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Agricultural Extension Worldwide - - Bog - Taylor & Francis Ltd - Plusbog.dk

Quantum Machine Learning - - Bog - Taylor & Francis Ltd - Plusbog.dk

Applied Genetic Programming and Machine Learning - Yoshihiko Hasegawa - Bog - Taylor & Francis Ltd - Plusbog.dk

Postharvest Extension and Capacity Building for the Developing World - - Bog - Taylor & Francis Ltd - Plusbog.dk

Postharvest Extension and Capacity Building for the Developing World - - Bog - Taylor & Francis Ltd - Plusbog.dk

It is estimated that around 1.3 billion tons per year of food produced for human consumption, which is about one-third of all food produced, is either lost or wasted globally. Reduction of the postharvest losses is being considered as one of the sustainable ways to ensure world food security. Postharvest Extension and Capacity Building for the Developing World provides information on postharvest extension/outreach programs, capacity building, and practical methodologies for postharvest extension professionals and food science teachers, food processing trainers, and outreach specialists who work in the field. The book provides information on training of postharvest trainers, food loss assessment methods, capacity building in universities and agro-industry, distance education methods, models for cost effective postharvest/food processing extension work, success stories, and lessons learned from past projects and programs. The book is divided into four sections. Section I explains postharvest loss assessments methods, Section II is on capacity building, and Sections III and IV focus on training and postharvest extension models. Food loss assessment methodologies are highlighted from several high-profile institutions and it is envisioned that researchers and postharvest extension personnel will benefit from the development and field testing of a hybrid methodology, incorporating the strengths and utilizing the best practices from each of the methodologies in current use. Chapters cover postharvest extension work and capacity building in a wide range of regions.

DKK 496.00
1

The Cooperative Extension Service - James A Christenson - Bog - Taylor & Francis Ltd - Plusbog.dk

The Cooperative Extension Service - James A Christenson - Bog - Taylor & Francis Ltd - Plusbog.dk

The Cooperative Extension Service, a publicly supported educational agency, is continually struggling to define its proper function and purpose in our changing society. Should its mission be broadly based or narrowly focused? Should staff members be generalists or specialists? Should its clients be primarily rural or urban, farm or nonfarm? What role should Extension play in the information networks of the twenty-first century? Professors Warner and Christenson take a broad look at these and other questions concerning where the Extension Service has been, how well it is doing, and where it ought to go. Theirs is, first, the only comprehensive national survey that looks at the total Extension organization rather than at just one program area. Second, it expresses the viewpoint of Extension clients and the public, rather than that of the organization''s staff; and third, it combines outside survey information with data recorded in the Extension Management Information System (EMIS) and other routine agency reports. The authors evaluate, among other things, the extent of public awareness of the agency and its four major program areas (agriculture, home economics, 4-H, and community development), determine the users and nonusers of the programs and the accessibility of programs to the general population, identify the level of satisfaction with existing programs, and outline priorities and policy issues for the future.

DKK 448.00
1

Privatization and the Crisis of Agricultural Extension: The Case of Pakistan - Ahmed Munir - Bog - Taylor & Francis Ltd - Plusbog.dk

The Human Factor in Machine Translation - - Bog - Taylor & Francis Ltd - Plusbog.dk

Extension Innovation Method - Chunyan Yang - Bog - Taylor & Francis Ltd - Plusbog.dk

Functional Reverse Engineering of Machine Tools - - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Learning for Healthcare - - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Learning for Healthcare - - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Learning for Healthcare: Handling and Managing Data provides in-depth information about handling and managing healthcare data through machine learning methods. This book expresses the long-standing challenges in healthcare informatics and provides rational explanations of how to deal with them. Machine Learning for Healthcare: Handling and Managing Data provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of machine learning applications. These are illustrated in a case study which examines how chronic disease is being redefined through patient-led data learning and the Internet of Things. This text offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare. Readers will discover the ethical implications of machine learning in healthcare and the future of machine learning in population and patient health optimization. This book can also help assist in the creation of a machine learning model, performance evaluation, and the operationalization of its outcomes within organizations. It may appeal to computer science/information technology professionals and researchers working in the area of machine learning, and is especially applicable to the healthcare sector. The features of this book include: - - A unique and complete focus on applications of machine learning in the healthcare sector. - - - An examination of how data analysis can be done using healthcare data and bioinformatics. - - - An investigation of how healthcare companies can leverage the tapestry of big data to discover new business values. - - - An exploration of the concepts of machine learning, along with recent research developments in healthcare sectors. -

