Models, Algorithms, and Technologies for Network Analysis: Proceedings of the Second International Conference on Network Analysis

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Sklep: KrainaKsiazek.pl

Models, Algorithms and Technologies for Network Analysis: From the Third International Conference on Network Analysis

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Sklep: KrainaKsiazek.pl

Models, Algorithms, and Technologies for Network Analysis: Net 2016, Nizhny Novgorod, Russia, May 2016

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Sklep: KrainaKsiazek.pl

Algorithms And Models For Network Data And Link Analysis

Książki Obcojęzyczne>Angielskie>Computing & information technology>Computer programming / software development>Algorithms & data structure...

0x0081e40600000000

Sklep: Gigant.pl

Emerging Technologies in Breast Imaging & Mammography American Scientific Publishers

Medycyna > English Division

This book contains over 30 review chapters covering all new and emerging technologies for breast imaging and mammography including a wide variety of topics; General morphology of benign and malignant breast lesions, Breast Optical Imaging, Hybrid Ultrasound and Optical technique for Breast Cancer Imaging, Digital Breast Tomosynthesis, In vivo optical characterization of the female breast by time resolved diffuse spectroscopy, Optical Mammography, Microwave Breast Imaging, Molecular Imaging of Breast Ultrasound, Scintimmamography for Breast Imaging, Phase Contrast Mammography, Elasticity Imaging, On the Measurement of the DQE for Mammographic Imaging Detectors, Physics of Mammography Detectors, Breast tomosynthesis of amorphous selenium (a-Se) based full field digital mammography (FFDM) system, Breast Imaging using PET, MR Elastography of Breast Imaging, Tomosynthesis for C-Arm Breast Imaging, Characterization and Detection of architectural distortion in mammograms using orientation fields, MR-X-ray Fusion for Breast Modeling, FFDMUS: Full Field Digital Mammography and Ultrasound System, Vibro-accoustography for Breast Imaging, Stereo Breast Imaging, CAD Algorithms for Evaluation of Mammography Displays, Application in Surgical Breast Imaging using 3-D Ultrasound and CT, Telemammography: Application of Telemedicine in Breast Imaging, Neural Network System for Analysis of Breast Nuclei in Histopathological Sections, Automated detection for architectural distortions with retraction and spiculation on mammograms, 3-D Interventional Ultrasound for Breast Imaging, Medical Displays for Mammography, Analytical and Monte Carlo X-ray Spectra Modeling in Mammography, Infra-red Technology for Breast Imaging, Future of Breast Imaging Technologies: Cost Vs. Effective Therapy, etc. Figures, illustrations and photos are presented in colors for high quality imaging.

Sklep: Ksiazki-medyczne.eu

Cap-Analysis Gene Expression World Scientific Publishing Co Ltd

Medycyna > English Division

The output of eukaryotic genomes is much more complex than expected. Genes produce different variants of RNAs from multiple promoters. One of the ultimate targets of biological analysis is to establish a relationship between the messenger RNAs that are transcribed from the genome and the genomic regions that control their expression - the promoters - in order to decipher the networks that regulate gene expression and the transcription factors that act as master regulators of transcriptional control. Novel technologies have recently appeared that allow deciphering of transcriptional network, based on the identification of the starting site of gene transcription, with the simultaneous measurement of expression level and identification of the promoter elements.These tagging technologies (including cap-analysis gene expression - CAGE - and others) are further boosted from the development of the novel generation of sequencing instruments, which allow transcriptional profiling by sequencing at the cost of microarray experiments. This book is a guide for users of new technologies, as it includes accurately proven protocols, allowing readers to prepare their samples for experiments. Additionally, it provides a guide for the bioinformatics tools that are available for the analysis of the obtained tags, including the design of the software, the sources and web information where they can be downloaded. Finally, the book provides examples of the application of these technologies to identify promoters, annotate genomes, identify new RNAs and reconstruct models of transcriptional control. Although examples mainly regard mammalians, the discussion expands to other groups of eukaryotes, where these approaches are complementing genome sequencing. "This is a comprehensive introduction to a powerful and important new technique that is transforming our understanding of gene regulation. This volume contains everything you need to know to become an expert in CAGE, from library construction to bioinformatic analysis of the voluminous data sets." --Gene E. Robinson, University of Illinois

