Zdzislaw Polkowski – författare

This book focuses on environmental sustainability by employing elements of engineering and green computing through modern educational concepts and solutions. It visualizes the potential of artificial intelligence, enhanced by business activities and strategies for rapid implementation, in manufacturing and green technology.

This book covers utilization of renewable resources and implementation of the latest energy-generation technologies. It discusses how to save natural resources from depletion and illustrates facilitation of green technology in industry through usage of advanced materials. The book also covers environmental sustainability and current trends in manufacturing.

The book provides the basic concepts of green technology, along with the technology aspects, for researchers, faculty, and students.

This book focuses on environmental sustainability by employing elements of engineering and green computing through modern educational concepts and solutions. It visualizes the potential of artificial intelligence, enhanced by business activities and strategies for rapid implementation, in manufacturing and green technology.

This book covers utilization of renewable resources and implementation of the latest energy-generation technologies. It discusses how to save natural resources from depletion and illustrates facilitation of green technology in industry through usage of advanced materials. The book also covers environmental sustainability and current trends in manufacturing.

The book provides the basic concepts of green technology, along with the technology aspects, for researchers, faculty, and students.

Escalating urbanization and energy consumption have increased the demand for green engineering solutions and intelligent systems to mitigate environmental hazards and offer a more sustainable future. Green engineering technologies help to create sustainable, eco-friendly designs and solutions with the aid of updated tools, methods, designs, and innovations. These technologies play a significant role in optimizing sustainability in various areas of energy, agriculture, waste management, and bioremediation and include green computing and artificial intelligence (AI) applications. Green Engineering and Technology: Innovations, Design, and Architectural Implementation examines the most recent advancements in green technology, across multiple industries, and outlines the opportunities of emerging and future innovations, as well as practical real-world implementation.

Features:

Green Engineering and Technology: Innovations, Design, and Architectural Implementation serves as a useful and practical guide for practicing engineers, researchers, and students alike.

Escalating urbanization and energy consumption have increased the demand for green engineering solutions and intelligent systems to mitigate environmental hazards and offer a more sustainable future. Green engineering technologies help to create sustainable, eco-friendly designs and solutions with the aid of updated tools, methods, designs, and innovations. These technologies play a significant role in optimizing sustainability in various areas of energy, agriculture, waste management, and bioremediation and include green computing and artificial intelligence (AI) applications. Green Engineering and Technology: Innovations, Design, and Architectural Implementation examines the most recent advancements in green technology, across multiple industries, and outlines the opportunities of emerging and future innovations, as well as practical real-world implementation.

Features:

Green Engineering and Technology: Innovations, Design, and Architectural Implementation serves as a useful and practical guide for practicing engineers, researchers, and students alike.

The book intends to cover various problematic aspects of emerging smart computing and self-adapting technologies comprising of machine learning, artificial intelligence, deep learning, robotics, cloud computing, fog computing, data mining algorithms, including emerging intelligent and smart applications related to these research areas. Further coverage includes implementation of self-adaptation architecture for smart devices, self-adaptive models for smart cities and self-driven cars, decentralized self-adaptive computing at the edge networks, energy-aware AI-based systems, M2M networks, sensors, data analytics, algorithms and tools for engineering self-adaptive systems, and so forth.

Acts as guide to Self-healing and Self-adaptation based fully automatic future technologies Discusses about Smart Computational abilities and self-adaptive systems Illustrates tools and techniques for data management and explains the need to apply, and data integration for improving efficiency of big data Exclusive chapter on the future of self-stabilizing and self-adaptive systems of systems Covers fields such as automation, robotics, medical sciences, biomedical and agricultural sciences, healthcare and so forth

This book is aimed researchers and graduate students in machine learning, information technology, and artificial intelligence.

The book intends to cover various problematic aspects of emerging smart computing and self-adapting technologies comprising of machine learning, artificial intelligence, deep learning, robotics, cloud computing, fog computing, data mining algorithms, including emerging intelligent and smart applications related to these research areas. Further coverage includes implementation of self-adaptation architecture for smart devices, self-adaptive models for smart cities and self-driven cars, decentralized self-adaptive computing at the edge networks, energy-aware AI-based systems, M2M networks, sensors, data analytics, algorithms and tools for engineering self-adaptive systems, and so forth.

Acts as guide to Self-healing and Self-adaptation based fully automatic future technologies Discusses about Smart Computational abilities and self-adaptive systems Illustrates tools and techniques for data management and explains the need to apply, and data integration for improving efficiency of big data Exclusive chapter on the future of self-stabilizing and self-adaptive systems of systems Covers fields such as automation, robotics, medical sciences, biomedical and agricultural sciences, healthcare and so forth

This book is aimed researchers and graduate students in machine learning, information technology, and artificial intelligence.

