The Role of Natural Antioxidants in Brain Disorders (1版)

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Oxidative stress and neuroinflammation are considered causative factors in various neurological disorders such as Parkinson’s and Alzheimer’s diseases. Antioxidants are chemicals that bind with oxidative species and nullify their effect from causing damage to biological molecules. Endogenous antioxidants are produced by our body, however most of them are obtained from external sources, primarily through diet, called dietary antioxidants. Major sources of antioxidants are brightly colored fruits, vegetables, cereals, legumes, and herbs. Other very effective sources are berries, green tea, and dark chocolate. These compounds have the potential to hinder neurodegeneration, reduce neuronal death and improve memory as well as cognitive functions. Based on the complex nature of antioxidants and oxidative stress, particular antioxidants such as vitamin E, vitamin C or β-carotene are beneficial in protecting cells, organs and tissues against oxidative damage. The Role of Natural Antioxidants in Brain Disorders describes various neuroprotective effects and their physiological phenomenon mediated by antioxidants to maintain and regulate the general health biomarkers against brain disorders. The important role of antioxidants, diet and lifestyle in managing brain disorders is covered, as is their use in conjunction with conventional therapies against oxidative stress. Both exogenous and endogenous antioxidants are explored in full. By focusing on the role of oxidative stress as a triggering mechanism for various brain disorders and the use of antioxidant foods in conjunction with traditional therapies in combating and preventing them, this is a valuable source for researchers in food science, nutrition, health science and physiology.

Sensor Data Analysis and Management: The Role of Deep Learning

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Discover detailed insights into the methods, algorithms, and techniques for deep learning in sensor data analysis Sensor Data Analysis and Management: The Role of Deep Learning delivers an insightful and practical overview of the applications of deep learning techniques to the analysis of sensor data. The book collects cutting-edge resources into a single collection designed to enlighten the reader on topics as varied as recent techniques for fault detection and classification in sensor data, the application of deep learning to Internet of Things sensors, and a case study on high-performance computer gathering and processing of sensor data. The editors have curated a distinguished group of perceptive and concise papers that show the potential of deep learning as a powerful tool for solving complex modelling problems across a broad range of industries, including predictive maintenance, health monitoring, financial portfolio forecasting, and driver assistance. The book contains real-time examples of analyzing sensor data using deep learning algorithms and a step-by-step approach for installing and training deep learning using the Python keras library. Readers will also benefit from the inclusion of: A thorough introduction to the Internet of Things for human activity recognition, based on wearable sensor data An exploration of the benefits of neural networks in real-time environmental sensor data analysis Practical discussions of supervised learning data representation, neural networks for predicting physical activity based on smartphone sensor data, and deep-learning analysis of location sensor data for human activity recognition An analysis of boosting with XGBoost for sensor data analysis Perfect for industry practitioners and academics involved in deep learning and the analysis of sensor data, Sensor Data Analysis and Management: The Role of Deep Learning will also earn a place in the libraries of undergraduate and graduate students in data science and computer science programs.