A hazardous gas detection system for aerospace and commercial applications / / G.W. Hunter [and six others]

• Record Nr.: UNINA9910706201203321
• Autore: Hunter G. W (Gary W.)
• Pubbl/distr/stampa: Cleveland, Ohio : , : National Aeronautics and Space Administration, Lewis Research Center, , November 1998
• Descrizione fisica: 1 online resource (10 pages) : illustrations
• Collana: NASA/TM ; ; 1998-208817
• Soggetti: Gas detectors; Hazards; Computer programs; Hardware; Aerospace safety
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: "November 1998."; "Prepared for the 34th Joint Propulsion Conference and Exhibit cosponsored by AIAA, ASME, SAE, and ASEE, Cleveland, Ohio, July 12-15, 1998."; "Performing organization: National Aeronautics and Space Administration, Lewis Research Center"--Report documentation page.; "AIAA-98-3614."
• Nota di bibliografia: Includes bibliographical references (page 10).

Advanced deep learning with TensorFlow 2 and Keras : apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more / / Rowel Atienza

• Record Nr.: UNINA9910795281003321
• Autore: Atienza Rowel
• Pubbl/distr/stampa: Birmingham, UK : , : Packt Publishing, , 2020
• ISBN: 1-83882-572-X
• Edizione: [Second edition.]
• Descrizione fisica: 1 online resource (1 volume) : illustrations
• Soggetti: Artificial intelligence; Machine learning; Python (Computer program language); Neural networks (Computer science)
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Sommario/riassunto: Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and Keras Key Features Explore the most advanced deep learning techniques that drive modern AI results New coverage of unsupervised deep learning using mutual information, object detection, and semantic segmentation Completely updated for TensorFlow 2.x Book Description Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more ; cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance. Next, you'll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI. What you will learn Use mutual information maximization techniques to perform unsupervised learning Use segmentation to identify the pixel-wise class of each object in an image Identify both the bounding box and class of objects in an image using object detection Learn the building blocks for advanced techniques - MLPss, CNN, and RNNs Understand deep neural networks - including ResNet and DenseNet Understand and build autoregressive models – autoencoders, VAEs, and GANs Discover and implement deep reinforcement learning methods Who this book is for This is not an introductory book, so fluency with Python is required. The reader should also be familiar with some machine learning approaches, and practical experience with DL will also be hel...

Plant Essential Oil with Biological Activity

• Record Nr.: UNINA9910566457203321
• Autore: Elshafie Hazem Salaheldin
• Pubbl/distr/stampa: Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022
• Descrizione fisica: 1 online resource (264 p.)
• Soggetti: Medicine and Nursing; Pharmacology
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Sommario/riassunto: Plant essential oils (PEOs) are hydrophobic liquids that contain volatile chemical components that are derived from various plant parts. They are among the most important plant natural products because of their diverse biological features as well as their therapeutic and nutritional applications. In addition, several aromatic PEOs are used to flavor food and add aromas to incense in the culinary sector. Recently, many PEOs have demonstrated promising antimicrobial activity against different post-harvest diseases and have been considered as possible natural alternatives for chemical treatments. This Special Issue titled "Plant Essential Oil with Biological Activity" provided an overview of several elements of PEOs, including their biological applications, antimicrobial activities, bio-pharmaceutical properties, principal single constituents, and mechanisms of action. This Special Issues fills in knowledge gaps and aids in the advancement of EO applications around the world. This issue contains thirteen research articles and two review papers that address a wide range of topics and applications relevant to the bioactivity of PEOs.