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https://www.selleckchem.com/products/c646.html The latest generation of artificial neural networks (ANNs) exploits capabilities such as online learning, fast training, high level knowledge representation, online evolution, learning by data and inferring rules.Wearable electronics is also developing rapidly and represents an important enabling technology to deploy physical and practical (noninvasive) devices using AI-based models for early prediction of neurodegenerative diseases and of intelligent prostheses.Here we describe how to apply advanced brain-inspired methods for inference and prediction, the evolving fuzzy neural network (EFuNN) paradigm and the spiking neural network (SNN) paradigm, and the system requirements to develop a wearable electronic prosthesis for functional rehabilitation.Recently, digitization of biomedical processes has accelerated, in no small part due to the use of machine learning techniques which require large amounts of labeled data. This chapter focuses on the prerequisite steps to the training of any algorithm data collection and labeling. In particular, we tackle how data collection can be set up with scalability and security to avoid costly and delaying bottlenecks. Unprecedented amounts of data are now available to companies and academics, but digital tools in the biomedical field encounter a problem of scale, since high-throughput workflows such as high content imaging and sequencing can create several terabytes per day. Consequently data transport, aggregation, and processing is challenging.A second challenge is maintenance of data security. Biomedical data can be personally identifiable, may constitute important trade-secrets, and be expensive to produce. Furthermore, human biomedical data is often immutable, as is the case with genetic information. These factors make securing this type of data imperative and urgent. Here we address best practices to achieve security, with a focus on practicality and scalability. We also address

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