Relevant bibliographies by topics / Software classification

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Software classification'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Software classification"

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Konet, I. M., and T. P. Pylypiuk. "PEDAGOGICAL SOFTWARE FOR PHYSICS: CLASSIFICATION, ANALYSIS, CREATION TOOLS." Collection of scientific papers of Kamianets-Podilskyi National Ivan Ohiienko University. Pedagogical series, no. 24 (November 29, 2018): 63–66. http://dx.doi.org/10.32626/2307-4507.2018-24.63-66.

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Warintarawej, P., M. Huchard, M. Lafourcade, A. Laurent, and P. Pompidor. "Software understanding: Automatic classification of software identifiers." Intelligent Data Analysis 19, no. 4 (2015): 761–78. http://dx.doi.org/10.3233/ida-150744.

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Feoktistov, Aleksandr G., Aleksandr S. Korsukov, and Olga Yu Basharina. "CLASSIFICATION OF SCALABLE SOFTWARE COMPLEXES." Proceedings of Irkutsk State Technical University 21, no. 11 (2017): 92–103. http://dx.doi.org/10.21285/1814-3520-2017-11-92-103.

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Singh, Mradul. "Software Bug Classification and Assignment." IOSR Journal of Engineering 3, no. 7 (2013): 01–03. http://dx.doi.org/10.9790/3021-03730103.

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Shelekhov, V. I. "Program Classification in Software Engineering." PROGRAMMNAYA INGENERIA 7, no. 12 (2016): 531–38. http://dx.doi.org/10.17587/prin.7.531-538.

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Ji, Shujuan, and Xiaohong Bao. "Research on Software Hazard Classification." Procedia Engineering 80 (2014): 407–14. http://dx.doi.org/10.1016/j.proeng.2014.09.098.

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Negishi, Hirokazu. "Tentative classification of global software." Behaviour & Information Technology 4, no. 2 (1985): 163–70. http://dx.doi.org/10.1080/01449298508901796.

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Tichý, Lubomír. "JUICE, software for vegetation classification." Journal of Vegetation Science 13, no. 3 (2002): 451–53. http://dx.doi.org/10.1111/j.1654-1103.2002.tb02069.x.

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Yusof, Yuhanis, and Qusai Hussein Ra. "Automation of Software Artifacts Classification." International Journal of Soft Computing 5, no. 3 (2010): 109–15. http://dx.doi.org/10.3923/ijscomp.2010.109.115.

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Lavrischeva, E. M. "Classification of software engineering disciplines." Cybernetics and Systems Analysis 44, no. 6 (2008): 791–96. http://dx.doi.org/10.1007/s10559-008-9053-5.

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Dissertations / Theses on the topic "Software classification"

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Wang, Hui. "Software Defects Classification Prediction Based On Mining Software Repository." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-216554.

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An important goal during the cycle of software development is to find and fix existing defects as early as possible. This has much to do with software defects prediction and management. Nowadays,many  big software development companies have their own development repository, which typically includes a version control system and a bug tracking system. This has no doubt proved useful for software defects prediction. Since the 1990s researchers have been mining software repository to get a deeper understanding of the data. As a result they have come up with some software defects prediction models the past few years. There are basically two categories among these prediction models. One category is to predict how many defects still exist according to the already captured defects data in the earlier stage of the software life-cycle. The other category is to predict how many defects there will be in the newer version software according to the earlier version of the software defects data. The complexities of software development bring a lot of issues which are related with software defects. We have to consider these issues as much as possible to get precise prediction results, which makes the modeling more complex. This thesis presents the current research status on software defects classification prediction and the key techniques in this area, including: software metrics, classifiers, data pre-processing and the evaluation of the prediction results. We then propose a way to predict software defects classification based on mining software repository. A way to collect all the defects during the development of software from the Eclipse version control systems and map these defects with the defects information containing in software defects tracking system to get the statistical information of software defects, is described. Then the Eclipse metrics plug-in is used to get the software metrics of files and packages which contain defects. After analyzing and preprocessing the dataset, the tool(R) is used to build a prediction models on the training dataset, in order to predict software defects classification on different levels on the testing dataset, evaluate the performance of the model and comparedifferent models’ performance.
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Dijkstra, Semme Josua. "Software tools developed for seafloor classification." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0031/NQ62170.pdf.

