检索结果-内蒙古大学图书馆

2020年

作者： Fred Nwanganga Mike Chapple

ISBN: (数字)9781119591542

ISBN: (纸本)9781119591511

Guides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular r programming language Machine learning—a branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit instructions—allows organizations to reveal patterns in their data and incorporate predictive analytics into their decision-making process. Practical Machine Learning in r provides a hands-on approach to solving business problems with intelligent, self-learning computer algorithms. Bestselling author and data analytics experts Fred Nwanganga and Mike Chapple explain what machine learning is, demonstrate its organizational benefits, and provide hands-on examples created in the r programming language. A perfect guide for professional self-taught learners or students in an introductory machine learning course, this reader-friendly book illustrates the numerous real-world business uses of machine learning approaches. Clear and detailed chapters cover data wrangling, r programming with the popular rStudio tool, classification and regression techniques, performance evaluation, and more. Explores data management techniques, including data collection, exploration and dimensionality reduction Covers unsupervised learning, where readers identify and summarize patterns using approaches such as apriori, eclat and clustering Describes the principles behind the Nearest Neighbor, Decision Tree and Naive Bayes classification techniques Explains how to evaluate and choose the right model, as well as how to improve model performance using ensemble methods such as random Forest and XGBoost Practical Machine Learning in r is a must-have guide for business analysts, data scientists, and other professionals interested in leveraging the power of AI to solve business problems, as well as students and independent learners seeking to enter the field.

关键词： machine learning machine learning business business analytics machine learning r machine learning examples machine learning guide r programming r Studio machine learning r data management r data clustering r algorithms artificial intelligence data science supervised learning unsupervised learning

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Time Series Data Analysis by Integration of r and Hadoop via Hive

Time Series Data Analysis by Integration of R and Hadoop via...

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International Conference on Intelligent Sustainable Systems (ICISS)

作者： Krishna, Ch Nanda Vamsi, N. V. G. K. Aishwarya, N. Sree ramya, G. Kalyan, Ch Pavan Velagapudi Ramakrishna Siddhartha Engn Coll Dept Informat Technol Vijayawada 520007 Andhra Pradesh India

ISBN: (纸本)9781538677995

Time Series data is the trending data at present. The time series means data generated for every second. This kind of data is produced by smart meters so we are working on smart meter data for analysis. Keen power meters have cleared route to utilize power proficiently contrasted with that about advanced meters. These sorts from claiming meters have been orchestrated crosswise over exactly nations since those 2000s. Utility organizations need aid utilizing such meters should provide exact Vitality utilization information with their customers. The inconceivable measure from claiming information produced Eventually Tom's perusing advanced mobile meters each interim camwood be utilized to information analytics and Different insights could be inferred for example, determining the power demand, actualizing ToU (Time of Use) duty rates and so forth throughout this way, observing and stock arrangement of all instrumentation may be enha. Which will help both utility organizations and in addition inhabitants. Comparison between two different methods were done by analyzing data. One is the ArIMA model and another one is the Exponential Smoothing method. We compare these two methods by AIC measurements of their MAPE values.

关键词： Time Series data eco-system: Hadoop Demand of Electricity ArIMA Exponential Smoothing models r programming MAPE values

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Statistical Analysis with r For Dummies 1

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丛书名： Computers

2017年

作者： Schmuller Joseph

ISBN: (数字)9781119337263

ISBN: (纸本)9781119337065

Understanding the world of r programming and analysis has never been easier Most guides to r, whether books or online, focus on r functions and procedures. But now, thanks to Statistical Analysis with r For Dummies, you have access to a trusted, easy-to-follow guide that focuses on the foundational statistical concepts that r addresses—as well as step-by-step guidance that shows you exactly how to implement them using r programming. People are becoming more aware of r every day as major institutions are adopting it as a standard. Part of its appeal is that it's a free tool that's taking the place of costly statistical software packages that sometimes take an inordinate amount of time to learn. Plus, r enables a user to carry out complex statistical analyses by simply entering a few commands, making sophisticated analyses available and understandable to a wide audience. Statistical Analysis with r For Dummies enables you to perform these analyses and to fully understand their implications and results. Gets you up to speed on the #1 analytics/data science software tool Demonstrates how to easily find, download, and use cutting-edge community-reviewed methods in statistics and predictive modeling Shows you how r offers intel from leading researchers in data science, free of charge Provides information on using r Studio to work with r Get ready to use r to crunch and analyze your data—the fast and easy way!

