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Artículos de revistas sobre el tema "AI"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 25 de julio de 2025

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1

albarracíín, pilar. "Ai, Ai, Ai." Gastronomica 8, no. 4 (2008): 122. http://dx.doi.org/10.1525/gfc.2008.8.4.122.

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Sachin, Samrat Medavarapu. "Demystifying AI: A Comprehensive Review of Explainable AI Techniques and Applications." European Journal of Advances in Engineering and Technology 10, no. 6 (2023): 49–52. https://doi.org/10.5281/zenodo.13627267.

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Explainable Artificial Intelligence (XAI) seeks to make AI systems more transparent and understandable to users. This review examines the various techniques developed to achieve explainability in AI models and their applications across different domains. We discuss methods such as feature attribution, model simplification, and example-based explanations, highlighting their strengths and limitations. Additionally, we explore the importance of XAI in critical fields like healthcare, finance, and law. The findings underscore the necessity of explainability for trust, accountability, and ethical A

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3

Shubham, Dighikar. "How Artificial Intelligence Influences the Marketing." Recent Trends in Data Mining and Business Forecasting 1, no. 1 (2020): 1–5. https://doi.org/10.5281/zenodo.3779624.

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Artificial Intelligence is the new revolution in the business and in past few years it is indulged in various fields like science, business, medicine, automation and education, AI is also reached in the marketing business. Most of the big companies are using AI for their advertisement in digital platforms. AI has been changing the way of marketing and sales with consumer perception and its behaviour. The author of the paper is suggested the research about an AI, what it is and how deeply it is applied in marketing and what marketing practitioners can do with AI implication. The author of the p

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Bals, Kees. "Ai! AI..." PodoPost 37, no. 4 (2024): 3. http://dx.doi.org/10.1007/s12480-024-2612-0.

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Euchner, Jim. "Little ai, Big AI—Good AI, Bad AI." Research-Technology Management 62, no. 3 (2019): 10–12. http://dx.doi.org/10.1080/08956308.2019.1587280.

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Baraheem, Samah S., and Tam V. Nguyen. "AI vs. AI: Can AI Detect AI-Generated Images?" Journal of Imaging 9, no. 10 (2023): 199. http://dx.doi.org/10.3390/jimaging9100199.

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The proliferation of Artificial Intelligence (AI) models such as Generative Adversarial Networks (GANs) has shown impressive success in image synthesis. Artificial GAN-based synthesized images have been widely spread over the Internet with the advancement in generating naturalistic and photo-realistic images. This might have the ability to improve content and media; however, it also constitutes a threat with regard to legitimacy, authenticity, and security. Moreover, implementing an automated system that is able to detect and recognize GAN-generated images is significant for image synthesis mo

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7

McColl, Ewen. "AI, AI, Oh." Dental Update 52, no. 6 (2025): 369–70. https://doi.org/10.12968/denu.2025.52.6.369.

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Souratn, Jain. "Revolutionizing business consulting with generative AI: Exploring transformative models for strategic decision-making, innovation, and operational excellence." World Journal of Advanced Research and Reviews 22, no. 2 (2024): 2273–84. https://doi.org/10.5281/zenodo.14719859.

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Automotive AI is at the forefront of rapidly disrupting the business consulting industry by revitalizing core issues in strategy formulation and management, innovation, and process improvement. This technology helps consultants solve problems by dealing with large amounts of material, thinking outside the box regarding business solutions, and optimizing processes at a higher level than before. Businesses can benefit from AI technology because AI models can make a deeper data analysis, automate processes, improve decision-making, and provide the setting for future improvement. In this paper, th

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9

Kim, Minjeong. "Analyzing the Serial Multiple Mediating Effect of AI Acceptance Intention and AI Literacy on Pre-service Teachers’ AI Course Taking and AI Teaching Efficacy." Journal of The Korean Association of Information Education 28, no. 4 (2024): 407–17. http://dx.doi.org/10.14352/jkaie.2024.28.4.407.

