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Добірка наукової літератури з теми "MACHINE LEARNING TOOL"

Автор: Grafiati
Опубліковано: 26 жовтня 2023

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Статті в журналах з теми "MACHINE LEARNING TOOL"

1

E, Prabhakar, Suresh Kumar V.S, Nandagopal S, and Dhivyaa C.R. "Mining Better Advertisement Tool for Government Schemes Using Machine Learning." International Journal of Psychosocial Rehabilitation 23, no. 4 (December 20, 2019): 1122–35. http://dx.doi.org/10.37200/ijpr/v23i4/pr190439.

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Monostori, László. "Learning procedures in machine tool monitoring." Computers in Industry 7, no. 1 (February 1986): 53–64. http://dx.doi.org/10.1016/0166-3615(86)90009-6.

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3

Kokar, Mieczyslaw M., Jerzy Letkowski, and Thomas F. Callahan. "Learning to monitor a machine tool." Journal of Intelligent & Robotic Systems 12, no. 2 (June 1995): 103–25. http://dx.doi.org/10.1007/bf01258381.

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4

Gittler, Thomas, Stephan Scholze, Alisa Rupenyan, and Konrad Wegener. "Machine Tool Component Health Identification with Unsupervised Learning." Journal of Manufacturing and Materials Processing 4, no. 3 (September 2, 2020): 86. http://dx.doi.org/10.3390/jmmp4030086.

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Анотація:
Unforeseen machine tool component failures cause considerable losses. This study presents a new approach to unsupervised machine component condition identification. It uses test cycle data of machine components in healthy and various faulty conditions for modelling. The novelty in the approach consists of the time series representation as features, the filtering of the features for statistical significance, and the use of this feature representation to train a clustering model. The benefit in the proposed approach is its small engineering effort, the potential for automation, the small amount of data necessary for training and updating the model, and the potential to distinguish between multiple known and unknown conditions. Online measurements on machines in unknown conditions are performed to predict the component condition with the aid of the trained model. The approach was exemplarily tested and verified on different healthy and faulty states of a grinding machine axis. For the accurate classification of the component condition, different clustering algorithms were evaluated and compared. The proposed solution demonstrated encouraging results as it accurately classified the component condition. It requires little data, is straightforward to implement and update, and is able to precisely differentiate minor differences of faults in test cycle time series.
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5

Baltruschat, Marcel, and Paul Czodrowski. "Machine learning meets pKa." F1000Research 9 (February 13, 2020): 113. http://dx.doi.org/10.12688/f1000research.22090.1.

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Анотація:
We present a small molecule pKa prediction tool entirely written in Python. It predicts the macroscopic pKa value and is trained on a literature compilation of monoprotic compounds. Different machine learning models were tested and random forest performed best given a five-fold cross-validation (mean absolute error=0.682, root mean squared error=1.032, correlation coefficient r2 =0.82). We test our model on two external validation sets, where our model performs comparable to Marvin and is better than a recently published open source model. Our Python tool and all data is freely available at https://github.com/czodrowskilab/Machine-learning-meets-pKa.
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Baltruschat, Marcel, and Paul Czodrowski. "Machine learning meets pKa." F1000Research 9 (April 27, 2020): 113. http://dx.doi.org/10.12688/f1000research.22090.2.

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Анотація:
We present a small molecule pKa prediction tool entirely written in Python. It predicts the macroscopic pKa value and is trained on a literature compilation of monoprotic compounds. Different machine learning models were tested and random forest performed best given a five-fold cross-validation (mean absolute error=0.682, root mean squared error=1.032, correlation coefficient r2 =0.82). We test our model on two external validation sets, where our model performs comparable to Marvin and is better than a recently published open source model. Our Python tool and all data is freely available at https://github.com/czodrowskilab/Machine-learning-meets-pKa.
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Finlay, Janet. "Machine learning: A tool to support usability?" Applied Artificial Intelligence 11, no. 7-8 (October 1997): 633–51. http://dx.doi.org/10.1080/088395197117966.

