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Artificial intelligence in dentistry
Provides an introduction to next-generation applications and technologies for improving diagnostic accuracy and prediction of treatment outcomes in dentistry through the use of artificial intelligence (AI) and machine learning (ML). The authors attempt to bridge the gap between dental research and global health outcomes, as well as provide a comprehensive guide to general and clinical aspects of dental and oral health issues and the etiology, prevalence, assessment, and management of these conditions.
Artificial intelligence for oral health care : Applications and future prospects
Offers a deeper understanding of the foundational concepts of AI, its practical applications in oral health, and the possibilities that lie ahead. From oral pathology to maxillofacial surgery, prosthodontics to orthodontics, and endodontics to dental education, it presents compelling, evidence-based insights into how AI is changing the landscape of dentistry.
Advanced dental biomaterials
Advanced Dental Biomaterials is an invaluable reference for researchers and clinicians within the biomedical industry and academia. The book can be used by both an experienced researcher/clinician learning about other biomaterials or applications that may be applicable to their current research or as a guide for a new entrant into the field who needs to gain an understanding of the primary challenges, opportunities, most relevant biomaterials, and key applications in dentistry.
3D imaging in endodontics : A new era in diagnosis and treatment
Full understanding of the role of cone beam computed tomography (CBCT) in helping to solve many of the most challenging problems in endodontics. It will shorten the learning curve in application of this exciting imaging technology in a variety of contexts: difficult diagnostic cases, treatment planning, evaluation of internal tooth anatomy prior to root canal therapy, nonsurgical and surgical treatments, early detection and treatment of resorptive defects, and outcomes assessment. The ability to obtain an accurate 3D representation of a tooth and the surrounding structures by means of noninvasive CBCT imaging is changing the approach to clinical decision making in endodontics. Clinicians long accustomed to working in very small, three-dimensional spaces are no longer constrained by the limitations of two-dimensional imaging. The challenges of mastering the new technology can, however, be daunting. The detailed guidance contained in this book will help endodontists to take full advantage of the important benefits offered by CBCT.
3D imaging in endodontics : A new era in diagnosis and treatment
Provides readers with a full understanding of the role of cone beam computed tomography (CBCT) in helping to solve many of the most challenging problems in endodontics. It will shorten the learning curve in application of this exciting imaging technique in a variety of contexts: difficult diagnostic cases, treatment planning, evaluation of internal tooth anatomy prior to root canal therapy, nonsurgical and surgical treatments, early detection and treatment of resorptive defects, and outcomes assessment.
Machine learning for civil and environmental engineers : A practical approach to data-driven analysis, explainability, and causality
Introduces engineers and engineering students to the applications of artificial intelligence (AI), machine learning (ML), and machine intelligence (MI) in relation to civil and environmental engineering projects and problems, presenting state-of-the-art methodologies and techniques to develop and implement algorithms in the engineering domain.
Machine Learning Applications in Civil Engineering
Discusses machine learning and deep learning models for different civil engineering applications. These models work for stochastic methods wherein internal processing is done using randomized prototypes. The book explains various machine learning model designs that will assist researchers to design multi domain systems with maximum efficiency. It introduces Machine Learning and its applications to different Civil Engineering tasks, including Basic Machine Learning Models for data pre-processing, models for data representation, classification models for Civil Engineering Applications, Bioinspired Computing models for Civil Engineering, and their case studies.
Artificial intelligence in mechatronics and civil engineering : Bridging the gap
Recent studies highlight the application of artificial intelligence, machine learning, and simulation techniques in engineering. This book covers the successful implementation of different intelligent techniques in various areas of engineering focusing on common areas between mechatronics and civil engineering. The power of artificial intelligence and machine learning techniques in solving some examples of real-life problems in engineering is highlighted in this book. The implementation process to design the optimum intelligent models is discussed in this book.
Artificial intelligence and machine learning techniques for civil engineering
Offers state-of-the-art contributions in the area of AI and its applications in the field of civil engineering presenting methods and implementation of AI and machine learning in multiple facets of civil engineering
Aging, shaking, and cracking of infrastructures : From mechanics to concrete dams and nuclear structures
Focuses on the safety assessment of existing structures subjected to multi-hazard scenarios through advanced numerical methods. Whereas the focus is on concrete dams and nuclear containment structures, the presented methodologies can also be applied to other large-scale ones. This book is composed of seven sections: Fundamentals: theoretical coverage of solid mechnics, plasticity, fracture mechanics, creep, / seismology, dynamic analysis, probability and statistics / Damage: that can affect concrete structures, such as cracking of concrete, AAR, chloride ingress, and rebar corrosion, / Finite Element: formulation for both linear and nonlinear analysis including stress, heat and fracture mechanics, / Engineering Models: for soil/fluid-structure interaction, uncertainty quantification, probablilistic and random finite element analysis, machine learning, performance based earthquake engineering, ground motion intensity measures, seismic hazard analysis, capacity/fragility functions and damage indeces, / Applications to dams through potential failure mode analyses, risk-informed decision making, deterministic and probabilistic examples, / Applications to nuclear structures through modeling issues, aging management programs, critical review of some analyses, / Other applications and case studies: massive RC structures and bridges, detailed assessment of a nuclear containment structure evaluation for license renewal.
