Littérature scientifique sur le sujet « Artificial datasets »
Créez une référence correcte selon les styles APA, MLA, Chicago, Harvard et plusieurs autres
Sommaire
Consultez les listes thématiques d’articles de revues, de livres, de thèses, de rapports de conférences et d’autres sources académiques sur le sujet « Artificial datasets ».
À côté de chaque source dans la liste de références il y a un bouton « Ajouter à la bibliographie ». Cliquez sur ce bouton, et nous générerons automatiquement la référence bibliographique pour la source choisie selon votre style de citation préféré : APA, MLA, Harvard, Vancouver, Chicago, etc.
Vous pouvez aussi télécharger le texte intégral de la publication scolaire au format pdf et consulter son résumé en ligne lorsque ces informations sont inclues dans les métadonnées.
Articles de revues sur le sujet "Artificial datasets"
Serrano-Pérez, Jonathan, et L. Enrique Sucar. « Artificial datasets for hierarchical classification ». Expert Systems with Applications 182 (novembre 2021) : 115218. http://dx.doi.org/10.1016/j.eswa.2021.115218.
Texte intégralLychev, Andrey V. « Synthetic Data Generation for Data Envelopment Analysis ». Data 8, no 10 (27 septembre 2023) : 146. http://dx.doi.org/10.3390/data8100146.
Texte intégralPetráš, Jaroslav, Marek Pavlík, Ján Zbojovský, Ardian Hyseni et Jozef Dudiak. « Benford’s Law in Electric Distribution Network ». Mathematics 11, no 18 (10 septembre 2023) : 3863. http://dx.doi.org/10.3390/math11183863.
Texte intégralDasari, Kishore Babu, et Nagaraju Devarakonda. « TCP/UDP-Based Exploitation DDoS Attacks Detection Using AI Classification Algorithms with Common Uncorrelated Feature Subset Selected by Pearson, Spearman and Kendall Correlation Methods ». Revue d'Intelligence Artificielle 36, no 1 (28 février 2022) : 61–71. http://dx.doi.org/10.18280/ria.360107.
Texte intégralKusetogullari, Huseyin, Amir Yavariabdi, Abbas Cheddad, Håkan Grahn et Johan Hall. « ARDIS : a Swedish historical handwritten digit dataset ». Neural Computing and Applications 32, no 21 (29 mars 2019) : 16505–18. http://dx.doi.org/10.1007/s00521-019-04163-3.
Texte intégralMorgan, Maria, Carla Blank et Raed Seetan. « Plant disease prediction using classification algorithms ». IAES International Journal of Artificial Intelligence (IJ-AI) 10, no 1 (1 mars 2021) : 257. http://dx.doi.org/10.11591/ijai.v10.i1.pp257-264.
Texte intégralSaul, Marcia, et Shahin Rostami. « Assessing performance of artificial neural networks and re-sampling techniques for healthcare datasets ». Health Informatics Journal 28, no 1 (janvier 2022) : 146045822210871. http://dx.doi.org/10.1177/14604582221087109.
Texte intégralGau, Michael-Lian, Huong-Yong Ting, Teck-Hock Toh, Pui-Ying Wong, Pei-Jun Woo, Su-Woan Wo et Gek-Ling Tan. « Effectiveness of Using Artificial Intelligence for Early Child Development Screening ». Green Intelligent Systems and Applications 3, no 1 (9 mai 2023) : 1–13. http://dx.doi.org/10.53623/gisa.v3i1.229.
Texte intégralGHAFFARI, REZA, IOAN GROSU, DACIANA ILIESCU, EVOR HINES et MARK LEESON. « DIMENSIONALITY REDUCTION FOR SENSORY DATASETS BASED ON MASTER–SLAVE SYNCHRONIZATION OF LORENZ SYSTEM ». International Journal of Bifurcation and Chaos 23, no 05 (mai 2013) : 1330013. http://dx.doi.org/10.1142/s0218127413300139.
Texte intégralPavlov, Nikolay A., Anna E. Andreychenko, Anton V. Vladzymyrskyy, Anush A. Revazyan, Yury S. Kirpichev et Sergey P. Morozov. « Reference medical datasets (MosMedData) for independent external evaluation of algorithms based on artificial intelligence in diagnostics ». Digital Diagnostics 2, no 1 (30 avril 2021) : 49–66. http://dx.doi.org/10.17816/dd60635.
Texte intégralThèses sur le sujet "Artificial datasets"
Hilton, Erwin. « Visual datasets for artificial intelligence agents ». Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119553.
Texte intégralThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 41).