This book brings insight into data science and offers applications and implementation strategies. It includes current developments and future directions and covers the concept of data science along with its origins. It focuses on the mechanisms of extracting data along with classifications, architectural concepts, and business intelligence with predictive analysis.

Data Science in Engineering and Management: Applications, New Developments, and Future Trends introduces the concept of data science, its use, and its origins, as well as presenting recent trends, highlighting future developments; discussing problems and offering solutions. It provides an overview of applications on data linked to engineering and management perspectives and also covers how data scientists, analysts, and program managers who are interested in productivity and improving their business can do so by incorporating a data science workflow effectively.

This book is useful to researchers involved in data science and can be a reference for future research. It is also suitable as supporting material for undergraduate and graduate-level courses in related engineering disciplines.

This book brings insight into data science and offers applications and implementation strategies. It includes current developments and future directions and covers the concept of data science along with its origins. It focuses on the mechanisms of extracting data along with classifications, architectural concepts, and business intelligence with predictive analysis.

Data Science in Engineering and Management: Applications, New Developments, and Future Trends introduces the concept of data science, its use, and its origins, as well as presenting recent trends, highlighting future developments; discussing problems and offering solutions. It provides an overview of applications on data linked to engineering and management perspectives and also covers how data scientists, analysts, and program managers who are interested in productivity and improving their business can do so by incorporating a data science workflow effectively.

This book is useful to researchers involved in data science and can be a reference for future research. It is also suitable as supporting material for undergraduate and graduate-level courses in related engineering disciplines.

This book focuses on industrial development, design, implementation, and transformation using technologies such as Artificial Intelligence, Machine Learning, the Internet of Things (IoT), Big Data Analysis, and Blockchain. It incorporates complex processes, functions, and various other elements as one central component of digital systems.

Industrial Transformation: Implementation and Essential Components and Processes of Digital Systems discusses the industry transformation aligned with the computerization of manufacturing and the required skills needed to build a new workforce. This book covers the role that AI plays in the management of resource flow and decision-making in the transformation of operations, as well as supply chain management. It presents sustainability and efficiency with IoT, Machine Learning, Data Analysis, and Blockchain technologies as it focuses on industrial development, design, and implementation. This book showcases the incorporation of complex processes and functions as one central component of digital systems and explores current trends that are working to accelerate industrial transformation. Case studies are also included, depicting the technologies that are influencing the transition into the fourth Industrial Revolution, such as industrial infrastructure, biodiversity, and enhanced productivity.

This book is aimed at researchers, scholars, and students that require real-time knowledge and applications where the transformation and implementation of digital systems in the manufacturing sector are needed.

This book focuses on industrial development, design, implementation, and transformation using technologies such as Artificial Intelligence, Machine Learning, the Internet of Things (IoT), Big Data Analysis, and Blockchain. It incorporates complex processes, functions, and various other elements as one central component of digital systems.

Industrial Transformation: Implementation and Essential Components and Processes of Digital Systems discusses the industry transformation aligned with the computerization of manufacturing and the required skills needed to build a new workforce. This book covers the role that AI plays in the management of resource flow and decision-making in the transformation of operations, as well as supply chain management. It presents sustainability and efficiency with IoT, Machine Learning, Data Analysis, and Blockchain technologies as it focuses on industrial development, design, and implementation. This book showcases the incorporation of complex processes and functions as one central component of digital systems and explores current trends that are working to accelerate industrial transformation. Case studies are also included, depicting the technologies that are influencing the transition into the fourth Industrial Revolution, such as industrial infrastructure, biodiversity, and enhanced productivity.

This book is aimed at researchers, scholars, and students that require real-time knowledge and applications where the transformation and implementation of digital systems in the manufacturing sector are needed.

This book discusses the newest approach to online learning systems in higher education. As e-Learning platforms change their mechanisms for data processing and storage, there is a need to move these systems toward being more efficient and smarter. This book covers online learning systems and their application to large-scale data along with the technological aspects of these processes and problem-solving methods.

Online Learning Systems: Methods and Applications with Large-Scale Data discusses the efficiency measurement and environmental impact of online education. The book offers a parametric evaluation and categorization of online learning systems and provides an exploration of big data ecosystems in cloud computing. Descriptive analytical methods that assist in finding solutions for big data challenges are also covered within the book.