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8

Caté, Antoine, Lorenzo Perozzi, Erwan Gloaguen, and Martin Blouin. "Machine learning as a tool for geologists." Leading Edge 36, no. 3 (March 2017): 215–19. http://dx.doi.org/10.1190/tle36030215.1.

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Whitehall, B. L., S. C. Y. Lu, and R. E. Stepp. "CAQ: A machine learning tool for engineering." Artificial Intelligence in Engineering 5, no. 4 (October 1990): 189–98. http://dx.doi.org/10.1016/0954-1810(90)90020-5.

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Wang, Zhi‐Lei, Toshio Ogawa, and Yoshitaka Adachi. "A Machine Learning Tool for Materials Informatics." Advanced Theory and Simulations 3, no. 1 (November 18, 2019): 1900177. http://dx.doi.org/10.1002/adts.201900177.

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Дисертації з теми "MACHINE LEARNING TOOL"

1

Wusteman, Judith. "EBKAT : an explanation-based knowledge acquisition tool." Thesis, University of Exeter, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.280682.

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Cooper, Clayton Alan. "Milling Tool Condition Monitoring Using Acoustic Signals and Machine Learning." Case Western Reserve University School of Graduate Studies / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1575539872711423.

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BUBACK, SILVANO NOGUEIRA. "USING MACHINE LEARNING TO BUILD A TOOL THAT HELPS COMMENTS MODERATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2011. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=19232@1.

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Анотація:
Uma das mudanças trazidas pela Web 2.0 é a maior participação dos usuários na produção do conteúdo, através de opiniões em redes sociais ou comentários nos próprios sites de produtos e serviços. Estes comentários são muito valiosos para seus sites pois fornecem feedback e incentivam a participação e divulgação do conteúdo. Porém excessos podem ocorrer através de comentários com palavrões indesejados ou spam. Enquanto para alguns sites a própria moderação da comunidade é suficiente, para outros as mensagens indesejadas podem comprometer o serviço. Para auxiliar na moderação dos comentários foi construída uma ferramenta que utiliza técnicas de aprendizado de máquina para auxiliar o moderador. Para testar os resultados, dois corpora de comentários produzidos na Globo.com foram utilizados, o primeiro com 657.405 comentários postados diretamente no site, e outro com 451.209 mensagens capturadas do Twitter. Nossos experimentos mostraram que o melhor resultado é obtido quando se separa o aprendizado dos comentários de acordo com o tema sobre o qual está sendo comentado.
One of the main changes brought by Web 2.0 is the increase of user participation in content generation mainly in social networks and comments in news and service sites. These comments are valuable to the sites because they bring feedback and motivate other people to participate and to spread the content. On the other hand these comments also bring some kind of abuse as bad words and spam. While for some sites their own community moderation is enough, for others this impropriate content may compromise its content. In order to help theses sites, a tool that uses machine learning techniques was built to mediate comments. As a test to compare results, two datasets captured from Globo.com were used: the first one with 657.405 comments posted through its site and the second with 451.209 messages captured from Twitter. Our experiments show that best result is achieved when comment learning is done according to the subject that is being commented.
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Binsaeid, Sultan Hassan. "Multisensor Fusion for Intelligent Tool Condition Monitoring (TCM) in End Milling Through Pattern Classification and Multiclass Machine Learning." Scholarly Repository, 2007. http://scholarlyrepository.miami.edu/oa_dissertations/7.