Marketing effectiveness : Applying marketing science for brand growth
Contrary to popular belief marketing effectiveness is not just about the measuring of ROI. The lens of effectiveness must be applied to all marketing mix elements, from strategy to pricing and product, to media and advertising. It's a strategic shift that demands robust evidence-based decisions and consistent application in order to grow. Written by leading marketing practitioner, Sorin Patilinet, this book enables mid-senior level marketers to integrate the scientific methods and advanced measurements required for true marketing effectiveness into their marketing strategies, in order to reap the benefits of strong customer understanding and developing decision-making processes for growth. Covering everything from neuroscience and its application to marketing to advanced analytics and machine learning models, this book provides a comprehensive practical guide for marketers.
Marketing analytics : A machine learning approach
Gives a comprehensive overview of marketing analytics, incorporating machine learning methods of data analysis that automates analytical model building. The volume covers the important aspects of marketing analytics, including segmentation and targeting analysis, statistics for marketing, marketing metrics, consumer buying behavior, neuromarketing techniques for consumer analytics, new product development, forecasting sales and price, web and social media analytics, and much more.
Machine learning for risk calculations : A practitioner's view
Fundamental Approximation Methods. Machine Learning -- Deep Neural Nets -- Chebyshev Tensors -- The toolkit - plugging in approximation methods. Introduction: why is a toolkit needed -- Composition techniques -- Tensors in TT format and Tensor Extension Algorithms -- Sliding Technique -- The Jacobian projection technique -- Hybrid solutions - approximation methods and the toolkit.
Liquidity, markets and trading in action : An interdisciplinary perspective
This book addresses four standard business school subjects: microeconomics, macroeconomics, finance and information systems as they relate to trading, liquidity, and market structure. It provides a detailed examination of the impact of trading costs and other impediments of trading that the authors call “frictions”. It also presents an interactive simulation model of equity market trading, TraderEx, that enables students to implement trading decisions in different market scenarios and structures. Addressing these topics shines a bright light on how a real-world financial market operates, and the simulation provides students with an experiential learning opportunity that is informative and fun.
Learning in Economic Systems with Expectations Feedback
Recently economists have more and more focussed on scenarios in which agents' views of the world may be erroneous. These notes introduce the concept of perfect forecasting rules which provide best least-squares predictions along the evolution of an economic system.
Learning at speed : How to upskill and reskill your workforce at pace to drive business performance
Learning at Speed is a practical book which brings together the best from lean and agile methodologies to show how they can be applied to learning and development (L&D) to improve individual and organizational performance. This provides a people development framework which can be used to deconstruct a learning strategy and optimise each element for improved results. It shows how to identify learning barriers and possible solutions, leverage company data to understand learning needs and how to assess the most effective learning resources and delivery channels.
Learning Analytics : Using Talent Data to Improve Business Outcomes
Effective evaluation and measurement of learning and development initiatives is critical to maximise the impact of training, identify gaps for improvement and ensure that efforts are aligned to the business' needs. Learning Analytics outlines how analytical approaches can respond to these challenges, the types and benefits of technological solutions and how to ask the right questions of organizational data in order to build a learning organization that boosts performance and competitive advantage.
Knowledge Solutions : Tools, Methods, and Approaches to Drive Organizational Performance
Covers topics in knowledge management and competence in strategy development, management techniques, collaboration mechanisms, knowledge sharing and learning, as well as knowledge capture and storage. Presented in accessible “chunks,” it includes more than 120 topics that are essential to high-performance organizations. The extensive use of quotes by respected experts juxtaposed with relevant research to counterpoint or lend weight to key concepts; “cheat sheets” that simplify access and reference to individual articles; as well as the grouping of many of these topics under recurrent themes make this book unique. In addition, it provides scalable tried-and-tested tools, method and approaches for improved organizational effectiveness.
Knowledge Management and Management Learning : Extending the Horizons of Knowledge-Based Management
The book begins with an extended introduction and theoretical framework. Contributing authors have written chapters that add to both the framework and the practical consequences of knowledge management. These chapters suggest many lessons learned that will find considerable use in practice. Some of these chapters include an investigation of the "do’s and don’ts" virtual learning based on real-life cases, the use of design teams for group learning, the role of language and the creation of common ground between company and client, culture as a dynamic and non-linear constructed concept, innovation and knowledge management and more. The book offers an exceptional range of contributions within a developing paradigm. Within this context, the book illustrates why and how of knowledge management is important for companies.
Knowledge Management : Organizational and Technological Dimensions
Presents a unique blend of articles which combines both conceptual and practical concerns related to devising and implementing sustainable Knowledge Management systems and solutions in contemporary global organizations. The book's contributors are among the leading thinkers and practitioners in this growing field. The seamless synthesis of the human, organizational, and technological dimensions of Knowledge Management makes this book a definitive guide for academics and practising managers alike.
