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Статті в журналах з теми "Prediction of quality"
Liang, Yun-Chia, Yona Maimury, Angela Hsiang-Ling Chen, and Josue Rodolfo Cuevas Juarez. "Machine Learning-Based Prediction of Air Quality." Applied Sciences 10, no. 24 (December 21, 2020): 9151. http://dx.doi.org/10.3390/app10249151.
Повний текст джерелаMuharsyah, Robi, Dian Nur Ratri, and Damiana Fitria Kussatiti. "Improving prediction quality of sea surface temperature (SST) in Niño3.4 region using Bayesian Model Averaging." IOP Conference Series: Earth and Environmental Science 893, no. 1 (November 1, 2021): 012028. http://dx.doi.org/10.1088/1755-1315/893/1/012028.
Повний текст джерелаPanchal, D. S., M. B. Shelke, S. S. Kawathekar, and S. N. Deshmukh. "Prediction of Healthcare Quality Using Sentiment Analysis." Indian Journal Of Science And Technology 16, no. 21 (June 3, 2023): 1603–13. http://dx.doi.org/10.17485/ijst/v16i21.2506.
Повний текст джерелаMartens, M., and H. Martens. "Near-Infrared Reflectance Determination of Sensory Quality of Peas." Applied Spectroscopy 40, no. 3 (March 1986): 303–10. http://dx.doi.org/10.1366/0003702864509114.
Повний текст джерелаKim, Donghyun, Heechan Han, Wonjoon Wang, Yujin Kang, Hoyong Lee, and Hung Soo Kim. "Application of Deep Learning Models and Network Method for Comprehensive Air-Quality Index Prediction." Applied Sciences 12, no. 13 (July 1, 2022): 6699. http://dx.doi.org/10.3390/app12136699.
Повний текст джерелаGANESAN, K., TAGHI M. KHOSHGOFTAAR, and EDWARD B. ALLEN. "CASE-BASED SOFTWARE QUALITY PREDICTION." International Journal of Software Engineering and Knowledge Engineering 10, no. 02 (April 2000): 139–52. http://dx.doi.org/10.1142/s0218194000000092.
Повний текст джерелаGonçalves, Mateus Teles Vital, Gota Morota, Paulo Mafra de Almeida Costa, Pedro Marcus Pereira Vidigal, Marcio Henrique Pereira Barbosa, and Luiz Alexandre Peternelli. "Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits." PLOS ONE 16, no. 3 (March 4, 2021): e0236853. http://dx.doi.org/10.1371/journal.pone.0236853.
Повний текст джерелаKouadri, Wissam Mammar, Mourad Ouziri, Salima Benbernou, Karima Echihabi, Themis Palpanas, and Iheb Ben Amor. "Quality of sentiment analysis tools." Proceedings of the VLDB Endowment 14, no. 4 (December 2020): 668–81. http://dx.doi.org/10.14778/3436905.3436924.
Повний текст джерелаAsiah, Mat, Khidzir Nik Zulkarnaen, Deris Safaai, Mat Yaacob Nik Nurul Hafzan, Mohamad Mohd Saberi, and Safaai Siti Syuhaida. "A Review on Predictive Modeling Technique for Student Academic Performance Monitoring." MATEC Web of Conferences 255 (2019): 03004. http://dx.doi.org/10.1051/matecconf/201925503004.
Повний текст джерелаVeeramalai, S., Mr T. Praveen, and S. Pradeepa Natarajan. "Cost Based On Product Quality Prediction Using Datamining." International Journal of Trend in Scientific Research and Development Special Issue, Special Issue-Active Galaxy (June 30, 2018): 38–42. http://dx.doi.org/10.31142/ijtsrd14564.
Повний текст джерелаДисертації з теми "Prediction of quality"
KUNTE, DEEPTI SHRIRAM. "Sound Quality Prediction Using Neural Networks." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-283336.