Sklep: Ksiazki-medyczne.eu

PyTorch Recipes APress

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Learn how to use PyTorch to build neural network models using code snippets updated for this second edition. This book includes new chapters covering topics such as distributed PyTorch modeling, deploying PyTorch models in production, and developments around PyTorch with updated code.You'll start by learning how to use tensors to develop and fine-tune neural network models and implement deep learning models such as LSTMs, and RNNs. Next, you'll explore probability distribution concepts using PyTorch, as well as supervised and unsupervised algorithms with PyTorch. This is followed by a deep dive on building models with convolutional neural networks, deep neural networks, and recurrent neural networks using PyTorch. This new edition covers also topics such as Scorch, a compatible module equivalent to the Scikit machine learning library, model quantization to reduce parameter size, and preparing a model for deployment within a production system. Distributed parallel processing for balancing PyTorch workloads, using PyTorch for image processing, audio analysis, and model interpretation are also covered in detail. Each chapter includes recipe code snippets to perform specific activities.By the end of this book, you will be able to confidently build neural network models using PyTorch. What You Will Learn Utilize new code snippets and models to train machine learning models using PyTorch Train deep learning models with fewer and smarter implementations Explore the PyTorch framework for model explainability and to bring transparency to model interpretation Build, train, and deploy neural network models designed to scale with PyTorch Understand best practices for evaluating and fine-tuning models using PyTorch Use advanced torch features in training deep neural networks Explore various neural network models using PyTorch Discover functions compatible with sci-kit learn compatible models Perform distributed PyTorch training and execution Who This Book Is ForMachine learning engineers, data scientists and Python programmers and software developers interested in learning the PyTorch framework.

Sklep: Libristo.pl

Engineering Mathematics I Springer International Publishing AG

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This book highlights the latest advances in engineering mathematics with a main focus on the mathematical models, structures, concepts, problems and computational methods and algorithms most relevant for applications in modern technologies and engineering. In particular, it features mathematical methods and models of applied analysis, probability theory, differential equations, tensor analysis and computational modelling used in applications to important problems concerning electromagnetics, antenna technologies, fluid dynamics, material and continuum physics and financial engineering. The individual chapters cover both theory and applications, and include a wealth of figures, schemes, algorithms, tables and results of data analysis and simulation. Presenting new methods and results, reviews of cutting-edge research, and open problems for future research, they equip readers to develop new mathematical methods and concepts of their own, and to further compare and analyse the methods and results discussed. The book consists of contributed chapters covering research developed as a result of a focused international seminar series on mathematics and applied mathematics and a series of three focused international research workshops on engineering mathematics organised by the Research Environment in Mathematics and Applied Mathematics at M?lardalen University from autumn 2014 to autumn 2015: the International Workshop on Engineering Mathematics for Electromagnetics and Health Technology; the International Workshop on Engineering Mathematics, Algebra, Analysis and Electromagnetics; and the 1st Swedish-Estonian International Workshop on Engineering Mathematics, Algebra, Analysis and Applications. It serves as a source of inspiration for a broad spectrum of researchers and research students in applied mathematics, as well as in the areas of applications of mathematics considered in the book. ?

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Fundamentals of Data Science ACADEMIC PR INC

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Fundamentals of Data Science: Theory and Practicepresents basic and advanced concepts in data science along with real-life applications. The book provides students, researchers, and professionals at different levels a good understanding of the concepts of data science, machine learning, data mining, and analytics. Data science is an evolving area of study that is extensively used in solving real-life problems. It is not just about machine learning, statistics, or databases. Instead, it is a comprehensive study of a number of topics that help extract novel knowledge from data, starting with preparing the data, applying suitable intelligent learning models, and interpreting the outcome. The models applied are not

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Perspectives in Modern Project Scheduling Springer-Verlag New York Inc.

