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19th International Conference on Computational Science and Its Applications (ICCSA)

作者： Ferrao, Maria Eugenia Prata, Paula Univ Beira Interior Covilha Portugal Ctr Appl Math & Econ CEMAPRE Lisbon Portugal Inst Telecomunicacoes IT Covilha Portugal

ISBN: (纸本)9783030243029

This article shows how to conduct multiple imputation in big identifiable data for educational research purposes. The r statistical package and procedures to handle missing data applied for the purpose of this study were "BaylorEdPsych" and "mi". Firstly, we checked that every dataset rejected the null hypothesis for Missing Completely At random (MCAr), using the function "LittleMCAr". Simulated and real data analyses were conducted. results suggest that the improvement of the quality of imputation requires alternative methods to be developed.

关键词： Multiple imputation r programming Big data Education research

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Agile release Planning Using Natural Language Processing Algorithm

Agile Release Planning Using Natural Language Processing Alg...

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Amity International Conference on Artificial Intelligence (AICAI)

作者： Sharma, Sarika Kumar, Deepak Amity Univ AIIT Noida India

ISBN: (纸本)9781538693469

once the requirement is gathered in agile, it is broken down into smaller pre-defined format called user stories. These user stories are then scoped in various sprint releases and delivered accordingly. release planning in Agile becomes challenging when the number of user stories goes up in hundreds. In such scenarios it is very difficult to manually identify similar user stories and package them together into a release. Hence, this paper suggests application of natural language processing algorithms for identifying similar user stories and then scoping them into a release This paper takes the approach to build a word corpus for every project release identified in the project and then to convert the provided user stories into a vector of string using Java utility for calculating top 3 most occurring words from the given project corpus in a user story. Once all the user stories are represented as vector array then by using rV coefficient NLP algorithm the user stories are clustered into various releases of the software project. Using the proposed approach, the release planning for large and complex software engineering projects can be simplified resulting into efficient planning in less time. The automated commercial tools like JIrA and rally can be enhanced to include suggested algorithms for managing release planning in Agile.

关键词： Natural Language Processing Algorithm Software Engineering Agile Methodology release Planning r programming

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A Novel Technique to Design Optimum Bituminous Mix Designs Based on r Studio and Autograph with Integration 9

A Novel Technique to Design Optimum Bituminous Mix Designs B...

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9th International Conference of Materials Processing and Characterization (ICMPC)

作者： Soujanya, Ch. Priya, G. Akhila Penkiramu Kumar, C. Naveen VNRVJIET Hyderbad Telangana India VNRVJIET Dept Civil Engn Hyderbad Telangana India

road has become an entangled part of construction. The construction of pavement involves immense expense of speculation. Different methods are involved to obtain required optimum gradation mix in which Trial and Error method is categorized as prior. The sequence of this method leads to Time consumption, Wastage of materials and most likely it is not precised. To overcome the above limitations a new tool in r-programming is adopted to generate accurate results. This paper emphasis on traditional methods like Midpoint gradation, Trial and Error, Excel solver and compared with new optimisation technique developed by r programming used in Bituminous Mix Design for Dense Bituminous Macadam (Grade-II). Aggregate gradation is said to be optimum when it reaches maximum stability in which the particle arrangement is proper where the smaller are packed with larger particles which reduces the voids and particle to particle contact is achieved. This paper studies on calculating the aggregate gradation area by methods like Trapezoidal and Simpsons rule and has observed that r programming with least gradation area of 4.66 has obtained and by calculating gradation area with integration r programming has got least area of 7.892 compared with different methods. This implies that aggregate gradation mix of r programming is said to be optimum as it is more stable and dense (C) 2019 Published by Elsevier Ltd.

关键词： r programming Autograph tool Optimisation Aggregate blending

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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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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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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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programming Plus Subject Expertise: A Combined Approach for Approval Profile Modification

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COLLECTION MANAGEMENT 2018年第2期43卷 89-100页

作者： Gao, Wenli Turner, Cherie Ke, Irene Univ Houston Lib Houston TX USA

This article focused on the analysis of four years of circulation data from print monographs acquired through an approval plan and firm ordering and incorporated interlibrary loan data to compare purchases with demand. A group of subject areas, including communication, psychology, and engineering were selected as examples for in-depth analysis. Using r programming, the authors automated an analysis to identify areas needing attention. The results from the analysis, together with librarians' subject expertise, enabled librarians to adjust monograph purchases accordingly.

