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Towards a Fully Automated Extraction and Interpretation of Tabular Data Using Machine Learning
UPTEC F 19050 Examensarbete 30 hp August 2019 Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Per Hedbrant Master Thesis in Engineering Physics Department of Engineering Sciences Uppsala University Sweden Abstract Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Teknisk- naturvetenskaplig fakultet UTH-enheten Motivation A challenge for researchers at CBCS is the ability to efficiently manage the Besöksadress: different data formats that frequently are changed. Significant amount of time is Ångströmlaboratoriet Lägerhyddsvägen 1 spent on manual pre-processing, converting from one format to another. There are Hus 4, Plan 0 currently no solutions that uses pattern recognition to locate and automatically recognise data structures in a spreadsheet. Postadress: Box 536 751 21 Uppsala Problem Definition The desired solution is to build a self-learning Software as-a-Service (SaaS) for Telefon: automated recognition and loading of data stored in arbitrary formats. The aim of 018 – 471 30 03 this study is three-folded: A) Investigate if unsupervised machine learning Telefax: methods can be used to label different types of cells in spreadsheets. B) 018 – 471 30 00 Investigate if a hypothesis-generating algorithm can be used to label different types of cells in spreadsheets. C) Advise on choices of architecture and Hemsida: technologies for the SaaS solution. http://www.teknat.uu.se/student Method A pre-processing framework is built that can read and pre-process any type of spreadsheet into a feature matrix. Different datasets are read and clustered. An investigation on the usefulness of reducing the dimensionality is also done. -
Kwame Nkrumah University of Science and Technology, Kumasi
KWAME NKRUMAH UNIVERSITY OF SCIENCE AND TECHNOLOGY, KUMASI, GHANA Assessing the Social Impacts of Illegal Gold Mining Activities at Dunkwa-On-Offin by Judith Selassie Garr (B.A, Social Science) A Thesis submitted to the Department of Building Technology, College of Art and Built Environment in partial fulfilment of the requirement for a degree of MASTER OF SCIENCE NOVEMBER, 2018 DECLARATION I hereby declare that this work is the result of my own original research and this thesis has neither in whole nor in part been prescribed by another degree elsewhere. References to other people’s work have been duly cited. STUDENT: JUDITH S. GARR (PG1150417) Signature: ........................................................... Date: .................................................................. Certified by SUPERVISOR: PROF. EDWARD BADU Signature: ........................................................... Date: ................................................................... Certified by THE HEAD OF DEPARTMENT: PROF. B. K. BAIDEN Signature: ........................................................... Date: ................................................................... i ABSTRACT Mining activities are undertaken in many parts of the world where mineral deposits are found. In developing nations such as Ghana, the activity is done both legally and illegally, often with very little or no supervision, hence much damage is done to the water bodies where the activities are carried out. This study sought to assess the social impacts of illegal gold mining activities at Dunkwa-On-Offin, the capital town of Upper Denkyira East Municipality in the Central Region of Ghana. The main objectives of the research are to identify factors that trigger illegal mining; to identify social effects of illegal gold mining activities on inhabitants of Dunkwa-on-Offin; and to suggest effective ways in curbing illegal mining activities. Based on the approach to data collection, this study adopts both the quantitative and qualitative approach. -
Statistical Process Control Tools: a Practical Guide for Jordanian Industrial Organizations