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Анотація:
In a fully automated manufacturing environment, instant detection of condition state of the cutting tool is essential to the improvement of productivity and cost effectiveness. In this paper, a tool condition monitoring system (TCM) via machine learning (ML) and machine ensemble (ME) approach was developed to investigate the effectiveness of multisensor fusion when machining 4340 steel with multi-layer coated and multi-flute carbide end mill cutter. Feature- and decision-level information fusion models utilizing assorted combinations of sensors were studied against selected ML algorithms and their majority vote ensemble to classify gradual and transient tool abnormalities. The criterion for selecting the best model does not only depend on classification accuracy but also on the simplicity of the implemented system where the number of features and sensors is kept to a minimum to enhance the efficiency of the online acquisition system. In this study, 135 different features were extracted from sensory signals of force, vibration, acoustic emission and spindle power in the time and frequency domain by using data acquisition and signal processing modules. Then, these features along with machining parameters were evaluated for significance by using different feature reduction techniques. Specifically, two feature extraction methods were investigated: independent component analysis (ICA), and principal component analysis (PCA) and two feature selection methods were studied, chi square and correlation-based feature selection (CFS). For various multi-sensor fusion models, an optimal feature subset is computed. Finally, ML algorithms using support vector machine (SVM), multilayer perceptron neural networks (MLP), radial basis function neural network (RBF) and their majority voting ensemble were studied for selected features to classify not only flank wear but also breakage and chipping. In this research, it has been found that utilizing the multisensor feature fusion technique under majority vote ensemble gives the highest classification performance. In addition, SVM outperformed other ML algorithms while CFS feature selection method surpassed other reduction techniques in improving classification performance and producing optimal feature sets for different models.
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Gert, Oskar. "Using Machine Learning as a Tool to Improve Train Wheel Overhaul Efficiency." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-171121.

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Анотація:
This thesis develops a method for using machine learning in a industrial pro-cess. The implementation of this machine learning model aimed to reduce costsand increase efficiency of train wheel overhaul in partnership with the AustrianFederal Railroads, Oebb. Different machine learning models as well as categoryencodings were tested to find which performed best on the data set. In addition,differently sized training sets were used to determine whether size of the trainingset affected the results. The implementation shows that Oebb can save moneyand increase efficiency of train wheel overhaul by using machine learning andthat continuous training of prediction models is necessary because of variationsin the data set.
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EDIN, ANTON, and MARIAM QORBANZADA. "E-Learning as a tool to support the integration of machine learning in product development processes." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279757.

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Анотація:
This research is concerned with possible applications of e-Learning as an alternative to onsite training sessions when supporting the integration of machine learning into the product development process. Mainly, its aim was to study if e-learning approaches are viable for laying a foundation for making machine learning more accessible in integrated product development processes. This topic presents itself as interesting as advances in the general understanding of it enable better remote learning as well as general scalability of knowledge transfer. To achieve this two groups of employees belonging to the same corporate group but working in two very different geographical regions where asked to participate in a set of training session created by the authors. One group received the content via in-person workshops whereas the other was invited to a series of remote tele-conferences. After both groups had participated in the sessions, some member where asked to be interviewed. Additionally. The authors also arranged for interviews with some of the participants’ direct managers and project leaders to compare the participants’ responses with some stakeholders not participating in the workshops. A combination of a qualitative theoretical analysis together with the interview responses was used as the base for the presented results. Respondents indicated that they preferred the onsite training approach, however, further coding of interview responses showed that there was little difference in the participants ability to obtain knowledge. Interestingly, while results point towards e-learning as a technology with many benefits, it seems as though other shortcomings, mainly concerning the human interaction between learners, may hold back its full potential and thereby hinder its integration into product development processes.
Detta forskningsarbete fokuserar på tillämpningar av elektroniska utlärningsmetoder som alternativ till lokala lektioner vid integrering av maskininlärning i produktutvecklingsprocessen. Framförallt är syftet att undersöka om det går att använda elektroniska utlärningsmetoder för att göra maskininlärning mer tillgänglig i produktutvecklingsprocessen. Detta ämne presenterar sig som intressant då en djupare förståelse kring detta banar väg för att effektivisera lärande på distans samt skalbarheten av kunskapsspridning. För att uppnå detta bads två grupper av anställda hos samma företagsgrupp, men tillhörande olika geografiska områden att ta del i ett upplägg av lektioner som författarna hade tagit fram. En grupp fick ta del av materialet genom seminarier, medan den andra bjöds in till att delta i en serie tele-lektioner. När båda deltagargrupper hade genomgått lektionerna fick några deltagare förfrågningar om att bli intervjuade. Några av deltagarnas direkta chefer och projektledare intervjuades även för att kunna jämföra deltagarnas åsikter med icke-deltagande intressenter. En kombination av en kvalitativ teoretisk analys tillsammans med svaren från intervjuerna användes som bas för de presenterade resultaten. Svarande indikerade att de föredrog träningarna som hölls på plats, men vidare kodning av intervjusvaren visade på undervisningsmetoden inte hade större påverkningar på deltagarnas förmåga att ta till sig materialet. Trots att resultatet pekar på att elektroniskt lärande är en teknik med många fördelar verkar det som att brister i teknikens förmåga att integrera mänsklig interaktion hindrar den från att nå sitt fulla potential och därigenom även hindrar dess integration i produktutvecklingsprocessen.
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Bheemireddy, Shruthi. "MACHINE LEARNING-BASED ONTOLOGY MAPPING TOOL TO ENABLE INTEROPERABILITY IN COASTAL SENSOR NETWORKS." MSSTATE, 2009. http://sun.library.msstate.edu/ETD-db/theses/available/etd-09222009-200303/.