In this thesis, I designed and implemented two visual dataset generation tool frameworks. With these tools, I introduce procedurally generated new data to test VQA agents and other visual Al models on. The first tool is Spatial IQ Generative Dataset (SIQGD). This tool generates images based on the Raven's Progressive Matrices spatial IQ examination metric. The second tool is a collection of 3D models along with a Blender3D extension that renders images of the models from multiple viewpoints along with their depth maps.
by Erwin Hilton.
M. Eng.
Siddique, Nahian A. « PATTERN RECOGNITION IN CLASS IMBALANCED DATASETS ». VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4480.
Texte intégralLundberg, Oskar. « Decentralized machine learning on massive heterogeneous datasets : A thesis about vertical federated learning ». Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-444639.
Texte intégralHorečný, Peter. « Metody segmentace obrazu s malými trénovacími množinami ». Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-412996.
Texte intégralWoods, Brent J. « Computer-Aided Detection of Malignant Lesions in Dynamic Contrast Enhanced MRI Breast and Prostate Cancer Datasets ». The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1218155270.
Texte intégralYasarer, Hakan. « Decision making in engineering prediction systems ». Diss., Kansas State University, 2013. http://hdl.handle.net/2097/16231.
Texte intégralDepartment of Civil Engineering
Yacoub M. Najjar
Access to databases after the digital revolutions has become easier because large databases are progressively available. Knowledge discovery in these databases via intelligent data analysis technology is a relatively young and interdisciplinary field. In engineering applications, there is a demand for turning low-level data-based knowledge into a high-level type knowledge via the use of various data analysis methods. The main reason for this demand is that collecting and analyzing databases can be expensive and time consuming. In cases where experimental or empirical data are already available, prediction models can be used to characterize the desired engineering phenomena and/or eliminate unnecessary future experiments and their associated costs. Phenomena characterization, based on available databases, has been utilized via Artificial Neural Networks (ANNs) for more than two decades. However, there is a need to introduce new paradigms to improve the reliability of the available ANN models and optimize their predictions through a hybrid decision system. In this study, a new set of ANN modeling approaches/paradigms along with a new method to tackle partially missing data (Query method) are introduced for this purpose. The potential use of these methods via a hybrid decision making system is examined by utilizing seven available databases which are obtained from civil engineering applications. Overall, the new proposed approaches have shown notable prediction accuracy improvements on the seven databases in terms of quantified statistical accuracy measures. The proposed new methods are capable in effectively characterizing the general behavior of a specific engineering/scientific phenomenon and can be collectively used to optimize predictions with a reasonable degree of accuracy. The utilization of the proposed hybrid decision making system (HDMS) via an Excel-based environment can easily be utilized by the end user, to any available data-rich database, without the need for any excessive type of training.
Gusarov, Nikita. « Performances des modèles économétriques et de Machine Learning pour l’étude économique des choix discrets de consommation ». Electronic Thesis or Diss., Université Grenoble Alpes, 2024. http://www.theses.fr/2024GRALE001.
Texte intégralThis thesis is a cross-disciplinary study of discrete choice modeling, addressing both econometrics and machine learning (ML) techniques applied to individual choice modeling. The problematic arises from insufficient points of contact among users (economists and engineers) and data scientists, who pursue different objectives, although using similar techniques. To bridge this interdisciplinary gap, the PhD work proposes a unified framework for model performance analysis. It facilitates the comparison of data analysis techniques under varying assumptions and transformations.The designed framework is suitable for a variety of econometrics and ML models. It addresses the performance comparison task from the research procedure perspective, incorporating all the steps potentially affecting the performance perceptions. To demonstrate the framework’s capabilities we propose a series of 3 applied studies. In those studies the model performance is explored face to the changes in (1) sample size and balance, resulting from data collection; (2) changes in preferences structure within population, reflecting incorrect behavioral assumptions; and (3) model selection, directly intertwined with the performance perception
Matsumoto, Élia Yathie. « A methodology for improving computed individual regressions predictions ». Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/3/3142/tde-12052016-140407/.
Texte intégralEsta pesquisa propõe uma metodologia para melhorar previsões calculadas por um modelo de regressão, sem a necessidade de modificar seus parâmetros ou sua arquitetura. Em outras palavras, o objetivo é obter melhores resultados por meio de ajustes nos valores computados pela regressão, sem alterar ou reconstruir o modelo de previsão original. A proposta é ajustar os valores previstos pela regressão por meio do uso de estimadores de confiabilidade individuais capazes de indicar se um determinado valor estimado é propenso a produzir um erro considerado crítico pelo usuário da regressão. O método proposto foi testado em três conjuntos de experimentos utilizando três tipos de dados diferentes. O primeiro conjunto de experimentos trabalhou com dados produzidos artificialmente, o segundo, com dados transversais extraídos no repositório público de dados UCI Machine Learning Repository, e o terceiro, com dados do tipo séries de tempos extraídos do ISO-NE (Independent System Operator in New England). Os experimentos com dados artificiais foram executados para verificar o comportamento do método em situações controladas. Nesse caso, os experimentos alcançaram melhores resultados para dados limpos artificialmente produzidos e evidenciaram progressiva piora com a adição de elementos aleatórios. Os experimentos com dados reais extraído das bases de dados UCI e ISO-NE foram realizados para investigar a aplicabilidade da metodologia no mundo real. O método proposto foi capaz de melhorar os valores previstos por regressões em cerca de 95% dos experimentos realizados com dados reais.
