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Author: Aitazaz Ahsan Farooque Publisher: Frontiers Media SA ISBN: 283254407X Category : Science Languages : en Pages : 172
Book Description
The Agricultural industry is always at risk - crops are affected by weather, diseases and pests. When a global pandemic hits suddenly, it becomes very difficult to manage various processes because most are not digital. In parallel, the rapid increase in the population and urbanization, demands more food production on less land. Farming under the demand pressure by increasing input consumption, leads to increases costs and negative impacts to the environment such as decreasing soil fertility continuously over time. There is therefore the need to move beyond traditional farming. To produce more yield in the agricultural field, there is the need to dig deep into the technological field and apply sensors, Internet of Things (IoT), big data analytics, cloud computing and machine learning (ML) and deep learning (DL) techniques. Better decision making, prediction and reliability depend upon high level of knowledge base and perceptions.
Author: Aitazaz Ahsan Farooque Publisher: Frontiers Media SA ISBN: 283254407X Category : Science Languages : en Pages : 172
Book Description
The Agricultural industry is always at risk - crops are affected by weather, diseases and pests. When a global pandemic hits suddenly, it becomes very difficult to manage various processes because most are not digital. In parallel, the rapid increase in the population and urbanization, demands more food production on less land. Farming under the demand pressure by increasing input consumption, leads to increases costs and negative impacts to the environment such as decreasing soil fertility continuously over time. There is therefore the need to move beyond traditional farming. To produce more yield in the agricultural field, there is the need to dig deep into the technological field and apply sensors, Internet of Things (IoT), big data analytics, cloud computing and machine learning (ML) and deep learning (DL) techniques. Better decision making, prediction and reliability depend upon high level of knowledge base and perceptions.
Author: Pavlos Stylianos Georgilakis Publisher: Springer Science & Business Media ISBN: 1848826672 Category : Technology & Engineering Languages : en Pages : 427
Book Description
Spotlight on Modern Transformer Design introduces a novel approach to transformer design using artificial intelligence (AI) techniques in combination with finite element method (FEM). Today, AI is widely used for modeling nonlinear and large-scale systems, especially when explicit mathematical models are difficult to obtain or completely lacking. Moreover, AI is computationally efficient in solving hard optimization problems. Many numerical examples throughout the book illustrate the application of the techniques discussed to a variety of real-life transformer design problems, including: • problems relating to the prediction of no-load losses; • winding material selection; • transformer design optimisation; • and transformer selection. Spotlight on Modern Transformer Design is a valuable learning tool for advanced undergraduate and graduate students, as well as researchers and power engineering professionals working in electric utilities and industries, public authorities, and design offices.
Author: Jim Mellon Publisher: Harriman House Limited ISBN: 0993047874 Category : Business & Economics Languages : en Pages : 298
Book Description
Moo’s Law is the latest title from successful investor Jim Mellon, to help readers understand the investment landscape in cultivated and plant-based proteins and materials. Jim has a vision that within the next couple of decades world agriculture will be radically transformed by the advent of cultivated meat technology. This book grounds the reader in why such an advancement is absolutely necessary and informs them of the investments they could make to become part of the New Agricultural Revolution themselves. The harrowing effects on our environment, animal cruelty in food and fashion, and the struggling ability to feed the world's ever-growing population gives us no choice but to grow meat in labs or derive our proteins from plant-based sources. Not only this, he outlines what he sees as the major hurdles to the industry's success in terms of scalability of production and the smart designing of regulatory frameworks to stimulate innovation in this sector. The future of food is being developed in labs across the world - it will be cleaner, safer, more ethical and, importantly soon, cheaper too! Once price parity with conventional meats is reached, there will be no turning back -- this is Moo's Law™.
Author: Siddhartha Bhattacharyya Publisher: Walter de Gruyter GmbH & Co KG ISBN: 3110551438 Category : Computers Languages : en Pages : 194
Book Description
This volume comprises six well-versed contributed chapters devoted to report the latest fi ndings on the applications of machine learning for big data analytics. Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. The possible challenges in this direction include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. Big data analytics is the process of examining large and varied data sets - i.e., big data - to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions. This volume is intended to be used as a reference by undergraduate and post graduate students of the disciplines of computer science, electronics and telecommunication, information science and electrical engineering. THE SERIES: FRONTIERS IN COMPUTATIONAL INTELLIGENCE The series Frontiers In Computational Intelligence is envisioned to provide comprehensive coverage and understanding of cutting edge research in computational intelligence. It intends to augment the scholarly discourse on all topics relating to the advances in artifi cial life and machine learning in the form of metaheuristics, approximate reasoning, and robotics. Latest research fi ndings are coupled with applications to varied domains of engineering and computer sciences. This field is steadily growing especially with the advent of novel machine learning algorithms being applied to different domains of engineering and technology. The series brings together leading researchers that intend to continue to advance the fi eld and create a broad knowledge about the most recent research.