DKK 993.00
1

Machine Learning for Healthcare - - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Learning for Healthcare - - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Learning for Healthcare: Handling and Managing Data provides in-depth information about handling and managing healthcare data through machine learning methods. This book expresses the long-standing challenges in healthcare informatics and provides rational explanations of how to deal with them. Machine Learning for Healthcare: Handling and Managing Data provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of machine learning applications. These are illustrated in a case study which examines how chronic disease is being redefined through patient-led data learning and the Internet of Things. This text offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare. Readers will discover the ethical implications of machine learning in healthcare and the future of machine learning in population and patient health optimization. This book can also help assist in the creation of a machine learning model, performance evaluation, and the operationalization of its outcomes within organizations. It may appeal to computer science/information technology professionals and researchers working in the area of machine learning, and is especially applicable to the healthcare sector. The features of this book include: - - A unique and complete focus on applications of machine learning in the healthcare sector. - - - An examination of how data analysis can be done using healthcare data and bioinformatics. - - - An investigation of how healthcare companies can leverage the tapestry of big data to discover new business values. - - - An exploration of the concepts of machine learning, along with recent research developments in healthcare sectors. -

DKK 467.00
1

Machine Intelligence - - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Intelligence - - Bog - Taylor & Francis Ltd - Plusbog.dk

Machines are being systematically empowered to be interactive and intelligent in their operations, offerings. and outputs. There are pioneering Artificial Intelligence (AI) technologies and tools. Machine and Deep Learning (ML/DL) algorithms, along with their enabling frameworks, libraries, and specialized accelerators, find particularly useful applications in computer and machine vision, human machine interfaces (HMIs), and intelligent machines. Machines that can see and perceive can bring forth deeper and decisive acceleration, automation, and augmentation capabilities to businesses as well as people in their everyday assignments. Machine vision is becoming a reality because of advancements in the computer vision and device instrumentation spaces. Machines are increasingly software-defined. That is, vision-enabling software and hardware modules are being embedded in new-generation machines to be self-, surroundings, and situation-aware. Machine Intelligence: Computer Vision and Natural Language Processing emphasizes computer vision and natural language processing as drivers of advances in machine intelligence. The book examines these technologies from the algorithmic level to the applications level. It also examines the integrative technologies enabling intelligent applications in business and industry. Features: - Motion images object detection over voice using deep learning algorithms - Ubiquitous computing and augmented reality in HCI - Learning and reasoning in Artificial Intelligence - Economic sustainability, mindfulness, and diversity in the age of artificial intelligence and machine learning - Streaming analytics for healthcare and retail domains Covering established and emerging technologies in machine vision, the book focuses on recent and novel applications and discusses state-of-the-art technologies and tools.

DKK 630.00
1

Homing the Machine in Architecture - - Bog - Taylor & Francis Ltd - Plusbog.dk

Homing the Machine in Architecture - - Bog - Taylor & Francis Ltd - Plusbog.dk

Homing the Machine in Architecture is a series of conversations on the ways designers, practitioners, historians, and theorists orient themselves within the world of architectural digital fabrication. To “home” a digital fabrication machine is to send it back to its origin point—a point that can be specified by the fabricator in advance of the fabrication process or by the defaults that are pre-programmed into the machine. The homing process is necessary and productive since it determines the physical point at which the machine (and the maker) begin making—every time that architectural designers begin to digitally fabricate something new, they first need to home the machine. This book gathers first- and second-hand accounts of the origins of individual “digi-fab” practices from the emergence of advanced prototyping tools to the contemporary moment. It features interviews, essays, and case studies organized around three questions: What are the possible histories of digital fabrication in architecture? How do designers orient themselves in this emergent discipline? What conceptual original points do architectural designers return to when they home their machines? The discourse that emerges from this collection aims to reach practicing architects using digital fabrication, as well as upper-level students and academics of digital architecture, architectural theory, and architectural history.

