Relevant bibliographies by topics / Multilabel approach

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Multilabel approach'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Multilabel approach"

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Li, Hua, Deyu Li, Yanhui Zhai, Suge Wang, and Jing Zhang. "A Variable Precision Attribute Reduction Approach in Multilabel Decision Tables." Scientific World Journal 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/359626.

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Owing to the high dimensionality of multilabel data, feature selection in multilabel learning will be necessary in order to reduce the redundant features and improve the performance of multilabel classification. Rough set theory, as a valid mathematical tool for data analysis, has been widely applied to feature selection (also called attribute reduction). In this study, we propose a variable precision attribute reduct for multilabel data based on rough set theory, calledδ-confidence reduct, which can correctly capture the uncertainty implied among labels. Furthermore, judgement theory and disc
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Alzanin, Samah M., Abdu Gumaei, Md Azimul Haque, and Abdullah Y. Muaad. "An Optimized Arabic Multilabel Text Classification Approach Using Genetic Algorithm and Ensemble Learning." Applied Sciences 13, no. 18 (2023): 10264. http://dx.doi.org/10.3390/app131810264.

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Multilabel classification of Arabic text is an important task for understanding and analyzing social media content. It can enable the categorization and monitoring of social media posts, the detection of important events, the identification of trending topics, and the gaining of insights into public opinion and sentiment. However, multilabel classification of Arabic contents can present a certain challenge due to the high dimensionality of the representation and the unique characteristics of the Arabic language. In this paper, an effective approach is proposed for Arabic multilabel classificat
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Chen, Zhe, Hongli Zhang, Lin Ye, and Shang Li. "An Approach Based on Multilevel Convolution for Sentence-Level Element Extraction of Legal Text." Wireless Communications and Mobile Computing 2021 (December 24, 2021): 1–12. http://dx.doi.org/10.1155/2021/1043872.

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In the judicial field, with the increase of legal text data, the extraction of legal text elements plays a more and more important role. In this paper, we propose a sentence-level model of legal text element extraction based on the structure of multilabel text classification. Our proposed model contains an encoder and an improved decoder. The encoder applies multilevel convolutional neural networks (CNN) and Long Short-Term Memory (LSTM) as feature extraction networks to extract local neighborhood and context information from legal text, and a decoder applies LSTM with multiattention and full
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Bhattacharya, Debshree, and Manoj Kumar Nigam. "A Multilabel Approach for Fault Detection and Classification of Transmission Lines using Binary Relevance." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7 (2023): 261–69. http://dx.doi.org/10.17762/ijritcc.v11i7.7934.

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In Contemporary automation systems, Fault detection and classification of electrical transmission lines in grid systems are given top priority. The broad application of Machine Learning (ML) methods has enabled the substitute of conventional methods of fault identification and classification. These methods are more effective ones that can identify faults early on using a significant quantity of sensory data. So detecting simultaneous failures is difficult in the context of distracting the noise and several faults in the transmission lines. This study contributes by offering a unique way for co
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Basu, Kaustav, Vincent Debusschere, Seddik Bacha, Ujjwal Maulik, and Sanghamitra Bondyopadhyay. "Nonintrusive Load Monitoring: A Temporal Multilabel Classification Approach." IEEE Transactions on Industrial Informatics 11, no. 1 (2015): 262–70. http://dx.doi.org/10.1109/tii.2014.2361288.

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Kumari, H. M. N. S., and U. M. M. P. K. Nawarathne. "Machine Failure Prediction Using Multilabel Classification Methods." Journal of Advances in Engineering and Technology 2, no. 2 (2024): 37–45. http://dx.doi.org/10.54389/oknw9621.

