Literatura científica selecionada sobre o tema "Artificial datasets"
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Artigos de revistas sobre o assunto "Artificial datasets"
Serrano-Pérez, Jonathan, e L. Enrique Sucar. "Artificial datasets for hierarchical classification". Expert Systems with Applications 182 (novembro de 2021): 115218. http://dx.doi.org/10.1016/j.eswa.2021.115218.
Texto completo da fonteLychev, Andrey V. "Synthetic Data Generation for Data Envelopment Analysis". Data 8, n.º 10 (27 de setembro de 2023): 146. http://dx.doi.org/10.3390/data8100146.
Texto completo da fontePetráš, Jaroslav, Marek Pavlík, Ján Zbojovský, Ardian Hyseni e Jozef Dudiak. "Benford’s Law in Electric Distribution Network". Mathematics 11, n.º 18 (10 de setembro de 2023): 3863. http://dx.doi.org/10.3390/math11183863.
Texto completo da fonteDasari, Kishore Babu, e 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, n.º 1 (28 de fevereiro de 2022): 61–71. http://dx.doi.org/10.18280/ria.360107.
Texto completo da fonteKusetogullari, Huseyin, Amir Yavariabdi, Abbas Cheddad, Håkan Grahn e Johan Hall. "ARDIS: a Swedish historical handwritten digit dataset". Neural Computing and Applications 32, n.º 21 (29 de março de 2019): 16505–18. http://dx.doi.org/10.1007/s00521-019-04163-3.
Texto completo da fonteMorgan, Maria, Carla Blank e Raed Seetan. "Plant disease prediction using classification algorithms". IAES International Journal of Artificial Intelligence (IJ-AI) 10, n.º 1 (1 de março de 2021): 257. http://dx.doi.org/10.11591/ijai.v10.i1.pp257-264.
Texto completo da fonteSaul, Marcia, e Shahin Rostami. "Assessing performance of artificial neural networks and re-sampling techniques for healthcare datasets". Health Informatics Journal 28, n.º 1 (janeiro de 2022): 146045822210871. http://dx.doi.org/10.1177/14604582221087109.
Texto completo da fonteGau, Michael-Lian, Huong-Yong Ting, Teck-Hock Toh, Pui-Ying Wong, Pei-Jun Woo, Su-Woan Wo e Gek-Ling Tan. "Effectiveness of Using Artificial Intelligence for Early Child Development Screening". Green Intelligent Systems and Applications 3, n.º 1 (9 de maio de 2023): 1–13. http://dx.doi.org/10.53623/gisa.v3i1.229.
Texto completo da fonteGHAFFARI, REZA, IOAN GROSU, DACIANA ILIESCU, EVOR HINES e MARK LEESON. "DIMENSIONALITY REDUCTION FOR SENSORY DATASETS BASED ON MASTER–SLAVE SYNCHRONIZATION OF LORENZ SYSTEM". International Journal of Bifurcation and Chaos 23, n.º 05 (maio de 2013): 1330013. http://dx.doi.org/10.1142/s0218127413300139.
Texto completo da fontePavlov, Nikolay A., Anna E. Andreychenko, Anton V. Vladzymyrskyy, Anush A. Revazyan, Yury S. Kirpichev e Sergey P. Morozov. "Reference medical datasets (MosMedData) for independent external evaluation of algorithms based on artificial intelligence in diagnostics". Digital Diagnostics 2, n.º 1 (30 de abril de 2021): 49–66. http://dx.doi.org/10.17816/dd60635.
Texto completo da fonteTeses / dissertações sobre o assunto "Artificial datasets"
Hilton, Erwin. "Visual datasets for artificial intelligence agents". Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119553.
Texto completo da fonteThis 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.
Texto completo da fonteLundberg, 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.
Texto completo da fonteHorečný, 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.
Texto completo da fonteWoods, 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.