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Konda, Swetha Reddy. "Classification of software components based on clustering." Morgantown, W. Va. : [West Virginia University Libraries], 2007. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5510.

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Thesis (M.S.)--West Virginia University, 2007.<br>Title from document title page. Document formatted into pages; contains vi, 59 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 57-59).
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Lester, Neil. "Assisting the software reuse process through classification and retrieval of software models." Thesis, University of Ulster, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.311531.

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Henningsson, Kennet. "A Fault Classification Approach to Software Process Improvement." Licentiate thesis, Karlskrona : Blekinge Institute of Technology [Blekinge tekniska högskola], 2005. http://www.bth.se/fou/Forskinfo.nsf/allfirst2/2b9d5998e26ed1b2c12571230047386b?OpenDocument.

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Lamont, Morné Michael Connell. "Binary classification trees : a comparison with popular classification methods in statistics using different software." Thesis, Stellenbosch : Stellenbosch University, 2002. http://hdl.handle.net/10019.1/52718.

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Thesis (MComm) -- Stellenbosch University, 2002.<br>ENGLISH ABSTRACT: Consider a data set with a categorical response variable and a set of explanatory variables. The response variable can have two or more categories and the explanatory variables can be numerical or categorical. This is a typical setup for a classification analysis, where we want to model the response based on the explanatory variables. Traditional statistical methods have been developed under certain assumptions such as: the explanatory variables are numeric only and! or the data follow a multivariate normal distribution. hl practice such assumptions are not always met. Different research fields generate data that have a mixed structure (categorical and numeric) and researchers are often interested using all these data in the analysis. hl recent years robust methods such as classification trees have become the substitute for traditional statistical methods when the above assumptions are violated. Classification trees are not only an effective classification method, but offer many other advantages. The aim of this thesis is to highlight the advantages of classification trees. hl the chapters that follow, the theory of and further developments on classification trees are discussed. This forms the foundation for the CART software which is discussed in Chapter 5, as well as other software in which classification tree modeling is possible. We will compare classification trees to parametric-, kernel- and k-nearest-neighbour discriminant analyses. A neural network is also compared to classification trees and finally we draw some conclusions on classification trees and its comparisons with other methods.<br>AFRIKAANSE OPSOMMING: Beskou 'n datastel met 'n kategoriese respons veranderlike en 'n stel verklarende veranderlikes. Die respons veranderlike kan twee of meer kategorieë hê en die verklarende veranderlikes kan numeries of kategories wees. Hierdie is 'n tipiese opset vir 'n klassifikasie analise, waar ons die respons wil modelleer deur gebruik te maak van die verklarende veranderlikes. Tradisionele statistiese metodes is ontwikkelonder sekere aannames soos: die verklarende veranderlikes is slegs numeries en! of dat die data 'n meerveranderlike normaal verdeling het. In die praktyk word daar nie altyd voldoen aan hierdie aannames nie. Verskillende navorsingsvelde genereer data wat 'n gemengde struktuur het (kategories en numeries) en navorsers wil soms al hierdie data gebruik in die analise. In die afgelope jare het robuuste metodes soos klassifikasie bome die alternatief geword vir tradisionele statistiese metodes as daar nie aan bogenoemde aannames voldoen word nie. Klassifikasie bome is nie net 'n effektiewe klassifikasie metode nie, maar bied baie meer voordele. Die doel van hierdie werkstuk is om die voordele van klassifikasie bome uit te wys. In die hoofstukke wat volg word die teorie en verdere ontwikkelinge van klassifikasie bome bespreek. Hierdie vorm die fondament vir die CART sagteware wat bespreek word in Hoofstuk 5, asook ander sagteware waarin klassifikasie boom modelering moontlik is. Ons sal klassifikasie bome vergelyk met parametriese-, "kernel"- en "k-nearest-neighbour" diskriminant analise. 'n Neurale netwerk word ook vergelyk met klassifikasie bome en ten slote word daar gevolgtrekkings gemaak oor klassifikasie bome en hoe dit vergelyk met ander metodes.
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Williams, Byron Joseph. "A FRAMEWORK FOR ASSESSING THE IMPACT OF SOFTWARE CHANGES TO SOFTWARE ARCHITECTURE USING CHANGE CLASSIFICATION." MSSTATE, 2006. http://sun.library.msstate.edu/ETD-db/theses/available/etd-04172006-150444/.

