This book describes the application of vision-sensing technologies in welding processes, one of the key technologies in intelligent welding manufacturing. Gas tungsten arc welding (GTAW) is one of the main welding techniques and has a wide range of applications in the manufacturing industry. As such, the book also explores the application of AI technologies, such as vision sensing and machine learning, in GTAW process sensing and feature extraction and monitoring, and presents the state-of-the-art in computer vision, image processing and machine learning to detect welding defects using non-destructive methods in order to improve welding productivity. Featuring the latest research from ORNL (Oak Ridge National Laboratory) using digital image correlation technology, this book will appeal to researchers, scientists and engineers in the field of advanced manufacturing.

This book records a unique attempt over a ten-year period to use stochastic optimization in the natural language processing domain. Setting the work against the background of the logical rule-based approach, the author provides a context for understanding the differences in assumptions about the nature of language and cognition.

This book is interesting and full of new ideas. It provokes the curiosity of the readers. The book targets both researchers and practitioners. The students and the researchers will acquire knowledge about ant colony optimization and its possible applications as well as practitioners will find new ideas and solutions of their combinatorial optimization and decision-making problems. Ant colony optimization is between the best method for solving difficult optimization problems arising in real life and industry. It has obtained distinguished results on some applications with very restrictive constraints. The reader will find theoretical aspects of ant method as well as applications on a variety of problems. The following applications could be mentioned: multiple knapsack problem, which is an important economical problem; grid scheduling problem; GPS surveying problem; E. coli cultivation modeling; wireless sensor network positioning; image edges detection; workforce planning.

This book introduces a variety of advanced machine learning approaches covering the areas of neural networks, fuzzy logic, and hybrid intelligent systems for the determination and diagnosis of cancer. Moreover, the tactical solutions of machine learning have proved its vast range of significance and, provided novel solutions in the medical field for the diagnosis of disease. This book also explores the distinct deep learning approaches that are capable of yielding more accurate outcomes for the diagnosis of cancer. In addition to providing an overview of the emerging machine and deep learning approaches, it also enlightens an insight on how to evaluate the efficiency and appropriateness of such techniques and analysis of cancer data used in the cancer diagnosis. Therefore, this book focuses on the recent advancements in the machine learning and deep learning approaches used in the diagnosis of different types of cancer along with their research challenges and future directions for the targeted audience including scientists, experts, Ph.D. students, postdocs, and anyone interested in the subjects discussed.

This book is the sixth volume of the successful book series on Robot Operating System: The Complete Reference. The objective of the book is to provide the reader with comprehensive coverage of the Robot Operating Systems (ROS) and the latest trends and contributed systems. ROS is currently considered as the primary development framework for robotics applications. There are seven chapters organized into three parts. Part I presents two chapters on the emerging ROS 2.0 framework; in particular, ROS 2.0 is become increasingly mature to be integrated into the industry. The first chapter from Amazon AWS deals with the challenges that ROS 2 developers will face as they transition their system to be commercial-grade. The second chapter deals with reactive programming for both ROS1 and ROS. In Part II, two chapters deal with advanced robotics, namely on the usage of robots in farms, and the second deals with platooning systems. Part III provides three chapters on ROS navigation. The first chapter deals with the use of deep learning for ROS navigation. The second chapter presents a detailed tuning guide on ROS navigation and the last chapter discusses SLAM for ROS applications. I believe that this book is a valuable companion for ROS users and developers to learn more ROS capabilities and features.

This book is a significant contribution to the subject of mining time-changing data streams and addresses the design of learning algorithms for this purpose. It introduces new contributions on several different aspects of the problem, identifying research opportunities and increasing the scope for applications. It also includes an in-depth study of stream mining and a theoretical analysis of proposed methods and algorithms. The first section is concerned with the use of an adaptive sliding window algorithm (ADWIN). Since this has rigorous performance guarantees, using it in place of counters or accumulators, it offers the possibility of extending such guarantees to learning and mining algorithms not initially designed for drifting data. Testing with several methods, including Naive Bayes, clustering, decision trees and ensemble methods, is discussed as well. The second part of the book describes a formal study of connected acyclic graphs, or 'trees', from the point of view of closure-based mining, presenting efficient algorithms for subtree testing and for mining ordered and unordered frequent closed trees. Lastly, a general methodology to identify closed patterns in a data stream is outlined. This is applied to develop an incremental method, a sliding-window based method, and a method that mines closed trees adaptively from data streams. These are used to introduce classification methods for tree data streams.

