Literatura científica selecionada sobre o tema "Machine learnings"
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Artigos de revistas sobre o assunto "Machine learnings"
Li, Tianshu. "Fintech Application in Banking Operations - Application of Machine Learning in Mitigating Bank Derivatives Counterparty Risks". Asian Business Research 4, n.º 3 (8 de outubro de 2019): 1. http://dx.doi.org/10.20849/abr.v4i3.652.
Texto completo da fonteMakarov, Vladimir, Christophe Chabbert, Elina Koletou, Fotis Psomopoulos, Natalja Kurbatova, Samuel Ramirez, Chas Nelson, Prashant Natarajan e Bikalpa Neupane. "Good machine learning practices: Learnings from the modern pharmaceutical discovery enterprise". Computers in Biology and Medicine 177 (julho de 2024): 108632. http://dx.doi.org/10.1016/j.compbiomed.2024.108632.
Texto completo da fonteKim, Jin Kook. "A Study on the Estimation Model for the Visitors to Let’s Run Park Using Machine Learning". Korean Journal of Sport Science 32, n.º 3 (30 de setembro de 2021): 411–18. http://dx.doi.org/10.24985/kjss.2021.32.3.411.
Texto completo da fonteMalik, Sehrish, e DoHyeun Kim. "Improved Control Scheduling Based on Learning to Prediction Mechanism for Efficient Machine Maintenance in Smart Factory". Actuators 10, n.º 2 (31 de janeiro de 2021): 27. http://dx.doi.org/10.3390/act10020027.
Texto completo da fontePREETHAM S, M C CHANDRASHEKHAR e M Z KURIAN. "METHODOLOGY FOR IMPLEMENTATION OF PREDICTION MODEL FOR STUDENTS USING MACHINE LEARNING". international journal of engineering technology and management sciences 7, n.º 3 (2023): 764–66. http://dx.doi.org/10.46647/ijetms.2023.v07i03.116.
Texto completo da fonteKurniawan, Robi, e Shunsuke Managi. "Forecasting annual energy consumption using machine learnings: Case of Indonesia". IOP Conference Series: Earth and Environmental Science 257 (10 de maio de 2019): 012032. http://dx.doi.org/10.1088/1755-1315/257/1/012032.
Texto completo da fonteSingh, Priyanka, Chakshu Garg, Aman Namdeo, Krishna Mohan Agarwal e Rajesh Kumar Rai. "Development of Prediction models for Bond Strength of Steel Fiber Reinforced Concrete by Computational Machine Learning". E3S Web of Conferences 220 (2020): 01097. http://dx.doi.org/10.1051/e3sconf/202022001097.
Texto completo da fonteDas, Aditi. "Automatic Personality Identification using Machine Learning". International Journal for Research in Applied Science and Engineering Technology 9, n.º VI (30 de junho de 2021): 3528–34. http://dx.doi.org/10.22214/ijraset.2021.35386.
Texto completo da fonteMalinda Sari Sembiring, Windi Astuti, Iskandar Muda,. "The Influence of Cloud Computing, Artificial Intelligence, Machine Learnings and Digital Disruption on the Design of Accounting and Finance Functions Mediated by Data Processing". International Journal on Recent and Innovation Trends in Computing and Communication 11, n.º 11 (30 de novembro de 2023): 56–62. http://dx.doi.org/10.17762/ijritcc.v11i11.9087.
Texto completo da fonteSendak, Mark P., William Ratliff, Dina Sarro, Elizabeth Alderton, Joseph Futoma, Michael Gao, Marshall Nichols et al. "Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study". JMIR Medical Informatics 8, n.º 7 (15 de julho de 2020): e15182. http://dx.doi.org/10.2196/15182.
Texto completo da fonteTeses / dissertações sobre o assunto "Machine learnings"
Algohary, Ahmad. "PROSTATE CANCER RISK STRATIFICATION USING RADIOMICS FOR PATIENTS ON ACTIVE SURVEILLANCE: MULTI-INSTITUTIONAL USE CASES". Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1599231033923829.
