Academic Press 第10页

Application of Machine Learning in Agriculture
2 Jun. 2022
Author: Mohammad Ayoub Khan ,Rijwan Khan,Mohammad Aslam Ansari (Editor)
Publisher Finelybook 出版社: Academic Press (2 Jun. 2022)
Language 语言: English
pages 页数: 330 pages
ISBN-10 书号: 0323905501
ISBN-13 书号: 9780323905503

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Book Description
Application of Machine Learning in Smart Agriculture is the first book to present a multidisciplinary look at how technology can not only improve agricultural output, but the economic efficiency of that output as well. Through a global lens, the book approaches the subject from a technical perspective, providing important knowledge and insights for effective and efficient implementation and utilization of machine learning.
As artificial intelligence techniques are being used to increase yield through optimal planting, fertilizing, irrigation, and harvesting, these are only part of the complex picture which must also take into account the economic investment and its optimized return. The performance of machine learning models improves over time as the various mathematical and statistical models are proven. Presented in three parts, Application of Machine Learning in Smart Agriculture looks at the fundamentals of smart agriculture; the economics of the technology in the agricultural marketplace; and a diverse representation of the tools and techniques currently available, and in development.
This book is an important resource for advanced level students and professionals working with artificial intelligence, internet of things, technology and agricultural economics.
Addresses the technology of smart agriculture from a technical perspective
Reveals opportunities for technology to improve and enhance not only yield and quality, but the economic value of a food crop
Discusses physical instruments, simulations, sensors, and markets for machine learning in agriculture

Result Page Generation for Web Searching: Emerging Research and Opportunities (Advances in Web Technologies and Engineering)
Author: Mostafa Alli
Publisher Finelybook 出版社: IGI Global; 1st edition (December 18, 2020)
Language 语言: English
pages 页数: 126 pages
ISBN-10 书号: 1799809617
ISBN-13 书号: 9781799809616

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Book Description
Diversity in user queries makes it challenging for search engines to effectively return a set of relevant results. Both user intentions to search the web and types of queries are vastly varied; consequently, horizontal and vertical search engines are developed to answer user queries more efficiently. However, these search engines present a variety of problems in web searching.
Result Page Generation for Web Searching: Emerging Research and Opportunities is an essential reference publication that focuses on taking advantages from text and web mining in order to address the issues of recommendation and visualization in web searching. Highlighting a wide range of topics such as navigational searching, resource identification, and ambiguous queries, this book is ideally designed for computer engineers, web designers, programmers, academicians, researchers, and students.

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Result Page Generation for Web Searching 9781799809616.zip

Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms (Advances in Systems Analysis, Software Engineering, and High Performance Computing)
11 Mar. 2022
Author: Veljko Milutinović,Nenad Mitić,Aleksandar Kartelj(Author)
Publisher Finelybook 出版社: Engineering Science Reference (11 Mar. 2022)
Language 语言: English
pages 页数: 312 pages
ISBN-10 书号: 1799883507
ISBN-13 书号: 9781799883500

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Book Description
Based on current literature and cutting-edge advances in the machine learning field, there are four algorithms whose usage in new application domains must be explored: neural networks, rule induction algorithms, tree-based algorithms, and density-based algorithms. A number of machine learning related algorithms have been derived from these four algorithms. Consequently, they represent excellent underlying methods for extracting hidden knowledge from unstructured data, as essential data mining tasks. Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms presents widely used data-mining algorithms and explains their advantages and disadvantages, their mathematical treatment, applications, energy efficient implementations, and more. It presents research of energy efficient accelerators for machine learning algorithms. Covering topics such as control-flow implementation, approximate computing, and decision tree algorithms, this book is an essential resource for computer scientists, engineers, students and educators of higher education, researchers, and academicians.

