Deep Learning Based Applications (70 results)

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Softcover. Condition: New. Dust Jacket Condition: no dj. First. In Deep Learning Applications in Sound-Based Language Revitalization, the authors present a comprehensive exploration of the applications of deep learning in sound-based language revitalization. The book examines the latest research and developments in the field, highlighting the potential of deep learning to inform language documentation and revitalization efforts. From the analysis of audio data to the development of language learning tools, this book provides a detailed examination of the complex relationships between deep learning, language, and culture. By showcasing the latest breakthroughs in deep learning, Deep Learning Applications in Sound-Based Language Revitalization offers a compelling argument for the integration of deep learning into language revitalization efforts. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

Softcover. Condition: New. Dust Jacket Condition: no dj. First. Language is a fundamental aspect of human culture and identity, yet many languages are at risk of disappearing due to globalization and cultural assimilation. Deep Learning Applications in Sound-Based Language Revitalization presents a groundbreaking examination of the potential for deep learning technologies to support language revitalization efforts. By analyzing the latest research and applications in this field, this book provides a roadmap for linguists, language educators, and community leaders seeking to preserve and promote endangered languages. Publication Year: 2025. SHIPPING TERMS - Depending on your location we may ship your book from the following locations: France, United Kingdom, India, Australia, Canada or the USA. This item is printed on demand.

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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Image segmentation is a crucial aspect of clinical decision-making in the medical field. The integration of image segmentation techniques has dramatically enhanced healthcare delivery. Also, the advancement of deep learning, particularly Convolutional Neural Networks (CNNs), has brought about a significant transformation in medical image analysis. These advanced algorithms have shown exceptional abilities in identifying complex patterns and features in medical images, revolutionizing diagnostic imaging. However, the complexity and scale of these models present significant challenges. This requires a substantial amount of computational resources and expert knowledge for successful implementation. Addressing these challenges is crucial to fully exploit the potential of deep learning in the field of medical image segmentation. To address the challenges, this study combines metaheuristic optimization algorithms with deep learning. These algorithms, inspired by natural processes, provide an effective way to optimize the structure and parameters of CNNs, thus making the process of medical image segmentation more efficient.