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Aatmaj, Amol Salunke. "Cracking the Code: Self-Explaining AI Models for Transparent Decision Making in Complex Algorithms." International Journal for Multidisciplinary Research (IJFMR) 5, no. 4 (2023): 10. https://doi.org/10.5281/zenodo.8268922.

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This research paper explores self-explaining AI models that bridge the gap between complex black-box algorithms and human interpretability. The study focuses on techniques like LIME, SHAP, attention mechanisms, and rule-based systems to create locally interpretable models. By providing transparent and understandable explanations for AI predictions, these models enhance user trust and comprehension. Realworld applications in healthcare, finance, and autonomous systems are evaluated to demonstrate the effectiveness of self-explaining AI models. Ethical considerations regarding fairness, bias, an

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11

Holzinger, Andreas. "Explainable AI (ex-AI)." Informatik-Spektrum 41, no. 2 (2018): 138–43. http://dx.doi.org/10.1007/s00287-018-1102-5.

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Keller, Jim. "AI-eXplained (AI-X)." IEEE Computational Intelligence Magazine 17, no. 4 (2022): 3–4. http://dx.doi.org/10.1109/mci.2022.3201735.

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13

Baungarten-Leon, Emilio Isaac, Susana Ortega-Cisneros, Mohamed Abdelmoneum, Ruth Yadira Vidana Morales, and German Pinedo-Diaz. "The Genesis of AI by AI Integrated Circuit: Where AI Creates AI." Electronics 13, no. 9 (2024): 1704. http://dx.doi.org/10.3390/electronics13091704.

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The typical Integrated Circuit (IC) development process commences with formulating specifications in natural language and subsequently proceeds to Register Transfer Level (RTL) implementation. RTL code is traditionally generated through manual efforts, using Hardware Description Languages (HDL) such as VHDL or Verilog. High-Level Synthesis (HLS), on the other hand, converts programming languages to HDL; these methods aim to streamline the engineering process, minimizing human effort and errors. Currently, Electronic Design Automation (EDA) algorithms have been improved with the use of AI, with

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14

Ravindra, Kumar. "Biases in Artificial Intelligence Applications Affecting Human Life: A Review." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 1 (2021): 54–55. https://doi.org/10.35940/ijrte.A5719.0510121.

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The introduction of Artificial Intelligence has improved operations in almost every sector, industry, and part of human life. The use of AI has been vital in the department of justice, recruitment by organizations, facial recognition by police, and school admissions. The aim of introducing AI algorithms in various fields was to reduce human bias in decision-making. Despite the progress, there are ethical concerns that the AI algorithms also exhibit biases. The main reason behind the claim is because human developers are in charge of training data used by the algorithms. There are areas where t

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Siva, Karthik Devineni. "AI Data Quality Copilots: Enhancing Intelligent Systems with Real-Time Data Integrity, Scalability, and Ethical AI Practices." Journal of Scientific and Engineering Research 11, no. 10 (2024): 8–26. https://doi.org/10.5281/zenodo.13991320.

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AI has proven to be a quickly growing field; hence, data quality is essential when attaining proper, equitable, and lasting AI solutions. Data quality is critically important as organizations rely more on AI for their decisions, training, and decisions. This paper aims to establish AI Data Quality Copilots; sophisticated systems focused on addressing data-related issues through automatic real-time data quality evaluation and enhancement. Future issues – data drift, privacy, and inclusion – as verified by AI Data Quality Copilots will remain non-triggering for the AI model's reliabi

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Kortz, Mason, Jessica Fjeld, Hannah Hilligoss, and Adam Nagy. "Is Lawful AI Ethical AI?" Morals & Machines 2, no. 1 (2022): 60–65. http://dx.doi.org/10.5771/2747-5174-2022-1-60.

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Attempts to impose moral constraints on autonomous, artificial decision-making systems range from “human in the loop” requirements to specialized languages for machine-readable moral rules. Regardless of the approach, though, such proposals all face the challenge that moral standards are not universal. It is tempting to use lawfulness as a proxy for morality; unlike moral rules, laws are usually explicitly defined and recorded – and they are usually at least roughly compatible with local moral norms. However, lawfulness is a highly abstracted and, thus, imperfect substitute for morality, and i

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La Rosa, Michele, and Enrica Morlicchio. "Ai collaboratori e ai lettori." SOCIOLOGIA DEL LAVORO, no. 153 (March 2019): 7. http://dx.doi.org/10.3280/sl2019-153001.