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Operations Research began as a field with the mathematical scheduling of a massive project logistically supplying Europe with military equipment and goods during the 2nd World War. Today project scheduling research continues to grow in the variety of its theoretical models, in its magnitude, and in its application. As the world becomes more interrelated and complex, the wider its research is applied to an increasing number of project scheduling problems. §The book surveys the current state-of-the-art in this realm of operations research with chapters written by the respective leading experts on each topic. It will cover the range of the key models in the field, including deterministic, probabilistic, single- and multi-mode, single- and multi-objective, and a general model on discrete-continuous resources. Recent solution algorithms are systematically examined. The book summarizes the current developments and theoretical achievements in the field. These new developments include project uncertainty and grid resource management. §PERSPECTIVES IN MODERN PROJECT SCHEDULING systematically presents this dynamically developing area in three parts. Part I discusses new models of the project management process that are being developed. They include an alternative to the well-known PERT technique, consideration of disturbances during the project realization, introduction of new constraints like due dates or setup times, as well as a general discussion on classification of resources. §Part II outlines and evaluates the new algorithms that have been developed to solve the strongly NP-hard resource constrained project scheduling efficiently with acceptable accuracy. New lower bounds for the RCPSP are proposed, followed by smart justification technique and a series of Metaheuristics. Finally, a neural network approach is introduced.§Part III is devoted to new areas of applications of the project management models and the algorithms for example, pharmaceutical research, grid computing, factory pick-up of new cars, batch scheduling in process industries and make-to-order (project driven) manufacturing that have been developed to solve project scheduling problems in these application areas.§The book will be of interest to academics and students in project management and scheduling in Business Schools, Operations Research/Management Science, Computer Science & Engineering, Systems Analysis, and Applied Mathematics. In addition, decision makers, consultants and practitioners in industry, government and service organizations will also be interested in this book.

Sklep: Libristo.pl

Oxygen Forensic Detective z kluczem USB (zawiera 12 m-cy aktualizacji) GSM-Support, Kraków

Oprogramowanie użytkowe > Oxygen > Programy dla śledczych

Oxygen Forensic Detective (zawiera 12 m-cy aktualizacji) - to oprogramowanie dla komputerów klasy PC służące do wydobycia maksymalnej ilości informacji z telefonów komórkowych i smartfonów, w celach dochodzeniowo-śledczych. Program ten odegrał znaczącą rolę w śledztwach w postępowaniach kryminalnych i innych w ponad 20 krajach na całym świecie. Jednym z głównych zastosowań tego oprogramowania jest odzyskiwanie informacji, które mogą służyć jako dowód w postępowaniu sądowym. Firma GSM-SUPPORT posiada bezpośrednią autoryzację od producenta na sprzedaż oprogramowania firmy Oxygen Software. Supports live data acquisition from 11,000+ mobile devices running on iOS, Android, Windows 8, Windows Mobile 5/6, RIM(Blackberry), Symbian, Bada, Chinese MTK chipset, and feature phones. Offers advanced Oxygen Forensic

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Simulation-Based Optimization Springer-Verlag New York Inc.

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Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques - especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms.Key features of this revised and improved Second Edition include:

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Parallel Computing for Bioinformatics & Computational Biolog John Wiley & Sons

Inne 1

Discover how to streamline complex bioinformatics applications with parallel computing This publication enables readers to handle more complex bioinformatics applications and larger and richer data sets. As the editor clearly shows, using powerful parallel computing tools can lead to significant breakthroughs in deciphering genomes, understanding genetic disease, designing customized drug therapies, and understanding evolution. A broad range of bioinformatics applications is covered with demonstrations on how each one can be parallelized to improve performance and gain faster rates of computation. Current parallel computing techniques and technologies are examined, including distributed computing and grid computing. Readers are provided with a mixture of algorithms, experiments, and simulations that provide not only qualitative but also quantitative insights into the dynamic field of bioinformatics. Parallel Computing for Bioinformatics and Computational Biology is a contributed work that serves as a repository of case studies, collectively demonstrating how parallel computing streamlines difficult problems in bioinformatics and produces better results. Each of the chapters is authored by an established expert in the field and carefully edited to ensure a consistent approach and high standard throughout the publication. The work is organized into five parts: Algorithms and models Sequence analysis and microarrays Phylogenetics Protein folding Platforms and enabling technologies Researchers, educators, and students in the field of bioinformatics will discover how high-performance computing can enable them to handle more complex data sets, gain deeper insights, and make new discoveries.