Повний текст джерелаLjudkvalitet är ett viktigt mått som skildrar en maskins kvalitet såväl som bekvämlighet i dess användning. Det är emellertid ett subjektivt mått, inte bara är det svårt att fånga detta i förväg men också att det kräver både tid och dyra jurytestningar. Det är därför värdefullt att kunna effektivt förutsäga de resultaten av jurystudien från mätvärden som kan mätas objektivt. Syftet med arbetet är tvåfaldigt: det första är att etablera neuronnätsmodeller till att förutsäga subjektiva ljudkvalitetsmätvärden från objektiva mätvärden. Det andra är att tolka modellen till att kunna förstå den relativa betydelsen av varje objektivt mätvärde mot en specifik subjektiv bedömning. I sista hand syftar arbetet till att bana vägen för inkludering av mätvärden för ljudkvalitet i de tidiga designfaserna. Utifrån studien var det uppenbart att neuronnäts prestanda var åtminstone lika med eller bättre än de linjära eller kvadratiska modellerna. Anslutningsviktsmetoden, profilmetoden, störningsmetoden, den förbättrade stegvisa urvalsmetoden samt den linjära regressionsmetoden var tolkningsalgoritmerna som visade sig att fungera väl på alla simulerad datauppsättningar. De gav också jämförbara resultat på de verkliga datauppsättningarna. Neuronnät visade sig att ha potential att ge låga prediktionsfel samtidigt som de bibehåller tolkningsbarhet i applikationer för ljudkvalitet. Studien av dataknapphet gav det en uppfattning om storleken på prestandaförbättring som kan uppnås med mer data och kan fungera som en användbar input vid bestämning av antalet datapunkter.
Steel, Donald. "Software reliability prediction." Thesis, Abertay University, 1990. https://rke.abertay.ac.uk/en/studentTheses/4613ff72-9650-4fa1-95d1-1a9b7b772ee4.
Повний текст джерелаPeng, Huiping. "Air quality prediction by machine learning methods." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/55069.
Повний текст джерелаScience, Faculty of
Earth, Ocean and Atmospheric Sciences, Department of
Graduate
Hollier, M. P. "Audio quality prediction for telecomunications speech systems." Thesis, University of Essex, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.282496.
Повний текст джерелаMateus, Ana Teresa Moreirinha Vila Fernandes. "Quality management in laboratories- Effciency prediction models." Doctoral thesis, Universidade de Évora, 2021. http://hdl.handle.net/10174/29338.
Повний текст джерелаTaipale, T. (Taneli). "Improving software quality with software error prediction." Master's thesis, University of Oulu, 2015. http://urn.fi/URN:NBN:fi:oulu-201512042251.
Повний текст джерелаNykyaikainen ketterä ohjelmistokehitys on monimutkainen prosessi. Tämä väittämä pätee varsinkin isoihin projekteihin. Ohjelmistokehityksessä käytettävät työkalut helpottavat jo itsessään kehitystyötä, mutta ne myös säilövät tärkeää tilastotietoa. Tätä tilastotietoa voidaan käyttää koneoppimisjärjestelmän opettamiseen. Tällä tavoin koneoppimisjärjestelmä oppii tunnistamaan ohjelmistokehitystyölle ominaisia käyttäytymismalleja. Tämän opinnäytetyön lähtökohta on ohjelmistoprojekti, jonka on määrä toimia osana laajaa telekommunikaatioverkkoa. Tällainen ohjelmistoprojekti vaatii kalliin testauslaitteiston, mikä johtaa suoraan kalliiseen testausaikaan. Toisaalta yksikkötestaus ja koodikatselmointi ovat työmenetelmiä, jotka parantavat ohjelmiston laatua, mutta vaativat paljon ohjelmistoammattilaisten resursseja. Koska ohjelmointivirheet ovat ohjelmistoprojektin edetessä väistämättömiä, on näiden työkalujen tehokkuus tunnistaa ohjelmointivirheitä erityisen tärkeää onnistuneen projektin kannalta. Tässä opinnäytetyössä testaamisen ja muiden laadunvarmennustyökalujen tehokkuutta pyritään parantamaan käyttämällä hyväksi koneoppimisjärjestelmää. Koneoppimisjärjestelmä opetetaan tunnistamaan ohjelmointivirheet käyttäen historiatietoa projektissa aiemmin tehdyistä ohjelmointivirheistä. Koneoppimisjärjestelmän ennusteilla kohdennetaan testausta painottamalla virheen todennäköisimmin löytäviä testitapauksia. Työn lopputuloksena on koneoppimisjärjestelmä, joka pystyy ennustamaan ohjelmointivirheen todennäköisimmin sisältäviä tiedostomuutoksia. Tämän tiedon pohjalta on luotu raportteja kuten listaus todennäköisimmin virheen sisältävistä tiedostomuutoksista, koko ohjelmistoprojektin kattava kartta virheen rivikohtaisista todennäköisyyksistä sekä graafi, joka yhdistää ohjelmointivirhetiedot organisaatiotietoon. Alkuperäisenä tavoitteena ollutta testaamisen painottamista ei kuitenkaan saatu aikaiseksi vajaan testikattavuustiedon takia. Tämä opinnäytetyö toi esiin tärkeitä parannuskohteita projektin työtavoissa ja uusia näkökulmia ohjelmistokehitysprosessiin
Krishnamurthy, Janaki. "Quality Market: Design and Field Study of Prediction Market for Software Quality Control." NSUWorks, 2010. http://nsuworks.nova.edu/gscis_etd/352.