Patterns can be any number of items that occur repeatedly, whether in the behaviour of animals, humans, traffic, or even in the appearance of a design. As technologies continue to advance, recognizing, mimicking, and responding to all types of patterns becomes more precise. Pattern Recognition and Classification in Time Series Data focuses on intelligent methods and techniques for recognizing and storing dynamic patterns. Emphasizing topics related to artificial intelligence, pattern management, and algorithm development, in addition to practical examples and applications, this publication is an essential reference source for graduate students, researchers, and professionals in a variety of computer-related disciplines.

In this era of healthcare applications predominantly occupy both individuals as well as the healthcare industries, so the need for analytical reports becomes an essential component for success. Especially, the IoT applications employed for healthcare which generate a huge amount of data that needs to be analyzed to produce the expected reports. To accomplish this task, a cloud-based analytical solution will be the right choice by which the reports can be generated faster compared to the traditional ways. In this book, the different analytical methods coupled with AI to analyze the IoT data on the cloud are discussed. This book applies AI in edge analytics for healthcare applications, analyzes the impact of tools and techniques in edge analytics for healthcare, and provides security solutions for edge analytics in healthcare IoT. Each chapter provides in-depth details on how to apply different analytical methods and tools for analytics of healthcare applications devised using IoT. As the IoT devices are generating huge amounts of data, it is highly essential to do the analytics on the cloud and this book showcases the mechanisms that are going to be applied for it. Hence, this book provides a holistic idea on how to do edge analytics for healthcare IoT using AI.

This book presents intelligent data analysis as a tool to fight against COVID-19 pandemic. The intelligent data analysis includes machine learning, natural language processing, and computer vision applications to teach computers to use big data-based models for pattern recognition, explanation, and prediction. These functions are discussed in detail in the book to recognize (diagnose), predict, and explain (treat) COVID-19 infections, and help manage socio-economic impacts. It also discusses primary warnings and alerts; tracking and prediction; data dashboards; diagnosis and prognosis; treatments and cures; and social control by the use of intelligent data analysis. It provides analysis reports, solutions using real-time data, and solution through web applications details.

This book is a comprehensive collection of extended contributions from the Workshops on Computational Optimization 2019.Our everyday life is unthinkable without optimization. We try to minimize our effort and to maximize the achieved profit. Many real-world and industrial problems arising in engineering, economics, medicine and other domains can be formulated as optimization tasks. This book presents recent advances in computational optimization. The book includes important real problems like modeling of physical processes, wildfire and flood risk modeling, workforce planning, parameter settings for controlling different processes, optimal electrical vehicle modeling, bioreactor modeling and design of VLSI. It shows how to develop algorithms for them based on new intelligent methods like evolutionary computations, ant colony optimization, constrain programming and others. This research demonstrates how some real-world problems arising in engineering, economics and other domains can be formulated as optimization problems.

Knowledge, Learning, and Machine Intelligence (D. Michie). Relating Images, Concepts, and Words (D.L. Waltz). Methods for an Expert System to Access and External Database (G.W. Ernst, X. He). Perceptual Representation and Reasoning (B. Chandrasekaran, N.H. Narayanan). Feature Based, Collision Free Inspection Path Planning (F.L. Merat, O. Jeon). Of Using Constraint Logic Programming for Design of Mechanical Parts (L. Sterling). Explanation Facility for Neural Networks (L.F. Pau, T. Goetzsche). CompileTime Type Prediction and Type Checking for Common Lisp Programs (R. Beer). Cognitive Neuroethology (H.J. Chiel). Generating Polytope Intersection Configurations from a Symbolic Description Using CLP (G.M. Radack, M.J. Andersson). Agent (P.J. Drongowski). Index.