Sklep: Albertus.pl

XGBoost. The Extreme Gradient Boosting for Mining Applications Grin Publishing

Książki / Literatura obcojęzyczna

Technical Report from the year 2017 in the subject Computer Science - Internet, New Technologies, grade: 8, , language: English, abstract: Tree boosting has empirically proven to be a highly effective and versatile approach for data-driven modelling. The core argument is that tree boosting can adaptively determine the local neighbourhoods of the model thereby taking the bias-variance trade-off into consideration during model fitting.Recently, a tree boosting method known as XGBoost has gained popularity by providing higher accuracy. XGBoost further introduces some improvements which allow it to deal with the bias-variance trade-off even more carefully. In this research work, we propose to demonstrate the use of an adaptive procedure i.e. Learned Loss (LL) to update the loss function as the boosting proceeds.Accuracy of the proposed algorithm i.e. XGBoost with Learned Loss boosting function is evaluated using test/train method, K-fold cross validation, and Stratified cross validation method and compared with the state of the art algorithms viz. XGBoost, AdaBoost, AdaBoost-NN, Linear Regression(LR),Neural Network(NN), Decision Tree(DT), Support Vector Machine(SVM), bagging-DT, bagging-NN and Random Forest algorithms. The parameters evaluated are accuracy, Type 1 error and Type 2 error (in Percentages). This study uses total ten years of historical data from Jan 2007 to Aug 2017 of two stock market indices CNX Nifty and S&P BSE Sensex which are highly voluminous.Further, in this research work, we will investigate how XGBoost differs from the more traditional ensemble techniques. Moreover, we will discuss the regularization techniques that these methods offer and the effect these have on the models.In addition to this, we will attempt to answer the question of why XGBoost seems to win so many competitions. To do this, we will provide some arguments for why tree boosting, and in particular XGBoost, seems to be such a highly effective and versatile approach to predictive modelling. The core argument is that tree boosting can be seen to adaptively determine the local neighbourhoods of the model. Tree boosting can thus be seen to take the bias-variance trade off into consideration during model fitting. XGBoost further introduces some improvements which allow it to deal with the bias-variance trade off even more carefully.

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Book Of Dash No Starch Press,US

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Create stunning interactive dashboard applications in Python with the Dash visualization and data analysis tool. Build interfaces that make sense of your data, and make it pretty.A swift and practical introduction to building interactive data visualization apps in Python, known as dashboards. You ve seen dashboards before; think election result visualizations you can update in real time, or population maps you can filter by demographic. With the Python Dash library you ll create analytic dashboards that present data in effective, usable, elegant ways in just a few lines of code. The book is fast-paced and caters to those entirely new to dashboards. It will talk you through the necessary software, then get straight into building the dashboards themselves. You ll learn the basic format of a Dash app in a Twitter analysis dashboard that tracks numbers of likes over time. You ll then build up skills through three more sophisticated projects. The first compares world data in three areas: volume of internet usage, percentage of parliament seats held by women, and CO2 emissions; the second is a financial portfolio dashboard that models your investments; and the third is visualizesmachine learning algorithms. The final chapter sets you up with some useful final skills, like debugging your code and applying color themes. In this book you will:Create and run your first Dash appsUse the pandas library to manipulate and analyze social media and API dataCreate a variety of stunning and effective charts using PlotlyLearn to use bar charts, chloropleth maps, contour plots, and moreExamine and build on existing apps written by the pros Dash combines several technologies to get you building dashboards quickly and efficiently. This book will do the same.

Sklep: Libristo.pl