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Implementation of Machine Learning Algorithms Using Control-flow and Dataflow Paradigms 9781799883517.zip

Handbook of Research on Innovations and Applications of AI, IoT, and Cognitive Technologies (Advances in Computational Intelligence and Robotics)
Author: Jingyuan Zhao and V. Vinoth Kumar
Publisher Finelybook 出版社: Engineering Science Reference (25 Jun. 2021)
Language 语言: English
pages 页数: 612 pages
ISBN-10 书号: 1799868702
ISBN-13 书号: 9781799868705

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Book Description
Recently, artificial intelligence (AI), the internet of things (IoT), and cognitive technologies have successfully been applied to various research domains, including computer vision, natural language processing, voice recognition, and more. In addition, AI with IoT has made a significant breakthrough and a shift in technical direction to achieve high efficiency and adaptability in a variety of new applications. On the other hand, network design and optimization for AI applications addresses a complementary topic, namely the support of AI-based systems through novel networking techniques, including new architectures, as well as performance models for IoT systems. IoT has paved the way to a plethora of new application domains, at the same time posing several challenges as a multitude of devices, protocols, communication channels, architectures, and middleware exist. Big data generated Author: these devices calls for advanced learning and data mining techniques to effectively understand, learn, and reason with this volume of information, such as cognitive technologies. Cognitive technologies play a major role in developing successful cognitive systems which mimic "cognitive" functions associated with human intelligence, such as "learning" and "problem solving." Thus, there is a continuing demand for recent research in these two linked fields. The Handbook of Research on Innovations and Applications of AI, IoT, and Cognitive Technologies discusses the latest innovations and applications of AI, IoT, and cognitive-based smart systems. The chapters cover the intersection of these three fields in emerging and developed economies in terms of their respective development situation, public policies, technologies and intellectual capital, innovation systems, competition and strategies, marketing and growth capability, and governance and relegation models. These applications span areas such as healthcare, security and privacy, industrial systems, multidisciplinary sciences, and more. This book is ideal for technologists, IT specialists, policymakers, government officials, academics, students, and practitioners interested in the experiences of innovations and applications of AI, IoT, and cognitive technologies.

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Handbook of Research on Innovations and Applications of Ai, Iot, and Cognitive Technologies 9781799868705.zip

Applying Reinforcement Learning on Real-World Data with Practical Examples in Python (Synthesis Lectures on Artificial Intelligence and Machine Learning) 1st Edition
Author: Philip Osborne,Kajal Singh,Matthew E. Taylor(Author)
Language 语言: English
pages 页数: 109 pages
ISBN-10 书号: 1636393446
ISBN-13 书号: 9781636393445

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Book Description
Applying Reinforcement Learning on Real-World Data with Practical Examples in Python (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Reinforcement learning is a powerful tool in artificial intelligence in which virtual or physical agents learn to optimize their decision making to achieve long-term goals. In some cases, this machine learning approach can save programmers time, outperform existing controllers, reach super-human performance, and continually adapt to changing conditions. It has shown human level performance on a number of tasks (REF) and the methodology for automation in robotics and self-driving cars (REF). This book argues that these successes show reinforcement learning can be adopted successfully in many different situations, including robot control, stock trading, supply chain optimization, and plant control.
However, reinforcement learning has traditionally been limited to applications in virtual environments or simulations in which the setup is already provided. Furthermore, experimentation may be completed for an almost limitless number of attempts risk-free. In many real-life tasks, applying reinforcement learning is not as simple as (1) data is not in the correct form for reinforcement learning; (2) data is scarce, and (3) automation has limitations in the real-world.
Therefore, this book is written to help academics, domain specialists, and data enthusiast alike to understand the basic principles of applying reinforcement learning to real-world problems. This is achieved Author: focusing on the process of taking practical examples and modeling standard data into the correct form required to then apply basic agents. To further assist readers gain a deep and grounded understanding of the approaches, the book shows hand-calculated examples in full and then how this can be achieved in a more automated manner with code.
For decision makers who are interested in reinforcement learning as a solution but are not proficient, the book includes simple, non-technical examples in the introduction and case studies section. These provide context of what reinforcement learning offer but also the challenges and risks associated with applying it in practice. Specifically, these sections illustrate the differences between reinforcement learning and other machine learning approaches as well as how well-known companies have found success using the approach to their problems.