Volume 4, Number 6, December 2010 ISSN 1995-6665 JJMIE Pages 693 - 700 Jordan Journal of Mechanical and Industrial Engineering www.jjmie.hu.edu.jo Statistical Process Control Tools: A Practical guide for Jordanian Industrial Organizations Rami Hikmat Fouad*, Adnan Mukattash Department of Industrial Engineering, Hashemite University, Jordan. Abstract The general aim of this paper is to identify the key ingredients for successful quality management in any industrial organization. Moreover, to illustrate how is it important to realize the intergradations between Statistical Process Control (SPC) is seven tools (Pareto Diagram, Cause and Effect Diagram, Check Sheets, Process Flow Diagram, Scatter Diagram, Histogram and Control Charts), and how to effectively implement and to earn the full strength of these tools. A case study has been carried out to monitor real life data in a Jordanian manufacturing company that specialized in producing steel. Flow process chart was constructed, Check Sheets were designed, Pareto Diagram, scatter diagrams, Histograms was used. The vital few problems were identified; it was found that the steel tensile strength is the vital few problem and account for 72% of the total results of the problems. The principal aim of the project is to train quality team on how to held an effective Brainstorming session and exploit these data in cause and effect diagram construction. The major causes of nonconformities and root causes of the quality problems were specified, and possible remedies were proposed. © 2010 Jordan Journal of Mechanical and Industrial Engineering. All rights reserved Keywords: Statistical Process Control, Check sheets, Process Flow Diagram, Pareto Diagram, Histogram, Scatter Diagram, Control Charts, Brainstorming, and Cause and Effect Diagram. -
In This Issue
SCASA: SOUTHERN CALIFORNIA CHA P- E-Tidings Newsletter TER OF THE AMERI- CAN STATISTICAL ASSOCIATION SCASA Events and News VOLUME 8, ISSUES 1 - 2 JANUARY - FEBRUARY 2019 In This Issue Page 2: New SCASA board Pages 3-4: Presidential address Page 5: Book Club Page 6: Online store Page 7: Statistics Poster competition Page 8: DataFest Page 9: Careers Day Page 10: Applied Statistics Workshop Page 11: Traveling course Page 12: Job Opening Announcement Page 13: Dr. Normalcurvesaurus, Ph.D. presents The answer is at the bottom of this issue. http://community.amstat.org/scasa/newsletters VOLUME 8, ISSUES 1 - 2 P A G E 2 SCASA Officers 2019-2020 : CONGRATULATIONS TO ALL ELECTED AND RE-ELECTED!!! We have the newly elected SCASA board!!! Congratulations to Everyone!!! President: James Joseph, AKAKIA [[email protected]] President-Elect: Rebecca Le, County of Riverside [[email protected]] Immediate Past President: Olga Korosteleva, CSULB [[email protected]] Treasurer: Olga Korosteleva, CSULB [[email protected]] Secretary: Michael Tsiang, UCLA [[email protected]] Vice President of Professional Affairs: Anna Liza Antonio, Enterprise Analytics [[email protected]] Vice President of Academic Affairs: Shujie Ma, UCR [[email protected]] Vice President for Student Affairs: Anna Yu Lee, APU and Claremont Graduate University [[email protected]] The ASA Council of Chapters Representative: Harold Dyck, CSUSB [[email protected]] ENewsletter Editor-in-Chief: Olga Korosteleva, CSULB [[email protected]] Chair of the Applied Statistics Workshop Committee: James Joseph, AKAKIA [[email protected]] Treasurer of the Applied Statistics Workshop: Rebecca Le, County of Riverside [[email protected]] Webmaster: Anthony Doan, CSULB [[email protected]] http://community.amstat.org/scasa/newsletters P A G E 3 VOLUME 8, ISSUES 1 - 2 “GROW STRONG” Presidential Address Southern California may be the most diverse job market in the United States, if not the world. -
Assessing the Environmental Adaptation of Wildlife And
Assessing the Environmental Adaptation of Wildlife and Production Animals Production and Wildlife of Adaptation Assessing Environmental the Assessing the Environmental Adaptation of Wildlife and • Edward Narayan Edward • Production Animals Applications of Physiological Indices and Welfare Assessment Tools Edited by Edward Narayan Printed Edition of the Special Issue Published in Animals www.mdpi.com/journal/animals Assessing the Environmental Adaptation of Wildlife and Production Animals: Applications of Physiological Indices and Welfare Assessment Tools Assessing the Environmental Adaptation of Wildlife and Production Animals: Applications of Physiological Indices and Welfare Assessment Tools Editor Edward Narayan MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin Editor Edward Narayan The University of Queensland Australia Editorial Office MDPI St. Alban-Anlage 66 4052 Basel, Switzerland This is a reprint of articles from the Special Issue published online in the open access journal Animals (ISSN 2076-2615) (available at: https://www.mdpi.com/journal/animals/special issues/ environmental adaptation). For citation purposes, cite each article independently as indicated on the article page online and as indicated below: LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. Journal Name Year, Volume Number, Page Range. ISBN 978-3-0365-0142-0 (Hbk) ISBN 978-3-0365-0143-7 (PDF) © 2021 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher areproperly credited, which ensures maximum dissemination and a wider impact of our publications. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND. -
