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Fundamentals of Agricultural Extension - - Bog - NIPA - Plusbog.dk

Fundamentals of Agricultural Extension - - Bog - NIPA - Plusbog.dk

The book Fundamentals of Agricultural Extension Education provides a thorough understanding of extension education and its role in agricultural development. This book covers various aspects such as effective teaching methods, organizational setup, ICT applications, rural and community development, extension administration, transfer of technology, monitoring and evaluation, new trends in agricultural extension, communication principles, diffusion and adoption of innovations, and agriculture journalism. The text is structured into six units, comprising a total of nineteen chapters, and includes mind maps, multiple-choice questions, and comprehensive exercises encompassing short and long-form questions to facilitate knowledge assimilation. After completing this course, undergraduate students majoring in BSc Agriculture, Forestry, Horticulture, Fisheries, or Veterinary Sciences will be able to demonstrate the following course outcomes.

DKK 1070.00
1

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

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

This book presents the research into and application of machine learning in quantum computation, known as quantum machine learning (QML). It presents a comparison of quantum machine learning, classical machine learning, and traditional programming, along with the usage of quantum computing, toward improving traditional machine learning algorithms through case studies. In summary, the book: - - Covers the core and fundamental aspects of statistics, quantum learning, and quantum machines. - - - Discusses the basics of machine learning, regression, supervised and unsupervised machine learning algorithms, and artificial neural networks. - - - Elaborates upon quantum machine learning models, quantum machine learning approaches and quantum classification, and boosting. - - - Introduces quantum evaluation models, deep quantum learning, ensembles, and QBoost. - - - Presents case studies to demonstrate the efficiency of quantum mechanics in industrial aspects. - This reference text is primarily written for scholars and researchers working in the fields of computer science and engineering, information technology, electrical engineering, and electronics and communication engineering.

DKK 901.00
1

ICTs for Agricultural Extension: Global Experiments,Innovations and Experiences - R. Saravanan - Bog - New India Publishing Agency - Plusbog.dk

ICTs for Agricultural Extension: Global Experiments,Innovations and Experiences - R. Saravanan - Bog - New India Publishing Agency - Plusbog.dk

This book is an attempt to document the National Policy on ICTs in agricultural extension, ICT infrastructure scenario and related issues, case studies on innovative ICTs for agricultural extension initiatives Village knowledge centres, information kiosks, mobile ICT units, web portals, digital data base and networks, rural tele centres, farmer call centres, mobile telephony, video conference, offline multimedia CDs, decision support systems, expert systems, innovative community radio and television programmes, open distance learning etc. The agricultural extension students, academicians, scientists, practitioners, administrators and policy makers will find this compilation of the "ICTs for Agricultural Extension: Global Experiments, Innovations and Experiences" from twenty eight countries relevant to providing a framework for the design and implementation of sustainable ICT-enabled extension services for the agricultural development.

DKK 955.00
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 993.00
1

Cyber Security Meets Machine Learning - - Bog - Springer Verlag, Singapore - Plusbog.dk

Cyber Security Meets Machine Learning - - Bog - Springer Verlag, Singapore - Plusbog.dk

Machine learning boosts the capabilities of security solutions in the modern cyber environment. However, there are also security concerns associated with machine learning models and approaches: the vulnerability of machine learning models to adversarial attacks is a fatal flaw in the artificial intelligence technologies, and the privacy of the data used in the training and testing periods is also causing increasing concern among users. This book reviews the latest research in the area, including effective applications of machine learning methods in cybersecurity solutions and the urgent security risks related to the machine learning models. The book is divided into three parts: Cyber Security Based on Machine Learning; Security in Machine Learning Methods and Systems; and Security and Privacy in Outsourced Machine Learning. Addressing hot topics in cybersecurity and written by leading researchers in the field, the book features self-contained chapters to allow readers to select topics that are relevant to their needs. It is a valuable resource for all those interested in cybersecurity and robust machine learning, including graduate students and academic and industrial researchers, wanting to gain insights into cutting-edge research topics, as well as related tools and inspiring innovations.

