3,525 results
Search Results
2. Mobility restrictions in response to local epidemic outbreaks in rock-paper-scissors models
- Author
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J Menezes
- Subjects
epidemic ,mobility restrictions ,simulations ,rock-paper-scissors ,ecology ,artificial intelligence ,Science ,Physics ,QC1-999 - Abstract
We study a three-species cyclic model whose organisms are vulnerable to contamination with an infectious disease which propagates person-to-person. We consider that individuals of one species perform a self-preservation strategy by reducing the mobility rate to minimise infection risk whenever an epidemic outbreak reaches the neighbourhood. Running stochastic simulations, we quantify the changes in spatial patterns induced by unevenness in the cyclic game introduced by the mobility restriction strategy of organisms of one out of the species. Our findings show that variations in disease virulence impact the benefits of dispersal limitation reaction, with the relative reduction of the organisms’ infection risk accentuating in surges of less contagious or deadlier diseases. The effectiveness of the mobility restriction tactic depends on the deceleration level and the fraction of infected neighbours which is considered too dangerous, thus triggering the defensive strategy. If each organism promptly reacts to the arrival of the first viral vectors in its surroundings with strict mobility reduction, contamination risk decreases significantly. Our conclusions may help biologists understand the impact of defensive strategies in ecosystems during an epidemic.
- Published
- 2024
- Full Text
- View/download PDF
3. The 100 most influential papers in medical artificial intelligence; a bibliometric analysis
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Fatima Zahoor, Muhammad Abdullah, Muhammad Waleed Tahir, and Asif Islam
- Subjects
Artificial intelligence ,Machine learning ,Computer reasoning ,Machine intelligence ,Medicine - Abstract
Objective: To assess the current trends in the field of artificial intelligence in medicine by analysing 100 most cited original articles relevant to the field. Methods: The systematic review was conducted in September 2022, and comprised literature search on Scopus database for original articles only. Google and Medical Subject Headings databases were used as resources to extract key words. In order to cover a broad range of articles, original studies comprising human as well as non-human subjects, studies without abstract and studies in languages other than English were part of the inclusion criteria. There was no specific time period applied to the search and no specific selection was done regarding the journals in the database. The screening was done using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to extract the top 100 most cited articles in the field of artificial intelligence usage in medicine. Data was analysed using SPSS 23. Results: Of the 11,571 studies identified, 100(0.86%) were analysed in detail. The studies were published between 1986 and 2021, with a median of 43 citations (IQR 53) per article. The journal ‘Artificial Intelligence in Medicine’ accounted for the highest number 9(9%)) of articles, and the United States was the country of origin for most of the articles 36(36%). Conclusion: The trends, development and shortcomings in field of artificial intelligence usage in medicine need to be understood to conduct an effective research in areas that still need attention, and to guide the authorities to direct their funding accordingly.
- Published
- 2024
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4. Re-Examining the Future Prospects of Artificial Intelligence in Education in Light of the GDPR and ChatGPT
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John Y. H. Bai, Olaf Zawacki-Richter, and Wolfgang Muskens
- Abstract
Artificial intelligence in education (AIEd) is a fast-growing field of research. In previous work, we described efforts to explore the possible futures of AIEd by identifying key variables and their future prospects. This paper re-examines our discussions on the governance of data and the role of students and teachers by considering the implications of (1) a recent case related to the General Data Protection Regulation (GDPR) and (2) the release of ChatGPT, a generative AI model capable to producing 'human-like' text. These events raise questions for the future of AIEd and the underlying function of assessment, and highlight the importance of active student participation in the integration of AI in education. [This article has been presented in the 5th International Open & Distance Learning Conference-IODL 2022.]
- Published
- 2024
5. A novel artificial neural network approach for residual life estimation of paper insulation in oil‐immersed power transformers.