Texto completo da fonteStohr, Daniel Christoph [Verfasser]. "Die beruflichen Anforderungen der Digitalisierung hinsichtlich formaler, physischer und kompetenzspezifischer Aspekte : Eine Analyse von Stellenanzeigen mittels Methoden des Text Minings und Machine Learnings / Daniel Christoph Stohr". Frankfurt a.M. : Peter Lang GmbH, Internationaler Verlag der Wissenschaften, 2019. http://d-nb.info/1185347240/34.
Texto completo da fonteTebbifakhr, Amirhossein. "Machine Translation For Machines". Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/320504.
Texto completo da fonteDinakar, Karthik. "Lensing Machines : representing perspective in machine learning". Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112523.
Texto completo da fonteCataloged from PDF version of thesis. Due to the condition of the original material with text runs off the edges of the pages, the reproduction may have unavoidable flaws.
Includes bibliographical references (pages 167-172).
Generative models are venerated as full probabilistic models that randomly generate observable data given a set of latent variables that cannot be directly observed. They can be used to simulate values for variables in the model, allowing analysis by synthesis or model criticism, towards an iterative cycle of model specification, estimation, and critique. However, many datasets represent a combination of several viewpoints - different ways of looking at the same data that leads to various generalizations. For example, a corpus that has data generated by multiple people may be mixtures of several perspectives and can be viewed with different opinions by others. It isn't always possible to represent the viewpoints by clean separation, in advance, of examples representing each perspective and train a separate model for each point of view. In this thesis, we introduce lensing, a mixed-initiative technique to (i) extract lenses or mappings between machine-learned representations and perspectives of human experts, and (2) generate lensed models that afford multiple perspectives of the same dataset. We explore lensing of latent variable model in their configuration, parameter and evidential spaces. We apply lensing to three health applications, namely imbuing the perspectives of experts into latent variable models that analyze adolescent distress and crisis counseling.
by Karthik Dinakar.
Ph. D.
Roderus, Jens, Simon Larson e Eric Pihl. "Hadoop scalability evaluation for machine learning algorithms on physical machines : Parallel machine learning on computing clusters". Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20102.
Texto completo da fonteKent, W. F. "Machine learning for parameter identification of electric induction machines". Thesis, University of Liverpool, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399178.
Texto completo da fonteThorén, Daniel. "Radar based tank level measurement using machine learning : Agricultural machines". Thesis, Linköpings universitet, Programvara och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176259.
Texto completo da fonteRomano, Donato. "Machine Learning algorithms for predictive diagnostics applied to automatic machines". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22319/.
Texto completo da fonteSchneider, C. "Using unsupervised machine learning for fault identification in virtual machines". Thesis, University of St Andrews, 2015. http://hdl.handle.net/10023/7327.
Texto completo da fonteSOAVE, Elia. "Diagnostics and prognostics of rotating machines through cyclostationary methods and machine learning". Doctoral thesis, Università degli studi di Ferrara, 2022. http://hdl.handle.net/11392/2490999.
Texto completo da fonteNegli ultimi decenni, l’analisi vibrazionale è stata sfruttata per il monitoraggio di molti sistemi meccanici per applicazioni industriali. Nonostante molte pubblicazioni abbiano dimostrato come la diagnostica vibrazionale possa raggiungere risultati soddisfacenti, lo scenario industriale odierno è in profondo cambiamento, guidato dalla necessità di ridurre tempi e costi produttivi. In questa direzione, la ricerca deve concentrarsi sul miglioramento dell’efficienza computazionale delle tecniche di analisi del segnale applicate a fini diagnostici. Allo stesso modo, il mondo industriale richiede una sempre maggior attenzione per la manutenzione predittiva, al fine di stimare l’effettivo danneggiamento del sistema evitando così inutili fermi macchina per operazioni manutentive. In tale ambito, negli ultimi anni l’attività di ricerca si sta spostando verso lo sviluppo di modelli prognostici finalizzati alla stima della vita utile residua dei componenti. Tuttavia, è importante ricordare come i due ambiti siano strettamente