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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.
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.
Blockchain and Other Emerging Technologies for Digital Business Strategies
Aims to explore the aspects of strategic leadership in a digital context together with the cyber-physical relationships whilst performing business activities. Furthermore, this book looks to investigate the interactions from both the organization strategy including the cross-functional actors/stakeholders whom are operating within the organization and the various characteristics of operating in a cyber secure ecosystem.
Asset Sales : Their Role in Restructuring and Financing Firms
Examines the corporate asset market and the mechanisms of asset sale transactions. The book then focuses on the theory of finance in asset sales (the efficiency and financing theory) and the extensive empirical literature now available. In light of recent and rapid technological and digital advances, the last section presents new perspectives on analyzing asset sales transactions. Chiefly intended as a primer for PhD students and academics, the book offers a road map of the empirical research landscape and suggests future research directions.
Artificial Intelligence, Business and Civilization : Our Fate Made in Machines
Looks at what exactly artificial intelligence is, how it can be classified, how it differentiates from other concepts such as machine learning, big data, blockchain, or the Internet-of-Things, and how it has evolved and might evolve over time.
Applications of computational intelligence in data-driven trading
The main purpose of this book is pedagogical in nature, and it is specifically aimed at defining an adequate level of engineering and scientific clarity when it comes to the usage of the term “Artificial Intelligence,” especially as it relates to the financial industry.
AI in marketing, sales and service : How marketers without a data science degree can use AI, big data and bots
Shows you: how customer and market potential can be automatically identified and profiled; how media planning can be intelligently automated and optimized with AI and Big Data; how (chat)bots and digital assistants can make communication between companies and consumers more efficient and smarter; how you can optimize Customer Journeys based on Algorithmics and AI; and how to conduct market research in more efficient and smarter way.
Machine Learning in Document Analysis and Recognition
The objective of Document Analysis and Recognition (DAR) is to recognize the text and graphicalcomponents of a document and to extract information. With ?rst papers dating back to the 1960’s, DAR is a mature but still gr- ing research?eld with consolidated and known techniques. Optical Character Recognition (OCR) engines are some of the most widely recognized pr- ucts of the research in this ?eld, while broader DAR techniques are nowadays studied and applied to other industrial and o?ce automation systems. In the machine learning community, one of the most widely known - search problems addressed in DAR is recognition of unconstrained handwr- ten characters which has been frequently used in the past as a benchmark for evaluating machine learning algorithms, especially supervised classi?ers.
Learning Classifier Systems in Data Mining
Just over thirty years after Holland first presented the outline for Learning Classifier System paradigm, the ability of LCS to solve complex real-world problems is becoming clear. In particular, their capability for rule induction in data mining has sparked renewed interest in LCS. This book brings together work by a number of individuals who are demonstrating their good performance in a variety of domains.
Knowledge Representation Techniques : A Rough Set Approach
The basis for the material in this book centers around a long term research project with autonomous unmanned aerial vehicle systems. One of the main research topics in the project is knowledge representation and reasoning. The focus of the research has been on the development of tractable combinations of approximate and nonmonotonic reasoning systems. The techniques developed are based on intuitions from rough set theory. Efforts have been made to take theory into practice by instantiating research results in the context of traditional relational database or deductive database systems.
Kernel Based Algorithms for Mining Huge Data Sets : Supervised, Semi-supervised, and Unsupervised Learning
"Kernel Based Algorithms for Mining Huge Data Sets" is the first book treating the fields of supervised, semi-supervised and unsupervised machine learning collectively. The book presents both the theory and the algorithms for mining huge data sets by using support vector machines (SVMs) in an iterative way. It demonstrates how kernel based SVMs can be used for dimensionality reduction (feature elimination) and shows the similarities and differences between the two most popular unsupervised techniques, the principal component analysis (PCA) and the independent component analysis (ICA).
Chemoinformatics : Theory, Practice, & Products
Chemoinformatics: Theory, Practice & Products covers theory, commercially available packages and applications of Chemoinformatics. Chemoinformatics is broadly defined as the use of information technology to assist in the acquisition, analysis and management of data and information relating to chemical compounds and their properties.The book also provides a summary of currently available, state-of-the-art, commercial Chemoinformatics products, with a specific focus on databases, toolkits, and modelling technologies designed for drug discovery.