- Author
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Nezami, Md. Manzar, Equbal, Md. Danish, Ansari, Md. Fahim, Alotaibi, Majed A., Malik, Hasmat, García Márquez, Fausto Pedro, and Hossaini, Mohammad Asef
- Subjects
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ARTIFICIAL neural networks , *POWER transformers , *TRANSFORMER insulation , *ARTIFICIAL intelligence , *MATHEMATICAL optimization - Abstract
Avoiding financial losses requires preventing catastrophic oil‐filled power transformer breakdowns. Continuous online transformer monitoring is needed. The authors use paper insulation to evaluate transformer health for continuous online transformer monitoring. The study suggests a new artificial intelligence method for estimating paper insulation residual life in oil‐immersed power transformers. The four artificial intelligence models use backpropagation‐based neural networks to predict paper insulation lifespan. Four primary transformer insulating paper failure indices—degree of polymerisation, 2‐furfuraldehyde, carbon monoxide, and carbon dioxide—form the basis of these models. Each model, including the backpropagation‐based neural networks, estimates paper insulation life using one failure index, along with moisture and temperature data. Optimisation techniques enhance hidden layer neurons and epoch count for improved performance. Results are validated against literature‐based life models, establishing a precise input–output correlation. This method accurately predicts the remaining useable life of power transformer paper insulation, enabling utilities to take proactive measures for safe and efficient transformer operation. [ABSTRACT FROM AUTHOR]
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- 2024
- Full Text
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6. Research Messages 2023: Informing + Influencing the Australian VET Sector
- Author
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National Centre for Vocational Education Research (NCVER) (Australia) and National Centre for Vocational Education Research (NCVER) (Australia)
- Abstract
Research messages is a summary of research produced by NCVER each year. This year's compilation includes a range of research activities undertaken during 2023, comprising of research reports, summaries, occasional papers, presentations, webinars, consultancies, submissions, the 32nd 'No Frills' national research conference, and various additions to VOCEDplus knowledge resources. "Research messages 2023" highlights the diverse range of research activities undertaken over the past year by the National Centre for Vocational Education Research (NCVER). This edition provides: (1) Key findings from NCVER's program of research; (2) Details of conferences, presentations, webinars, podcasts and other NCVER research communications; (3) Resources collated by NCVER designed to assist in informing the VET (vocational education and training) system and its related policies; and (4) A summary of NCVER discussion papers and submissions to government reviews.
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- 2024
7. Personalized Education for All: The Future of Open Universities
- Author
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Insung Jung
- Abstract
This paper charts a forward-looking roadmap for open universities, drawing upon their historical evolution and current practices. It advocates a shift toward a universally accessible, personalized education system. At the heart of this proposed advancement lies the customization of learning paths and experiences, where individualized advising and mentorship, and a variety of learning content, resources, and environments are essential. The study underscores the importance of integrating advanced technologies such as artificial intelligence and blockchain into the open and distance education system. Within the discourse, the paper delineates three primary areas for open universities to address: system transformation, expansion of openness, and integration of digital innovation. The concluding part of the paper offers possible strategic recommendations for policymakers and researchers of open universities. The essence of these recommendations is advocating for a universally personalized educational paradigm while making a strong case for addressing the digital divide, fostering strong partnerships at both global and community levels, and supporting the use of the latest technology to its fullest potential. By navigating this transformative journey, open universities are not just participating in the evolution of educational models but also poised to lead a revolution in the broader landscape of higher education.
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- 2024
8. Assessing the Feasibility of Processing a Paper-based Multilingual Social Needs Screening Questionnaire Using Artificial Intelligence
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Artificial intelligence ,Natural language interfaces ,Medical records ,Computational linguistics ,Language processing ,Machine learning ,Artificial intelligence ,Computers - Abstract
2024 APR 16 (VerticalNews) -- By a News Reporter-Staff News Editor at Information Technology Newsweekly -- According to news reporting based on a preprint abstract, our journalists obtained the following [...]
- Published
- 2024
9. Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper.
- Author
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Vinodkumar, Prasoon Kumar, Karabulut, Dogus, Avots, Egils, Ozcinar, Cagri, and Anbarjafari, Gholamreza
- Subjects
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DEEP learning , *COMPUTER vision , *GRAPH neural networks , *ARTIFICIAL intelligence , *MACHINE learning , *GENERATIVE adversarial networks - Abstract
The research groups in computer vision, graphics, and machine learning have dedicated a substantial amount of attention to the areas of 3D object reconstruction, augmentation, and registration. Deep learning is the predominant method used in artificial intelligence for addressing computer vision challenges. However, deep learning on three-dimensional data presents distinct obstacles and is now in its nascent phase. There have been significant advancements in deep learning specifically for three-dimensional data, offering a range of ways to address these issues. This study offers a comprehensive examination of the latest advancements in deep learning methodologies. We examine many benchmark models for the tasks of 3D object registration, augmentation, and reconstruction. We thoroughly analyse their architectures, advantages, and constraints. In summary, this report provides a comprehensive overview of recent advancements in three-dimensional deep learning and highlights unresolved research areas that will need to be addressed in the future. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
10. Driving Success in Pulp And Paper: The Power of Market Intelligence Platforms.
- Author
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Franklin, Savannah
- Subjects
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PAPER industry , *ARTIFICIAL intelligence , *RAW materials , *INDUSTRIAL management , *MACROECONOMICS - Published
- 2024
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