Cause and Effect Diagrams
Online Student Guide Cause and Effect Diagrams OpusWorks 2019, All Rights Reserved 1 Table of Contents LEARNING OBJECTIVES ....................................................................................................................................4 INTRODUCTION ..................................................................................................................................................4 WHAT IS A “ROOT CAUSE”? ......................................................................................................................................................... 4 WHAT IS A “ROOT CAUSE ANALYSIS”?...................................................................................................................................... 4 ADDRESSING THE ROOT CAUSE .................................................................................................................................................. 5 ROOT CAUSE ANALYSIS: THREE BASIC STEPS ........................................................................................................................ 5 CAUSE AND EFFECT TOOLS .......................................................................................................................................................... 6 FIVE WHYS ...........................................................................................................................................................6 THE FIVE WHYS ............................................................................................................................................................................ -
General Linear Models - Part II
Introduction to General and Generalized Linear Models General Linear Models - part II Henrik Madsen Poul Thyregod Informatics and Mathematical Modelling Technical University of Denmark DK-2800 Kgs. Lyngby October 2010 Henrik Madsen Poul Thyregod (IMM-DTU) Chapman & Hall October 2010 1 / 32 Today Test for model reduction Type I/III SSQ Collinearity Inference on individual parameters Confidence intervals Prediction intervals Residual analysis Henrik Madsen Poul Thyregod (IMM-DTU) Chapman & Hall October 2010 2 / 32 Tests for model reduction Assume that a rather comprehensive model (a sufficient model) H1 has been formulated. Initial investigation has demonstrated that at least some of the terms in the model are needed to explain the variation in the response. The next step is to investigate whether the model may be reduced to a simpler model (corresponding to a smaller subspace),. That is we need to test whether all the terms are necessary. Henrik Madsen Poul Thyregod (IMM-DTU) Chapman & Hall October 2010 3 / 32 Successive testing, type I partition Sometimes the practical problem to be solved by itself suggests a chain of hypothesis, one being a sub-hypothesis of the other. In other cases, the statistician will establish the chain using the general rule that more complicated terms (e.g. interactions) should be removed before simpler terms. In the case of a classical GLM, such a chain of hypotheses n corresponds to a sequence of linear parameter-spaces, Ωi ⊂ R , one being a subspace of the other. n R ⊆ ΩM ⊂ ::: ⊂ Ω2 ⊂ Ω1 ⊂ R ; where Hi : µ 2 -
Título Del Artículo