DKK 391.00
1

Applied Machine Learning Using mlr3 in R - - Bog - Taylor & Francis Ltd - Plusbog.dk

Applied Machine Learning Using mlr3 in R - - Bog - Taylor & Francis Ltd - Plusbog.dk

mlr3 is an award-winning ecosystem of R packages that have been developed to enable state-of-the-art machine learning capabilities in R. Applied Machine Learning Using mlr3 in R gives an overview of flexible and robust machine learning methods, with an emphasis on how to implement them using mlr3 in R. It covers various key topics, including basic machine learning tasks, such as building and evaluating a predictive model; hyperparameter tuning of machine learning approaches to obtain peak performance; building machine learning pipelines that perform complex operations such as pre-processing followed by modelling followed by aggregation of predictions; and extending the mlr3 ecosystem with custom learners, measures, or pipeline components. Features: - In-depth coverage of the mlr3 ecosystem for users and developers - Explanation and illustration of basic and advanced machine learning concepts - Ready to use code samples that can be adapted by the user for their application - Convenient and expressive machine learning pipelining enabling advanced modelling - Coverage of topics that are often ignored in other machine learning books The book is primarily aimed at researchers, practitioners, and graduate students who use machine learning or who are interested in using it. It can be used as a textbook for an introductory or advanced machine learning class that uses R, as a reference for people who work with machine learning methods, and in industry for exploratory experiments in machine learning.

DKK 656.00
1

Coverbal Synchrony in Human-Machine Interaction - - Bog - Taylor & Francis Ltd - Plusbog.dk

Coverbal Synchrony in Human-Machine Interaction - - Bog - Taylor & Francis Ltd - Plusbog.dk

Embodied conversational agents (ECA) and speech-based human–machine interfaces can together represent more advanced and more natural human–machine interaction. Fusion of both topics is a challenging agenda in research and production spheres. The important goal of human–machine interfaces is to provide content or functionality in the form of a dialog resembling face-to-face conversations. All natural interfaces strive to exploit and use different communication strategies that provide additional meaning to the content, whether they are human–machine interfaces for controlling an application or different ECA-based human–machine interfaces directly simulating face-to-face conversation. Coverbal Synchrony in Human-Machine Interaction presents state-of-the-art concepts of advanced environment-independent multimodal human–machine interfaces that can be used in different contexts, ranging from simple multimodal web-browsers (for example, multimodal content reader) to more complex multimodal human–machine interfaces for ambient intelligent environments (such as supportive environments for elderly and agent-guided household environments). They can also be used in different computing environments—from pervasive computing to desktop environments. Within these concepts, the contributors discuss several communication strategies, used to provide different aspects of human–machine interaction.

DKK 643.00
1

Practical Machine Learning with R - Carsten Lange - Bog - Taylor & Francis Ltd - Plusbog.dk

Practical Machine Learning with R - Carsten Lange - Bog - Taylor & Francis Ltd - Plusbog.dk

This textbook is a comprehensive guide to machine learning and artificial intelligence tailored for students in business and economics. It takes a hands-on approach to teach machine learning, emphasizing practical applications over complex mathematical concepts. Students are not required to have advanced mathematics knowledge such as matrix algebra or calculus. The author introduces machine learning algorithms, utilizing the widely used R language for statistical analysis. Each chapter includes examples, case studies, and interactive tutorials to enhance understanding. No prior programming knowledge is needed. The book leverages the tidymodels package, an extension of R, to streamline data processing and model workflows. This package simplifies commands, making the logic of algorithms more accessible by minimizing programming syntax hurdles. The use of tidymodels ensures a unified experience across various machine learning models. With interactive tutorials that students can download and follow along at their own pace, the book provides a practical approach to apply machine learning algorithms to real-world scenarios. In addition to the interactive tutorials, each chapter includes a Digital Resources section, offering links to articles, videos, data, and sample R code scripts. A companion website further enriches the learning and teaching experience: https://ai.lange-analytics.com . This book is not just a textbook; it is a dynamic learning experience that empowers students and instructors alike with a practical and accessible approach to machine learning in business and economics. Key Features: - Unlocks machine learning basics without advanced mathematics — no calculus or matrix algebra required. - Demonstrates each concept with R code and real-world data for a deep understanding — no prior programming knowledge is needed. - Bridges the gap between theory and real-world applications with hands-on interactive projects and tutorials in every chapter, guided with hints and solutions. - Encourages continuous learning with chapter-specific online resources—video tutorials, R-scripts, blog posts, and an online community. - Supports instructors through a companion website that includes customizable materials such as slides and syllabi to fit their specific course needs.