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Applying Reinforcement Learning on Real-World Data with Practical Examples in Python 9781636393445.rar

Deep Learning Systems: Algorithms,Compilers,and Processors for Large-scale Production (Synthesis Lectures on Computer Architecture)
by 作者: Andres Rodriguez
Publisher Finelybook 出版社: Morgan & Claypool (October 26,2020)
Language 语言: English
pages 页数: 265 pages
ISBN-10 书号: 1681739666
ISBN-13 书号: 9781681739663

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Book Description
This book describes deep learning systems: the algorithms,compilers,and processor components to efficiently train and deploy deep learning models for commercial applications.
The exponential growth in computational power is slowing at a time when the amount of compute consumed by 作者: state-of-the-art deep learning (DL) workloads is rapidly growing. Model size,serving latency,and power constraints are a significant challenge in the deployment of DL models for many applications. Therefore,it is imperative to codesign algorithms,compilers,and hardware to accelerate advances in this field with holistic system-level and algorithm solutions that improve performance,power,and efficiency.
Advancing DL systems generally involves three types of engineers: (1) data scientists that utilize and develop DL algorithms in partnership with domain experts,such as medical,economic,or climate scientists; (2) hardware designers that develop specialized hardware to accelerate the components in the DL models; and (3) performance and compiler engineers that optimize software to run more efficiently on a given hardware. Hardware engineers should be aware of the characteristics and components of production and academic models likely to be adopted by 作者: industry to guide design decisions impacting future hardware. Data scientists should be aware of deployment platform constraints when designing models. Performance engineers should support optimizations across diverse models,libraries,and hardware targets.
The purpose of this book is to provide a solid understanding of (1) the design,training,and applications of DL algorithms in industry; (2) the compiler techniques to map deep learning code to hardware targets; and (3) the critical hardware features that accelerate DL systems. This book aims to facilitate co-innovation for the advancement of DL systems. It is written for engineers working in one or more of these areas who seek to understand the entire system stack in order to better collaborate with engineers working in other parts of the system stack.
The book details advancements and adoption of DL models in industry,explains the training and deployment process,describes the essential hardware architectural features needed for today’s and future models,and details advances in DL compilers to efficiently execute algorithms across various hardware targets.
Unique in this book is the holistic exposition of the entire DL system stack,the emphasis on commercial applications,and the practical techniques to design models and accelerate their performance. The author is fortunate to work with hardware,software,data scientist,and research teams across many high-technology companies with hyperscale data centers. These companies employ many of the examples and methods provided throughout the book.

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Deep Learning Systems 9781681739663.zip

5G IoT and Edge Computing for Smart Healthcare (Intelligent Data-Centric Systems)
Author: Akash Kumar Bhoi,Victor Hugo Costa de Albuquerque,Samarendra Nath Sur,Paolo Barsocchi (Editor)
Publisher Finelybook 出版社: Academic Press; (April 12, 2022)
Language 语言: English
pages 页数: 324 pages
ISBN-10 书号: 032390548X
ISBN-13 书号: 9780323905480

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Book Description
5G IoT and Edge Computing for Smart Healthcare addresses the importance of a 5G IoT and Edge-Cognitive-Computing-based system for the successful implementation and realization of a smart-healthcare system. The book provides insights on 5G technologies, along with intelligent processing algorithms/processors that have been adopted for processing the medical data that would assist in addressing the challenges in computer-aided diagnosis and clinical risk analysis on a real-time basis. Each chapter is self-sufficient, solving real-time problems through novel approaches that help the audience acquire the right knowledge.
With the progressive development of medical and communication – computer technologies, the healthcare system has seen a tremendous opportunity to support the demand of today’s new requirements.
Focuses on the advancement of 5G in terms of its security and privacy aspects, which is very important in health care systems
Address advancements in signal processing and, more specifically, the cognitive computing algorithm to make the system more real-time
Gives insights into various information-processing models and the architecture of layers to realize a 5G based smart health care system

Transcultural Artificial Intelligence and Robotics in Health and Social Care
Author: Irena Papadopoulos,Christina Koulouglioti,Chris Papadopoulos,Antonio Sgorbissa(Author)
Publisher Finelybook 出版社: Academic Press; (May 9, 2022)
Language 语言: English
pages 页数: 310 pages
ISBN-10 书号: 0323904076
ISBN-13 书号: 9780323904070