Software for learning and for doing statistics and probability – Looking back and looking forward from a personal perspective Software para aprender y para hacer estadística y probabilidad – Mirando atrás y adelante desde una perspectiva personal Rolf Biehler Universität Paderborn, Germany Abstract The paper discusses requirements for software that supports both the learning and the doing of statistics. It looks back into the 1990s and looks forward to new challenges for such tools stemming from an updated conception of statistical literacy and challenges from big data, the exploding use of data in society and the emergence of data science. A focus is on Fathom, TinkerPlots and Codap, which are looked at from the perspective of requirements for tools for statistics education. Experiences and success conditions for using these tools in various educational contexts are reported, namely in primary and secondary education and pre- service and in-service teacher education. New challenges from data science require new tools for education with new features. The paper finishes with some ideas and experience from a recent project on data science education at the upper secondary level Keywords: software for learning and doing statistics, key attributes of software, data science education, statistical literacy Resumen El artículo discute los requisitos que el software debe cumplir para que pueda apoyar tanto el aprendizaje como la práctica de la estadística. Mira hacia la década de 1990 y hacia el futuro, para identificar los nuevos desafíos que para estas herramientas surgen de una concepción actualizada de la alfabetización estadística y los desafíos que plantean el uso de big data, la explosión del uso masivo de datos en la sociedad y la emergencia de la ciencia de los datos. -
Root Cause Analysis of Defects in Automobile Fuel Pumps: a Case Study
International Journal of Management, IT & Engineering Vol. 7 Issue 4, April 2017, ISSN: 2249-0558 Impact Factor: 7.119 Journal Homepage: http://www.ijmra.us, Email: [email protected] Double-Blind Peer Reviewed Refereed Open Access International Journal - Included in the International Serial Directories Indexed & Listed at: Ulrich's Periodicals Directory ©, U.S.A., Open J-Gage as well as in Cabell‟s Directories of Publishing Opportunities, U.S.A ROOT CAUSE ANALYSIS OF DEFECTS IN AUTOMOBILE FUEL PUMPS: A CASE STUDY Saurav Adhikari* Nilesh Sachdeva* Dr. D.R. Prajapati** ABSTRACT Quality can be directly measured from the degree to which customer requirements are satisfied. Some problems were reported by the customers of the automobile company under study in the fuel pumps; which is used in an automobile to transfer the fuel from fuel tank to fuel injection system after filtration.This paper presents the implementation of Quality Control tools– Check Sheet, Fishbone Diagram(or Ishikawa Diagram), ParetoChartand 5-Why analysis tools for identification and elimination of the root cause/s responsible for malfunctioning of the fuel pump in customers‟ cars. From the Check sheet and Pareto analysis, two major defects were identified which accounted for more than 80% of the problems being reported. The root causes of these two defects affecting the product quality of the company were then further analyzed using the 5- Why analysis. Keywords: Quality Control Tools, Ishikawa Diagram, Pareto Chart, 5-Why Analysis * Undergraduate Student, Department of Mechanical Engineering, PEC University of Technology, (formerly Punjab Engineering College), Chandigarh ** Associate Professor& Corresponding Author, Department of Mechanical Engineering, PEC University of Technology (formerly Punjab Engineering College), Chandigarh 90 International journal of Management, IT and Engineering http://www.ijmra.us, Email: [email protected] ISSN: 2249-0558Impact Factor: 7.119 1. -
Stats: Data and Models We’Ve Taken This Principle Still Further
Sample Preface. Not for Distribution. PREFACE tats: Data and Models, fifth edition, has been especially exciting to develop. The book you hold steps beyond our previous editions in several important ways. Of Scourse, we’ve kept our conversational style and anecdotes,1 but we’ve enriched that material with tools for teaching about randomness, sampling distribution models, and inference throughout the book. And we’ve expanded discussions of models for data to introduce models with more than two variables earlier in the text. We’ve taken our inspiration both from our experience in the classroom and from the 2016 revision of the Guidelines for Assessment and Instruction in Statistics Education (GAISE) report adopted by the American Statistical Association. As a result, we increased the text’s innovative uses of technology to encourage more statistical thinking, while maintaining its traditional core concepts and coverage. You’ll notice that, to expand our attention beyond just one or two variables, we’ve adjusted the order of some topics. Innovations