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Анотація:
In todays world, ontologies are being widely used for data integration tasks and solving information heterogeneity problems on the web because of their capability in providing explicit meaning to the information. The growing need to resolve the heterogeneities between different information systems within a domain of interest has led to the rapid development of individual ontologies by different organizations. These ontologies designed for a particular task could be a unique representation of their project needs. Thus, integrating distributed and heterogeneous ontologies by finding semantic correspondences between their concepts has become the key point to achieve interoperability among different representations. In this thesis, an advanced instance-based ontology matching algorithm has been proposed to enable data integration tasks in ocean sensor networks, whose data are highly heterogeneous in syntax, structure, and semantics. This provides a solution to the ontology mapping problem in such systems based on machine-learning methods and string-based methods.
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8

Hashmi, Muhammad Ali S. M. Massachusetts Institute of Technology. "Said-Huntington Discourse Analyzer : a machine-learning tool for classifying and analyzing discourse." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/98543.

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Анотація:
Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2015.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 71-74).
Critical discourse analysis (CDA) aims to understand the link "between language and the social" (Mautner and Baker, 2009), and attempts to demystify social construction and power relations (Gramsci, 1999). On the other hand, corpus linguistics deals with principles and practice of understanding the language produced within large amounts of textual data (Oostdijk, 1991). In my thesis, I have aimed to combine, using machine learning, the CDA approach with corpus linguistics with the intention of deconstructing dominant discourses that create, maintain and deepen fault lines between social groups and classes. As an instance of this technological framework, I have developed a tool for understanding and defining the discourse on Islam in the global mainstream media sources. My hypothesis is that the media coverage in several mainstream news sources tends to contextualize Muslims largely as a group embroiled in conflict at a disproportionately large level. My hypothesis is based on the assumption that discourse on Islam in mainstream global media tends to lean toward the dangerous "clash of civilizations" frame. To test this hypothesis, I have developed a prototype tool "Said-Huntington Discourse Analyzer" that machine classifies news articles on a normative scale -- a scale that measures "clash of civilization" polarization in an article on the basis of conflict. The tool also extracts semantically meaningful conversations for a media source using Latent Dirichlet Allocation (LDA) topic modeling, allowing the users to discover frames of conversations on the basis of Said-Huntington index classification. I evaluated the classifier on human-classified articles and found that the accuracy of the classifier was very high (99.03%). Generally, text analysis tools uncover patterns and trends in the data without delineating the 'ideology' that permeates the text. The machine learning tool presented here classifies media discourse on Islam in terms of conflict and non-conflict, and attempts to put light on the 'ideology' that permeates the text. In addition, the tool provides textual analysis of news articles based on the CDA methodologies.
by Muhammad Ali Hashmi.
S.M.
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9

McCoy, Mason Eugene. "A Twitter-Based Prediction Tool for Digital Currency." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/theses/2302.