关键词： Statistical Analysis r statistical analysis with r guide to r r functions r functions and procedures statistical concepts r programming programming with r free statistical software free statistics software data science data science software tool r Studio using r Studio using r Studio with r predictive modeling Statistical Analysis with r For Dummies Joseph Schmuller

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Simultaneous Mass Transfer and Chemical reactions in Engineering Science 1

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2023年

作者： Bertram K. C. Chan

ISBN: (数字)9783527823529

ISBN: (纸本)9783527346653

Simultaneous Mass Transfer and Chemical reactions in Engineering Science A comprehensive look at the basic science of diffusional process and mass transfer Mass transfer as a principle is an essential part of numerous unit operations in biomolecular, chemical, and process engineering; crystallization, distillation, and membrane separation processes, for example, use this important method. Given this significance – particularly in engineering design where these processes occur – understanding the design and analysis of such unit operations must begin with a basic understanding of how simultaneous mass transfer and the chemical reactions that influence these occurrences. It is also vital to be aware of the most up-to-date technologies for analyzing and predicting the phenomena. Given the significance of this process, Simultaneous Mass Transfer and Chemical reactions in Engineering Science is an important resource as it introduces the reader to the complex subject of simultaneous mass transfer with biochemical and chemical reactions and gives them the tools to develop an applicable design. Analyzing the systems of simultaneous mass transfer and reactions is at the core of this book, as all known design approaches are carefully examined and compared. The volume also provides the reader with a working knowledge of the latest technologies – with a special focus on the open-sourced computer programming language r – and how these tools are an essential resource in quantitative assessment in analysis models. Simultaneous Mass Transfer and Chemical reactions in Engineering Science provides a working knowledge of the latest information on simultaneous mass transfer and reactions by focusing on the analysis of this process, as well as discussing the existence and distinctive quality of the solutions to the Simultaneous Mass Transfer and Chemical reactions in Engineering Science readers will also find: A theoretical basis of each design model that is carefully stated, compared,

关键词： Mathematical models diffusion models for environmental transport data analysis r programming data processing data entry array and matrix data-frame for r computation univariate data bivariate data multivariate data biostatistics

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Data Science Support at the Academic Library

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JOUrNAL OF LIBrArY ADMINISTrATION 2019年第3期59卷 241-257页

作者： Oliver, Jeffrey C. Kollen, Christine Hickson, Benjamin rios, Fernando Univ Arizona Univ Lib Off Digital Innovat & Stewardship Tucson AZ 85721 USA

Data science is a rapidly growing field with applications across all scientific domains. The demand for support in data science literacy is outpacing available resources at college campuses. The academic library is uniquely positioned to provide training and guidance in a number of areas relevant to data science. The University of Arizona Libraries has built a successful data science support program, focusing on computational literacy, geographic information systems, and reproducible science. Success of the program has largely been due to the strength of library personnel and strategic partnerships with units outside of the library. Academic libraries can support campus data science needs through professional development of current staff and recruitment of new personnel with expertise in data-intensive domains.

关键词： data visualization GIS python r programming reproducibility

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Development of Statistical Computational Tools Through Pharmaceutical Drug Development and Manufacturing Life Cycle 39th

Development of Statistical Computational Tools Through Pharm...

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39th Annual Midwest Biopharmaceutical Statistics Workshop (MBSW)

作者： Li, Fasheng Wang, Ke Pfizer Inc Pharmaceut Sci & Mfg Stat MS 8220-2356Eastern Point Rd Groton CT 06340 USA

ISBN: (纸本)9783319673868;9783319673851

Statisticians at Pfizer who support Chemistry, Manufacturing, and Controls (CMC), and regulatory Affairs (reg CMC) have developed many statistical r-based computational tools to enable high efficiency, consistency, and fast turnaround in their routine statistical support to drug product and manufacturing process development. Most tools have evolved into web-based applications for convenient access by statisticians and colleagues across the company. These tools cover a wide range of areas, such as product stability and shelf life or clinical use period estimation, process parameter criticality assessment, and design space exploration through experimental design and parametric bootstrapping. In this article, the general components of these r-programmed web-based computational tools are introduced, and their successful applications are demonstrated through an application of estimating a drug product shelf life based on stability data.

关键词： Statistical computing Web based tool r programming regression analysis Design of experiment

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IQPC 2015 TrACK: WATEr DETECTION AND CLASSIFICATION ON MULTI-SOUrCE rEMOTE SENSING AND TErrAIN DATA

IQPC 2015 TRACK: WATER DETECTION AND CLASSIFICATION ON MULTI...

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ISPrS Geospatial Week

作者： Olasz, A. Kristof, D. Belenyesi, M. Bakos, K. Kovacs, Z. Balazs, B. Szabo, Sz. FOMI Inst Geodesy Cartog & Remote Sensing H-1149 Budapest Hungary Univ Debrecen Dept Phys Geog & Geoinformat Syst Debrecen Hungary

ISBN: (纸本)9781629935201

Since 2013, the EU FP7 research project "IQmulus" encourages the participation of the whole scientific community as well as specific user groups in the IQmulus Processing Contest (IQPC). This year, IQPC 2015 consists of three processing tasks (tracks), from which "Water detection and classification on multi-source remote sensing and terrain data" is introduced in the present paper. This processing track addresses a particular problem in the field of big data processing and management with the objective of simulating a realistic remote sensing application scenario. The main focus is on the detection of water surfaces (natural waters, flood, inland excess water, other water-affected categories) using remotely sensed data. Multiple independent data sources are available and different tools could be used for data processing and evaluation. The main challenge is to identify the right combination of data and methods to solve the problem in the most efficient way. Although the first deadline for submitting track solutions has passed and the track has been successfully concluded, the track organizers decided to keep the possibility of result submission open to enable collecting a variety of approaches and solutions for this interesting problem.