Technology One of the new GAISE guidelines states: Use technology to explore concepts and analyze data. We think a modern statistics text should recognize from the start that statistics is practiced with technology. And so should our students. You won’t find tedious calcula- tions worked by hand. You will find equation forms that favor intuition over calculation. You’ll find extensive use of real data—even large datasets. Throughout, you’ll find a focus on statistical thinking rather than calculation. The question that motivates each of our hundreds of examples is not “How do you calculate the answer?” but “How do you think about the answer?” For this edition of Stats: Data and Models we’ve taken this principle still further. -
Antibiotic De-Escalation Experience in the Setting of Emergency Department: a Retrospective, Observational Study
Journal of Clinical Medicine Article Antibiotic De-escalation Experience in the Setting of Emergency Department: A Retrospective, Observational Study Silvia Corcione 1,2, Simone Mornese Pinna 1 , Tommaso Lupia 3,* , Alice Trentalange 1, Erika Germanò 1, Rossana Cavallo 4, Enrico Lupia 1 and Francesco Giuseppe De Rosa 1,2 1 Department of Medical Sciences, University of Turin, 10126 Turin, Italy; [email protected] (S.C.); [email protected] (S.M.P.); [email protected] (A.T.); [email protected] (E.G.); [email protected] (E.L.); [email protected] (F.G.D.R.) 2 Tufts University School of Medicine, Boston, MA 02129, USA 3 Infectious Diseases Unit, Cardinal Massaia Hospital, 14100 Asti, Italy 4 Microbiology and Virology Unit, University of Turin, 10126 Turin, Italy; [email protected] * Correspondence: [email protected]; Tel.: +39-0141-486-404 or +39-3462-248-637 Abstract: Background: Antimicrobial de-escalation (ADE) is a part of antimicrobial stewardship strategies aiming to minimize unnecessary or inappropriate antibiotic exposure to decrease the rate of antimicrobial resistance. Information regarding the effectiveness and safety of ADE in the setting of emergency medicine wards (EMW) is lacking. Methods: Adult patients admitted to EMW and receiving empiric antimicrobial treatment were retrospectively studied. The primary outcome was the rate and timing of ADE. Secondary outcomes included factors associated with early ADE, length of stay, and in-hospital mortality. Results: A total of 336 patients were studied. An initial regimen combining two agents was prescribed in 54.8%. Ureidopenicillins and carbapenems were Citation: Corcione, S.; Mornese the most frequently empiric treatment prescribed (25.1% and 13.6%). -
Integrating Food Production and Biodiversity
Integrating Food Production and Biodiversity Energy and Scale Issues in Implementation Kristina Belfrage Faculty of Natural Resources and Agricultural Sciences Department of Urban and Rural Development Uppsala Doctoral Thesis Swedish University of Agricultural Sciences Uppsala 2014 Acta Universitatis agriculturae Sueciae 2014:57 Cover: Landscape at the research site, Roslagen, Sweden (photo: M.Olsson) ISSN 1652-6880 ISBN (print version) 978-91-576-8062-4 ISBN (electronic version) 978-91-576-8063-1 © 2014 Kristina Belfrage, Uppsala Print: SLU Service/Repro, Uppsala 2014 Integrating food production and biodiversity – energy and scale issues in implementation Abstract The aim of this thesis was to test the hypotheses that (1) biodiversity at a farm level differs between small and large farms, and (2) it is possible to combine high biodiversity at farm level with high food production, sustainable nutrient circulation, and self-sufficiency in fuels. In the research area in SE Sweden, six small farms (<52 ha) and six large farms (>135 ha) were selected for the studies. The farm with the highest biodiversity was selected as a case study farm for the productivity and biofuel studies. Differences in biodiversity between small and large farms were assessed by comparing number of birds and herbaceous plant species plus the number of bird territories, bumblebees, and butterflies. Both on-farm heterogeneity and surrounding landscape heterogeneity were measured by calculating the Shannon-Wiener Diversity Index. Productivity was measured as the number of people supplied with food with different livestock combinations and types of biofuels. The biofuel scenarios were evaluated regarding their impact on the number of people supplied with food, and NPK fluxes at farm level.