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BIRMINGHAM, WILLIAM P. "The AI in AI EDAM." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21, no. 1 (2007): 5–6. http://dx.doi.org/10.1017/s0890060407070035.

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A natural question is why AI in design? Although the design applications written about in the journal vary widely, the common thread is that researchers use AI techniques to implement their ideas. The use of AI techniques for design applications, at least when AI EDAM was started, was partially a reaction against the predominant design methods based on some form of optimization. Knowledge-based techniques, particularly rule-based systems of various sorts, were very popular. One of the draws of these methods, I believe, was their ability to represent knowledge that is hard or awkward to represe

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19

笹嶋, 宗彦. "AI戦略（AI Strategy）". Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 35, № 2 (2023): 25. http://dx.doi.org/10.3156/jsoft.35.2_25_1.

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20

Manning, Ryan Vincent. "Tech Limited: AI is AI." Architectural Design 94, no. 3 (2024): 94–101. http://dx.doi.org/10.1002/ad.3060.

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AbstractWhat might a conversation between a highly trained generator that calculates the probability of the next word in a sentence, and the jumbled mess of organic neurons inside the head of a human architect look like? Through such a fictitious dialogue, architectural designer and educator Ryan Vincent Manning explores issues of human inquisitiveness, uniqueness and agency, human‐ machine interfaces, machine intelligence, AI latent spaces and the assimilation of design originality into free‐access, ubiquitous machine code that is adding to huge potential datasets.

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ZAVATIN (CHILEA), Iuliana, Ioana Maria POPESCU (IACOBESCU), Rodica LUPU (ȘTEFĂNESCU), and Andra-Elena ORLOSCHI. "AI REGULATION VS. AI STANDARDIZATION." Revista Economica 76, no. 3 (2024): 113–26. https://doi.org/10.56043/reveco-2024-0030.

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The key specificity of Artificial Intelligence today is that it is a fast-evolving field with yet unknown capabilities and potential. In technical research as well as in media there are new emerging AI-related topics almost every day. We hear a lot about risks of AI but also about so many useful AI applications that help people and companies benefit from better lives or enhanced results of their activity. Businesses developing AI solutions worldwide face significant challenges not only in designing processes but also in putting on the market safe, efficient and reliable technologies. Thus, the

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Chilea, Iuliana, Maria Popescu (Iacobescu) Ioana, Rodica Lupu, and Andra ORLOSCHI. "AI REGULATION VS. AI STANDARDIZATION." Revista Economica 76, no. 3 (2024): 113–26. https://doi.org/10.56043/reveco-2024-0030.

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The key specificity of Artificial Intelligence today is that it is a fast-evolving field with yet unknown capabilities and potential. In technical research as well as in media there are new emerging AI-related topics almost every day. We hear a lot about risks of AI but also about so many useful AI applications that help people and companies benefit from better lives or enhanced results of their activity. Businesses developing AI solutions worldwide face significant challenges not only in designing processes but also in putting on the market safe, efficient and reliable technologies. Thus, the

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23

Park, SunJu. "AI education perception of pre-service teachers according to AI learning experience, Interest in AI education, and Major." Journal of The Korean Association of Information Education 25, no. 1 (2021): 103–11. http://dx.doi.org/10.14352/jkaie.2021.25.1.103.

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24

van Wynsberghe, Aimee. "Sustainable AI: AI for sustainability and the sustainability of AI." AI and Ethics 1, no. 3 (2021): 213–18. http://dx.doi.org/10.1007/s43681-021-00043-6.

