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Haematology
A book in the Illustrated Colour Text series, this excellent guide simplifies and explains complex concepts in hematology and presents ideas in a clear, visual, easy-to-understand format. Concise chapters break down topics into manageable, well-illustrated discussions, for a practical introduction to the major diseases and their treatment.
Haematology E-Book
This textbook on haematology in the Illustrated Colour Text series is suitable for medical students, junior doctors and others needing a concise and practical introduction to the subject. - Concise and manageable coverage of the major blood diseases and their treatment. - Does not overload the reader with science, unlike some competing books. - Focus is on the haematology patient and on the practical aspects of the subject. - Makes use of the attractive features of the ICT series - double page spreads, lots of colour illustrations, summary boxes. - Updating to take account of recenty developments in stem cell biology. - Increased emphasis on genomic and proteomic techniques in the diagnosis of haematological malignancy. - Use of new imaging techniques including MRI and PET. - Description of advances in treatment of leukaemia and lymphoma and in diagnosis and management of patients with thrombophilia and venous thrombosis. - Development of new antimicrobial agents in management of immunosuppressed patients with infection. - New double page spread on palliative care of patients with blood disorders. - New illustrations.
Basic Integrated Circuit Engineering
-- Solutions manual to accompany Basic integrated circuit engineering. [By] Douglas J. Hamilton [and] William G. Howard. N.Y., McGraw-Hill, 1976. 280p.
Statistical Implicative Analysis
Statistical implicative analysis is a data analysis method created by Régis Gras almost thirty years ago which has a significant impact on a variety of areas ranging from pedagogical and psychological research to data mining. Statistical implicative analysis (SIA) provides a framework for evaluating the strength of implications; such implications are formed through common knowledge acquisition techniques in any learning process, human or artificial. This new concept has developed into a unifying methodology, and has generated a powerful convergence of thought between mathematicians, statisticians, psychologists, specialists in pedagogy and last, but not least, computer scientists specialized in data mining. This volume collects significant research contributions of several rather distinct disciplines that benefit from SIA. Contributions range from psychological and pedagogical research, bioinformatics, knowledge management, and data mining.
Research and Development in Intelligent Systems XXIX
The papers in this volume are the refereed papers presented at AI-2012, the Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, held in Cambridge in December 2012 in both the technical and the application streams. They present new and innovative developments and applications, divided into technical stream sections on Data Mining, Data Mining and Machine Learning, Planning and Optimisation, and Knowledge Management and Prediction, followed by application stream sections on Language and Classification, Recommendation, Practical Applications and Systems, and Data Mining and Machine Learning. The volume also includes the text of short papers presented as posters at the conference. This is the twenty-ninth volume in the Research and Development in Intelligent Systems series, which also incorporates the twentieth volume in the Applications and Innovations in Intelligent Systems series. These series are essential reading for those who wish to keep up to date with developments in this important field.
Handbook of Philosophical Logic
The fourteenth volume of the Second Edition covers central topics in philosophical logic that have been studied for thousands of years, since Aristotle: Inconsistency, Causality, Conditionals, and Quantifiers. These topics are central in many applications of logic in central disciplines and this book is indispensable to any advanced student or researcher using logic in these areas. The chapters are comprehensive and written by major figures in the field.
Advances in Artificial Intelligence
This book constitutes the refereed proceedings of the 18th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2005, held in Victoria, Canada in May 2005. The revised full papers and 19 revised short papers presented were carefully reviewed and selected from 135 submission. The papers are organized in topical sections on agents, constraint satisfaction and search, data mining, knowledge representation and reasoning, machine learning, natural language processing, and reinforcement learning.
Rough Sets and Current Trends in Computing
In recent years rough set theory has attracted the attention of many researchers and practitioners all over the world, who have contributed essentially to its development and applications. Weareobservingagrowingresearchinterestinthefoundationsofroughsets, including the various logical, mathematical and philosophical aspects of rough sets. Some relationships have already been established between rough sets and other approaches, and also with a wide range of hybrid systems. As a result, rough sets are linked with decision system modeling and analysis of complex systems, fuzzy sets, neural networks, evolutionary computing, data mining and knowledge discovery, pattern recognition, machine learni...
Proceedings of the Fourth SIAM International Conference on Data Mining
The Fourth SIAM International Conference on Data Mining continues the tradition of providing an open forum for the presentation and discussion of innovative algorithms as well as novel applications of data mining. This is reflected in the talks by the four keynote speakers who discuss data usability issues in systems for data mining in science and engineering, issues raised by new technologies that generate biological data, ways to find complex structured patterns in linked data, and advances in Bayesian inference techniques. This proceedings includes 61 research papers.
Data Profiling
Data profiling refers to the activity of collecting data about data, {i.e.}, metadata. Most IT professionals and researchers who work with data have engaged in data profiling, at least informally, to understand and explore an unfamiliar dataset or to determine whether a new dataset is appropriate for a particular task at hand. Data profiling results are also important in a variety of other situations, including query optimization, data integration, and data cleaning. Simple metadata are statistics, such as the number of rows and columns, schema and datatype information, the number of distinct values, statistical value distributions, and the number of null or empty values in each column. More...