Integrated Science: Science without Borders" is the first volume of the INTEGRATED SCIENCE Book series, aiming to publish the results of the most updated ideas and reviews in transdisciplinary fields and to highlight the integration of discrete disciplines, including formal sciences, physical-chemical sciences and engineering, biological sciences, medical sciences, and social sciences. This volume primarily focuses on the research involving the integration of two or more academic fields offering an innovative, borderless view, which is one of the main focuses of the Universal Scientific Education and Research Network (USERN). The whole world is suffering from complex problems; these are borderless problems; thus, a borderless solution could merely solve such complex issues. Transdisciplinarity is a domain, that researchers work jointly, using a shared conceptual framework, drawing together disciplinary-specific theories, concepts, and approaches to address common problems. Lack of confidence, lack of expertise, complexities of healthcare, the confusing nature of healthcare environments, and lack of organization and standardization are the obstacles of successful scientific communication. Consequently, this book provides an overview of the essential elements of transdisciplinary studies and integrated science. The unique aspect of this book -privileging it from other books- is covering all aspects of science as harmonies of a single symphony.

The science of simulation and modeling (SM) is multifaceted and complex due to the numerous applications involved, particularly since SM applications range from nuclear reaction to supermarket queuing. Simulation and Modeling: Current Technologies and Applications offers insight into the computer science aspect of simulation and modeling while integrating the business practices of SM.Simulation and Modeling: Current Technologies and Applications includes current issues related to simulation, such as: Web-based simulation, virtual reality, augmented reality, and artificial intelligence. This book depicts different methods, views, theories, and applications of simulations in one volume.

This book highlights original approaches of modelling and intelligent control of cyber-physical systems covering both theoretical and practical aspects. The novel contribution of the book covers the transformation of scientific research and their results into applications for cyber-physical systems design and operation during the whole life cycle in different domains. Given its scope, the book offers an excellent reference book for researchers and other readers in the fields of cyber-physical systems modelling and intelligent control, space exploration and practical implementation of cyber-physical systems. The book also benefits researchers and practitioners in artificial intelligence and machine learning, as described results can be applied in cyber-physical systems design and cost-effectively maintenance. The target audience of this book also includes practitioners and experts, as well as state authorities and representatives of international organizations interested in creating mechanisms for implementing Cyber-Physical Systems projects.

This book is focused on the use of deep learning (DL) and artificial intelligence (AI) as tools to advance the fields of malware detection and analysis. The individual chapters of the book deal with a wide variety of state-of-the-art AI and DL techniques, which are applied to a number of challenging malware-related problems. DL and AI based approaches to malware detection and analysis are largely data driven and hence minimal expert domain knowledge of malware is needed. This book fills a gap between the emerging fields of DL/AI and malware analysis. It covers a broad range of modern and practical DL and AI techniques, including frameworks and development tools enabling the audience to innovate with cutting-edge research advancements in a multitude of malware (and closely related) use cases.

This book contains the proceedings of the 12th KES International Conference on Sustainability and Energy in Buildings 2020 (SEB20) held in Split, Croatia, during 24-26 June 2020 organized by KES International. SEB20 invited contributions on a range of topics related to sustainable buildings and explored innovative themes regarding sustainable energy systems. The aim of the conference is to bring together researchers, and government and industry professionals to discuss the future of energy in buildings, neighbourhoods and cities from a theoretical, practical, implementation and simulation perspective. The conference formed an exciting chance to present, interact and learn about the latest research and practical developments on the subject. The conference attracted submissions from around the world. Submissions for the Full-Paper Track were subjected to a blind peer-review process. Only the best of these were selected for presentation at the conference and publication in these proceedings. It is intended that this book provides a useful and informative snapshot of recent research developments in the important and vibrant area of sustainability in energy and buildings.

This book constitutes the full research papers and short monographs developed on the base of the refereed proceedings of the International Conference: Information and Communication Technologies for Research and Industry (ICIT 2020). The book brings accepted research papers which present mathematical modelling, innovative approaches and methods of solving problems in the sphere of control engineering and decision making for the various fields of studies: industry and research, energy efficiency and sustainability, ontology-based data simulation, theory and use of digital signal processing, cognitive systems, robotics, cybernetics, automation control theory, image and sound processing, image recognition, technologies, and computer vision. The book contains also several analytical reviews on using smart city technologies in Russia. The central audience of the book are researchers, industrial practitioners and students from the following areas: Adaptive Systems, Human-Robot Interaction, Artificial Intelligence, Smart City and Internet of Things, Information Systems, Mathematical Modelling, and the Information Sciences.

