Through activities, approaches, and examples, this resource highlights concrete strategies for incorporating material culture into K–16 art classrooms, as well as museum and community settings. Chapters are written by luminaries in the field and organized around various aspects of material culture, including object study, the role of technology, and multisensory art. “Learning Things is a resource abounding in lucid insights into how everyday objects impact teaching and learning in art. I am certain this book will quickly become a foundational text in our field.” —Juan Carlos Castro, chair, NAEA Research Commission “Filled with excellent examples and teaching strategies, this book brings to life the interdisciplinary stories objects hold and the ways we can use them in research and teaching.” —Deborah L. Smith-Shank, The Ohio State University “In this intimate and educative book, Doug Blandy and Paul Bolin invite us to consider how things come into appearance and take form in the uses to which they are put. If you have ever wondered how we find and lose ourselves in the things that we create, collect, or carry with us, then, this book is for you.” —Dónal O’Donoghue, The University of British Columbia

Digital technology has enabled a number of internet-enabled devices that generate huge volumes of data from different systems. This large amount of heterogeneous data requires efficient data collection, processing, and analytical methods. Deep Learning is one of the latest efficient and feasible solutions that enable smart devices to function independently with a decision-making support system. Convergence of Deep Learning and Internet of Things: Computing and Technology contributes to technology and methodology perspectives in the incorporation of deep learning approaches in solving a wide range of issues in the IoT domain to identify, optimize, predict, forecast, and control emerging IoT systems. Covering topics such as data quality, edge computing, and attach detection and prediction, this premier reference source is a comprehensive resource for electricians, communications specialists, mechanical engineers, civil engineers, computer scientists, students and educators of higher education, librarians, researchers, and academicians.

This book presents the latest developments in deep learning-enabled healthcare tools and technologies and offers practical ideas for using the IoT with deep learning (motion-based object data) to deal with human dynamics and challenges including critical application domains, technologies, medical imaging, drug discovery, insurance fraud detection and solutions to handle relevant challenges. This book covers real-time healthcare applications, novel solutions, current open challenges, and the future of deep learning for next-generation healthcare. It includes detailed analysis of the utilization of the IoT with deep learning and its underlying technologies in critical application areas of emergency departments such as drug discovery, medical imaging, fraud detection, Alzheimer's disease, and genomes. Presents practical approaches of using the IoT with deep learning vision and how it deals with human dynamics Offers novel solution for medical imaging including skin lesion detection, cancer detection, enhancement techniques for MRI images, automated disease prediction, fraud detection, genomes, and many more Includes the latest technological advances in the IoT and deep learning with their implementations in healthcare Combines deep learning and analysis in the unified framework to understand both IoT and deep learning applications Covers the challenging issues related to data collection by sensors, detection and tracking of moving objects and solutions to handle relevant challenges Postgraduate students and researchers in the departments of computer science, working in the areas of the IoT, deep learning, machine learning, image processing, big data, cloud computing, and remote sensing will find this book useful.

Health Economics and Financing Encapsulates different case studies where green-IOT and machine learning can be used for making significant progress towards improvising the quality of life and sustainable environment. The Internet of Things (IoT) is an evolving idea which is responsible for connecting billions of devices that acquire, perceive, and communicate data from their surroundings. Because this transmission of data uses significant energy, improving energy efficiency in IOT devices is a significant topic for research. The green internet of things (G-IoT) makes it possible for IoT devices to use less energy since intelligent processing and analysis are fundamental to constructing smart IOT applications with large data sets. Machine learning (ML) algorithms that can predict sustainable energy consumption can be used to prepare guidelines to make IoT device implementation easier. Green Internet of Things and Machine Learning lays the foundation of in-depth analysis of principles of Green-Internet of Things (G-IoT) using machine learning. It outlines various green ICT technologies, explores the potential towards diverse real-time areas, as well as highlighting various challenges and obstacles towards the implementation of G-IoT in the real world. Also, this book provides insights on how the machine learning and green IOT will impact various applications: It covers the Green-IOT and ML-based smart computing, ML techniques for reducing energy consumption in IOT devices, case studies of G-IOT and ML in the agricultural field, smart farming, smart transportation, banking industry and healthcare. Audience The book will be helpful for research scholars and researchers in the fields of computer science and engineering, information technology, electronics and electrical engineering. Industry experts, particularly in R&D divisions, can use this book as their problem-solving guide.

This volume presents peer-reviewed papers of the First International Conference on Microelectronics, Communication Systems, Machine Learning, and the Internet of Things (MCMI-2020). This book discusses recent trends in technology and advancement in microelectronics, nano-electronics, VLSI design, IC technologies, wireless communications, optical communications, SoC, advanced instrumentations, signal processing, internet of things, machine learning, image processing, green energy, hybrid vehicles, weather forecasting, cloud computing, renewable energy, CMOS sensors, actuators, RFID, transducers, real-time embedded system, sensor network and applications, EDA design tools and techniques, fuzzy logic & artificial intelligence, high-performance computer architecture, AI-based robotics & applications, brain-computer interface, deep learning, advanced operating systems, supply chain development & monitoring, physical systems design, ICT applications, e-farming, information security, etc. It includes original papers based on theoretical, practical, experimental, simulations, development, application, measurement, and testing. The applications and solutions discussed in the book will serve as good reference material for young scholars, researchers, and academics.

Plan for six weeks of learning covering all six areas of learning and development of the EYFS through the topic of what things are made from. The Planning for Learning series is a series of topic books written around the Early Years Foundation Stage designed to make planning easy. This book takes you through six weeks of activities on the theme of what are things made from. Each activity is linked to a specific Early Learning Goal, and the book contains a skills overview so that practitioners can keep track of which areas of learning and development they are promoting. This book also includes a photocopiable page to give to parents with ideas for them to get involved with their children's topic, as well as ideas for bringing the six weeks of learning together.Weekly topics include a look at materials around us including paper, wood, fabric, wool and shiny materials. Count wooden bricks, make postcard collages and design shiny jewellery. Bring it all together with a jumble sale!

Machine Learning and the Internet of Medical Things in Healthcare discusses the applications and challenges of machine learning for healthcare applications. The book provides a platform for presenting machine learning-enabled healthcare techniques and offers a mathematical and conceptual background of the latest technology. It describes machine learning techniques along with the emerging platform of the Internet of Medical Things used by practitioners and researchers worldwide. The book includes deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. It also presents the concepts of the Internet of Things, the set of technologies that develops traditional devices into smart devices. Finally, the book offers research perspectives, covering the convergence of machine learning and IoT. It also presents the application of these technologies in the development of healthcare frameworks. Provides an introduction to the Internet of Medical Things through the principles and applications of machine learning Explains the functions and applications of machine learning in various applications such as ultrasound imaging, biomedical signal processing, robotics, and biomechatronics Includes coverage of the evolution of healthcare applications with machine learning, including Clinical Decision Support Systems, artificial intelligence in biomedical engineering, and AI-enabled connected health informatics, supported by real-world case studies