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Digital currencies (cryptocurrencies) are rapidly becoming commonplace in the global market. Trading is performed similarly to the stock market or commodities, but stock market prediction algorithms are not necessarily well-suited for predicting digital currency prices. In this work, we analyzed tweets with both an existing sentiment analysis package and a manually tailored "objective analysis," resulting in one impact value for each analysis per 15-minute period. We then used evolutionary techniques to select the most appropriate training method and the best subset of the generated features to include, as well as other parameters. This resulted in implementation of predictors which yielded much more profit in four-week simulations than simply holding a digital currency for the same time period--the results ranged from 28% to 122% profit. Unlike stock exchanges, which shut down for several hours or days at a time, digital currency prediction and trading seems to be of a more consistent and predictable nature.
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Lutero, Gianluca. "A Tool For Data Analysis Using Autoencoders." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20510/.

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In this thesis will be showed the design and development of SpeechTab, a web application that collects structured speech data from different subjects and a technique that try to tell which one is affected of cognitive decline or not.
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Книги з теми "MACHINE LEARNING TOOL"

1

Houser, David Allan. Machine learning as a quality improvement tool. Ottawa: National Library of Canada, 1996.

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2

Building intelligent agents: An apprenticeship multistrategy learning theory, methodology, tool and case studies. San Diego: Academic Press, 1998.

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3

Learning computer numerical control. Albany, NY: Delmar Publishers, 1992.

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4

Cost-sensitive machine learning. Boca Raton, FL: CRC Press, 2012.

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5

Khosrowpour, Mehdi, and Information Resources Management Association. Machine learning: Concepts, methodologies, tools and applications. Hershey, PA: Information Science Reference, 2012.

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6

Eibe, Frank, and Hall Mark A, eds. Data mining: Practical machine learning tools and techniques. 3rd ed. Burlington, MA: Morgan Kaufmann, 2011.

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7

Machine learning: A probabilistic perspective. Cambridge, MA: MIT Press, 2012.

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8

Castiello, Maria Elena. Computational and Machine Learning Tools for Archaeological Site Modeling. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-88567-0.

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Pardalos, Panos M., Stamatina Th Rassia, and Arsenios Tsokas, eds. Artificial Intelligence, Machine Learning, and Optimization Tools for Smart Cities. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-84459-2.

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Witten, I. H. Data mining: Practical machine learning tools and techniques with Java implementations. San Francisco, Calif: Morgan Kaufmann, 2000.

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Частини книг з теми "MACHINE LEARNING TOOL"

1

Olson, Randal S., and Jason H. Moore. "TPOT: A Tree-Based Pipeline Optimization Tool for Automating Machine Learning." In Automated Machine Learning, 151–60. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05318-5_8.

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Patil, Nilesh M., Tanmay P. Rane, and Anmol A. Panjwani. "ML Suite: An Auto Machine Learning Tool." In Machine Learning for Predictive Analysis, 483–89. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7106-0_48.

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3

Nguifo, Engelbert Mephu, and Patrick Njiwoua. "Using lattice-based framework as a tool for feature extraction." In Machine Learning: ECML-98, 304–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0026700.

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4

Morin, Emmanuel, and Emmanuelle Martienne. "Using a Symbolic Machine Learning Tool to Refine Lexico-syntactic Patterns." In Machine Learning: ECML 2000, 292–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45164-1_31.

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5

Anvari, Hamidreza, and Paul Lu. "iPerfOPS: A Tool for Machine Learning-Based Optimization Through Protocol Selection." In Machine Learning for Networking, 36–55. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36183-8_4.