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Software developers must produce software that can be changed without the risk of degrading the software architecture. One way to address software changes is to classify their causes and effects. A software change classification mechanism allows engineers to develop a common approach for handling changes. This information can be used to show the potential impact of the change. The goal of this research is to develop a change classification scheme that can be used to address causes of architectural degradation. This scheme can be used to model the effects of changes to software architecture. This research also presents a study of the initial architecture change classification scheme. The results of the study indicated that the classification scheme was easy to use and provided some benefit to developers. In addition, the results provided some evidence that changes of different types (in this classification scheme) required different amounts of effort to implement.
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Graham, Martin. "Visualising multiple overlapping classification hierarchies." Thesis, Edinburgh Napier University, 2001. http://researchrepository.napier.ac.uk/Output/2430.

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The revision or reorganisation of hierarchical data sets can result in many possible hierarchical classifications composed of the same or overlapping data sets existing in parallel with each other. These data sets are difficult for people to handle and conceptualise, as they try to reconcile the different perspectives and structures that such data represents. One area where this situation occurs is the study of botanical taxonomy, essentially the classification and naming of plants. Revisions, new discoveries and new dimensions for classifying plants lead to a proliferation of classifications over the same set of plant data. Taxonomists would like a method of exploring these multiple overlapping hierarchies for interesting information, correlations, or anomalies. The application and extension of Information Visualisation (IV) techniques, the graphical display of abstract information, is put forward as a solution to this problem. Displaying the multiple classification hierarchies in a visually appealing manner along with powerful interaction mechanisms for examination and exploration of the data allows taxonomists to unearth previously hidden information. This visualisation gives detail that previous visualisations and statistical overviews cannot offer. This thesis work has extended previous IV work in several respects to achieve this goal. Compact, yet full and unambiguous, hierarchy visualisations have been developed. Linking and brushing techniques have been extended to work on a higher class of structure, namely overlapping trees and hierarchies. Focus and context techniques have been pushed to achieve new effects across the visually distinct representations of these multiple hierarchies. Other data types, such as multidimensional data and large cluster hierarchies have also been displayed using the final version of the visualisation.
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Lee, Kee Khoon. "Interpretable classification model for automotive material fatigue." Thesis, University of Southampton, 2002. https://eprints.soton.ac.uk/361578/.

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Manley, Gary W. "The classification and evaluation of Computer-Aided Software Engineering tools." Thesis, Monterey, California: Naval Postgraduate School, 1990. http://hdl.handle.net/10945/34910.

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Approved for public release; distribution unlimited.<br>The use of Computer-Aided Software Engineering (CASE) tools has been viewed as a remedy for the software development crisis by achieving improved productivity and system quality via the automation of all or part of the software engineering process. The proliferation and tremendous variety of tools available have stretched the understanding of experienced practitioners and has had a profound impact on the software engineering process itself. To understand what a tool does and compare it to similar tools is a formidable task given the existing diversity of functionality. This thesis investigates what tools are available, proposes a general classification scheme to assist those investigating tools to decide where a tool falls within the software engineering process and identifies a tool's capabilities and limitations. This thesis also provides guidance for the evaluation of a tool and evaluates three commercially available tools.
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Books on the topic "Software classification"

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Yang, Xiang, and SpringerLink (Online service), eds. Software Similarity and Classification. Springer London, 2012.

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Cesare, Silvio, and Yang Xiang. Software Similarity and Classification. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2909-7.

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Lester, Neil. Assisting the software reuse process through classification and retrieval of software models. The Author], 2000.

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Jain, Anil K. Real-Time Object Measurement and Classification. Springer Berlin Heidelberg, 1988.

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Paget, François. Vers & virus: Classification, lutte anti-virale et perspectives. Dunod, 2005.

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Akc̜akaya, H. R. RAMAS red list: Threatened species classifications under uncertainty. Applied Biomathematics, 1999.

