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Titlebook: Artificial Intelligence Research; 4th Southern African Anban Pillay,Edgar Jembere,Aurona J. Gerber Conference proceedings 2023 The Editor(s

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發(fā)表于 2025-3-21 17:08:48 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Artificial Intelligence Research
期刊簡稱4th Southern African
影響因子2023Anban Pillay,Edgar Jembere,Aurona J. Gerber
視頻videohttp://file.papertrans.cn/163/162134/162134.mp4
學(xué)科分類Communications in Computer and Information Science
圖書封面Titlebook: Artificial Intelligence Research; 4th Southern African Anban Pillay,Edgar Jembere,Aurona J. Gerber Conference proceedings 2023 The Editor(s
影響因子This book constitutes the refereed proceedings of the 4th Southern African Conference on Artificial Intelligence Research, SACAIR 2023, held in Muildersdrift, South Africa, in December 2023.?.The 22 full papers presented in these proceedings were carefully reviewed and selected from 66 submissions. The papers are organized in the following topical sections:?Responsible and Ethical AI Track;?Socio-Technical and Human-Centered AI Track;?Algorithmic, and Data?Driven and Symbolic AI..
Pindex Conference proceedings 2023
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Exploring the Ethical and Societal Concerns of Generative AI in Internet of Things (IoT) Environmentevolutionise many aspects of our lives today. Generative AI, a subset of AI technologies, has a unique ability to create and manipulate content based on patterns learned from vast datasets, and when combined with the interconnected nature of IoT devices, they hold great promise across industries. Le
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Let’s Play Games: Using No-Code AI to?Reduce Human Cognitive Load During AI Solution Developmentgnitive capacity. Cognitive fit theory is an established theory that proposes that task completion performance is enhanced when there is a congruent relationship between the problem statement and task execution. Despite efforts to simplify the tasks, the tasks may still pose a challenge when it come
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Voice Conversion for?Stuttered Speech, Instruments, Unseen Languages and?Textually Described Voicesncreasingly realistic output. But what happens when models are fed with non-standard data, such as speech from a user with a speech impairment? We investigate how a recent voice conversion model performs on non-standard downstream voice conversion tasks. We use a simple but robust approach called k-
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