DKK 816.00
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 Learning in Clinical Neuroscience - - Bog - Springer Nature Switzerland AG - Plusbog.dk

Machine Learning in Clinical Neuroscience - - Bog - Springer Nature Switzerland AG - Plusbog.dk

This book bridges the gap between data scientists and clinicians by introducing all relevant aspects of machine learning in an accessible way, and will certainly foster new and serendipitous applications of machine learning in the clinical neurosciences. Building from the ground up by communicating the foundational knowledge and intuitions first before progressing to more advanced and specific topics, the book is well-suited even for clinicians without prior machine learning experience. Authored by a wide array of experienced global machine learning groups, the book is aimed at clinicians who are interested in mastering the basics of machine learning and who wish to get started with their own machine learning research. The volume is structured in two major parts: The first uniquely introduces all major concepts in clinical machine learning from the ground up, and includes step-by-step instructions on how to correctly develop and validate clinical prediction models. It also includes methodological and conceptual foundations of other applications of machine learning in clinical neuroscience, such as applications of machine learning to neuroimaging, natural language processing, and time series analysis. The second part provides an overview of some state-of-the-art applications of these methodologies. The Machine Intelligence in Clinical Neuroscience (MICN) Laboratory at the Department of Neurosurgery of the University Hospital Zurich studies clinical applications of machine intelligence to improve patient care in clinical neuroscience. The group focuses on diagnostic, prognostic and predictive analytics that aid in decision-making by increasing objectivity and transparency to patients. Other major interests of our group members are in medical imaging, and intraoperative applications of machine vision.

DKK 986.00
1

Machine Guarding Handbook - Frank R. Spellman - Bog - Government Institutes - Plusbog.dk

Machine Guarding Handbook - Frank R. Spellman - Bog - Government Institutes - Plusbog.dk

Machine Guarding Handbook is a must-have reading for safety engineers and managers in manufacturing and other industrial settings who need to incorporate an effective machine guarding safety program, meet OSHA requirements, and protect workers. It provides a basic overview of OSHA''s requirements, making compliance easier to achieve, thus preventing the risk of worker injury or mutilation and reducing the occurrence of costly penalties and OSHA audits. This 106-page book explores and discusses the hazards of unguarded machines, common safeguarding methods, the safeguarding of machines and robots, the importance of guarding, the varying methods of machine guarding, training, inspection and maintenance, and safeguarding techniques. In addition, it provides both regulatory information and the material required to implement a viable machine guarding program. Special features include a sample lockout/tagout program, checklists, a machine-guarding case study, chapter summaries, and the complete OSHA Machine Guarding and Lockout/Tagout Standards.

DKK 936.00
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

Machine Learning Applications - - Bog - John Wiley & Sons Inc - Plusbog.dk

Machine Learning Applications - - Bog - John Wiley & Sons Inc - Plusbog.dk

Machine Learning Applications Practical resource on the importance of Machine Learning and Deep Learning applications in various technologies and real-world situations Machine Learning Applications discusses methodological advancements of machine learning and deep learning, presents applications in image processing, including face and vehicle detection, image classification, object detection, image segmentation, and delivers real-world applications in healthcare to identify diseases and diagnosis, such as creating smart health records and medical imaging diagnosis, and provides real-world examples, case studies, use cases, and techniques to enable the reader’s active learning. Composed of 13 chapters, this book also introduces real-world applications of machine and deep learning in blockchain technology, cyber security, and climate change. An explanation of AI and robotic applications in mechanical design is also discussed, including robot-assisted surgeries, security, and space exploration. The book describes the importance of each subject area and detail why they are so important to us from a societal and human perspective. Edited by two highly qualified academics and contributed to by established thought leaders in their respective fields, Machine Learning Applications includes information on: Content based medical image retrieval (CBMIR), covering face and vehicle detection, multi-resolution and multisource analysis, manifold and image processing, and morphological processing Smart medicine, including machine learning and artificial intelligence in medicine, risk identification, tailored interventions, and association rules AI and robotics application for transportation and infrastructure (e.g., autonomous cars and smart cities), along with global warming and climate change Identifying diseases and diagnosis, drug discovery and manufacturing, medical imaging diagnosis, personalized medicine, and smart health records With its practical approach to the subject, Machine Learning Applications is an ideal resource for professionals working with smart technologies such as machine and deep learning, AI, IoT, and other wireless communications; it is also highly suitable for professionals working in robotics, computer vision, cyber security and more.

DKK 923.00
1

An Introduction to Machine Learning - Sanjay Churiwala - Bog - Springer Nature Switzerland AG - Plusbog.dk

An Introduction to Machine Learning - Sanjay Churiwala - Bog - Springer Nature Switzerland AG - Plusbog.dk

Just like electricity, Machine Learning will revolutionize our life in many ways - some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually builds up the depth, covering many of the present day machine learning algorithms, ending in Deep Learning and Reinforcement Learning algorithms. The book also covers some of the popular Machine Learning applications. The material in this book is agnostic to any specific programming language or hardware so that readers can try these concepts on whichever platforms they are already familiar with. - Offers a comprehensive introduction to Machine Learning, while not assuming any prior knowledge of the topic; - Provides a complete overview of available techniques and algorithms in conceptual terms, covering various application domains of machine learning; - Not tied to any specific software language or hardware implementation.