Texto completo da fonteYasarer, Hakan. "Decision making in engineering prediction systems". Diss., Kansas State University, 2013. http://hdl.handle.net/2097/16231.
Texto completo da fonteDepartment 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.
Texto completo da fonteThis 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/.
Texto completo da fonteEsta 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/.
Texto completo da fonteMattiussi, 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/.
Texto completo da fonteLivros sobre o assunto "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.
Texto completo da fonteMountantonakis, M. Services for Connecting and Integrating Big Numbers of Linked Datasets. IOS Press, Incorporated, 2021.
Encontre o texto completo da fonteChirimuuta, 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.
Texto completo da fonteVillez, Kris, Daniel Aguado, Janelcy Alferes, Queralt Plana, Maria Victoria Ruano e Oscar Samuelsson, eds. Metadata Collection and Organization in Wastewater Treatment and Wastewater Resource Recovery Systems. IWA Publishing, 2024. http://dx.doi.org/10.2166/9781789061154.
Texto completo da fonteCapítulos de livros sobre o assunto "Artificial datasets"
Arbelaitz, Olatz, Ibai Gurrutxaga, Javier Muguerza e Jesús María Pérez. "Applying Resampling Methods for Imbalanced Datasets to Not So Imbalanced Datasets". In Advances in Artificial Intelligence, 111–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40643-0_12.
Texto completo da fonteKhangamwa, Gift, Terence van Zyl e Clint J. van Alten. "Towards a Methodology for Addressing Missingness in Datasets, with an Application to Demographic Health Datasets". In Artificial Intelligence Research, 169–86. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-22321-1_12.
Texto completo da fonteCerquides, Jesús, Maite López-Sánchez, Santi Ontañón, Eloi Puertas, Anna Puig, Oriol Pujol e Dani Tost. "Classification Algorithms for Biomedical Volume Datasets". In Current Topics in Artificial Intelligence, 143–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881216_16.
Texto completo da fonteSebé, Francesc, Josep Domingo-Ferrer e Agustí Solanas. "Noise-Robust Watermarking for Numerical Datasets". In Modeling Decisions for Artificial Intelligence, 134–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11526018_14.
Texto completo da fonteGarcía-Álvarez, David, Javier Lara Hinojosa e Francisco José Jurado Pérez. "Global Thematic Land Use Cover Datasets Characterizing Artificial Covers". In 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.
Texto completo da fonteKim, Minho, Hyunjin Yoo e R. S. Ramakrishna. "Cluster Validation for High-Dimensional Datasets". In 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.
Texto completo da fonteFerreira, Carlos Abreu, João Gama e Vítor Santos Costa. "Sequential Pattern Mining in Multi-relational Datasets". In 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.
Texto completo da fonteShamaev, Dmitry. "Synthetic Datasets and Medical Artificial Intelligence Specifics". In 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.
Texto completo da fonteEwen, Nicolas, Tamer Abdou e Ayse Bener. "Applications of Feature Selection Techniques on Large Biomedical Datasets". In Advances in Artificial Intelligence, 543–48. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18305-9_57.
Texto completo da fonteWang, Xiaoguang, Stan Matwin, Nathalie Japkowicz e Xuan Liu. "Cost-Sensitive Boosting Algorithms for Imbalanced Multi-instance Datasets". In Advances in Artificial Intelligence, 174–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38457-8_15.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Artificial datasets"
Da Silva, Ronnypetson, Valter M. Filho e Mario Souza. "Interaffection of Multiple Datasets with Neural Networks in Speech Emotion Recognition". In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/eniac.2020.12141.
Texto completo da fonteGhafourian, Sarvenaz, Ramin Sharifi e Amirali Baniasadi. "Facial Emotion Recognition in Imbalanced Datasets". In 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.
Texto completo da fonteFang, Yili, Chaojie Shen, Huamao Gu, Tao Han e Xinyi Ding. "TDG4Crowd:Test Data Generation for Evaluation of Aggregation Algorithms in Crowdsourcing". In 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.