关键词： Approval plan firm orders interlibrary loan r programming usage statistics

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Predicting short-term stock prices using ensemble methods and online data sources

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EXPErT SYSTEMS WITH APPLICATIONS 2018年 112卷 258-273页

作者： Weng, Bin Lu, Lin Wang, Xing Megahed, Fadel M. Martinez, Waldyn Auburn Univ Dept Ind & Syst Engn Auburn AL 36849 USA Miami Univ Dept Informat Syst & Analyt Oxford OH 45056 USA

With the ubiquity of the Internet, platforms such as: Google, Wikipedia and the like can provide insights pertaining to firms' financial performance as well as capture the collective interest of traders through search trends, number of web page visitors and/or financial news sentiment. Information emanating from these platforms can significantly affect, or be affected by, changes in the stock market. The overarching goal of this paper is to develop a financial expert system that incorporates these features to predict short term stock prices. Our expert system is comprised of two main modules: a knowledge base and an artificial intelligence (AI) platform. The "knowledge base" for our expert system captures: (a) historical stock prices;(b) several well-known technical indicators;(c) counts and sentiment scores of published news articles for a given stock;(d) trends in Google searches for the given stock ticker;and (e) number of unique visitors for pertinent Wikipedia pages. Once the data is collected, we use a structured approach for data preparation. Then, the AI platform trains four machine learning ensemble methods: (a) a neural network regression ensemble;(b) a support vector regression ensemble;(c) a boosted regression tree;and (d) a random forest regression. In the cross-validation phase, the AI platform picks the "best" ensemble for a given stock. To evaluate the efficacy of our expert system, we first present a case study based on the Citi Group stock ($C) with data collected from 01/01/2013 - 12/31/2016. We show the expert system can predict the 1-day ahead $C stock price with a mean absolute percent error (MAPE) <= 1.50% and the 1-10 day ahead with a MAPE <= 1.89%, which is better than the reported results in the literature. We show that the use of features extracted from online sources does not substitute the traditional financial metrics, but rather supplements them to improve upon the prediction performance of machine learning based methods. To highlight

关键词： Big data Ensembles Google trends r programming Sentiment analysis Wikipedia

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A Survey of Orbitrap All Ion Fragmentation Analysis Assessed by an r MetaboList Package to Study Small-Molecule Metabolites

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CHrOMATOGrAPHIA 2018年第7期81卷 981-994页

作者： Sentandreu, Enrique Peris-Diaz, Manuel D. Sweeney, Shannon r. Chiou, Jennifer Munoz, Nathalie Tiziani, Stefano Dell Pediat Res Inst 1400 Barbara Jordan Blvd Austin TX 78723 USA Univ Texas Austin Dept Nutr Sci Austin TX 78712 USA Univ Texas Austin Inst Cellular & Mol Biol Austin TX 78712 USA Univ Wroclaw Fac Biotechnol Dept Chem Biol F Joliot Curie 14a PL-50383 Wroclaw Poland

Leukemia cell and melanoma tumor tissue extracts were studied for small (mostly m/z < 250) polar metabolites by LC-ESI-HrMSn analysis powered by a hybrid Quadrupole-Orbitrap. MS data were simultaneously acquired in fast polarity switching mode operating in MS1 and MS/MS (All Ion Fragmentation, AIF) full-scan analyses at high mass resolution. Positive metabolite assignments were achieved by AIF analysis considering at least two characteristic transitions. Targeted metabolite profiling was achieved by the relative quantification of 18 metabolites through spiking of their respective deuterated counterparts. Manual data processing of MS1 and AIF scans were compared for the accurate determination of natural metabolites and their deuterated analogs by chromatographic alignment and peak area integration. Evaluation of manual and automated (MetaboList r package) AIF data processing yielded comparable results. The versatility of AIF analysis also enabled the untargeted metabolite profiling of leukemia and melanoma samples in which 22 and 53 compounds were, respectively, identified outside those studied by labeling. The main limitation of this method was that low abundance metabolites with scan rates below 8 scans/peak could not be accurately quantified by AIF analysis. The combination of AIF analysis with MetaboList r package represents an opportunity to move towards automated, faster, and more global metabolomics approaches supported by an entirely flexible open source data processing platform freely available from Comprehensive r Archive Network (CrAN, https://***/package=MetaboList).

关键词： Liquid chromatography High resolution mass spectrometry Orbitrap All ion fragmentation Targeted analysis Untargeted analysis Metabolomics r programming MS/MS automated data processing MetaboList

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