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Burkov, Aleksey. Technical operation of electric ships. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1048423.

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The book investigates the issues related to improving the efficiency of technical operation of ship electric drives, developed their classification. Identified ship's drives, having low reliability, designed and implemented technical solutions to increase their reliability. The appropriateness of the integrated assessment within the tasks of a mathematical and physical modeling. Developed and implemented mathematical and physical models for studies of electric drives. The proposed method, an algorithmic software, and made payments of contactors for work in the proposed technical solutions.&#x0D; Designed for those who specializiruetsya in the field of the theory and practice of ship electric drives. Useful for the learning process in the system of higher Maritime education.
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Logunova, Oksana, Petr Romanov, and Elena Il'ina. Processing of experimental data on a computer. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1064882.

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The textbook provides information about the main methods and tools for automating computational processes used in data processing; methods for representing and generating models of experimental data; data models and classification of processing tasks; and the organization of the user interface in automated systems for processing experimental data. Contains structured chapters on the specifics of experimental research. The features of using software for processing experimental data are clearly and logically described. Theoretical material and basic algorithms for processing experimental data used in industrial statistics are presented. Examples of processing experimental data in the field of metallurgy and management in higher education are given. Meets the requirements of the Federal state educational standards of higher education of the latest generation. For students and postgraduates of higher educational institutions.
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Office, General Accounting. Computer security: FAA needs to improve controls over use of foreign nationals to remediate and review software : report to the Chairman, Committee on Science, House of Representatives. The Office, 1999.

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Grigor'ev, Anatoliy, and Evgeniy Isaev. Methods and algorithms of data processing. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1032305.

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The tutorial deals with selected methods and algorithms of data processing, the sequence of solving problems of processing and analysis of data to create models behavior of the object taking into account all the components of its mathematical model. Describes the types of technological methods for the use of software and hardware for solving problems in this area. The algorithms of distributions, regressions vremenny series, transform them with the aim of obtaining mathematical models and prediction of the behavior information and economic systems (objects).&#x0D; The second edition is supplemented by materials that are in demand by researchers in the part of the correct use of clustering algorithms. Are elements of the classification algorithms to identify their capabilities, strengths and weaknesses. Are the procedures of justification and verify the adequacy of the results of the cluster analysis, conducted a comparison and evaluation of different clustering techniques, given information about visualization of multidimensional data and examples of practical application of clustering algorithms.&#x0D; Meets the requirements of Federal state educational standards of higher education of the last generation.&#x0D; For students of economic specialties, specialists, and graduate students.
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Book chapters on the topic "Software classification"

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Herrera, Francisco, Francisco Charte, Antonio J. Rivera, and María J. del Jesus. "Multilabel Software." In Multilabel Classification. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41111-8_9.

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Berztiss, Alfs. "Classification of Software." In Software Methods for Business Reengineering. Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-3980-2_8.

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Cesare, Silvio, and Yang Xiang. "Software Birthmark Similarity." In Software Similarity and Classification. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2909-7_8.

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Helfrich, James. "Classification of Attack." In Security for Software Engineers. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9780429506475-5.

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Handoyo, Eko, Slinger Jansen, and Sjaak Brinkkemper. "Software Ecosystem Roles Classification." In Lecture Notes in Business Information Processing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39336-5_21.

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Qin, Zheng, Huidi Zhang, Xin Qin, et al. "Classification and Software Culture." In Fundamentals of Software Culture. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0701-0_3.

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Cesare, Silvio, and Yang Xiang. "Software Similarity Searching and Classification." In Software Similarity and Classification. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2909-7_9.

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Ruczinski, Ingo, Charles Kooperberg, and Michael LeBlanc. "Logic Regression — Methods and Software." In Nonlinear Estimation and Classification. Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21579-2_21.

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Klein, Harald. "Classification of Text Analysis Software." In Classification and Knowledge Organization. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-59051-1_37.

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Cesare, Silvio, and Yang Xiang. "Introduction." In Software Similarity and Classification. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2909-7_1.

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Conference papers on the topic "Software classification"

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"HRV Computation as Embedded Software." In The First International Workshop on Biosignal Processing and Classification. SciTePress - Science and and Technology Publications, 2005. http://dx.doi.org/10.5220/0001195401500157.

