it may raise a series of social and legal risks, mainly including violation of personal privacy and data security issues, infringement of intellectual property rights, generation of misleading and false content, and exacerbation of discrimination and prejudice. However, the traditional AI governance paradigm oriented towards conventional AI may not be adequately adapted to generative AI with generalized potential and based on big models. In order to encourage the innovative development of ge

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Alva, Lingareddy. "AI-Driven Data Mesh with Generative AI for Enterprise Analytics." European Journal of Computer Science and Information Technology 13, no. 37 (2025): 97–108. https://doi.org/10.37745/ejcsit.2013/vol13n3797108.

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This article explores the transformative integration of generative AI capabilities with Data Mesh architecture to revolutionize enterprise analytics. Beginning with examining traditional data architectures' limitations, the discussion highlights how centralized proceeds towards creating bottlenecks that impede innovation and time-to-insight. The Data Mesh paradigm is presented as a fundamental shift that decentralizes data ownership while maintaining federated governance. The integration of generative AI within this framework enables natural language interfaces, synthetic data generation, auto

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Yan, Tingting. "Research on Personal Information Protection in the Context of Generative Artificial Intelligence." Scientific Journal Of Humanities and Social Sciences 6, no. 7 (2024): 116–24. http://dx.doi.org/10.54691/vhyxgj39.

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Personal information is the foundation of generative AI, and ChatGPT-like generative AI needs to process a large amount of personal information at various stages such as model training, model generation, and model optimization, which also has a certain impact on traditional personal information protection rules. During the information collection phase, generative AI may fugitive the informed consent rules and infringe on the privacy rights of information subjects. In the information utilization stage, generative AI may impact basic personal information processing rules such as the principle of

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Jennifer Bakowaa Sarfo, Samuel Aremora, Mary Opeyemi Adebote, Kehinde M. Balogun, and Abayomi Taiwo Fashina. "Generative AI: A new frontier for agric extension service in Africa - revolutionizing farmer information access." Computer Science & IT Research Journal 6, no. 2 (2025): 94–103. https://doi.org/10.51594/csitrj.v6i2.1870.

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Extension services in agriculture are essential in the dissemination of critical information and sustainable agricultural techniques to farmers in Africa. Nevertheless, such services tend to be limited by their reach, language constraints, and lack of context-specific information. With the potential to generate new and context-specific content, Generative Artificial Intelligence (AI) offers a revolutionary means to overcome these constraints and improve agricultural knowledge transfer. This paper investigates the future of Generative AI, with components including Natural Language Processing (N

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Researcher. "THE CRITICAL ROLE OF DATA ENGINEERING IN GENERATIVE AI." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 829–40. https://doi.org/10.5281/zenodo.13475740.

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This article explores the intricate relationship between data engineering and generative AI (Gen AI), highlighting the critical role that data engineering plays in the development, deployment, and optimization of Gen AI systems. It delves into the nature of generative AI and its revolutionary capabilities across various domains, from text and image generation to music composition and code creation. The symbiotic relationship between data engineering and Gen AI is examined in detail, covering key aspects such as data collection and curation, preprocessing and transformation, scalable infrastruc

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Researcher. "THE CRITICAL ROLE OF DATA ENGINEERING IN GENERATIVE AI." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 829–40. https://doi.org/10.5281/zenodo.13475740.

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This article explores the intricate relationship between data engineering and generative AI (Gen AI), highlighting the critical role that data engineering plays in the development, deployment, and optimization of Gen AI systems. It delves into the nature of generative AI and its revolutionary capabilities across various domains, from text and image generation to music composition and code creation. The symbiotic relationship between data engineering and Gen AI is examined in detail, covering key aspects such as data collection and curation, preprocessing and transformation, scalable infrastruc

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Researcher. "THE CRITICAL ROLE OF DATA ENGINEERING IN GENERATIVE AI." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 829–40. https://doi.org/10.5281/zenodo.13475740.

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This article explores the intricate relationship between data engineering and generative AI (Gen AI), highlighting the critical role that data engineering plays in the development, deployment, and optimization of Gen AI systems. It delves into the nature of generative AI and its revolutionary capabilities across various domains, from text and image generation to music composition and code creation. The symbiotic relationship between data engineering and Gen AI is examined in detail, covering key aspects such as data collection and curation, preprocessing and transformation, scalable infrastruc

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Researcher. "THE CRITICAL ROLE OF DATA ENGINEERING IN GENERATIVE AI." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 829–40. https://doi.org/10.5281/zenodo.13475740.

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This article explores the intricate relationship between data engineering and generative AI (Gen AI), highlighting the critical role that data engineering plays in the development, deployment, and optimization of Gen AI systems. It delves into the nature of generative AI and its revolutionary capabilities across various domains, from text and image generation to music composition and code creation. The symbiotic relationship between data engineering and Gen AI is examined in detail, covering key aspects such as data collection and curation, preprocessing and transformation, scalable infrastruc

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Researcher. "THE CRITICAL ROLE OF DATA ENGINEERING IN GENERATIVE AI." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 829–40. https://doi.org/10.5281/zenodo.13475740.

