Ming Yang – författare

This book explores the transformative potential of machine learning (ML) technologies in agriculture. It delves into specific applications, such as crop monitoring, disease detection, and livestock management, demonstrating how artificial intelligence/machine learning (AI/ML) can optimize resource management and improve overall productivity in farming practices.

Sustainable Farming through Machine Learning: Enhancing Productivity and Efficiency provides an in-depth overview of AI and ML concepts relevant to the agricultural industry. It discusses the challenges faced by the agricultural sector and how AI/ML can address them. The authors highlight the use of AI/ML algorithms for plant disease and pest detection and examine the role of AI/ML in supply chain management and demand forecasting in agriculture. It includes an examination of the integration of AI/ML with agricultural robotics for automation and efficiency. The authors also cover applications in livestock management, including feed formulation and disease detection; they also explore the use of AI/ML for behavior analysis and welfare assessment in livestock. Finally, the authors also explore the ethical and social implications of using such technologies.

This book can be used as a textbook for students in agricultural engineering, precision farming, and smart agriculture. It can also be a reference book for practicing professionals in machine learning, and deep learning working on sustainable agriculture applications.

This book explores the transformative potential of machine learning (ML) technologies in agriculture. It delves into specific applications, such as crop monitoring, disease detection, and livestock management, demonstrating how artificial intelligence/machine learning (AI/ML) can optimize resource management and improve overall productivity in farming practices.

Sustainable Farming through Machine Learning: Enhancing Productivity and Efficiency provides an in-depth overview of AI and ML concepts relevant to the agricultural industry. It discusses the challenges faced by the agricultural sector and how AI/ML can address them. The authors highlight the use of AI/ML algorithms for plant disease and pest detection and examine the role of AI/ML in supply chain management and demand forecasting in agriculture. It includes an examination of the integration of AI/ML with agricultural robotics for automation and efficiency. The authors also cover applications in livestock management, including feed formulation and disease detection; they also explore the use of AI/ML for behavior analysis and welfare assessment in livestock. Finally, the authors also explore the ethical and social implications of using such technologies.

This book can be used as a textbook for students in agricultural engineering, precision farming, and smart agriculture. It can also be a reference book for practicing professionals in machine learning, and deep learning working on sustainable agriculture applications.

The book provides invaluable insights into the transformative role of AI and ML in security, offering essential strategies and real-world applications to effectively navigate the complex landscape of today’s cyber threats.

Protecting and Mitigating Against Cyber Threats delves into the dynamic junction of artificial intelligence (AI) and machine learning (ML) within the domain of security solicitations. Through an exploration of the revolutionary possibilities of AI and ML technologies, this book seeks to disentangle the intricacies of today’s security concerns. There is a fundamental shift in the security soliciting landscape, driven by the extraordinary expansion of data and the constant evolution of cyber threat complexity. This shift calls for a novel strategy, and AI and ML show great promise for strengthening digital defenses. This volume offers a thorough examination, breaking down the concepts and real-world uses of this cutting-edge technology by integrating knowledge from cybersecurity, computer science, and related topics. It bridges the gap between theory and application by looking at real-world case studies and providing useful examples.

Protecting and Mitigating Against Cyber Threats provides a roadmap for navigating the changing threat landscape by explaining the current state of AI and ML in security solicitations and projecting forthcoming developments, bringing readers through the unexplored realms of AI and ML applications in protecting digital ecosystems, as the need for efficient security solutions grows. It is a pertinent addition to the multi-disciplinary discussion influencing cybersecurity and digital resilience in the future.

Readers will find in this book:

Audience

Cybersecurity professionals, researchers, academics, industry professionals, technology enthusiasts, policymakers, and strategists interested in the dynamic intersection of artificial intelligence (AI), machine learning (ML), and cybersecurity.

The book provides invaluable insights into the transformative role of AI and ML in security, offering essential strategies and real-world applications to effectively navigate the complex landscape of today’s cyber threats.

Protecting and Mitigating Against Cyber Threats delves into the dynamic junction of artificial intelligence (AI) and machine learning (ML) within the domain of security solicitations. Through an exploration of the revolutionary possibilities of AI and ML technologies, this book seeks to disentangle the intricacies of today’s security concerns. There is a fundamental shift in the security soliciting landscape, driven by the extraordinary expansion of data and the constant evolution of cyber threat complexity. This shift calls for a novel strategy, and AI and ML show great promise for strengthening digital defenses. This volume offers a thorough examination, breaking down the concepts and real-world uses of this cutting-edge technology by integrating knowledge from cybersecurity, computer science, and related topics. It bridges the gap between theory and application by looking at real-world case studies and providing useful examples.

Protecting and Mitigating Against Cyber Threats provides a roadmap for navigating the changing threat landscape by explaining the current state of AI and ML in security solicitations and projecting forthcoming developments, bringing readers through the unexplored realms of AI and ML applications in protecting digital ecosystems, as the need for efficient security solutions grows. It is a pertinent addition to the multi-disciplinary discussion influencing cybersecurity and digital resilience in the future.

Readers will find in this book:

Audience

Cybersecurity professionals, researchers, academics, industry professionals, technology enthusiasts, policymakers, and strategists interested in the dynamic intersection of artificial intelligence (AI), machine learning (ML), and cybersecurity.