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6

Herrmann, Jürgen, Reiner Ackermann, Jörg Peters, and Detlef Reipa. "A multistrategy learning system and its integration into an interactive floorplanning tool." In Machine Learning: ECML-94, 138–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-57868-4_55.

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Inza, Iñaki, Borja Calvo, Rubén Armañanzas, Endika Bengoetxea, Pedro Larrañaga, and José A. Lozano. "Machine Learning: An Indispensable Tool in Bioinformatics." In Methods in Molecular Biology, 25–48. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60327-194-3_2.

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8

Kokate, Meera B., Bhushan T. Patil, and Geetha Subramanian. "Machine Learning as a Smart Manufacturing Tool." In Proceedings of International Conference on Intelligent Manufacturing and Automation, 359–66. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4485-9_37.

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9

El Amine Lazouni, Mohammed, Mostafa El Habib Daho, Nesma Settouti, Mohammed Amine Chikh, and Saïd Mahmoudi. "Machine Learning Tool for Automatic ASA Detection." In Modeling Approaches and Algorithms for Advanced Computer Applications, 9–16. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00560-7_5.

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Cacciari, I., and G. F. Pocobelli. "Machine Learning: A Novel Tool for Archaeology." In Handbook of Cultural Heritage Analysis, 961–1002. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-60016-7_33.

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Тези доповідей конференцій з теми "MACHINE LEARNING TOOL"

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Chong, Ang Boon, Ch'ng Pei Chun, Cheah Wye Luon, Ngo Seow Yin, Lee Ching Yee, Chin Kevin Che Chau, Ng Kok Aur, and Nor Affizal Amirah. "Implementation Tool Machine Learning Features Evaluation." In 2023 IEEE Symposium on Industrial Electronics & Applications (ISIEA). IEEE, 2023. http://dx.doi.org/10.1109/isiea58478.2023.10212351.

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Li, Ming, and Mihai Burzo. "Tool Wear Monitoring Using Machine Learning." In 2021 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2021. http://dx.doi.org/10.1109/ccece53047.2021.9569060.

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Hernandez, Alejandra, Clara Gomez, Marina Galli, Jonathan Crespo, and Ramon Barber. "PLAYING AND LEARNING TOOL BASED ON MACHINE LEARNING." In 10th annual International Conference of Education, Research and Innovation. IATED, 2017. http://dx.doi.org/10.21125/iceri.2017.0528.

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Letford, Flynn, Max Rogers, Xun Xu, and Yuqian Lu. "Machine Learning to Empower a Cyber-Physical Machine Tool." In 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). IEEE, 2020. http://dx.doi.org/10.1109/case48305.2020.9216842.

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Song, Wontaek, In-Wook Oh, Joon-Soo Lee, Chaeeun Kim, Eunseok Nam, and Byung-Kwon Min. "Energy Consumption Estimation of Machine Tool Using Machine Learning." In International Conference of Asian Society for Precision Engineering and Nanotechnology. Singapore: Research Publishing Services, 2022. http://dx.doi.org/10.3850/978-981-18-6021-8_or-06-0260.html.

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Liu, Chia-Ruei, Li-Hua Duan, Po-Wei Chen, and Chao-Chun Yang. "Monitoring Machine Tool Based on External Physical Characteristics of the Machine Tool Using Machine Learning Algorithm." In 2018 First International Conference on Artificial Intelligence for Industries (AI4I). IEEE, 2018. http://dx.doi.org/10.1109/ai4i.2018.8665696.

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Moysen, Jessica, Lorenza Giupponi, and Josep Mangues-Bafalluy. "A machine learning enabled network planning tool." In 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). IEEE, 2016. http://dx.doi.org/10.1109/pimrc.2016.7794909.