The book is written for academicians, which includes teachers, students, and higher education policymakers who believe in transforming the education industry, as well as research scholars and those working in education technology and artificial intelligence. Industry professionals involved in education management and those working in e-Learning companies will also find this book useful.

This book discusses the newest approach to online learning systems in higher education. As e-Learning platforms change their mechanisms for data processing and storage, there is a need to move these systems toward being more efficient and smarter. This book covers online learning systems and their application to large-scale data along with the technological aspects of these processes and problem-solving methods.

Online Learning Systems: Methods and Applications with Large-Scale Data discusses the efficiency measurement and environmental impact of online education. The book offers a parametric evaluation and categorization of online learning systems and provides an exploration of big data ecosystems in cloud computing. Descriptive analytical methods that assist in finding solutions for big data challenges are also covered within the book.

The book is written for academicians, which includes teachers, students, and higher education policymakers who believe in transforming the education industry, as well as research scholars and those working in education technology and artificial intelligence. Industry professionals involved in education management and those working in e-Learning companies will also find this book useful.

The Handbook of Computational Sciences is a comprehensive collection of research chapters that brings together the latest advances and trends in computational sciences and addresses the interdisciplinary nature of computational sciences, which

require expertise from multiple disciplines to solve complex problems.

This edited volume covers a broad range of topics, including computational physics, chemistry, biology, engineering, finance, and social sciences. Each chapter provides an in-depth discussion of the state-of-the-art techniques and methodologies used in the respective field. The book also highlights the challenges and opportunities for future research in these areas.

The volume pertains to applications in the areas of imaging, medical imaging, wireless and WS networks, IoT with applied areas, big data for various applicable solutions, etc. This text delves deeply into the core subject and then broadens to encompass the interlinking, interdisciplinary, and cross-disciplinary sections of other relevant areas. Those areas include applied, simulation, modeling, real-time, research applications, and more.

Audience

Because of the book’s multidisciplinary approach, it will be of value to many researchers and engineers in different fields including computational biologists, computational chemists, and physicists, as well as those in life sciences, neuroscience, mathematics, and software engineering.

The Handbook of Computational Sciences is a comprehensive collection of research chapters that brings together the latest advances and trends in computational sciences and addresses the interdisciplinary nature of computational sciences, which

require expertise from multiple disciplines to solve complex problems.

This edited volume covers a broad range of topics, including computational physics, chemistry, biology, engineering, finance, and social sciences. Each chapter provides an in-depth discussion of the state-of-the-art techniques and methodologies used in the respective field. The book also highlights the challenges and opportunities for future research in these areas.

The volume pertains to applications in the areas of imaging, medical imaging, wireless and WS networks, IoT with applied areas, big data for various applicable solutions, etc. This text delves deeply into the core subject and then broadens to encompass the interlinking, interdisciplinary, and cross-disciplinary sections of other relevant areas. Those areas include applied, simulation, modeling, real-time, research applications, and more.

Audience

Because of the book’s multidisciplinary approach, it will be of value to many researchers and engineers in different fields including computational biologists, computational chemists, and physicists, as well as those in life sciences, neuroscience, mathematics, and software engineering.

Encapsulates different case studies where green-IOT and machine learning can be used for making significant progress towards improvising the quality of life and sustainable environment.

The Internet of Things (IoT) is an evolving idea which is responsible for connecting billions of devices that acquire, perceive, and communicate data from their surroundings. Because this transmission of data uses significant energy, improving energy efficiency in IOT devices is a significant topic for research. The green internet of things (G-IoT) makes it possible for IoT devices to use less energy since intelligent processing and analysis are fundamental to constructing smart IOT applications with large data sets. Machine learning (ML) algorithms that can predict sustainable energy consumption can be used to prepare guidelines to make IoT device implementation easier.

Green Internet of Things and Machine Learning lays the foundation of in-depth analysis of principles of Green-Internet of Things (G-IoT) using machine learning. It outlines various green ICT technologies, explores the potential towards diverse real-time areas, as well as highlighting various challenges and obstacles towards the implementation of G-IoT in the real world. Also, this book provides insights on how the machine learning and green IOT will impact various applications: It covers the Green-IOT and ML-based smart computing, ML techniques for reducing energy consumption in IOT devices, case studies of G-IOT and ML in the agricultural field, smart farming, smart transportation, banking industry and healthcare.

Audience

The book will be helpful for research scholars and researchers in the fields of computer science and engineering, information technology, electronics and electrical engineering. Industry experts, particularly in R&D divisions, can use this book as their problem-solving guide.