Повний текст джерелаWallner, Björn. "Protein Structure Prediction : Model Building and Quality Assessment." Doctoral thesis, Stockholm University, Department of Biochemistry and Biophysics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-649.
Повний текст джерелаProteins play a crucial roll in all biological processes. The wide range of protein functions is made possible through the many different conformations that the protein chain can adopt. The structure of a protein is extremely important for its function, but to determine the structure of protein experimentally is both difficult and time consuming. In fact with the current methods it is not possible to study all the billions of proteins in the world by experiments. Hence, for the vast majority of proteins the only way to get structural information is through the use of a method that predicts the structure of a protein based on the amino acid sequence.
This thesis focuses on improving the current protein structure prediction methods by combining different prediction approaches together with machine-learning techniques. This work has resulted in some of the best automatic servers in world – Pcons and Pmodeller. As a part of the improvement of our automatic servers, I have also developed one of the best methods for predicting the quality of a protein model – ProQ. In addition, I have also developed methods to predict the local quality of a protein, based on the structure – ProQres and based on evolutionary information – ProQprof. Finally, I have also performed the first large-scale benchmark of publicly available homology modeling programs.
Wallner, Björn. "Protein structure prediction : model building and quality assessment /." Stockholm : Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-649.
Повний текст джерелаBrun, Daniel, and Colin Lawless. "Quality Prediction in Jet Printing Using Neural Networks." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278882.
Повний текст джерелаYtmonteringsteknologi är en väletablerad metod som används inom tillverkningen av kommersiell elektronik, och kravet på dessa maskiner ökar i takt med att elektronikens komplexitet ökar och storleken på komponenterna minskar. Mycronic är ett företag vars fokus ligger i att möta dessa krav med deras högteknologiska jet printing - och pick-and-place-maskiner. Detta examensarbete har utförts på Mycronic och har fokuserat på jet printing-maskinen MY700. På grund av okända faktorer kan kvaliteten på den deponerade lodpastan från maskinen variera över tid. Det var därför intressant att övervaka variabler hos maskinen för att få mer kunskap om orsaken till den varierande kvaliteten och också för att kunna upptäcka förändringar i kvaliteten. I det här projektet har temperaturen mätts på tre kritiska positioner på ejektorn samt även strömmen som går genom det piezoelektriska ställdonet. Dessa data gavs till ett neuralt nätverk för att göra kvalitetsprognoser med avseende på diametern på deponeringarna av lodpasta. Olika kombinationer av sensordata användes för att utvärdera hur de olika sensorerna påverkade det neurala nätverkets prestanda. Därigenom kunde en bättre förståelse av hur stor påverkan de olika variablerna hade på kvaliteten på deponeringarna uppnås. Resultaten indikerar att strömmen var mer betydelsefull än temperaturen för att göra kvalitetsprognoser. Om bara temperaturdata användes lyckades inte det neurala nätverket göra exakta förutsägelser för kvalitetsavvikelser, medan med bara strömdata eller båda kombinerade kunde bättre förutsägelser göras. Strömdatan förbättrade också prestandan hos det neurala nätverket när jobb med olika diametrar användes. Slutsatsen är att ingen av de tre temperatursensorerna förbättrade prestandan signifikant, och det fanns inga betydande skillnader mellan dem, medan strömmen förbättrade prestandan.
Книги з теми "Prediction of quality"
Mittag, Gabriel. Deep Learning Based Speech Quality Prediction. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-91479-0.
Повний текст джерелаNational Symposium on Hydrology (India) (11th 2004 Roorkee, India). Water quality: Monitoring, modelling, and prediction. Edited by Jain C. K, Trivedi R. C, Sharma K. D, National Institute of Hydrology (India), and India. Central Pollution Control Board. New Delhi: Allied Publishers, 2004.
Знайти повний текст джерелаBelmudez, Benjamin. Audiovisual Quality Assessment and Prediction for Videotelephony. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14166-4.