connessi, essendo la diagnostica la base su cui fondare l’efficacia di ciascun modello prognostico. Su questa base, questa tesi è stata incentrata su queste due diverse, ma tra loro connesse, aree al fine di identificare e predire possibile cause di cedimento su macchine rotanti per applicazioni industriali. La prima parte della tesi è concentrata sullo sviluppo di un nuovo indicatore di blind deconvolution per l’identificazione di difetti su organi rotanti sulla base della teoria ciclostazionaria. Il criterio presentato vuole andare a ridurre il costo computazionale richiesto dalla blind deconvolution tramite l’utilizzo della serie di Fourier-Bessel grazie alla sua natura modulata, maggiormente affine alla tipica firma vibratoria del difetto. L’indicatore proposto viene accuratamente confrontato con il suo analogo basato sulla classica serie di Fourier considerando sia segnali simulati che segnali di vibrazione reali. Il confronto vuole dimostrare il miglioramento fornito dal nuovo criterio in termini sia di minor numero di operazioni richieste dall’algoritmo che di efficacia diagnostica anche in condizioni di segnale molto rumoroso. Il contributo innovativo di questa parte riguarda la combinazione di ciclostazionarietà e serie di Furier-Bessel che porta alla definizione di un nuovo criterio di blind deconvolution in grado di mantenere l’efficacia diagnostica della ciclostazionarietà ma con un minor tempo computazionale per venire incontro alle richieste del mondo industriale. La second parte riguarda la definizione di un nuovo modello prognostico, appartenente alla famiglia degli hidden Markov models, costruito partendo da una distribuzione Gaussiana generalizzata. L’obbiettivo del metodo proposto è una miglior riproduzione della reale distribuzione dei dati, in particolar modo negli ultimi stadi del danneggiamento. Infatti, la comparsa e l’evoluzione del difetto comporta una modifica della distribuzione delle osservazioni fra i diversi stati. Di conseguenza, una densità di probabilità generalizzata permette la modificazione della forma della distribuzione tramite diversi valori dei parametri del modello. Il metodo proposto viene confrontato con il classico hidden Markov model di base Gaussiana in termini di qualità di riproduzione della distribuzione e predizione della sequenza di stati tramite l’analisi di alcuni test di rottura su cuscinetti volventi e sistemi complessi. L’innovatività di questa parte è data dalla definizione di un algoritmo iterativo per la stima dei parametri del modello nell’ipotesi di distribuzione Gaussiana generalizzata, sia nel caso monovariato che multivariato, partendo dalle osservazioni sul sistema fisico in esame.
Livros sobre o assunto "Machine learnings"
Ertekin, Şeyda. Algorithms for efficient learning systems: Online and active learning approaches. Saarbrücken: VDM Verlag Dr. Müller, 2009.
Encontre o texto completo da fonteCampbell, Colin. Learning with support vector machines. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.
Encontre o texto completo da fonteBoyle, Brandon H. Support vector machines: Data analysis, machine learning, and applications. Hauppauge, N.Y: Nova Science Publishers, 2011.
Encontre o texto completo da fonteZhou, Zhi-Hua. Machine Learning. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-1967-3.
Texto completo da fonteJung, Alexander. Machine Learning. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8193-6.
Texto completo da fonteMitchell, Tom M., Jaime G. Carbonell e Ryszard S. Michalski. Machine Learning. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-2279-5.
Texto completo da fonteFernandes de Mello, Rodrigo, e Moacir Antonelli Ponti. Machine Learning. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94989-5.
Texto completo da fonteBell, Jason. Machine Learning. Indianapolis, IN, USA: John Wiley & Sons, Inc, 2014. http://dx.doi.org/10.1002/9781119183464.
Texto completo da fonteHuang, Kaizhu, Haiqin Yang, Irwin King e Michael Lyu. Machine Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-79452-3.
Texto completo da fonteJebara, Tony. Machine Learning. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-9011-2.
Texto completo da fonteCapítulos de livros sobre o assunto "Machine learnings"
Heesen, Bernd. "Grundlagen des Machine Learnings mit R". In Künstliche Intelligenz und Machine Learning mit R, 111–398. Wiesbaden: Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-41576-1_6.
Texto completo da fonteAugust, Stephanie E., e Audrey Tsaima. "Artificial Intelligence and Machine Learning: An Instructor’s Exoskeleton in the Future of Education". In Innovative Learning Environments in STEM Higher Education, 79–105. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-58948-6_5.
