Examines the recent advances in preventive medicine, from genomics to early detection, and how they are transforming health care, and advocates a new strategy for thinking about health, one that incorporates cutting-edge technology.

Today’s world is bounded by data, from morning to night each and all work is associated to data. The usage of computer and its technology is rapidly growing in many different fields like Education, banking sector, bioinformatics field, business, health cares and Industry. In all ways, everywhere data is created and this information is stored in various hubs or data wares houses. There is huge amount of data and it is created by increasing usage of computer. There is rapidly growth of data generated by all systems and it can be used for deriving models by assessing useful relationship among input and output dependencies. Consequently, there is presently shifted a model since classical modelling and it investigates to develop a model and the equivalent analyses from stored data. Government organizations, scientific institutions, administration offices and businesses have all dedicated huge resources to assembly and putting away information. Now a days, Data can possibly assist organizations with improving tasks and make quicker, progressively powerful decisions. The information or data is gathered from various sources including messages, cell phones, applications, databases, servers and different methods. This information is collected, arranged, controlled and put in meaningful information. This meaningful information would assist to an organization with valuable understanding to hold the clients for expand the income and improved the business activities. The government organizations and companies are gathering the useful information to support to manage human resources.

The book focuses on different variants of decision tree induction but also describes the meta-learning approach in general which is applicable to other types of machine learning algorithms. The book discusses different variants of decision tree induction and represents a useful source of information to readers wishing to review some of the techniques used in decision tree learning, as well as different ensemble methods that involve decision trees. It is shown that the knowledge of different components used within decision tree learning needs to be systematized to enable the system to generate and evaluate different variants of machine learning algorithms with the aim of identifying the top-most performers or potentially the best one. A unified view of decision tree learning enables to emulate different decision tree algorithms simply by setting certain parameters. As meta-learning requires running many different processes with the aim of obtaining performance results, a detailed description of the experimental methodology and evaluation framework is provided. Meta-learning is discussed in great detail in the second half of the book. The exposition starts by presenting a comprehensive review of many meta-learning approaches explored in the past described in literature, including for instance approaches that provide a ranking of algorithms. The approach described can be related to other work that exploits planning whose aim is to construct data mining workflows. The book stimulates interchange of ideas between different, albeit related, approaches.

Presents a detailed study of the major design components that constitute a top-down decision-tree induction algorithm, including aspects such as split criteria, stopping criteria, pruning and the approaches for dealing with missing values. Whereas the strategy still employed nowadays is to use a 'generic' decision-tree induction algorithm regardless of the data, the authors argue on the benefits that a bias-fitting strategy could bring to decision-tree induction, in which the ultimate goal is the automatic generation of a decision-tree induction algorithm tailored to the application domain of interest. For such, they discuss how one can effectively discover the most suitable set of components of decision-tree induction algorithms to deal with a wide variety of applications through the paradigm of evolutionary computation, following the emergence of a novel field called hyper-heuristics. "Automatic Design of Decision-Tree Induction Algorithms" would be highly useful for machine learning and evolutionary computation students and researchers alike.

This book not only discusses the important topics in the area of machine learning and combinatorial optimization, it also combines them into one. This was decisive for choosing the material to be included in the book and determining its order of presentation. Decision trees are a popular method of classification as well as of knowledge representation. At the same time, they are easy to implement as the building blocks of an ensemble of classifiers. Admittedly, however, the task of constructing a near-optimal decision tree is a very complex process. The good results typically achieved by the ant colony optimization algorithms when dealing with combinatorial optimization problems suggest the possibility of also using that approach for effectively constructing decision trees. The underlying rationale is that both problem classes can be presented as graphs. This fact leads to option of considering a larger spectrum of solutions than those based on the heuristic. Moreover, ant colony optimization algorithms can be used to advantage when building ensembles of classifiers. This book is a combination of a research monograph and a textbook. It can be used in graduate courses, but is also of interest to researchers, both specialists in machine learning and those applying machine learning methods to cope with problems from any field of R&D.