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Lovstad, M., and V. Guttormsen. "Impact Of Common Structural Rules On Software For Ship Design And Strength Assessment." In Developments in Classification & International Regulation 2007. RINA, 2007. http://dx.doi.org/10.3940/rina.dcir.2007.05.

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Wohlin, Claes, and Darja mite. "Classification of Software Transfers." In 2012 19th Asia-Pacific Software Engineering Conference (APSEC). IEEE, 2012. http://dx.doi.org/10.1109/apsec.2012.137.

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Lin, Jianhui. "On Malicious Software Classification." In 2008 International Symposium on Intelligent Information Technology Application Workshops. IEEE, 2008. http://dx.doi.org/10.1109/iita.workshops.2008.106.

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Heasley, J. N., and Coryn A. L. Bailer-Jones. "The Pan-STARRS Data Processing and Science Analysis Software Systems." In CLASSIFICATION AND DISCOVERY IN LARGE ASTRONOMICAL SURVEYS: Proceedings of the International Conference: “Classification and Discovery in Large Astronomical Surveys”. AIP, 2008. http://dx.doi.org/10.1063/1.3059075.

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Alrumaih, Hala, Abdulrahman Mirza, and Hessah Alsalamah. "Toward Automated Software Requirements Classification." In 2018 21st Saudi Computer Society National Computer Conference (NCC). IEEE, 2018. http://dx.doi.org/10.1109/ncg.2018.8593012.

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Gao, Junting, Liping Zhang, Fengrong Zhao, and Ye Zhai. "Research on Software Defect Classification." In 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2019. http://dx.doi.org/10.1109/itnec.2019.8729440.

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Owens, Womack, and Gonzalez. "Software error classification using Purify." In Proceedings of International Conference on Software Maintenance. IEEE, 1996. http://dx.doi.org/10.1109/icsm.1996.564994.

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Chunting Yang. "The classification and cultivating software engineers." In 2010 International Conference on Artificial Intelligence and Education (ICAIE). IEEE, 2010. http://dx.doi.org/10.1109/icaie.2010.5641099.

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Majidi, Emad, Mahdieh Alemi, and Hasan Rashidi. "Software Architecture: A Survey and Classification." In 2010 Second International Conference on Communication Software and Networks. IEEE, 2010. http://dx.doi.org/10.1109/iccsn.2010.94.

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Reports on the topic "Software classification"

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Firth, Robert, Bill Wood, Rich Pethia, Lauren Roberts, and Vicky Mosley. A Classification Scheme for Software Development Methods. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada200606.

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Boisvert, Ronald F., Sally E. Howe, and David K. Kahaner. The guide to available mathematical software problem classification system. National Institute of Standards and Technology, 1990. http://dx.doi.org/10.6028/nist.ir.4475.

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Firth, Robert, Vicky Mosley, Richard Pethla, Lauren Roberts, and William Wood. A Guide to the Classification and Assessment of Software Engineering Tools. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada213968.

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Samson, Scott, Dmitry Goldgof, Thomas Hopkins, and Lawrence Hall. Development of Automated Image Analysis Software for Suspended Marine Particle Classification. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada627430.

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Samson, Scott, Dmitry Goldgol, Thomas Hopkins, and Lawrence Hall. Development of Automated Image Analysis Software for Suspended Marine Particle Classification. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada620262.

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Meng, Zhaoyi. High Performance Computing and Real Time Software for High Dimensional Data Classification. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1485604.

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Lasko, Kristofer, and Sean Griffin. Monitoring Ecological Restoration with Imagery Tools (MERIT) : Python-based decision support tools integrated into ArcGIS for satellite and UAS image processing, analysis, and classification. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40262.