Gualandi, Giacomo. « Analisi di dataset in campo finanziario mediante reti neurali LSTM ». Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19623/.
Texte intégralMattiussi, Vlad. « Una Rassegna di Dataset e Applicazioni Innovative di Intelligenza Artificiale per Affrontare la Pandemia da COVID19 ». Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21844/.
Texte intégralLivres sur le sujet "Artificial datasets"
Shi, Feng. Learn About Artificial Neural Networks in Python With Data From the Adult Census Income Dataset (1996). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom : SAGE Publications, Ltd., 2019. http://dx.doi.org/10.4135/9781526497093.
Texte intégralMountantonakis, M. Services for Connecting and Integrating Big Numbers of Linked Datasets. IOS Press, Incorporated, 2021.
Trouver le texte intégralChirimuuta, Mazviita. The Development and Application of Efficient Coding Explanation in Neuroscience. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198777946.003.0009.
Texte intégralVillez, Kris, Daniel Aguado, Janelcy Alferes, Queralt Plana, Maria Victoria Ruano et Oscar Samuelsson, dir. Metadata Collection and Organization in Wastewater Treatment and Wastewater Resource Recovery Systems. IWA Publishing, 2024. http://dx.doi.org/10.2166/9781789061154.
Texte intégralChapitres de livres sur le sujet "Artificial datasets"
Arbelaitz, Olatz, Ibai Gurrutxaga, Javier Muguerza et Jesús María Pérez. « Applying Resampling Methods for Imbalanced Datasets to Not So Imbalanced Datasets ». Dans Advances in Artificial Intelligence, 111–20. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40643-0_12.
Texte intégralKhangamwa, Gift, Terence van Zyl et Clint J. van Alten. « Towards a Methodology for Addressing Missingness in Datasets, with an Application to Demographic Health Datasets ». Dans Artificial Intelligence Research, 169–86. Cham : Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-22321-1_12.
Texte intégralCerquides, Jesús, Maite López-Sánchez, Santi Ontañón, Eloi Puertas, Anna Puig, Oriol Pujol et Dani Tost. « Classification Algorithms for Biomedical Volume Datasets ». Dans Current Topics in Artificial Intelligence, 143–52. Berlin, Heidelberg : Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881216_16.
Texte intégralSebé, Francesc, Josep Domingo-Ferrer et Agustí Solanas. « Noise-Robust Watermarking for Numerical Datasets ». Dans Modeling Decisions for Artificial Intelligence, 134–43. Berlin, Heidelberg : Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11526018_14.
Texte intégralGarcía-Álvarez, David, Javier Lara Hinojosa et Francisco José Jurado Pérez. « Global Thematic Land Use Cover Datasets Characterizing Artificial Covers ». Dans Land Use Cover Datasets and Validation Tools, 419–42. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90998-7_21.
Texte intégralKim, Minho, Hyunjin Yoo et R. S. Ramakrishna. « Cluster Validation for High-Dimensional Datasets ». Dans Artificial Intelligence : Methodology, Systems, and Applications, 178–87. Berlin, Heidelberg : Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30106-6_18.
Texte intégralFerreira, Carlos Abreu, João Gama et Vítor Santos Costa. « Sequential Pattern Mining in Multi-relational Datasets ». Dans Current Topics in Artificial Intelligence, 121–30. Berlin, Heidelberg : Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14264-2_13.
Texte intégralShamaev, Dmitry. « Synthetic Datasets and Medical Artificial Intelligence Specifics ». Dans Data Science and Algorithms in Systems, 519–28. Cham : Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-21438-7_41.
Texte intégralEwen, Nicolas, Tamer Abdou et Ayse Bener. « Applications of Feature Selection Techniques on Large Biomedical Datasets ». Dans Advances in Artificial Intelligence, 543–48. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18305-9_57.
Texte intégralWang, Xiaoguang, Stan Matwin, Nathalie Japkowicz et Xuan Liu. « Cost-Sensitive Boosting Algorithms for Imbalanced Multi-instance Datasets ». Dans Advances in Artificial Intelligence, 174–86. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38457-8_15.