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Liu, Yelin, Yang Liu, Tsong Yueh Chen, and Zhi Quan Zhou. "A Testing Tool for Machine Learning Applications." In ICSE '20: 42nd International Conference on Software Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3387940.3392694.

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Li, Wei, Ming Yang, and Zi-cai Wang. "Flexible Simulation Data Collection and Replay Tool." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258607.

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Wu, Yu-Chuan, and Zhi-Yong Zuo. "Temperature Intelligent Control System Research for Quencher Machine Tool." In 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370236.

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Звіти організацій з теми "MACHINE LEARNING TOOL"

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Ludwig, Jens, and Sendhil Mullainathan. Machine Learning as a Tool for Hypothesis Generation. Cambridge, MA: National Bureau of Economic Research, March 2023. http://dx.doi.org/10.3386/w31017.

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Xie, Bin. DiagSoftfailure: Automated Soft-Failure Diagnostic Tool Using Machine Learning for Network Users. Office of Scientific and Technical Information (OSTI), November 2019. http://dx.doi.org/10.2172/1575995.

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Yoo, Shinjae, Yonggang Cui, Ji Hwan Park, Yuewei Lin, and Yihui Ren. Development of a software tool for IAEA use of the YOLOv3 machine learning algorithm. Office of Scientific and Technical Information (OSTI), February 2019. http://dx.doi.org/10.2172/1494041.

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Chou, Roger, Tracy Dana, and Kanaka D. Shetty. Testing a Machine Learning Tool for Facilitating Living Systematic Reviews of Chronic Pain Treatments. Agency for Healthcare Research and Quality (AHRQ), November 2020. http://dx.doi.org/10.23970/ahrqepcmethtestingmachinelearning.

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Frash, Luke, and Bulbul Ahmmed. GeoThermalCloud: A Machine Learning Tool for Discovery, Exploration, and Development of Hidden Geothermal Resources. Office of Scientific and Technical Information (OSTI), September 2023. http://dx.doi.org/10.2172/2007326.

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Alonso-Robisco, Andrés, José Manuel Carbó, and José Manuel Carbó. Machine Learning methods in climate finance: a systematic review. Madrid: Banco de España, February 2023. http://dx.doi.org/10.53479/29594.

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Анотація:
Preventing the materialization of climate change is one of the main challenges of our time. The involvement of the financial sector is a fundamental pillar in this task, which has led to the emergence of a new field in the literature, climate finance. In turn, the use of Machine Learning (ML) as a tool to analyze climate finance is on the rise, due to the need to use big data to collect new climate-related information and model complex non-linear relationships. Considering the proliferation of articles in this field, and the potential for the use of ML, we propose a review of the academic literature to assess how ML is enabling climate finance to scale up. The main contribution of this paper is to provide a structure of application domains in a highly fragmented research field, aiming to spur further innovative work from ML experts. To pursue this objective, first we perform a systematic search of three scientific databases to assemble a corpus of relevant studies. Using topic modeling (Latent Dirichlet Allocation) we uncover representative thematic clusters. This allows us to statistically identify seven granular areas where ML is playing a significant role in climate finance literature: natural hazards, biodiversity, agricultural risk, carbon markets, energy economics, ESG factors & investing, and climate data. Second, we perform an analysis highlighting publication trends; and thirdly, we show a breakdown of ML methods applied by research area.
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Gates, Allison, Michelle Gates, Shannon Sim, Sarah A. Elliott, Jennifer Pillay, and Lisa Hartling. Creating Efficiencies in the Extraction of Data From Randomized Trials: A Prospective Evaluation of a Machine Learning and Text Mining Tool. Agency for Healthcare Research and Quality (AHRQ), August 2021. http://dx.doi.org/10.23970/ahrqepcmethodscreatingefficiencies.

