Text databases

Ebook Visual C# 2005 Demystified includes contents: Chapter 1: getting started with your first windows program, chapter 2: writing your first code, chapter 3: controls, chapter 4: storing information-data types and variables, chapter 5: letting the program do the math-arithmetic operators, chapter 6: making comparisons-comparison and logical operators, chapter 7: making choices-if and switch control structures, chapter 8: repeating yourself-loops and arrays, chapter 9: organizing your code with methods, chapter 10: helper forms, chapter 11: menus, chapter 12: toolbars, chapter 13: accessing...

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File formats handbook is divided into several parts, includes contents: Part 1 describes various dBASE compatible formats. The applications covered are dBASE, Clipper and FoxPro. Part 2 deals with formats used by a number of spreadsheet programs. The formats used by LOTUS 1-2-3 and EXCEL are described, together with the specifications of data exchange formats such as DIFF, SYLK and so on. Part 3, in the area of word processing, the number of formats is huge. This part describes the formats for MS-WORD, WordPerfect and AMI PRO.

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Precision oncology involves analysis of individual cancer samples to understand the genes and pathways involved in the development and progression of a cancer. To improve patient care, knowledge of diagnostic, prognostic, predisposing, and drug response markers is essential.

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This book is intended as an introduction to computer vision for a broad audience. It provides necessary theory and examples for students and practicioners who will work in fields where significant information must be extracted automatically from images. The book should be a useful resource book for professionals, a text for both undergraduate and beginning graduate courses, and a resource for enrichment of college or even high school projects. Our goals were to provide a basic set of fundamental concepts and algorithms and also discuss some of the exciting evolving application areas.

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This book aims to provide a gentle and application-oriented introduction to those topics. Motivation and application-development considerations, rather than state-of-the-art research, are the main focus. Examples are extensively used in the text, and a brief selected reading section appears at the end of each chapter for readers who want more information. Special attention is given to the design issues raised by the new trends. The book is structured as follows: Part i: fundamentals; part ii: advanced technologies; part iii: advanced design issues.

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Ebook "Machine learning and its applications" includes content: Comparing machine learning and knowledge discovery in databases -- an application to knowledge discovery in texts, unsupervised learning of probabilistic concept hierarchies, function decomposition in machine learning, case based reasoning,...and other contents.

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With ebook "PostgreSQL for beginners" you will learn: installation on Linux and Windows; connecting to a server, writing SQL queries, and using transactions; learning SQL on a demo database; about full-text search capabilities; working with JSON data; using PostgreSQL with your application;...

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Book "Beginning C# 6 Programming with Visual Studio 2015" provides step-by-step directions for programming with C# in the .NET framework. Beginning with programming essentials, such as variables, flow control, and object-oriented programming, this authoritative text moves into more complicated topics, such as web and Windows programming and data access within both database and XML environments.

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In this twelfth edition, we added some new features and reorganized some coverage to provide a better flow of material. Aside from enhancing the already strong coverage of database design, we made other improvements in the topical coverage. In particular, the continued growth of Big Data and NoSQL technologies have challenged the status quo in the database industry. Therefore, we created an entire new chapter, Big Data Analytics and NoSQL, to help students grasp the key aspects of these complex new technologies and challenges.

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To determine the effects of using unstructured clinical text in machine learning (ML) for prediction, early detection, and identification of sepsis. Materials and methods: PubMed, Scopus, ACM DL, dblp, and IEEE Xplore databases were searched. Articles utilizing clinical text for ML or natural language processing (NLP) to detect, identify, recognize, diagnose, or predict the onset, development, progress, or prognosis of systemic inflammatory response syndrome, sepsis, severe sepsis, or septic shock were included.

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presents the following content: Chapter 1: introduction; chapter 2: data, information, and knowledge; chapter 3: representation of information; chapter 4: attribute content and values; chapter 5: models of virtual data structure; chapter 6: the physical structure of data; chapter 7: querying the information retrieval system; chapter 8: interpretation and execution of query statements; chapter 9: text searching; chapter 10: system-computed relevance and ranking; chapter 11: search feedback and iteration; chapter 12: multi-database searching and mapping; chapter 13: search strategy; chapter 1...

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This thesis adopts a systematic and novel approach to evaluating what scholars have written via a comprehensive and technical examination of the literature (articles published between 1988 and 2016 across ten computerized databases). Major articles were systematically reviewed using a machine-based text analysis, Leximancer, revealing which themes pervade the field of scholarship based on what is written.

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Lecture Data mining: Lesson 22. The main topics covered in this chapter include: text and web search; text databases; information retrieval; basic measures for text retrieval; information retrieval techniques; full-text scanning; Tex - Detailed outline;... Please refer to the content of document.

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Part 1 of book "Searching multimedia databases by content" provide with knowledge about: database indexing methods; introduction to relational DBMS; primary key access methods; secondary key access methods; spatial access methods (SAMS); access methods for text;...

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Semantic matching has received much attention in the database, AI, KDD, Web, and Semantic Web communities. Recently, many works have also applied deep learning (DL) to semantic matching. In this paper we survey this fast growing topic. We define the semantic matching problem, categorize its variations into a taxonomy, and describe important applications. We describe DL solutions for important variations of semantic matching. Finally, we discuss future R&D directions.

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Scientific literature carries a wealth of information crucial for research, but only a fraction of it is present as structured information in databases and therefore can be analyzed using traditional data analysis tools.

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Biomarkers and target-specific phenotypes are important to targeted drug design and individualized medicine, thus constituting an important aspect of modern pharmaceutical research and development. More and more, the discovery of relevant biomarkers is aided by in silico techniques based on applying data mining and computational chemistry on large molecular databases.

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A popular query from scientists reading a biomedical abstract is to search for topic-related documents in bibliographic databases. Such a query is challenging because the amount of information attached to a single abstract is little, whereas classification-based retrieval algorithms are optimally trained with large sets of relevant documents.

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The scientific literature contains millions of microbial gene identifiers within the full text and tables, but these annotations rarely get incorporated into public sequence databases. We propose to utilize the Open Access (OA) subset of PubMed Central (PMC) as a gene annotation database and have developed an R package called pmcXML to automatically mine and extract locus tags from full text, tables and supplements.

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We aim to automatically extract species names of bacteria and their locations from webpages. This task is important for exploiting the vast amount of biological knowledge which is expressed in diverse natural language texts and putting this knowledge in databases for easy access by biologists.

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