The Chinese government set a target to reduce China’s carbon intensity by 40%-45% in 2020 at its 2005 level. To achieve this target, the government has allocated targets to provinces, cities, and large enterprises, and selected five pilot provinces and eight cities for CO2 emission trading. Such emission trading process will involve decentralization, optimization, and negotiation. The prime objective of this book is to perform academic research on simulating the negotiation process. Through this research, a methodological framework and its implementation are set up to analyze, model and facilitate the process of negotiation among central government and individual energy producers under environmental, economical and social constraints.

Negotiation In Decentralization: Case Study Of China''s Carbon Trading In The Power Sector discusses research carried out on negotiation issues in China regarding Chinese power sector reform over the past 30 years. Results show that conflicts exist between power groups and the national government, and that the most current negotiation topics in China''s power industry are demand and supply management, capital investment, energy prices, and CO2 emission mitigations.

Negotiation In Decentralization: Case Study Of China''s Carbon Trading In The Power Sector is written for government policy makers, energy and environment industry investors, energy program and project managers, environment conservation specialists, university professors, researchers, and graduate students. It aims to provide a methodology and a tool that can resolve difficult negotiation issues and change a loss-loss situation to a win-win situation for key players in a decentralized system, including government policymakers, energy producers, and environment conservationists.

Energy efficiency plays and will continue to play an important role in the world to save energy and mitigate greenhouse gas (GHG) emissions. However, little is known on how much additional capital should be invested to ensure using energy efficiently as it should be, and very little is known which sub-areas, technologies, and countries shall achieve maximum greenhouse gas emissions mitigation per dollar of investment in energy efficiency worldwide.

Analyzing completed and slowly moving energy efficiency projects by the Global Environment Facility during 1991-2010, Closing the Gap: GEF Experiences in Global Energy Efficiency evaluates impacts of multi-billion-dollar investments in the world energy efficiency. It covers the following areas:

1.       Reviewing the world energy efficiency investment and disclosing the global energy efficiency gap and market barriers that cause the gap;

2.       Leveraging private funds with public funds and other resources in energy efficiency investments; using these funds in tangible and intangible asset investments;

3.       Investment effectiveness indollars per metric ton of CO2 emissions mitigation in 10 energy efficiency sub-areas;

4.       Major barriers causing failure and abandonments in energy efficiency investments;

5.       Quantification of direct and indirect CO2 emissions mitigations inside and outside a project boundary; and

6.       Classification and estimation of CO2 emissions mitigations from tangible and intangible asset investments.

Closing the Gap: GEF Experiences in Global Energy Efficiency can serve as a handbook for policymakers, project investors and managers, and project implementation practitioners in need of benchmarks in energy efficiency project investments for decision-making. It can also be used by students, researchers and other professionals in universities and research institutions in methodology development for evaluating energy efficiency projects and programs.

This book discusses how energy efficiency benefits the global environment, national energy security, local pollution mitigation, natural resource conservation, and utility bill reduction. In addition, this book provides many hands-on skills and knowledge to identify and develop energy efficiency projects. The literature review shows that energy efficiency has become the first fuel in the world energy supply. With empirical analyses, this book indicates that without continued investment in energy efficiency, neither China nor the U.S. could achieve their carbon emission reduction targets that were announced on November 13, 2014 during the Beijing 2014 APEC meeting. The authors argue that energy efficiency will become the first tool to mitigate climate change. These include (1) identifying energy efficiency barriers, (2) developing energy policies, (3) investing in energy efficient technologies, (4) undertaking project cost-effectiveness analysis, (5) de-risking and financing energy efficiency projects; (6) developing and managing energy service companies, and (7) promoting urban transport efficiency. Two case studies in energy efficiency improvement in electrical motors and industrial boilers are also presented. This book is written for college and university students, practitioners, researchers, consultants, project developers, and policy makers who want to dedicate their professional careers in global energy efficiency improvement, climate change mitigation, local clean air initiatives, and energy bill reduction.

This book provides insight into domino effects in industrial chemical sites and process industries. It is about the integration of safety and security resources to prevent and mitigate domino effects in the process industries. It explains how chemical industrial areas, comprised of various hazardous installations, are susceptible to a chain of undesired events, or domino effects, triggered by accidental events or intentional attacks and then presents solutions to prevent them.

Firstly, the book provides a dynamic graph approach to model the domino effects induced by accidental fire or intentional fire, considering the spatial-temporal evolution of fires. Then, a dynamic risk assessment method based on a discrete dynamic event tree is proposed to assess the likelihood of VCEs and the vulnerability of installations, addressing the time dependencies in vapor cloud dispersion and the uncertainty of delayed ignitions. A dynamic methodology based on dynamic graphs and Monte Carlois provided to assess the vulnerability of individuals and installations exposed to multi-hazards, such as fire, explosion and toxic release during escalation events. Based on these domino effect models, an economic approach is developed to integrate safe and security resources, obtaining the most cost–benefit protection strategy for preventing domino effects. Finally, a resilience-based approach is provided to find out the most cost-resilient way to protect chemical industrial areas, addressing possible domino effects.

This book constitutes the refereed proceedings of the 17 International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2016, held in Yangzhou, China, in October 2016.

The 68 full papers presented were carefully reviewed and selected from 115 submissions. They provide a valuable and timely sample of latest research outcomes in data engineering and automated learning ranging from methodologies, frameworks, and techniques to applications including various topics such as evolutionary algorithms; deep learning; neural networks; probabilistic modeling; particle swarm intelligence; big data analysis; applications in regression, classification, clustering, medical and biological modeling and predication; text processing and image analysis.