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AbstractWhile there is a growing effort towards AI for Sustainability (e.g. towards the sustainable development goals) it is time to move beyond that and to address the sustainability of developing and using AI systems. In this paper I propose a definition of Sustainable AI; Sustainable AI is a movement to foster change in the entire lifecycle of AI products (i.e. idea generation, training, re-tuning, implementation, governance) towards greater ecological integrity and social justice. As such, Sustainable AI is focused on more than AI applications; rather, it addresses the whole sociotechnical

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Janssen, T. "SP-0541 AI^2: When the AI checks the AI." Radiotherapy and Oncology 182 (May 2023): S427. http://dx.doi.org/10.1016/s0167-8140(23)67400-6.

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Dong, Hwa Kim. "AI Curriculum Design for Korea K-12 AI Education Through Analyzing AI Education Curriculum." International Journal of Recent Technology and Engineering (IJRTE) 12, no. 3 (2023): 72–81. https://doi.org/10.35940/ijrte.C7860.0912323.

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This paper deals with the curriculum design of Korean K-12 AI education. AI is the core technology of 4th wave and it is impact is wide and strong. Therefore, every country has s strong nurture system for AI manpower. However, there are few materials about curriculum design and operation experience documentation because AI was an interest since 2016, and its education is very new. In Korea, they started a program for teacher manpower education in 2020. Therefore, there is no manpower that had an experience in the design and operation of the K-12 AI curriculum. The AI curriculum is a core param

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27

Bordalejo, Barbara, Davide Pafumi, Frank Onuh, AKM Iftekhar Khalid, Morgan S. Pearce, and Daniel O'Donnell. ""Scarlet Cloak and the Forest Adventure": a preliminary study of the impact of AI on commonly used writing tools." International Journal of Educational Technology in Higher Education volume 22, no. 6 (2025): 1–25. https://doi.org/10.5281/zenodo.15014534.

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This paper explores the growing complexity of detecting and differentiating generative AI from other AI interventions. Initially prompted by noticing how tools like Grammarly were being flagged by AI detection software, it examines how these popular tools such as Grammarly, EditPad, Writefull, and AI models such as ChatGPT and Microsoft Bing Copilot affect human-generated texts and how accurately current AI-detection systems, including Turnitin and GPTZero, can assess texts for use of these tools. The results highlight that widely used writing aids, even those not primarily generative, can tri

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Yoo, Sunghee. "Ethical Issues Posed by ‘Generative-AI’ (G-AI) - Response strategies for ‘Good AI Society’." Journal of the Korean Bioethics Association 24, no. 1 (2023): 1–29. http://dx.doi.org/10.37305/jkba.2023.06.24.1.1.

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Bokdash, Saed. "AI-Imam AI- Shaikh M. Aabid AI- Sendi AI-Ansari and his Scientific and.Feqhia Efforts." Journal of King Abdulaziz University-Educational Sciences 16, no. 1 (2003): 95–191. http://dx.doi.org/10.4197/edu.16-1.6.

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Kumar, Neeraj Sachdev, and Mehra Aryan. "An Exploration of Philosophical Ideas of Select Thinkers in Artificial Intelligence." International Journal of Social Science And Human Research 05, no. 12 (2022): 5455–57. https://doi.org/10.5281/zenodo.7417694.

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We attempt to explore the presence and working of philosophical ideas of select thinkers in the whole architecture of artificial intelligence. This exploration delves into the philosophical connections relating to the proof of the existence of artificial intelligence. It further examines the philosophical perspectives of strong and weak versions of artificial intelligence, language processing and the inner working of artificial intelligence itself.

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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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Lee, Keun-woo. "AI as a tool, AI era and Response of the Criminal Law." Gachon Law Review 18, no. 1 (2025): 41–68. https://doi.org/10.15335/glr.2025.18.1.002.

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Miller, David P., R. James Firby, Paul A. Fishwick, and Jeff Rothenberg. "AI." ACM Transactions on Modeling and Computer Simulation 2, no. 4 (1992): 269–84. http://dx.doi.org/10.1145/149516.149519.

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Clancey, William J. "AI." ACM Computing Surveys 27, no. 3 (1995): 320–22. http://dx.doi.org/10.1145/212094.212110.