Why do people in a certain group behave the way they do? And, more importantly, what specific criteria was used by the group in question? This book presents a method for answering these questions.

Innovations and Advances in Computer Sciences and Engineering includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art research projects in the areas of Computer Science, Software Engineering, Computer Engineering, and Systems Engineering and Sciences. Innovations and Advances in Computer Sciences and Engineering includes selected papers form the conference proceedings of the International Conference on Systems, Computing Sciences and Software Engineering (SCSS 2008) which was part of the International Joint Conferences on Computer, Information and Systems Sciences and Engineering (CISSE 2008).

This book provides a detailed description of machine learning algorithms in data analytics, data science life cycle, Python for machine learning, linear regression, logistic regression, and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for real-world examples in electrical, oil and gas, e-commerce, and hi-tech industries. The focus is on Python programming for machine learning and patterns involved in decision science for handling data. Features: Explains the basic concepts of Python and its role in machine learning. Provides comprehensive coverage of feature engineering including real-time case studies. Perceives the structural patterns with reference to data science and statistics and analytics. Includes machine learning-based structured exercises. Appreciates different algorithmic concepts of machine learning including unsupervised, supervised, and reinforcement learning. This book is aimed at researchers, professionals, and graduate students in data science, machine learning, computer science, and electrical and computer engineering.

This substantially revised and expanded edition of the The Drama Therapy Decision Tree provides an integrated model for therapeutic decision-making by uniting drama therapy interventions with diagnostic information, individual and group processes, psychological distance, the drama therapy pie, and global outcomes. This book is a practical guide in four sections, not a checklist. Rather than using a standardized protocol that makes the decisions for the therapist, drama therapy is based on dynamic, embodied, creative action with participants in the here and now. Conscious planning on the part of the drama therapist before the session supports spontaneity and creativity, preparing them to make good therapeutic decisions in the moment during the session. The opening section guides readers through the foundational principles leading readers into Section Two, The Decision Tree, which is a series of questions for early career drama therapists to ask themselves as they prepare treatment plans for clients. Diversity, Equity, and Ethics are covered in Section Three from the point of view of creative arts therapy practitioners. Section Four looks at Integrating the Five Phases of Treatment with the Drama Therapy Pie, following different populations (diagnosis) of clients through the five phases of group therapy in order to illustrate how the Decision Tree supports intervention choice in the different phases of treatment. The authors strive to provide a common language for communicating what drama therapists do and how they do it in order to demystify drama therapy for other mental health and medical professionals. Using the decision tree as a guide, early career drama therapists can move forward confidently and ground their work with participants in an integrated system. An online searchable database of drama therapy interventions provides descriptions, therapeutic outcomes addressed, and other useful information provides a wealth of additional supporting material. There is also a separate online resource of deroling activities. The online resources can also be an asset for non-drama therapists who are wanting to incorporate a more active and embodied component safely into their work, particularly in terms of warm-ups, closure, and deroling.

Unmanned Driving Systems for Smart Trains explores the core technologies involved in unmanned driving systems for smart railways and trains, from foundational theory to the latest advances. The volume introduces the key technologies, research results and frontiers of the field. Each chapter includes practical cases to ground theory in practice. Seven chapters cover key aspects of unmanned driving systems for smart trains, including performance evaluation, algorithm-based reasoning and learning strategy, main control parameters, data mining and processing, energy saving optimization and control, and intelligent algorithm simulation platforms. This book will help researchers find solutions in developing better unmanned driving systems. - Responds to the expansion of smart railways and the adoption of unmanned global systems - Covers core technologies of unmanned driving systems for smart trains - Details a large number of case studies and experimental designs for unmanned railway systems - Adopts a multidisciplinary view where disciplines intersect at key points - Gives both foundational theory and the latest theoretical and practical advances for unmanned railways