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Titlebook: Handbook of Artificial Intelligence in Healthcare; Vol. 1 - Advances an Chee-Peng Lim,Ashlesha Vaidya,Lakhmi C. Jain Book 2022 The Editor(s

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樓主: Wilson
51#
發(fā)表于 2025-3-30 08:12:37 | 只看該作者
https://doi.org/10.1057/9780230114029ore complex ones, which combine recurrent networks with convolutional models. Experimental results have shown the feasibility of the approach, and the superiority of composite architectures, which have led to higher accuracy values.
52#
發(fā)表于 2025-3-30 15:19:00 | 只看該作者
Artificial Intelligence in Remote Photoplethysmography: Remote Heart Rate Estimation from Video Imagn these methods are discussed for solving movement artifacts and illumination changes. Deep learning methods are then reviewed, and a general overview of the datasets available for remote photoplethysmography learning is furnished.
53#
發(fā)表于 2025-3-30 19:27:01 | 只看該作者
54#
發(fā)表于 2025-3-31 00:17:47 | 只看該作者
https://doi.org/10.1007/978-981-13-1177-2of stereotyped responses collected in a systematic way, labeled as patterns. Those movements and sounds, represents how patterns in audio and video relate to stimuli from the environment. Findings allow to discriminate when and how there is a reaction, an autistic verbal behavior.
55#
發(fā)表于 2025-3-31 02:02:24 | 只看該作者
Autistic Verbal Behavior Language Parameterizationof stereotyped responses collected in a systematic way, labeled as patterns. Those movements and sounds, represents how patterns in audio and video relate to stimuli from the environment. Findings allow to discriminate when and how there is a reaction, an autistic verbal behavior.
56#
發(fā)表于 2025-3-31 06:57:03 | 只看該作者
Book 2022 methodologies in specific healthcare problems, while the second volume is concerned with general practicality issues and challenges and future prospects in the healthcare context.?.The advent of digital and computing technologies has created a surge in the development of AI methodologies and their
57#
發(fā)表于 2025-3-31 11:00:35 | 只看該作者
58#
發(fā)表于 2025-3-31 16:44:58 | 只看該作者
Computer-Aided Detection of Depressive Severity Using Multimodal Behavioral Dataine behavioral features such as facial expressions and speech prosody will be introduced. From the experimental results of the baseline systems introduced in this chapter, readers can not only compare between the performance of different baseline features but also have a general understanding of computer-aided depressive severity diagnosis.
59#
發(fā)表于 2025-3-31 18:51:09 | 只看該作者
New Insights on Implementing and Evaluating Artificial Intelligence in Cardiovascular Carechallenges faced by personalized care delivery using multi-domain data patient health information. It discusses validated solutions for data management and Machine Learning approaches for combining the value of these complementary yet disparate data resources for patient-specific risk prediction modelling.
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