Повний текст джерелаHolman, Thomas B., Paul James Birch, Jason S. Carroll, Cynthia Doxey, Jeffry H. Larson, and Steven T. Linford. Premarital Prediction of Marital Quality or Breakup. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/b107947.
Повний текст джерелаHandbook of image quality: Characterization and prediction. New York: Marcel Dekker, 2002.
Знайти повний текст джерелаA, Kupecz Julie, Gluyas J. G, and Bloch S, eds. Reservoir quality prediction in sandstones and carbonates. Tulsa, Okla: American Association of Petroleum Geologists, 1997.
Знайти повний текст джерелаD, Meshri Indu, Ortoleva Peter J, and American Association of Petroleum Geologists., eds. Prediction of reservoir quality through chemical modeling. Tulsa, Okla., U.S.A: American Association of Petroleum Geologists, 1990.
Знайти повний текст джерелаPrediction and regulation of air pollution. Dordrecht: Kluwer Academic Publishers, 1991.
Знайти повний текст джерелаMöller, Sebastian. Assessment and Prediction of Speech Quality in Telecommunications. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-3117-0.
Повний текст джерелаSchiffner, Falk Ralph. Dimension-Based Quality Analysis and Prediction for Videotelephony. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56570-1.
Повний текст джерелаЧастини книг з теми "Prediction of quality"
Möller, Sebastian. "Quality Prediction." In Quality Engineering, 163–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-65615-0_9.
Повний текст джерелаMcGuffin, Liam J. "Model Quality Prediction." In Introduction to Protein Structure Prediction, 323–42. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470882207.ch15.
Повний текст джерелаAggarwal, K. K. "Reliability Prediction." In Topics in Safety, Reliability and Quality, 107–21. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1928-3_5.
Повний текст джерелаMöller, Sebastian. "Quality of Prediction Models." In Assessment and Prediction of Speech Quality in Telecommunications, 159–87. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-3117-0_7.
Повний текст джерелаSchiffner, Falk Ralph. "Quality Modeling and Prediction." In T-Labs Series in Telecommunication Services, 83–88. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56570-1_5.
Повний текст джерелаPourbafrani, Mahsa, Shreya Kar, Sebastian Kaiser, and Wil M. P. van der Aalst. "Remaining Time Prediction for Processes with Inter-case Dynamics." In Lecture Notes in Business Information Processing, 140–53. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_11.
Повний текст джерелаPandey, Ajeet Kumar, and Neeraj Kumar Goyal. "Background: Software Quality and Reliability Prediction." In Early Software Reliability Prediction, 17–33. India: Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-1176-1_2.
Повний текст джерелаMichael, Thilo. "Conversational Quality Predictions." In Simulating Conversations for the Prediction of Speech Quality, 101–21. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-31844-3_6.
Повний текст джерелаDuzbayev, Nurzhan, and Iman Poernomo. "Runtime Prediction of Queued Behaviour." In Quality of Software Architectures, 78–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11921998_10.
Повний текст джерелаDu, Shichang, and Lifeng Xi. "Surface Prediction." In High Definition Metrology Based Surface Quality Control and Applications, 265–91. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0279-8_7.
Повний текст джерелаТези доповідей конференцій з теми "Prediction of quality"
Padilla, Dionis A., Glenn V. Magwili, Luis Benjamin Z. Mercado, and Jean Tristan L. Reyes. "Air Quality Prediction using Recurrent Air Quality Predictor with Ensemble Learning." In 2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM). IEEE, 2020. http://dx.doi.org/10.1109/hnicem51456.2020.9400051.
Повний текст джерелаShihab, Emad. "Practical Software Quality Prediction." In 2014 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE, 2014. http://dx.doi.org/10.1109/icsme.2014.114.
Повний текст джерелаSazzad, Z. M. Parvez, Shouta Yamanaka, Yoshikazu Kawayokeita, and Yuukou Horita. "Stereoscopic image quality prediction." In 2009 International Workshop on Quality of Multimedia Experience (QoMEx 2009). IEEE, 2009. http://dx.doi.org/10.1109/qomex.2009.5246956.
Повний текст джерелаKodama, S., and I. Kataoka. "Study on Analytical Prediction of Forced Convective CHF in the Wide Range of Quality." In 10th International Conference on Nuclear Engineering. ASMEDC, 2002. http://dx.doi.org/10.1115/icone10-22128.