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Wu, Xianchao. "When Creative AI Meets Conversational AI." Journal of Natural Language Processing 28, no. 3 (2021): 881–87. http://dx.doi.org/10.5715/jnlp.28.881.

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Sūna, Laura, and Dagmar Hoffmann. "From AI imaginaries to AI literacy." MedieKultur: Journal of media and communication research 40, no. 76 (2024): 53–76. http://dx.doi.org/10.7146/mk.v40i76.137144.

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Based on the results of a qualitative study on how migrants experience technologies of automation in everyday life, the article describes users’ imaginaries of artificial intelligence as the overall technology behind different digital media applications. This encompasses the subjective idea of users about what AI technology is, what it can do, and what it should do. All respondents share a general understanding of AI as a feeling and awareness that the technology has its own logic of some kind, as articulated in recommendation algorithms on TikTok, YouTube or Netflix, language correction on Wh

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Moehle, Matthew R., Roopa Nandi, and Hardik Shah. "AI Practitioner November 2013 - AI Resources." AI Practitioner 15, no. 3 (2013): 71–75. http://dx.doi.org/10.12781/978-1-907549-17-5-10.

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Vaux, Janet. "AI is dead; long live AI." Expert Systems 11, no. 1 (1994): 49. http://dx.doi.org/10.1111/j.1468-0394.1994.tb00317.x.

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Johnson, Deborah G., and Mario Verdicchio. "Ethical AI is Not about AI." Communications of the ACM 66, no. 2 (2023): 32–34. http://dx.doi.org/10.1145/3576932.

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Hollands, Fiona, and Cynthia Breazeal. "Establishing AI Literacy before Adopting AI." Science Teacher 91, no. 2 (2024): 35–42. http://dx.doi.org/10.1080/00368555.2024.2308316.

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Sreejani, K. "Melody AI – An AI Music Generator." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42228.

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MELODY AI is a music generation system based on AI that converts textual descriptions into novel musical pieces. Built with the Hugging Face model, the system uses deep learning algorithms to create music from user-input prompts. The process entails processing user-provided prompts, representation of these prompts in musical form, and synthesis of related audio outputs. The model has been optimized to preserve rich musical structures, allowing for coherent and expressive music to be generated across various genres. The model is successful in preserving the spirit of user inputs, generating exp

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Kumar, Keetha Ajay. "Melody AI – An AI Music Generator." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50461.

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Abstract - MELODY AI is an innovative system designed to generate music using artificial intelligence by transforming textual descriptions into original compositions. Leveraging the MusicGen model from Hugging Face, the system employs advanced deep learning techniques to interpret user input and produce corresponding audio tracks. The workflow includes interpreting the user's prompt, translating it into a musical representation, and synthesizing it into audio form. The AI model has been fine-tuned to maintain musical richness and structural integrity, enabling it to generate expressive, genre-

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Goel, Ashok K., and David A. Joyner. "Using AI to Teach AI: Lessons from an Online AI Class." AI Magazine 38, no. 2 (2017): 48–59. http://dx.doi.org/10.1609/aimag.v38i2.2732.

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In fall 2014, we launched a foundational course in artificial intelligence (CS7637: Knowledge-Based AI) as part of the Georgia Institute of Technology's Online Master of Science in Computer Science program. We incorporated principles and practices from the cognitive and learning sciences into the development of the online AI course. We also integrated AI techniques into the instruction of the course, including embedding 100 highly focused intelligent tutoring agents in the video lessons. By now, more than 2000 students have taken the course. Evaluations have indicated that OMSCS students enjoy

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Miyamoto, Michiko. "Measuring AI Governance, AI Adoption and AI Strategy of Japanese Companies." International Journal of Membrane Science and Technology 10, no. 1 (2023): 649–57. http://dx.doi.org/10.15379/ijmst.v10i1.2627.