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This article explores the intricate relationship between data engineering and generative AI (Gen AI), highlighting the critical role that data engineering plays in the development, deployment, and optimization of Gen AI systems. It delves into the nature of generative AI and its revolutionary capabilities across various domains, from text and image generation to music composition and code creation. The symbiotic relationship between data engineering and Gen AI is examined in detail, covering key aspects such as data collection and curation, preprocessing and transformation, scalable infrastruc

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Vartiainen, Henriikka, Matti Tedre, and Ilkka Jormanainen. "Co-creating digital art with generative AI in K-9 education: Socio-material insights." International Journal of Education Through Art 19, no. 3 (2023): 405–23. http://dx.doi.org/10.1386/eta_00143_1.

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The rise of image-generating artificial intelligence (AI) tools has triggered changes in digital art and graphic design, provoking debates in the creative industry. However, scant research exists about children’s and youths’ insights into and encounters with generative AI. Building on sociocultural and new materialist perspectives, this exploratory study proposed to address this gap by exploring middle schoolers’ (N = 10) creative interaction with generative AI, particularly with text-to-image generative models. Qualitative content analyses of emerging learning activities evidenced how generat

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R, Prof Seema. "STABLE DIFFUSION TEXT TO IMAGE USING AI." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33350.

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The Stable Diffusion Text-to-Image Generation Project is an innovative endeavor in the field of generative adversarial networks (GANs) and natural language processing (NLP). This project aims to bridge the semantic gap between textual descriptions and visual content by utilizing the Stable Diffusion training framework to generate highly realistic and coherent images from text prompts. The project leverages recent advancements in deep learning techniques to tackle the challenging task of text-to image synthesis. The project introduces an innovative approach at the crossroads of generative adver

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Sinha, Himanshu. "The Identification of Network Intrusions with Generative Artificial Intelligence Approach for Cybersecurity." Journal of Web Applications and Cyber Security 2, no. 2 (2024): 20–29. http://dx.doi.org/10.48001/jowacs.2024.2220-29.

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Generative Artificial Intelligence (Generative AI) offers a paradigm shift to the way robots perceive and interact with data. Generative AI approaches aim to produce new data samples that closely match the original dataset, in contrast to standard AI models that concentrate on tasks such as categorisation or prediction. This ability has broad implications for a variety of fields, including network security. New generation of network intrusion defense brought forth by Generative AI is revolution using the area of network security. In this work, investigates the revolutionary potential of Genera

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Bryndin, Evgeniy. "Formation of reflexive generative A.I. with ethical measures of use." Research on Intelligent Manufacturing and Assembly 3, no. 1 (2024): 109–17. http://dx.doi.org/10.25082/rima.2024.01.003.

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The application of reflexive generative AI in the social sphere will improve the quality of life of individuals and society. Its commercial application will require compliance with ethical standard measures to ensure that its use does not cause harm. The development, implementation and use of an ethical standard for the use of reflexive generative AI will increase the safety of its use. The ethical use of generative AI by individuals should be automatically regulated by it. The reflection of generative AI is implemented by the AGI multilogic and ensures the validity of content generation.

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Bozkurt, Aras, and Ramesh C. Sharma. "Generative AI and Prompt Engineering: The Art of Whispering to Let the Genie Out of the Algorithmic World." Asian Journal of Distance Education 18, no. 2 (2023): i—vii. https://doi.org/10.5281/zenodo.8174941.

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The capabilities of generative AI in education, serving as a co-creator, highlight the crucial role of prompt engineering for optimal interactions between hum<strong>a</strong>n<strong>i</strong>ty and Large Language Models (LLMs) that utilize Natural Language Processing (NLP). Generative AI's potential lies in responding to well-crafted prompts, making them essential for unleashing its capabilities in generating authentic content. To optimize this process, prompt engineers, encompassing various stakeholders in education, must grasp how language nuances impact generative AI's responses

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Hari Kiran Vuyyuru. "How AI Chatbot works: Simplifying the Magic of Conversational AI." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 645–52. https://doi.org/10.32628/cseit25111273.

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This comprehensive article explores the fundamental mechanisms and capabilities of Generative AI, examining how this advanced language model achieves human-like conversational abilities. The article delves into the system's core components, including its sophisticated tokenization processes, context management mechanisms, next-token prediction capabilities, and training methodologies enhanced through human feedback. Through a detailed analysis of recent research findings, the article demonstrates how Generative AI chatbot transcends simple pattern matching to achieve complex reasoning, creativ

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Quintais, João Pedro. "Generative AI, copyright and the AI Act." Computer Law & Security Review 56 (April 2025): 106107. https://doi.org/10.1016/j.clsr.2025.106107.

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Panda, Meethun. "GENERATIVE AI FOR DATA-CENTRIC AI DEVELOPMENT." INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY 16, no. 1 (2025): 1521–34. https://doi.org/10.34218/ijcet_16_01_112.

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Kaustubh Kabtiyal, Manan Mistry,. "GenViz AI: Data Visualization using Generative AI." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45237.

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Data analysis plays a pivotal role in decision-making, but its complexity often makes it inaccessible to non-technical users. Traditional approaches require expertise in programming, statistics, and visualization tools, creating a barrier for many. This research introduces a system leveraging Artificial Intelligence (AI), Large Language Models (LLMs), and PandasAI to simplify data analysis. The system integrates Retrieval-Augmented Generation (RAG) techniques, allowing users to upload datasets (e.g., Excel files) and query them in natural language. With the ability to process, summarize, and v

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Kesavan, Harish. "AI-Powered Document Summarizer Using Generative AI." International Journal of Science, Engineering and Technology 13, no. 2 (2025): 1–3. https://doi.org/10.61463/ijset.vol.13.issue2.411.

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