DKK 986.00
1

Machine Learning and the City - S Carta - Bog - John Wiley and Sons Ltd - Plusbog.dk

Machine Learning and the City - S Carta - Bog - John Wiley and Sons Ltd - Plusbog.dk

Machine Learning and the City Explore the applications of machine learning and artificial intelligence to the built environment Machine Learning and the City: Applications in Architecture and Urban Design delivers a robust exploration of machine learning (ML) and artificial intelligence (AI) in the context of the built environment. Relevant contributions from leading scholars in their respective fields describe the ideas and techniques that underpin ML and AI, how to begin using ML and AI in urban design, and the likely impact of ML and AI on the future of city design and planning. Each section couples theoretical and technical chapters, authoritative references, and concrete examples and projects that illustrate the efficacy and power of machine learning in urban design. The book also includes: An introduction to the probabilistic logic that underpins machine learningComprehensive explorations of the applications of machine learning and artificial intelligence to urban environmentsPractical discussions of the consequences of applied machine learning and the future of urban design Perfect for designers approaching machine learning and AI for the first time, Machine Learning and the City: Applications in Architecture and Urban Design will also earn a place in the libraries of urban planners and engineers involved in urban design.

DKK 804.00
1

Cost-Sensitive Machine Learning - - Bog - Taylor & Francis Inc - Plusbog.dk

Cost-Sensitive Machine Learning - - Bog - Taylor & Francis Inc - Plusbog.dk

In machine learning applications, practitioners must take into account the cost associated with the algorithm. These costs include: - - Cost of acquiring training data - Cost of data annotation/labeling and cleaning - Computational cost for model fitting, validation, and testing - Cost of collecting features/attributes for test data - Cost of user feedback collection - Cost of incorrect prediction/classification - Cost-Sensitive Machine Learning is one of the first books to provide an overview of the current research efforts and problems in this area. It discusses real-world applications that incorporate the cost of learning into the modeling process. The first part of the book presents the theoretical underpinnings of cost-sensitive machine learning. It describes well-established machine learning approaches for reducing data acquisition costs during training as well as approaches for reducing costs when systems must make predictions for new samples. The second part covers real-world applications that effectively trade off different types of costs. These applications not only use traditional machine learning approaches, but they also incorporate cutting-edge research that advances beyond the constraining assumptions by analyzing the application needs from first principles. Spurring further research on several open problems, this volume highlights the often implicit assumptions in machine learning techniques that were not fully understood in the past. The book also illustrates the commercial importance of cost-sensitive machine learning through its coverage of the rapid application developments made by leading companies and academic research labs.

DKK 976.00
1

Machine Learning for Cyber Security - - Bog - Springer Nature Switzerland AG - Plusbog.dk

Machine Learning for Cyber Security - - Bog - Springer Nature Switzerland AG - Plusbog.dk

This three volume book set constitutes the proceedings of the Third International Conference on Machine Learning for Cyber Security, ML4CS 2020, held in Xi''an, China in October 2020. The 118 full papers and 40 short papers presented were carefully reviewed and selected from 360 submissions. The papers offer a wide range of the following subjects: Machine learning, security, privacy-preserving, cyber security, Adversarial machine Learning, Malware detection and analysis, Data mining, and Artificial Intelligence.

DKK 800.00
1

Machine Learning for Cyber Security - - Bog - Springer Nature Switzerland AG - Plusbog.dk

Machine Learning for Cyber Security - - Bog - Springer Nature Switzerland AG - Plusbog.dk

This three volume book set constitutes the proceedings of the Third International Conference on Machine Learning for Cyber Security, ML4CS 2020, held in Xi''an, China in October 2020. The 118 full papers and 40 short papers presented were carefully reviewed and selected from 360 submissions. The papers offer a wide range of the following subjects: Machine learning, security, privacy-preserving, cyber security, Adversarial machine Learning, Malware detection and analysis, Data mining, and Artificial Intelligence.

DKK 967.00
1

Statistical Machine Learning - Richard Golden - Bog - Taylor & Francis Ltd - Plusbog.dk

Statistical Machine Learning - Richard Golden - Bog - Taylor & Francis Ltd - Plusbog.dk

The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular, the material in this text directly supports the mathematical analysis and design of old, new, and not-yet-invented nonlinear high-dimensional machine learning algorithms. Features: - - - Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised, unsupervised, and reinforcement machine learning algorithms - - - Matrix calculus methods for supporting machine learning analysis and design applications - - - Explicit conditions for ensuring convergence of adaptive, batch, minibatch, MCEM, and MCMC learning algorithms that minimize both unimodal and multimodal objective functions - - - Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification - This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics, computer science, electrical engineering, and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students, professional engineers, and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible. About the Author: Richard M. Golden (Ph.D., M.S.E.E., B.S.E.E.) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models.

DKK 993.00
1