Texto completo da fonteHe, Shuai, Yongchang Zhang, Rui Xie, Dongxiang Jiang e Anlong Ming. "Rethinking Image Aesthetics Assessment: Models, Datasets and Benchmarks". In 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.
Texto completo da fonte"DATAZAPPER: GENERATING INCOMPLETE DATASETS". In 1st International Conference on Agents and Artificial Intelligence. SciTePress - Science and and Technology Publications, 2009. http://dx.doi.org/10.5220/0001660700690076.
Texto completo da fonteAbdo, Thiago, e Fabiano Silva. "Iterative machine learning applied to annotation of text datasets". In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/eniac.2021.18268.
Texto completo da fonteCarvalho, Nathan F., André A. Gonçalves e Ana C. Lorena. "Collecting Meta-Data from the OpenML Public Repository". In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/eniac.2023.234286.
Texto completo da fonteKim, Seul ki, Kwihoon Kim e Taeyoung Kim. "Development of AI Educational Datasets Library Using Synthetic Dataset Generation Method". In 2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). IEEE, 2023. http://dx.doi.org/10.1109/icaiic57133.2023.10067000.
Texto completo da fonteMayo, Michael, e Quan Sun. "Evolving artificial datasets to improve interpretable classifiers". In 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014. http://dx.doi.org/10.1109/cec.2014.6900238.
Texto completo da fonteBhattacharjya, Debarun, Tian Gao, Nicholas Mattei e Dharmashankar Subramanian. "Cause-Effect Association between Event Pairs in Event Datasets". In 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.
Texto completo da fonteRelatórios de organizações sobre o assunto "Artificial datasets"
Johra, Hicham, Martin Veit, Mathias Østergaard Poulsen, Albert Daugbjerg Christensen, Rikke Gade, Thomas B. Moeslund e 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.
Texto completo da fonteCerulli, Giovanni. Machine Learning and AI for Research in Python. Instats Inc., 2023. http://dx.doi.org/10.61700/b7qz5fpva9dar469.
Texto completo da fonteCerulli, Giovanni. Machine Learning and AI for Research in Python. Instats Inc., 2023. http://dx.doi.org/10.61700/x92cmhdrxsvu7469.
Texto completo da fonteCerulli, Giovanni. Machine Learning and AI for Researchers in R. Instats Inc., 2023. http://dx.doi.org/10.61700/n8rzaz6kghskt469.
Texto completo da fonteCerulli, Giovanni. Machine Learning and AI for Researchers in R. Instats Inc., 2023. http://dx.doi.org/10.61700/atz7nxsz9afbm469.
Texto completo da fonteCerulli, Giovanni. Machine Learning and AI for Researchers in R. Instats Inc., 2023. http://dx.doi.org/10.61700/w5nn12uvjosgd469.
Texto completo da fonteChahal, Husanjot, Sara Abdulla, Jonathan Murdick e Ilya Rahkovsky. Mapping India’s AI Potential. Center for Security and Emerging Technology, março de 2021. http://dx.doi.org/10.51593/20200096.
Texto completo da fonteArnold, Zachary, Joanne Boisson, Lorenzo Bongiovanni, Daniel Chou, Carrie Peelman e Ilya Rahkovsky. Using Machine Learning to Fill Gaps in Chinese AI Market Data. Center for Security and Emerging Technology, fevereiro de 2021. http://dx.doi.org/10.51593/20200064.
Texto completo da fonteMusser, Micah, Rebecca Gelles, Catherine Aiken e Andrew Lohn. “The Main Resource is the Human”. Center for Security and Emerging Technology, abril de 2023. http://dx.doi.org/10.51593/20210071.
Texto completo da fonteChahal, Husanjot, Helen Toner e Ilya Rahkovsky. Small Data's Big AI Potential. Center for Security and Emerging Technology, setembro de 2021. http://dx.doi.org/10.51593/20200075.
Texto completo da fonte