Author: Joern Helbert Publisher: Elsevier ISBN: 0128187220 Category : Science Languages : en Pages : 234
Book Description
Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific understanding of planetary data and serves as a guide for selecting the right methods and tools for solving a variety of everyday problems in planetary science using machine learning. Illustrating ways to employ machine learning in practice with case studies, the book is clearly organized into four parts to provide thorough context and easy navigation. The book covers a range of issues, from data analysis on the ground to data analysis onboard a spacecraft, and from prioritization of novel or interesting observations to enhanced missions planning. This book is therefore a key resource for planetary scientists working in data analysis, missions planning, and scientific observation. - Includes links to a code repository for sharing codes and examples, some of which include executable Jupyter notebook files that can serve as tutorials - Presents methods applicable to everyday problems faced by planetary scientists and sufficient for analyzing large datasets - Serves as a guide for selecting the right method and tools for applying machine learning to particular analysis problems - Utilizes case studies to illustrate how machine learning methods can be employed in practice
Author: Linda Weintraub Publisher: Univ of California Press ISBN: 0520273613 Category : Art Languages : en Pages : 380
Book Description
This title documents the burgeoning eco art movement from A to Z, presenting a panorama of artistic responses to environmental concerns, from Ant Farms anti-consumer antics in the 1970s to Marina Zurkows 2007 animation that anticipates the havoc wreaked upon the planet by global warming.
Author: OECD Publisher: OECD Publishing ISBN: 9264967834 Category : Languages : en Pages : 282
Book Description
Food systems around the world face a triple challenge: providing food security and nutrition for a growing global population; supporting livelihoods for those working along the food supply chain; and contributing to environmental sustainability. Better policies hold tremendous promise for making progress in these domains.
Author: Amita Kapoor Publisher: Packt Publishing Ltd ISBN: 1788832760 Category : Computers Languages : en Pages : 382
Book Description
Build smarter systems by combining artificial intelligence and the Internet of Things—two of the most talked about topics today Key FeaturesLeverage the power of Python libraries such as TensorFlow and Keras to work with real-time IoT dataProcess IoT data and predict outcomes in real time to build smart IoT modelsCover practical case studies on industrial IoT, smart cities, and home automationBook Description There are many applications that use data science and analytics to gain insights from terabytes of data. These apps, however, do not address the challenge of continually discovering patterns for IoT data. In Hands-On Artificial Intelligence for IoT, we cover various aspects of artificial intelligence (AI) and its implementation to make your IoT solutions smarter. This book starts by covering the process of gathering and preprocessing IoT data gathered from distributed sources. You will learn different AI techniques such as machine learning, deep learning, reinforcement learning, and natural language processing to build smart IoT systems. You will also leverage the power of AI to handle real-time data coming from wearable devices. As you progress through the book, techniques for building models that work with different kinds of data generated and consumed by IoT devices such as time series, images, and audio will be covered. Useful case studies on four major application areas of IoT solutions are a key focal point of this book. In the concluding chapters, you will leverage the power of widely used Python libraries, TensorFlow and Keras, to build different kinds of smart AI models. By the end of this book, you will be able to build smart AI-powered IoT apps with confidence. What you will learnApply different AI techniques including machine learning and deep learning using TensorFlow and KerasAccess and process data from various distributed sourcesPerform supervised and unsupervised machine learning for IoT dataImplement distributed processing of IoT data over Apache Spark using the MLLib and H2O.ai platformsForecast time-series data using deep learning methodsImplementing AI from case studies in Personal IoT, Industrial IoT, and Smart CitiesGain unique insights from data obtained from wearable devices and smart devicesWho this book is for If you are a data science professional or a machine learning developer looking to build smart systems for IoT, Hands-On Artificial Intelligence for IoT is for you. If you want to learn how popular artificial intelligence (AI) techniques can be used in the Internet of Things domain, this book will also be of benefit. A basic understanding of machine learning concepts will be required to get the best out of this book.
Author: Andreas Sudmann Publisher: transcript Verlag ISBN: 3839447194 Category : Social Science Languages : en Pages : 335
Book Description
After a long time of neglect, Artificial Intelligence is once again at the center of most of our political, economic, and socio-cultural debates. Recent advances in the field of Artifical Neural Networks have led to a renaissance of dystopian and utopian speculations on an AI-rendered future. Algorithmic technologies are deployed for identifying potential terrorists through vast surveillance networks, for producing sentencing guidelines and recidivism risk profiles in criminal justice systems, for demographic and psychographic targeting of bodies for advertising or propaganda, and more generally for automating the analysis of language, text, and images. Against this background, the aim of this book is to discuss the heterogenous conditions, implications, and effects of modern AI and Internet technologies in terms of their political dimension: What does it mean to critically investigate efforts of net politics in the age of machine learning algorithms?
Author: Luis M. Camarinha-Matos Publisher: Springer Nature ISBN: 3030782883 Category : Computers Languages : en Pages : 360
Book Description
This book constitutes the refereed proceedings of the 12th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2021, held in Costa de Caparica, Portugal, in July 2021.* The 34 papers presented were carefully reviewed and selected from 92 submissions. The papers present selected results produced in engineering doctoral programs and focus on technological innovation for industry and service systems. Research results and ongoing work are presented, illustrated and discussed in the following areas: collaborative networks; smart manufacturing; cyber-physical systems and digital twins; intelligent decision making; smart energy management; communications and electronics; classification systems; smart healthcare systems; and medical devices. *The conference was held virtually. Chapters “Characteristics of Adaptable Control of Production Systems and the Role of Self-organization Towards Smart Manufacturing” and “Predictive Manufacturing: Enabling Technologies, Frameworks and Applications” are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Author: Aitazaz Ahsan Farooque
Author: Aitazaz Ahsan Farooque
Author: Pavlos Stylianos Georgilakis
Author: Jim Mellon
Author: Siddhartha Bhattacharyya
Author: Joern Helbert
Author: Linda Weintraub
Author: OECD
Author: Amita Kapoor
Author: Andreas Sudmann
Author: Luis M. Camarinha-Matos