Повний текст джерелаLincke, Rüdiger, Tobias Gutzmann, and Welf Löwe. "Software Quality Prediction Models Compared." In 2010 10th International Conference on Quality Software (QSIC). IEEE, 2010. http://dx.doi.org/10.1109/qsic.2010.9.
Повний текст джерелаGuirguis, Shawket, Fatma Zada, and Tawfik Khattab. "Quality Controlled Stock Prediction Model." In 2013 23rd International Conference on Computer Theory and Applications (ICCTA). IEEE, 2013. http://dx.doi.org/10.1109/iccta32607.2013.9529781.
Повний текст джерелаNagorny, Pierre, Maurice Pillet, Eric Pairel, Ronan Le Goff, Jerome Loureaux, Marlene Wali, and Patrice Kiener. "Quality prediction in injection molding." In 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA). IEEE, 2017. http://dx.doi.org/10.1109/civemsa.2017.7995316.
Повний текст джерелаS, Abhinav, Sahana Srinivasan, Aishwarya Ganesan, Anala M R, and Mamatha T. "Wireless Water Quality Monitoring and Quality Deterioration Prediction System." In 2019 26th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW). IEEE, 2019. http://dx.doi.org/10.1109/hipcw.2019.00013.
Повний текст джерелаWu, Dazhong, Yupeng Wei, and Janis Terpenny. "Surface Roughness Prediction in Additive Manufacturing Using Machine Learning." In ASME 2018 13th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/msec2018-6501.
Повний текст джерелаShruthi, P. "Wine Quality Prediction Using Data Mining." In 2019 International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE). IEEE, 2019. http://dx.doi.org/10.1109/icatiece45860.2019.9063846.
Повний текст джерелаЗвіти організацій з теми "Prediction of quality"
Wei, Jie. Magnet Quality and Collider Performance Prediction. Office of Scientific and Technical Information (OSTI), November 1996. http://dx.doi.org/10.2172/1119510.
Повний текст джерелаMurphy, D. D., W. M. Thomas, W. M. Evanco, and W. W. Agresti. Procedures for Applying Ada Quality Prediction Models. Fort Belvoir, VA: Defense Technical Information Center, December 1992. http://dx.doi.org/10.21236/ada264730.
Повний текст джерелаAgresti, W. W., W. M. Evanco, M. C. Smith, and D. R. Clarson. An Approach to Software Quality Prediction from Ada Designs. Fort Belvoir, VA: Defense Technical Information Center, December 1992. http://dx.doi.org/10.21236/ada264731.
Повний текст джерелаMcDonald, M. J. Quality prediction and mistake proofing: An LDRD final report. Office of Scientific and Technical Information (OSTI), March 1998. http://dx.doi.org/10.2172/650152.
Повний текст джерелаDi, Jiaqi, Xuanlin Li, Jingjing Yang, Luguang Li, and Xueqing Yu. Critical appraisal of the reporting quality of risk prediction models for idiopathic pulmonary fibrosis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2020. http://dx.doi.org/10.37766/inplasy2020.11.0105.
Повний текст джерелаVlek, R. J., D. J. M. Willems, and H. Rijgersberg. Requirements for implementation : a quality prediction system for soft fruit based on a Bayesian Belief Network. Wageningen: Wageningen Food and Biobased Research, 2018. http://dx.doi.org/10.18174/563391.
Повний текст джерелаThegeya, Aaron, Thomas Mitterling, Arturo Martinez Jr, Joseph Albert Niño Bulan, Ron Lester Durante, and Jayzon Mag-atas. Application of Machine Learning Algorithms on Satellite Imagery for Road Quality Monitoring: An Alternative Approach to Road Quality Surveys. Asian Development Bank, December 2022. http://dx.doi.org/10.22617/wps220587-2.
Повний текст джерелаVecherin, Sergey, Stephen Ketcham, Aaron Meyer, Kyle Dunn, Jacob Desmond, and Michael Parker. Short-range near-surface seismic ensemble predictions and uncertainty quantification for layered medium. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45300.
Повний текст джерелаJohnson, Paul C. Prediction of Groundwater Quality Improvement Down-Gradient of In Situ Permeable Treatment Barriers and Fully Remediated Source Zones. Fort Belvoir, VA: Defense Technical Information Center, December 2008. http://dx.doi.org/10.21236/ada602219.
Повний текст джерелаPeterson, Warren. PR-663-19600-Z01 Develop Guidance for Calculation of HCDP in Pipelines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), March 2020. http://dx.doi.org/10.55274/r0011659.
Повний текст джерела