November titles from Elsevier

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Please see the November catalog from Elsevier featuring their new publications in medicine, nursing, allied health and science.

Click on the image below or here to view the catalog.

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Perioperative Nursing
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Perioperative Nursing, 4e

Brigid M. Gillespie, Benjamin Lockwood, Erin Wakefield, Paula Foran
Elsevier
510 pages
ISBN: 9780729545068
November 2025

Indispensable resource for the novice perioperative nurse The most up-to-date resource about perioperative nursing in Australia and New Zealand. Perioperative Nursing: An Introduction provides an in-depth, standards-based overview for those undertaking specialist training. The text examines the roles and responsibilities of nurses working in perioperative care, including specialty areas such as anaesthesia, intraoperative patient care, post-anaesthesia care and day surgery. It brings theory to life by following the patient experience along their surgery journey. The fourth edition of this invaluable book has been updated by clinical experts to incorporate the most current national and international standards and guidelines around perioperative nursing in Australia and New Zealand.
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Mastering Medical Terminology
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Mastering Medical Terminology, 4e

Emma Barker, Stephane Gjorgioski, Melanie Tassos
Elsevier
680 pages
ISBN: 9780729545051
November 2025

Medical terms demystified Every health professional comes across unfamiliar medical terms from time to time. Mastering Medical Terminology teaches students and practitioners to decipher the meanings of word parts and understand complex terminology associated with the human body and common diseases and procedures. With a focus on Australian and New Zealand spelling and pronunciation conventions, this invaluable resource provides easy-to-understand descriptions of human anatomy and physiology by body system. It includes information about the common diseases and procedures that relate to these body systems. Also provided are comprehensive definitions of terminology, pronunciation guides and reinforcement of learning through a wide variety of exercises and practice questions. The fourth edition of this go-to textbook has been fully updated by a team of expert authors to reflect current medical terminology and the local healthcare environment.
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Advances in Fog Computing and the Internet of Things for Smart Healthcare
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Advances in Fog Computing and the Internet of Things for Smart Healthcare, 1e

Joseph Bamidele Awotunde, Akash Kumar Bhoi, Paolo Barsocchi, Victor Hugo Costa de Albuquerque
Elsevier
472 pages
ISBN: 9780443334412
November 2025

Advances in Fog Computing and the Internet of Things for Smart Healthcare delves into the transformative impact of fog computing and IoT on modern healthcare systems. This comprehensive guide educates researchers and graduate students on the fundamental concepts of these technologies, illustrating their practical applications in healthcare. Real-world use cases showcase the technologies' ability to enhance patient monitoring and personalized medicine. The book also addresses significant challenges such as privacy, security, data management, and regulatory compliance, providing strategies for overcoming them. Alongside its companion book, Fundamentals of Fog Computing and the Internet of Things for Smart Healthcare , this volume empowers various industries to leverage IoT technologies while optimizing performance and system efficiency. It highlights the crucial role of fog computing in processing data closer to the source, ensuring faster and more efficient data processing and contributing to the creation of effective IoT ecosystems. This synergy between fog computing and IoT facilitates smarter applications and services, advancing the landscape of healthcare with more efficient, personalized, and accessible services.
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Multimodal Learning Using Heterogeneous Data
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Multimodal Learning Using Heterogeneous Data, 1e

Saeid Eslamian, Preethi Nanjundan, Jossy P. George, Faezeh Eslamian
Elsevier
250 pages
ISBN: 9780443275289
November 2025

Multimodal Learning Using Heterogeneous Data is a comprehensive guide to the emerging field of multimodal learning, which focuses on integrating diverse data types such as text, images, and audio within a unified framework. The book delves into the challenges and opportunities presented by multimodal data and offers insights into the foundations, techniques, and applications of this interdisciplinary approach. It is intended for researchers and practitioners interested in learning more about multimodal learning and is a valuable resource for those working on projects involving data analysis from multiple modalities. The book begins with a comprehensive introduction, focusing on multimodal learning's foundational principles and the intricacies of heterogeneous data. It then delves into feature extraction, fusion techniques, and deep learning architectures tailored for multimodal data. It also covers transfer learning, pre-processing challenges, and cross-modal information retrieval. The book highlights the application of multimodal learning in specialized contexts such as sentiment analysis, data generation, medical imaging, and ethical considerations. Real-world case studies are woven into the narrative, illuminating the applications of multimodal learning in diverse domains such as natural language processing, multimedia content analysis, autonomous systems, and cognitive computing. The book concludes with an insightful exploration of multimodal data analytics across social media, surveillance, user behavior, and a forward-looking examination of future trends and practical implementations. As a collective resource, Multimodal Learning Using Heterogeneous Data illuminates the powerful utility of multimodal learning to elevate machine learning tasks while also highlighting the need for innovative solutions and methodologies. The book acknowledges the challenges associated with deep learning and the growing importance of ethical considerations in the collection and analysis of multimodal data. Overall, Multimodal Learning Using Heterogeneous Data provides an expansive panorama of this rapidly evolving field, its potential for future research and application, and its vital role in shaping machine learning's evolution.
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