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                <text>Faculty Publications</text>
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    <name>Book Chapter</name>
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          <name>Creator</name>
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              <text>Takore, Tamirat Tagesse; Krishna, Konda Hari; Kumar, K. Suresh; Kumar, R. Ganesh; Jain, Amit; Keerthika, T.</text>
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              <text>Applications, Enabling Technologies, Vision of Edge Artificial Intelligence for 6G</text>
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              <text>01-01-2025</text>
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              <text>Advances in AI for Cloud, Edge, and Mobile Computing Applications;pp.213-237</text>
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              <text>&lt;a href="https://www.scopus.com/pages/publications/105005693781?origin=resultslist" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/pages/publications/105005693781?origin=resultslist&lt;/a&gt;</text>
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              <text>Takore T.T., Department of Electrical and Computer Engineering, Wachemo University, India; Krishna K.H., Department of CSE, School of Computing, Mohan Babu University, Sree Sainath Nagar, Rangampeta, Andhra Pradesh, Tirupati, India; Kumar K.S., MBA Department, Panimalar Engineering College, Varadarajapuram, Poonamallee, Tamil Nadu, Chennai, India; Kumar R.G., Department of Computer Science and Engineering, CHRIST (Deemed to be University), School of Engineering and Technology, Kengeri Campus, Kanminike, Karnataka, Bangalore, India; Jain A., Computer Science and Engineering Department, OP Jindal University, Chhattisgarh, Raigarh, India; Keerthika T., Department of IT, Sri Krishna College of Engineering and Technology, Coimbatore, India</text>
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              <text>The advancement in applications of artificial intelligence technologies that propels the ongoing development of wireless networks. Envisioned as a transformative force, 6G is expected to shift wireless evolution from connecting devices to enabling intelligent connections. However, cutting-edge AI systems relying on deep learning and extensive data analytics demand significant computational and communication resources, leading to notable issues such as privacy breaches, network congestion, energy consumption and latency during both inference and training phases. Edge AI emerges as a disruptive technology for 6G by incorporating inference capabilities and model training at the networks edge. This approach seamlessly integrates intelligence, computation, communication and sensing enhancing the security, privacy, effectiveness and efficiency of the 6G technologies. The study includes the applications, enabling technologies dependable and scalable edge AI technology through an integrated approach that encompasses decentralized models of machine learning and strategies of wireless communication. We will delve into new wireless network design principles, optimization methods for resource allocation driven by service requirements, and an all-encompassing architecture of end-to-end system supporting edge AI. Additionally, we will touch upon application scenarios, hardware and software platforms, and standardization for facilitating the commercialization and industrial adoption of edge AI systems.  2025 Apple Academic Press, Inc.</text>
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          <name>Subject</name>
          <description>The topic of the resource</description>
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              <text>6G technologies; Edge AI; Federated learning; Intelligent healthcare; Internet of medical things (IoMT); Precision medicine</text>
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          <name>Publisher</name>
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              <text>Apple Academic Press</text>
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              <text>ISBN: 978-104038081-9; 978-177952068-5;</text>
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              <text>Restricted Access; Hardcopy may be available in the library</text>
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