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Book Description
Transcultural Artificial Intelligence and Robotics in Health and Social Care provides healthcare professionals with a deeper understanding of the incredible opportunities brought Author: the emerging field of AI robotics. In addition, it provides robotic researchers with the point-of-view of healthcare professionals to understand what the healthcare sector – as well as the market – really needs from robotics technology. Author: doing so, the book fills an important gap between both fields in order to leverage new developments and collaborative work in favor of global patients.
The book is aimed at the non-technical reader, especially health and social care professionals, and explains in a simple way the technological principles applied in the development of socially assistive humanoid AI robots (SAHR), the values which guide such developments, the ethics related to them, and research approaches in the field, with a focus on achieving a culturally competent SAHR.
Presents user-friendly and stage-Author: -stage information to help readers appreciate how AI robots work and how they can be integrated in their work environments
Explains why AI and socially assistive robotics need to be culturally competent
Helps reduce readers’ fears and change negative prejudices they may have about robots as a relevant tool for healthcare
Written Author: experts in AI robotics and the creators of transcultural health/social robotics
Informed Author: the largest trial conducted with real patients

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Transcultural Artificial Intelligence and Robotics in Health and Social Care 9780323904070.zip

Deep Learning in Bioinformatics: Techniques and Applications in Practice
Author: Habib Izadkhah |
Publisher Finelybook 出版社: Academic Press; (February 2, 2022)
Language 语言: English
pages 页数: 380 pages
ISBN-10 书号: 0128238224
ISBN-13 书号: 9780128238226

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Book Description
Deep Learning in Bioinformatics: Techniques and Applications in Practice introduces the topic in an easy-to-understand way, exploring how it can be utilized for addressing important problems in bioinformatics, including drug discovery, de novo molecular design, sequence analysis, protein structure prediction, gene expression regulation, protein classification, biomedical image processing and diagnosis, biomolecule interaction prediction, and in systems biology. The book also presents theoretical and practical successes of deep learning in bioinformatics, pointing out problems and suggesting future research directions. Dr. Izadkhah provides valuable insights and will help researchers use deep learning techniques in their biological and bioinformatics studies.
Introduces deep learning in an easy-to-understand way
Presents how deep learning can be utilized for addressing some important problems in bioinformatics
Presents the state-of-the-art algorithms in deep learning and bioinformatics
Introduces deep learning libraries in bioinformatics

Current Trends and Advances in Computer-Aided Intelligent Environmental Data Engineering (Intelligent Data-Centric Systems)
Author: Goncalo Marques and Joshua O. Ighalo
Publisher Finelybook 出版社: Academic Press; (April 5, 2022)
Language 语言: English
pages 页数: 474 pages
ISBN-10 书号: 0323855970
ISBN-13 书号: 9780323855976

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Book Description
Current Trends and Advances in Computer-Aided Intelligent Environmental Data Engineeringmerges computer engineering and environmental engineering. The book presents the latest finding on how data science and AI-based tools are being applied in environmental engineering research. This application involves multiple domains such as data science and artificial intelligence to transform the data collected Author: intelligent sensors into relevant and reliable information to support decision-making. These tools include fuzzy logic, knowledge-based systems, particle swarm optimization, genetic algorithms, Monte Carlo simulation, artificial neural networks, support vector machine, boosted regression tree, simulated annealing, ant colony algorithm, decision tree, immune algorithm, and imperialist competitive algorithm.
This book is a fundamental information source because it is the first book to present the foundational reference material in this new research field. Furthermore, it gives a critical overview of the latest cross-domain research findings and technological developments on the recent advances in computer-aided intelligent environmental data engineering.
Captures the application of data science and artificial intelligence for a broader spectrum of environmental engineering problems
Presents methods and procedures as well as case studies where state-of-the-art technologies are applied in actual environmental scenarios
Offers a compilation of essential and critical reviews on the application of data science and artificial intelligence to the entire spectrum of environmental engineering