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Monitoring the impacts of ecosystem restoration strategies requires both short-term and long-term land surface monitoring. The combined use of unmanned aerial systems (UAS) and satellite imagery enable effective landscape and natural resource management. However, processing, analyzing, and creating derivative imagery products can be time consuming, manually intensive, and cost prohibitive. In order to provide fast, accurate, and standardized UAS and satellite imagery processing, we have developed a suite of easy-to-use tools integrated into the graphical user interface (GUI) of ArcMap and ArcGIS Pro as well as open-source solutions using NodeOpenDroneMap. We built the Monitoring Ecological Restoration with Imagery Tools (MERIT) using Python and leveraging third-party libraries and open-source software capabilities typically unavailable within ArcGIS. MERIT will save US Army Corps of Engineers (USACE) districts significant time in data acquisition, processing, and analysis by allowing a user to move from image acquisition and preprocessing to a final output for decision-making with one application. Although we designed MERIT for use in wetlands research, many tools have regional or global relevancy for a variety of environmental monitoring initiatives.
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McCarthy, Noel, Eileen Taylor, Martin Maiden, et al. Enhanced molecular-based (MLST/whole genome) surveillance and source attribution of Campylobacter infections in the UK. Food Standards Agency, 2021. http://dx.doi.org/10.46756/sci.fsa.ksj135.

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This human campylobacteriosis sentinel surveillance project was based at two sites in Oxfordshire and North East England chosen (i) to be representative of the English population on the Office for National Statistics urban-rural classification and (ii) to provide continuity with genetic surveillance started in Oxfordshire in October 2003. Between October 2015 and September 2018 epidemiological questionnaires and genome sequencing of isolates from human cases was accompanied by sampling and genome sequencing of isolates from possible food animal sources. The principal aim was to estimate the contributions of the main sources of human infection and to identify any changes over time. An extension to the project focussed on antimicrobial resistance in study isolates and older archived isolates. These older isolates were from earlier years at the Oxfordshire site and the earliest available coherent set of isolates from the national archive at Public Health England (1997/8). The aim of this additional work was to analyse the emergence of the antimicrobial resistance that is now present among human isolates and to describe and compare antimicrobial resistance in recent food animal isolates. Having identified the presence of bias in population genetic attribution, and that this was not addressed in the published literature, this study developed an approach to adjust for bias in population genetic attribution, and an alternative approach to attribution using sentinel types. Using these approaches the study estimated that approximately 70% of Campylobacter jejuni and just under 50% of C. coli infection in our sample was linked to the chicken source and that this was relatively stable over time. Ruminants were identified as the second most common source for C. jejuni and the most common for C. coli where there was also some evidence for pig as a source although less common than ruminant or chicken. These genomic attributions of themselves make no inference on routes of transmission. However, those infected with isolates genetically typical of chicken origin were substantially more likely to have eaten chicken than those infected with ruminant types. Consumption of lamb’s liver was very strongly associated with infection by a strain genetically typical of a ruminant source. These findings support consumption of these foods as being important in the transmission of these infections and highlight a potentially important role for lamb’s liver consumption as a source of Campylobacter infection. Antimicrobial resistance was predicted from genomic data using a pipeline validated by Public Health England and using BIGSdb software. In C. jejuni this showed a nine-fold increase in resistance to fluoroquinolones from 1997 to 2018. Tetracycline resistance was also common, with higher initial resistance (1997) and less substantial change over time. Resistance to aminoglycosides or macrolides remained low in human cases across all time periods. Among C. jejuni food animal isolates, fluoroquinolone resistance was common among isolates from chicken and substantially less common among ruminants, ducks or pigs. Tetracycline resistance was common across chicken, duck and pig but lower among ruminant origin isolates. In C. coli resistance to all four antimicrobial classes rose from low levels in 1997. The fluoroquinolone rise appears to have levelled off earlier and among animals, levels are high in duck as well as chicken isolates, although based on small sample sizes, macrolide and aminoglycoside resistance, was substantially higher than for C. jejuni among humans and highest among pig origin isolates. Tetracycline resistance is high in isolates from pigs and the very small sample from ducks. Antibiotic use following diagnosis was relatively high (43.4%) among respondents in the human surveillance study. Moreover, it varied substantially across sites and was highest among non-elderly adults compared to older adults or children suggesting opportunities for improved antimicrobial stewardship. The study also found evidence for stable lineages over time across human and source animal species as well as some tighter genomic clusters that may represent outbreaks. The genomic dataset will allow extensive further work beyond the specific goals of the study. This has been made accessible on the web, with access supported by data visualisation tools.
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