DKK 810.00
1

Machine-to-Machine Communications - - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine-to-Machine Communications - - Bog - Taylor & Francis Ltd - Plusbog.dk

With the number of machine-to-machine (M2M)–enabled devices projected to reach 20 to 50 billion by 2020, there is a critical need to understand the demands imposed by such systems. Machine-to-Machine Communications: Architectures, Technology, Standards, and Applications offers rigorous treatment of the many facets of M2M communication, including its integration with current technology. Presenting the work of a different group of international experts in each chapter, the book begins by supplying an overview of M2M technology. It considers proposed standards, cutting-edge applications, architectures, and traffic modeling and includes case studies that highlight the differences between traditional and M2M communications technology. - Details a practical scheme for the forward error correction code design - Investigates the effectiveness of the IEEE 802.15.4 low data rate wireless personal area network standard for use in M2M communications - Identifies algorithms that will ensure functionality, performance, reliability, and security of M2M systems - Illustrates the relationship between M2M systems and the smart power grid - Presents techniques to ensure integration with and adaptation of existing communication systems to carry M2M traffic Providing authoritative insights into the technologies that enable M2M communications, the book discusses the challenges posed by the use of M2M communications in the smart grid from the aspect of security and proposes an efficient intrusion detection system to deal with a number of possible attacks. After reading this book, you will develop the understanding required to solve problems related to the design, deployment, and operation of M2M communications networks and systems.

DKK 505.00
1

Machine Learning for Business Analytics - - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Learning for Business Analytics - - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Learning is an integral tool in a business analyst’s arsenal because the rate at which data is being generated from different sources is increasing and working on complex unstructured data is becoming inevitable. Data collection, data cleaning, and data mining are rapidly becoming more difficult to analyze than just importing information from a primary or secondary source. The machine learning model plays a crucial role in predicting the future performance and results of a company. In real-time, data collection and data wrangling are the important steps in deploying the models. Analytics is a tool for visualizing and steering data and statistics. Business analysts can work with different datasets -- choosing an appropriate machine learning model results in accurate analyzing, forecasting the future, and making informed decisions. The global machine learning market was valued at $1.58 billion in 2017 and is expected to reach $20.83 billion in 2024 -- growing at a CAGR of 44.06% between 2017 and 2024. The authors have compiled important knowledge on machine learning real-time applications in business analytics. This book enables readers to get broad knowledge in the field of machine learning models and to carry out their future research work. The future trends of machine learning for business analytics are explained with real case studies. Essentially, this book acts as a guide to all business analysts. The authors blend the basics of data analytics and machine learning and extend its application to business analytics. This book acts as a superb introduction and covers the applications and implications of machine learning. The authors provide first-hand experience of the applications of machine learning for business analytics in the section on real-time analysis. Case studies put the theory into practice so that you may receive hands-on experience with machine learning and data analytics. This book is a valuable source for practitioners, industrialists, technologists, and researchers.