Texte intégralActes de conférences sur le sujet "Artificial datasets"
Da Silva, Ronnypetson, Valter M. Filho et Mario Souza. « Interaffection of Multiple Datasets with Neural Networks in Speech Emotion Recognition ». Dans Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/eniac.2020.12141.
Texte intégralGhafourian, Sarvenaz, Ramin Sharifi et Amirali Baniasadi. « Facial Emotion Recognition in Imbalanced Datasets ». Dans 9th International Conference on Artificial Intelligence and Applications (AIAPP 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120920.
Texte intégralFang, Yili, Chaojie Shen, Huamao Gu, Tao Han et Xinyi Ding. « TDG4Crowd:Test Data Generation for Evaluation of Aggregation Algorithms in Crowdsourcing ». Dans Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California : International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/333.
Texte intégralHe, Shuai, Yongchang Zhang, Rui Xie, Dongxiang Jiang et Anlong Ming. « Rethinking Image Aesthetics Assessment : Models, Datasets and Benchmarks ». Dans Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California : International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/132.
Texte intégral« DATAZAPPER : GENERATING INCOMPLETE DATASETS ». Dans 1st International Conference on Agents and Artificial Intelligence. SciTePress - Science and and Technology Publications, 2009. http://dx.doi.org/10.5220/0001660700690076.
Texte intégralAbdo, Thiago, et Fabiano Silva. « Iterative machine learning applied to annotation of text datasets ». Dans Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/eniac.2021.18268.
Texte intégralCarvalho, Nathan F., André A. Gonçalves et Ana C. Lorena. « Collecting Meta-Data from the OpenML Public Repository ». Dans Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/eniac.2023.234286.
Texte intégralKim, Seul ki, Kwihoon Kim et Taeyoung Kim. « Development of AI Educational Datasets Library Using Synthetic Dataset Generation Method ». Dans 2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). IEEE, 2023. http://dx.doi.org/10.1109/icaiic57133.2023.10067000.
Texte intégralMayo, Michael, et Quan Sun. « Evolving artificial datasets to improve interpretable classifiers ». Dans 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014. http://dx.doi.org/10.1109/cec.2014.6900238.
Texte intégralBhattacharjya, Debarun, Tian Gao, Nicholas Mattei et Dharmashankar Subramanian. « Cause-Effect Association between Event Pairs in Event Datasets ». Dans Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California : International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/167.
Texte intégralRapports d'organisations sur le sujet "Artificial datasets"
Johra, Hicham, Martin Veit, Mathias Østergaard Poulsen, Albert Daugbjerg Christensen, Rikke Gade, Thomas B. Moeslund et Rasmus Lund Jensen. Training and testing labelled image and video datasets of human faces for different indoor visual comfort and glare visual discomfort situations. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau542153983.
Texte intégralCerulli, Giovanni. Machine Learning and AI for Research in Python. Instats Inc., 2023. http://dx.doi.org/10.61700/b7qz5fpva9dar469.
Texte intégralCerulli, Giovanni. Machine Learning and AI for Research in Python. Instats Inc., 2023. http://dx.doi.org/10.61700/x92cmhdrxsvu7469.
Texte intégralCerulli, Giovanni. Machine Learning and AI for Researchers in R. Instats Inc., 2023. http://dx.doi.org/10.61700/n8rzaz6kghskt469.
Texte intégralCerulli, Giovanni. Machine Learning and AI for Researchers in R. Instats Inc., 2023. http://dx.doi.org/10.61700/atz7nxsz9afbm469.
Texte intégralCerulli, Giovanni. Machine Learning and AI for Researchers in R. Instats Inc., 2023. http://dx.doi.org/10.61700/w5nn12uvjosgd469.
Texte intégralChahal, Husanjot, Sara Abdulla, Jonathan Murdick et Ilya Rahkovsky. Mapping India’s AI Potential. Center for Security and Emerging Technology, mars 2021. http://dx.doi.org/10.51593/20200096.
Texte intégralArnold, Zachary, Joanne Boisson, Lorenzo Bongiovanni, Daniel Chou, Carrie Peelman et Ilya Rahkovsky. Using Machine Learning to Fill Gaps in Chinese AI Market Data. Center for Security and Emerging Technology, février 2021. http://dx.doi.org/10.51593/20200064.
Texte intégralMusser, Micah, Rebecca Gelles, Catherine Aiken et Andrew Lohn. “The Main Resource is the Human”. Center for Security and Emerging Technology, avril 2023. http://dx.doi.org/10.51593/20210071.
Texte intégralChahal, Husanjot, Helen Toner et Ilya Rahkovsky. Small Data's Big AI Potential. Center for Security and Emerging Technology, septembre 2021. http://dx.doi.org/10.51593/20200075.
Texte intégral