Conventionally, physicians follow a cognitive decision making process, the appropriateness of which develops with experience and knowledge gained from literature, lectures etc. Having inadequate knowledge or experience may lead to misdiagnosis of diseases consequently affecting the patient physically, emotionally, financially and so on. Using data mining techniques, it is possible to develop accurate diagnostic models that can be used in clinical decision making. Adopting such models in clinical practice may ensure better decision making thereby decreasing the rate of misdiagnosis, minimising the uneasiness, pain and anxiety that is associated with a disease through early detection and treatment. In this thesis, (i) the performance of standard classification algorithms in CKD detection was explored (ii) a new hybrid approach to accurately diagnose CKD is presented and (iii) the application of distributed random forest algorithm for developing generalized model for CKD diagnosis was proposed.
WWW is a rich domain of information and knowledge, a large number of new and professional users search data of their own interest. Now, in these days, the number of new users are rapidly increases, most of them are not aware about the security on the web and risk in internet usages. These users are sometimes become victims of fraud causes by the advertising networks. The internet users are attracted by the advertising and due to little greedy nature user is trapped in these advertising network.
Motion detection and tracking are an important technique in image processing with extensive applications in medicine, remote sensing, and various fields, with increasing popularity in surveillance systems, military applications, and healthcare, for example. Detection algorithms depending on the application. And in the selection of the technique required in most cases, which depends on the application and the image in question rather than fragmentation of the image.
Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapter starts with conceptual background information about the techniques, includes multiple examples using R to achieve results, and concludes with a case study. Written by Matt and Joshua F. Wiley, Advanced R Statistical Programming and Data Models shows you how to conduct data analysis using the popular R language. You´ll delve into the preconditions or hypothesis for various statistical tests and techniques and work through concrete examples using R for a variety of these next-level analytics. This is a must-have guide and reference on using and programming with the R language. What You´ll Learn Conduct advanced analyses in R including: generalized linear models, generalized additive models, mixed effects models, machine learning, and parallel processing Carry out regression modeling using R data visualization, linear and advanced regression, additive models, survival / time to event analysis Handle machine learning using R including parallel processing, dimension reduction, and feature selection and classification Address missing data using multiple imputation in R Work on factor analysis, generalized linear mixed models, and modeling intraindividual variability Who This Book Is For Working professionals, researchers, or students who are familiar with R and basic statistical techniques such as linear regression and who want to learn how to use R to perform more advanced analytics. Particularly, researchers and data analysts in the social sciences may benefit from these techniques. Additionally, analysts who need parallel processing to speed up analytics are given proven code to reduce time to result(s).
Are you attracted by the promises of agile methods but put off by the fanaticism of many agile texts? Would you like to know which agile techniques work, which ones do not matter much, and which ones will harm your projects? Then you need Agile! : the first exhaustive, objective review of agile principles, techniques and tools. Agile methods are one of the most important developments in software over the past decades, but also a surprising mix of the best and the worst. Until now every project and developer had to sort out the good ideas from the bad by themselves. This book spares you the pain. It offers both a thorough descriptive presentation of agile techniques and a perceptive analysis of their benefits and limitations. Agile! serves first as a primer on agile development : one chapter each introduces agile principles, roles, managerial practices, technical practices and artifacts. A separate chapter analyzes the four major agile methods: Extreme Programming, Lean Software, Scrum and Crystal. The accompanying critical analysis explains what you should retain and discard from agile ideas. It is based on Meyer´s thorough understanding of software engineering, and his extensive personal experience of programming and project management. He highlights the limitations of agile methods as well as their truly brilliant contributions - even those to which their own authors do not do full justice. Three important chapters precede the core discussion of agile ideas: an overview, serving as a concentrate of the entire book; a dissection of the intellectual devices used by agile authors; and a review of classical software engineering techniques, such as requirements analysis and lifecycle models, which agile methods criticize. The final chapters describe the precautions that a company should take during a transition to agile development and present an overall assessment of agile ideas. This is the first book to discuss agile methods, beyond the brouhaha, in the general context of modern software engineering. It is a key resource for projects that want to combine the best of established results and agile innovations.
This new and completely updated edition is an easy to implement, hands-on resource for usability in the real world. You´ll learn about the user requirements gathering stage of product development and find a variety of techniques. For each technique, you´ll understand how to prepare for and conduct the activity, as well as analyze and present the data - all in a practical and hands-on way. Each method presented provides different information about the user and their requirements (e.g., functional requirements, information architecture, task flows). The techniques can be used together to form a complete picture of the users´ requirements or they can be used separately to address specific product questions. These techniques have helped product teams understand the value of user requirements gathering by providing insight into how users work and what they need to be successful at their tasks. You´ll find case studies from industry-leading companies to demonstrate each method in action. After reading this book, you´ll be able to conduct any usability activity (e.g., getting buy-in from management, legal and ethical considerations, setting up your facilities, recruiting, and moderating activities) and be able to apply them to your own products.
Since starting my work career in the telecommunications industry more than 20 years ago, I have been looking for ways to apply tool supported development techniques. At a time when the need for professional software testing was gathering recognition and testers were not participating in development teams, I was correcting software errors in database systems. This experience guided my understanding of how important systematic software development is in preventing errors. My journey has been driven by the curiosity of what improvements new software development techniques can provide to the end product quality. This work presents a novel approach to Aspect-Oriented Modelling and testing to address the needs of Model-Based Testing. The approach aims at providing assistance for incremental test model creation as well as for abstract test purpose specification by referring to attributes of aspects. This methodology results in three advantages: testability of system under test quality attributes, a simple rule for composition, and better comprehension of test models. The usability of the method is demonstrated on the Home Rehabilitation System testing case-study.
This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches -which are based on optimization techniques ? together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models. Key Features Include: An introductory chapter on related mathematical toolsAll major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methodsA presentation of the physical reasoning, mathematical modeling and algorithmic implementation of each methodThe latest trends: Sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent modelingCase studies - protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization and echo cancellation, show how the theory can be appliedMATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code
A decision procedure is an algorithm that, given a decision problem, terminates with a correct yes/no answer. Here, the authors focus on theories that are expressive enough to model real problems, but are still decidable. Specifically, the book concentrates on decision procedures for first-order theories that are commonly used in automated verification and reasoning, theorem-proving, compiler optimization and operations research. The techniques described in the book draw from fields such as graph theory and logic, and are routinely used in industry. The authors introduce the basic terminology of satisfiability modulo theories and then, in separate chapters, study decision procedures for each of the following theories: propositional logic; equalities and uninterpreted functions; linear arithmetic; bit vectors; arrays; pointer logic; and quantified formulas.