Texto completo da fonteHan, Haihang, Tianjie Zhang, Qiao Dong, Xueqin Chen e Yangyang Wang. "Pavement roughness level classification based on logistic and decision tree machine learnings". In Green and Intelligent Technologies for Sustainable and Smart Asphalt Pavements, 400–405. London: CRC Press, 2021. http://dx.doi.org/10.1201/9781003251125-63.
Texto completo da fonteAwotunde, Joseph Bamidele, Sunday Adeola Ajagbe, Matthew A. Oladipupo, Jimmisayo A. Awokola, Olakunle S. Afolabi, Timothy O. Mathew e Yetunde J. Oguns. "An Improved Machine Learnings Diagnosis Technique for COVID-19 Pandemic Using Chest X-ray Images". In Communications in Computer and Information Science, 319–30. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89654-6_23.
Texto completo da fonteBringsjord, Selmer, Naveen Sundar Govindarajulu, Shreya Banerjee e John Hummel. "Do Machine-Learning Machines Learn?" In Studies in Applied Philosophy, Epistemology and Rational Ethics, 136–57. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96448-5_14.
Texto completo da fonteDai, Anni. "Co-creation: Space Reconfiguration by Architect and Agent Simulation Based Machine Learning". In Computational Design and Robotic Fabrication, 304–13. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8637-6_27.
Texto completo da fonteSödergård, Caj. "Summary of Potential and Exploitation of Big Data and AI in Bioeconomy". In Big Data in Bioeconomy, 417–23. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_32.
Texto completo da fontePuigbò, Jordi-Ysard, Xerxes D. Arsiwalla e Paul F. M. J. Verschure. "Challenges of Machine Learning for Living Machines". In Biomimetic and Biohybrid Systems, 382–86. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95972-6_41.
Texto completo da fonteWehenkel, Louis A. "Machine Learning". In Automatic Learning Techniques in Power Systems, 99–144. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5451-6_5.
Texto completo da fonteCios, Krzysztof J., Witold Pedrycz e Roman W. Swiniarski. "Machine Learning". In Data Mining Methods for Knowledge Discovery, 229–308. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5589-6_6.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Machine learnings"
Gaber, Ayman, Mohamed Mahmoud Zaki, Ahmed Maher Mohamed e Mohamed Abdellatif Beshara. "Cellular Network Power Control Optimization Using Unsupervised Machine Learnings". In 2019 International Conference on Innovative Trends in Computer Engineering (ITCE). IEEE, 2019. http://dx.doi.org/10.1109/itce.2019.8646611.
Texto completo da fonteGuajardo, Marco, Ahmed S. Omran e Howard Clark. "Fast model-driven target optimization methods using machine learnings". In Design-Technology Co-optimization XV, editado por Chi-Min Yuan e Ryoung-Han Kim. SPIE, 2021. http://dx.doi.org/10.1117/12.2587122.
Texto completo da fonteEshita, Kakeru, Kousei Nishizono, Ryusei Kunitake, Hirohumi Miyazima, Kenichi Arai e Toru Kobayashi. "Surface Roughness Prediction System for Blade Machining Using Machine Learnings". In 2023 IEEE 12th Global Conference on Consumer Electronics (GCCE). IEEE, 2023. http://dx.doi.org/10.1109/gcce59613.2023.10315442.
Texto completo da fontePowney, M., J. Masi, D. Austin, T. Citraningtyas, M. Dyrendahl, B. Alaei, S. Cornelius, F. Dias e P. Emmet. "Legacy Learnings to Future Insight – Characterising CCUS Sites Using Legacy Data with Machine Learning". In First EAGE Workshop on Hydrogen & CCS in LATAM. European Association of Geoscientists & Engineers, 2023. http://dx.doi.org/10.3997/2214-4609.202382004.
Texto completo da fonteSato, Keita, Masafumi Chida, Yoshihiro Hayakawa e Nahomi Miyamoto Fujiki. "Automatic Feature Extraction from Wearable Sensor Data by Use of Machine Learnings". In The 7th International Conference on Intelligent Systems and Image Processing 2019. The Institute of Industrial Application Engineers, 2019. http://dx.doi.org/10.12792/icisip2019.067.