As science continues to advance, researchers are continually gaining new insights into the way living beings behave and function, and into the composition of the smallest molecules. Most of these biological processes have been imitated by many scientific disciplines with the purpose of trying to solve different problems, one of which is artificial intelligence.""Advancing Artificial Intelligence through Biological Process Applications"" presents recent advances in the study of certain biological processes related to information processing that are applied to artificial intelligence. Describing the benefits of recently discovered and existing techniques to adaptive artificial intelligence and biology, this book will be a highly valued addition to libraries in the neuroscience, molecular biology, and behavioral science spheres.

During the COVID-19 pandemic, computational intelligence and computer-aided diagnosis (CAD) systems have supported the effective treatment of the virus. Artificial intelligence (AI) has been playing a significant role in the rapidly emerging healthcare sector in terms of CAD, software algorithms, hardware implementation, and applications in the medical field. Through this, the constraints of the traditional system must be addressed to innovate and shed light on emerging healthcare technologies. Computational Intelligence and Applications for Pandemics and Healthcare explores the state-of-the-art computational intelligence approaches in medical data and classifies existing computational techniques used in medical areas. It discusses the tactics and methods as well as the limitations and performances of computational intelligence applications for healthcare. The constraints of traditional healthcare systems are addressed by using CAD and computationally-intelligent medical data. Covering topics such as cloud-based monitoring systems, detection and diagnosis, and intelligent medical systems, this book is an excellent resource for computer scientists, government officials, medical students, medical professionals, hospitals, researchers, and academicians.

With the proliferation of GPS devices in daily life, trajectory data that records where and when people move is now readily available on a large scale. As one of the most typical representatives, it has now become widely recognized that taxi trajectory data provides rich opportunities to enable promising smart urban services. Yet, a considerable gap still exists between the raw data available, and the extraction of actionable intelligence. This gap poses fundamental challenges on how we can achieve such intelligence. These challenges include inaccuracy issues, large data volumes to process, and sparse GPS data, to name but a few. Moreover, the movements of taxis and the leaving trajectory data are the result of a complex interplay between several parties, including drivers, passengers, travellers, urban planners, etc. In this book, we present our latest findings on mining taxi GPS trajectory data to enable a number of smart urban services, and to bring us one step closer to the vision of smart mobility. Firstly, we focus on some fundamental issues in trajectory data mining and analytics, including data map-matching, data compression, and data protection. Secondly, driven by the real needs and the most common concerns of each party involved, we formulate each problem mathematically and propose novel data mining or machine learning methods to solve it. Extensive evaluations with real-world datasets are also provided, to demonstrate the effectiveness and efficiency of using trajectory data. Unlike other books, which deal with people and goods transportation separately, this book also extends smart urban services to goods transportation by introducing the idea of crowdshipping, i.e., recruiting taxis to make package deliveries on the basis of real-time information. Since people and goods are two essential components of smart cities, we feel this extension is bot logical and essential. Lastly, we discuss the most important scientific problems and open issues in mining GPS trajectory data.

This book constitutes the refereed proceedings of the 22nd IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2021, held in Saint-Etienne, and virtually in November 2021. The 70 papers (15 full and 55 short) presented with 5 industrial workshop papers were carefully reviewed and selected from 189 submissions. They provide a comprehensive overview of major challenges and recent advances in various domains related to the digital transformation and collaborative networks and their applications with a strong focus on the following areas related to the main theme of the conference: sustainable collaborative networks; sustainability via digitalization; analysis and assessment of business ecosystems; human factors in collaboration 4.0; maintenance and life-cycle management; policies and new digital services; safety and collaboration management; simulation and optimization; complex collaborative systems and ontologies; value co-creation in digitally enabled ecosystems; digitalization strategy in collaborative enterprises' networks; pathways and tools for DIHs; socio-technical perspectives on smart product-service systems; knowledge transfer and accelerated innovation in FoF; interoperability of IoT and CPS for industrial CNs; sentient immersive response network; digital tools and applications for collaborative healthcare; collaborative networks and open innovation in education 4.0; collaborative learning networks with industry and academia; and industrial workshop.

This book features high-quality, peer-reviewed research papers presented at the Fourth International Conference on Smart Technologies in Data Science and Communication (SMART-DSC 2021), held in Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India, on 18-19 February 2021. It includes innovative and novel contributions in the areas of data analytics, communication, and soft computing.