关键词： IQmulus Processing Contest remote Sensing Water detection and classification Multi-source remote sensing data processing resource optimization r programming

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Self-Admitted Technical Debt in r Packages: An Exploratory Study 18

Self-Admitted Technical Debt in R Packages: An Exploratory S...

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29th IEEE/ACM International Conference on Program Comprehension (ICPC) / 18th IEEE/ACM International Conference on Mining Software repositories (MSr)

作者： Vidoni, Melina RMIT Univ Sch Comp Technol Melbourne Vic Australia

ISBN: (纸本)9781728187105

Self-Admitted Technical Debt (SATD) is a particular case of Technical Debt (TD) where developers explicitly acknowledge their sub-optimal implementation decisions. Though previous studies have demonstrated that SATD is common in software projects and negatively impacts their maintenance, they have mostly approached software systems coded in traditional object-oriented programming (OOP), such as Java, C++ ***. This paper studies SATD in r packages, and reports results of a three-part study. The first part mined more than 500 r packages available on GitHub, and manually analysed more than 164k of comments to generate a dataset. The second part administered a crowd-sourcing to analyse the quality of the extracted comments, while the third part conducted a survey to address developers' perspectives regarding SATD comments. The main findings indicate that a large amount of outdated code is left commented, with SATD accounting for about 3% of comments. Code Debt was the most common type, but there were also traces of Algorithm Debt, and there is a considerable amount of comments dedicated to circumventing CrAN checks. Moreover, package authors seldom address the SATD they encounter and often add it as self-reminders.

关键词： Self-Admitted Technical Debt Mining Software repositories Empirical Software Engineering r programming

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Intra-patient Arrhythmia Heartbeat Modeling by Gibbs Sampling 11th

Intra-patient Arrhythmia Heartbeat Modeling by Gibbs Samplin...

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11th Mexican Conference on Pattern recognition (MCPr)

作者： ramirez-robles, Ethery Angel Jara-Maldonado, Miguel Etcheverry, Gibran Univ Americas Puebla Sta Catarina Martir Cholula Mexico

ISBN: (纸本)9783030210779;9783030210762

Heartbeat modeling allows to detect anomalies that reflect the functioning of the heart. Certain approaches face this problem by using Gaussian Mixture Models (GMMs) and other statistical classifiers by extracting the fiducial points provided by the MIT-BIH database. In this work, MIT-BIH database heartbeats are modeled into different heartbeat types from a single subject by using the Gibbs Sampling (GS) algorithm. Firstly, a data pre-processing step is performed;this step involves several tasks such as filtering the raw signals from the MIT-BIH database and reducing the heartbeat types to five. Secondly, the GS is applied to the resulting signals of one subject. Thirdly, the Euclidean distance between each heartbeat type is calculated, and lastly, the Bhattacharyya distance is used to classify heartbeats. The results obtained by the GS algorithm were also compared to results obtained by applying the Expectation Maximization (EM) algorithm to the same data-set. results allow to conclude that GS is a proper solution for separating each heartbeat type;by providing a significant difference between each heartbeat type which can be used for classification.

关键词： Arrhythmia Electrocardiogram Gibbs Sampling algorithm Expectation Maximization QrS complex r programming

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An Insight Platform for Clinicians to Identify Outbreaks of Antimicrobial resistant Bacteria

An Insight Platform for Clinicians to Identify Outbreaks of ...

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2024 IEEE MIT Undergraduate research Technology Conference, UrTC 2024

作者： Penugonda, Ajay Rock Ridge High School AshburnVA United States

ISBN: (纸本)9798331531003

Several surveillance systems have been developed to share data about the spread of antimicrobial resistance, a global public health crisis. However, often, data is limited in spatiotemporal resolution and scope, hampering the application of data shared in clinical practice. While whole-genome sequencing systems can provide information to help control clinical antimicrobial resistance, these systems require expertise from bioinformaticians. This paper discusses a pipeline that integrates whole genome-sequencing data with other genomic information, and a graphical user interface, which together provide an intuitive platform for clinicians to monitor antimicrobial resistance and gain applicable insights for clinical practice. © 2024 IEEE.

关键词： AMr antimicrobial resistance bioinformatics python r programming surveillance

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