Texto completo da fonteGera, Saksham, Mr Mridul e Kireet Joshi. "Regression Analysis And Future Forecasting Of COVID-19 Using Machine Learnings Algorithm". In 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 2021. http://dx.doi.org/10.1109/confluence51648.2021.9377065.
Texto completo da fonteJusman, Yessi, Muhammad Khoirul Anam, Sartika Puspita e Edwyn Saleh. "Machine Learnings of Dental Caries Images based on Hu Moment Invariants Features". In 2021 International Seminar on Application for Technology of Information and Communication (iSemantic). IEEE, 2021. http://dx.doi.org/10.1109/isemantic52711.2021.9573208.
Texto completo da fonteSrivastava, Priyank, Mainak Bandyopadhyay, Shantanu Chakraborty, Samarth Patwardhan e Huy Tran. "Classification of Wireline Formation Testing Responses Using Unsupervised Machine Learning Methods". In Offshore Technology Conference. OTC, 2022. http://dx.doi.org/10.4043/31892-ms.
Texto completo da fonteQumsiyeh, Emma, Miar Yousef e Malik Yousef. "ReScore Disease Groups Based on Multiple Machine Learnings Utilizing the Grouping-Scoring-Modeling Approach". In 15th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012379400003657.
Texto completo da fonteEmery, David J., Marcelo Guarido, Brian Russell e Daniel Trad. "Machine learnings and lessons learned on improvements to Castagna’s mudrock, Gardner’s density, and Faust’s velocity estimation". In Second International Meeting for Applied Geoscience & Energy. Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2022. http://dx.doi.org/10.1190/image2022-3749277.1.
Texto completo da fonteRelatórios de organizações sobre o assunto "Machine learnings"
Giannoulakis, Stylianos, e Arrigo Beretta. PR-471-18210-R01 Pump Failure and Performance Degradation Prediction. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), setembro de 2020. http://dx.doi.org/10.55274/r0011801.
Texto completo da fonteVesselinov, Velimir Valentinov. Machine Learning. Office of Scientific and Technical Information (OSTI), janeiro de 2019. http://dx.doi.org/10.2172/1492563.
Texto completo da fonteValiant, L. G. Machine Learning. Fort Belvoir, VA: Defense Technical Information Center, janeiro de 1993. http://dx.doi.org/10.21236/ada283386.
Texto completo da fonteChase, Melissa P. Machine Learning. Fort Belvoir, VA: Defense Technical Information Center, abril de 1990. http://dx.doi.org/10.21236/ada223732.
Texto completo da fonteKagie, Matthew J., e Park Hays. FORTE Machine Learning. Office of Scientific and Technical Information (OSTI), agosto de 2016. http://dx.doi.org/10.2172/1561828.
Texto completo da fonteLin, Youzuo, Shihang Feng e Esteban Rougier. Machine Learning Tutorial. Office of Scientific and Technical Information (OSTI), julho de 2022. http://dx.doi.org/10.2172/1876777.
Texto completo da fonteCaplin, Andrew, Daniel Martin e Philip Marx. Modeling Machine Learning. Cambridge, MA: National Bureau of Economic Research, outubro de 2022. http://dx.doi.org/10.3386/w30600.
Texto completo da fonteKelly, Bryan, e Dacheng Xiu. Financial Machine Learning. Cambridge, MA: National Bureau of Economic Research, julho de 2023. http://dx.doi.org/10.3386/w31502.
Texto completo da fonteVassilev, Apostol. Adversarial Machine Learning:. Gaithersburg, MD: National Institute of Standards and Technology, 2024. http://dx.doi.org/10.6028/nist.ai.100-2e2023.
Texto completo da fonteJunttila, Jukka, Ville Lämsä, Leonardo Espinosa Leal e Anssi Sillanpää. Feature engineering –based machine learning models for operational state recognition of rotating machines. Peeref, março de 2023. http://dx.doi.org/10.54985/peeref.2303p8483224.
Texto completo da fonte