DKK 542.00
1

Machine Learning - Jugal Kalita - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Learning - Jugal Kalita - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning. The book covers regression including regularization, tree-based methods including Random Forests and Boosted Trees, Artificial Neural Networks including Convolutional Neural Networks (CNNs), reinforcement learning, and unsupervised learning focused on clustering. Topics are introduced in a conceptual manner along with necessary mathematical details. The explanations are lucid, illustrated with figures and examples. For each machine learning method discussed, the book presents appropriate libraries in the R programming language along with programming examples. Features: - - Provides an easy-to-read presentation of commonly used machine learning algorithms in a manner suitable for advanced undergraduate or beginning graduate students, and mathematically and/or programming-oriented individuals who want to learn machine learning on their own. - - - Covers mathematical details of the machine learning algorithms discussed to ensure firm understanding, enabling further exploration - - - Presents worked out suitable programming examples, thus ensuring conceptual, theoretical and practical understanding of the machine learning methods. - This book is aimed primarily at introducing essential topics in Machine Learning to advanced undergraduates and beginning graduate students. The number of topics has been kept deliberately small so that it can all be covered in a semester or a quarter. The topics are covered in depth, within limits of what can be taught in a short period of time. Thus, the book can provide foundations that will empower a student to read advanced books and research papers.

DKK 476.00
1

Machine Learning for Neuroscience - Chuck Easttom - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Learning for Neuroscience - Chuck Easttom - Bog - Taylor & Francis Ltd - Plusbog.dk

This book addresses the growing need for machine learning and data mining in neuroscience. The book offers a basic overview of the neuroscience, machine learning and the required math and programming necessary to develop reliable working models. The material is presented in a easy to follow user-friendly manner and is replete with fully working machine learning code. Machine Learning for Neuroscience: A Systematic Approach, tackles the needs of neuroscience researchers and practitioners that have very little training relevant to machine learning. The first section of the book provides an overview of necessary topics in order to delve into machine learning, including basic linear algebra and Python programming. The second section provides an overview of neuroscience and is directed to the computer science oriented readers. The section covers neuroanatomy and physiology, cellular neuroscience, neurological disorders and computational neuroscience. The third section of the book then delves into how to apply machine learning and data mining to neuroscience and provides coverage of artificial neural networks (ANN), clustering, and anomaly detection. The book contains fully working code examples with downloadable working code. It also contains lab assignments and quizzes, making it appropriate for use as a textbook. The primary audience is neuroscience researchers who need to delve into machine learning, programmers assigned neuroscience related machine learning projects and students studying methods in computational neuroscience.

DKK 929.00
1

Practical Machine Learning - Noe E. Nnko - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Learning for Managers - Paul (university Of Auckland Geertsema - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Learning in Multimedia - - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Learning in Multimedia - - Bog - Taylor & Francis Ltd - Plusbog.dk

This book explores the interdisciplinary nature of machine learning in multimedia, highlighting its intersections with fields such as computer vision, natural language processing, and audio signal processing. Machine Learning in Multimedia: Unlocking the Power of Visual and Auditory Intelligence serves as a comprehensive guide to navigating this exciting terrain where artificial intelligence meets the rich tapestry of visual and auditory data. At its core, this book seeks to unravel the mysteries and unveil the potential of machine learning in the realm of multimedia. Whether it''s enhancing user experiences in virtual environments, revolutionizing medical diagnostics, or shaping the future of entertainment, the impact of machine learning in multimedia is profound and far-reaching. The journey begins with a thorough exploration of the foundational principles of machine learning, providing readers with a solid understanding of algorithms, models, and techniques tailored specifically for multimedia data. Through clear explanations and illustrative examples, readers will gain insights into how machine learning algorithms can be trained to extract meaningful patterns and insights from diverse forms of multimedia content. Moving beyond theory, this book delves into practical implementations and real-world applications of machine learning in multimedia. Through a series of case studies and examples, readers will witness firsthand how machine learning algorithms are transforming industries and reshaping the way we interact with multimedia content. Whether it''s improving image recognition accuracy in autonomous vehicles, enabling personalized recommendations in streaming platforms, or enhancing speech recognition systems for better accessibility, the possibilities are limitless. This book will be helpful to computer science, data science, and artificial intelligence researchers, students, and professionals looking to unlock the full potential of visual and auditory intelligence through the power of machine learning.

DKK 759.00
1

Digital Mayhem 3D Machine Techniques - - Bog - Taylor & Francis Ltd - Plusbog.dk