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Анотація:
Background. Machine learning tools that semi-automate data extraction may create efficiencies in systematic review production. We prospectively evaluated an online machine learning and text mining tool’s ability to (a) automatically extract data elements from randomized trials, and (b) save time compared with manual extraction and verification. Methods. For 75 randomized trials published in 2017, we manually extracted and verified data for 21 unique data elements. We uploaded the randomized trials to ExaCT, an online machine learning and text mining tool, and quantified performance by evaluating the tool’s ability to identify the reporting of data elements (reported or not reported), and the relevance of the extracted sentences, fragments, and overall solutions. For each randomized trial, we measured the time to complete manual extraction and verification, and to review and amend the data extracted by ExaCT (simulating semi-automated data extraction). We summarized the relevance of the extractions for each data element using counts and proportions, and calculated the median and interquartile range (IQR) across data elements. We calculated the median (IQR) time for manual and semiautomated data extraction, and overall time savings. Results. The tool identified the reporting (reported or not reported) of data elements with median (IQR) 91 percent (75% to 99%) accuracy. Performance was perfect for four data elements: eligibility criteria, enrolment end date, control arm, and primary outcome(s). Among the top five sentences for each data element at least one sentence was relevant in a median (IQR) 88 percent (83% to 99%) of cases. Performance was perfect for four data elements: funding number, registration number, enrolment start date, and route of administration. Among a median (IQR) 90 percent (86% to 96%) of relevant sentences, pertinent fragments had been highlighted by the system; exact matches were unreliable (median (IQR) 52 percent [32% to 73%]). A median 48 percent of solutions were fully correct, but performance varied greatly across data elements (IQR 21% to 71%). Using ExaCT to assist the first reviewer resulted in a modest time savings compared with manual extraction by a single reviewer (17.9 vs. 21.6 hours total extraction time across 75 randomized trials). Conclusions. Using ExaCT to assist with data extraction resulted in modest gains in efficiency compared with manual extraction. The tool was reliable for identifying the reporting of most data elements. The tool’s ability to identify at least one relevant sentence and highlight pertinent fragments was generally good, but changes to sentence selection and/or highlighting were often required.
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Lasko, Kristofer, and Elena Sava. Semi-automated land cover mapping using an ensemble of support vector machines with moderate resolution imagery integrated into a custom decision support tool. Engineer Research and Development Center (U.S.), November 2021. http://dx.doi.org/10.21079/11681/42402.

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Land cover type is a fundamental remote sensing-derived variable for terrain analysis and environmental mapping applications. The currently available products are produced only for a single season or a specific year. Some of these products have a coarse resolution and quickly become outdated, as land cover type can undergo significant change over a short time period. In order to enable on-demand generation of timely and accurate land cover type products, we developed a sensor-agnostic framework leveraging pre-trained machine learning models. We also generated land cover models for Sentinel-2 (20m) and Landsat 8 imagery (30m) using either a single date of imagery or two dates of imagery for mapping land cover type. The two-date model includes 11 land cover type classes, whereas the single-date model contains 6 classes. The models’ overall accuracies were 84% (Sentinel-2 single date), 82% (Sentinel-2 two date), and 86% (Landsat 8 two date) across the continental United States. The three different models were built into an ArcGIS Pro Python toolbox to enable a semi-automated workflow for end users to generate their own land cover type maps on demand. The toolboxes were built using parallel processing and image-splitting techniques to enable faster computation and for use on less-powerful machines.
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Cary, Dakota, and Daniel Cebul. Destructive Cyber Operations and Machine Learning. Center for Security and Emerging Technology, November 2020. http://dx.doi.org/10.51593/2020ca003.

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Анотація:
Machine learning may provide cyber attackers with the means to execute more effective and more destructive attacks against industrial control systems. As new ML tools are developed, CSET discusses the ways in which attackers may deploy these tools and the most effective avenues for industrial system defenders to respond.
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Harris, Philip. Physics Community Needs, Tools, and Resources for Machine Learning. Office of Scientific and Technical Information (OSTI), March 2022. http://dx.doi.org/10.2172/1873720.

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