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Purpose: This study aims to measure the level of AI governance and AI adoption among Japanese companies. Theoretical Framework: The research investigates the extent to which Japanese companies have implemented AI governance frameworks and the degree of AI adoption in their operations. The study also explores the relationship between AI governance, AI adoption, and AI strategy, providing insights into the factors that influence successful AI implementation. Design / Methodology / Approach: a survey questionnaire was administered to a representative sample of Japanese companies across various in

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Lee, Sun-Hyung, and Byung-Keun Kim. "Analysis of Factors and Pathways Influencing Continued Usage Intention of AI Technology: Focusing on the Mediating Effects of AI Usage and AI Trust." JOURNAL OF THE KOREA CONTENTS ASSOCIATION 25, no. 4 (2025): 282–94. https://doi.org/10.5392/jkca.2025.25.04.282.

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Tejesh, Reddy Singasani. "Enhancing Customer Experience through PEGA s AI Powered Decisioning." Journal of Scientific and Engineering Research 9, no. 12 (2022): 191–95. https://doi.org/10.5281/zenodo.13753089.

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In this paper, we will examine the revolutionary effect of PEGA AI-powered decisioning on improving CX. Following AI breakthroughs, it has become clear that companies have their sights set on automatic customer service to deliver a more personable and efficient experience for customers. PEGA's AI decisioning engine uses data-driven real-time insights to predict desired outcomes, better optimize the overall experience and increase customer satisfaction. This paper discusses the AI mechanisms of PEGA, demonstrates how they work using different use cases and emphasizes their power in real-time sc

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Pavan, Narayan L., Reddy R. Rohan, Gowda K. S. Punith, and P. V. Sagar. "AI-Based Heart Disease Prediction System." Recent Innovations in Wireless Network Security 7, no. 1 (2025): 48–55. https://doi.org/10.5281/zenodo.14793767.

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<em>Heart disease is a leading cause of death globally, requiring early and accurate detection for effective management. This study proposes an AI-based heart disease prediction system using machine learning algorithms to analyse patient data. Key features influencing heart disease are identified to enhance model accuracy and interpretability. The system, trained on publicly available datasets, achieves high prediction accuracy, demonstrating its potential as a reliable, cost-effective diagnostic tool. Integrating AI into clinical workflows can improve personalized care and patient outcomes. F

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Dr., Amartya Saha. "Understanding the Scope and Problems of Artificial Intelligence and Assisted Learning in Higher Education." Social Science Journal for Advanced Research 4, no. 5 (2024): 1–9. https://doi.org/10.5281/zenodo.13637234.

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Learning is a dynamic experience. We need to create learning experiences that assist all kinds of learners. Human beings have different patterns of learning. Assisted learning helps in decentralizing the learning experience in a more individualistic approach. Artificial Intelligence has helped in automating many tasks of administrative nature, evaluation and has even helped personalize learning, countering the problems of teacher exhaustion. Not just efficient grading use of AI through educational chatbots and applications can help in helping students get a 24/7 mentoring option which in a rea

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Özer, Niyazi. "Doing Educational Leadership in the AI Age: Redefining the Roles and Responsibilities of System Level Leaders." AI Research in Educational Leadership 1, no. 2 (2025): 1–7. https://doi.org/10.5281/zenodo.15166322.

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Technological advances have the potential to significantly impact various aspects of human life, including the educational sphere. It seems likely that the education of the future will be influenced by the advent of AI, and this will undoubtedly require a shift in thinking and action that is markedly distinct from past. In light of these developments, it would be beneficial for those in leadership positions within the education system, at all levels, including local and national education authorities, to undertake a thorough reassessment of their roles and responsibilities with regards to the

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Uma, Perumal, and Renganathan Vasantharajan. "Embedded AI Application in Defense UAV." Journal of Advances in Computational Intelligence Theory 4, no. 3 (2022): 1–9. https://doi.org/10.5281/zenodo.7446973.

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Embedded systems in combination with AI in defense systems is a boon for nations security. Various embedded hardware along with algorithms of AI, is a blend of computing techniques is a leading-edge tool for an army. Army drones, single or a swarm, can either inform the allies with necessary facts or can even misinform enemies with fake data is a game changer in a battle or military operation. The capabilities of embedded systems in addition edge and fog computing techniques to bring down latency, bandwidth and reduction in load to cloud servers. AI algorithms that can teach Intuition with adv

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