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Early detection of machine failure is crucial in every industrial setting as it may prevent unexpected process downtimes as well as system failures. However, machine learning (ML) models are increasingly being utilized to forecast system failures in industrial maintenance, and among them, multilabel classification techniques act as efficient methods. Therefore, this study analyzed machine failure data with five types of machine failures. Initially, a feature selection approach was also carried out in this study to determine the variables which directly cause machine failure. Furthermore, multi
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Lee, Jaesung, and Dae-Won Kim. "Scalable Multilabel Learning Based on Feature and Label Dimensionality Reduction." Complexity 2018 (September 24, 2018): 1–15. http://dx.doi.org/10.1155/2018/6292143.

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The data-driven management of real-life systems based on a trained model, which in turn is based on the data gathered from its daily usage, has attracted a lot of attention because it realizes scalable control for large-scale and complex systems. To obtain a model within an acceptable computational cost that is restricted by practical constraints, the learning algorithm may need to identify essential data that carries important knowledge on the relation between the observed features representing the measurement value and labels encoding the multiple target concepts. This results in an increase
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Do, Kien, Truyen Tran, Thin Nguyen, and Svetha Venkatesh. "Attentional multilabel learning over graphs: a message passing approach." Machine Learning 108, no. 10 (2019): 1757–81. http://dx.doi.org/10.1007/s10994-019-05782-6.

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Valverde-Albacete, Francisco J., and Carmen Peláez-Moreno. "A Formalization of Multilabel Classification in Terms of Lattice Theory and Information Theory: Concerning Datasets." Mathematics 12, no. 2 (2024): 346. http://dx.doi.org/10.3390/math12020346.

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Multilabel classification is a recently conceptualized task in machine learning. Contrary to most of the research that has so far focused on classification machinery, we take a data-centric approach and provide an integrative framework that blends qualitative and quantitative descriptions of multilabel data sources. By combining lattice theory, in the form of formal concept analysis, and entropy triangles, obtained from information theory, we explain from first principles the fundamental issues of multilabel datasets such as the dependencies of the labels, their imbalances, or the effects of t
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Sakr, Nehal A., Mervat Abu-ElKheir, A. Atwan, and H. H. Soliman. "A multilabel classification approach for complex human activities using a combination of emerging patterns and fuzzy sets." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 4 (2019): 2993. http://dx.doi.org/10.11591/ijece.v9i4.pp2993-3001.

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In our daily lives, humans perform different Activities of Daily Living (ADL), such as cooking, and studying. According to the nature of humans, they perform these activities in a sequential/simple or an overlapping/complex scenario. Many research attempts addressed simple activity recognition, but complex activity recognition is still a challenging issue. Recognition of complex activities is a multilabel classification problem, such that a test instance is assigned to a multiple overlapping activities. Existing data-driven techniques for complex activity recognition can recognize a maximum nu
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Dissertations / Theses on the topic "Multilabel approach"

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Lindig, León Cecilia. "Classification multilabels à partir de signaux EEG d'imaginations motrices combinées : application au contrôle 3D d'un bras robotique." Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0016/document.

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Les interfaces cerveau-ordinateur (ou BCI en anglais pour Brain-Computer Interfaces) mettent en place depuis le système nerveux central un circuit artificiel secondaire qui remplace l’utilisation des nerfs périphériques, permettant entre autres à des personnes ayant une déficience motrice grave d’interagir, uniquement à l’aide de leur activité cérébrale, avec différents types d’applications, tels qu’un système d’écriture, une neuro-prothèse, un fauteuil roulant motorisé ou un bras robotique. Une technique répandue au sein des BCI pour enregistrer l’activité cérébrale est l’électroencéphalograp
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Lindig, León Cecilia. "Classification multilabels à partir de signaux EEG d'imaginations motrices combinées : application au contrôle 3D d'un bras robotique." Electronic Thesis or Diss., Université de Lorraine, 2017. http://www.theses.fr/2017LORR0016.

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Les interfaces cerveau-ordinateur (ou BCI en anglais pour Brain-Computer Interfaces) mettent en place depuis le système nerveux central un circuit artificiel secondaire qui remplace l’utilisation des nerfs périphériques, permettant entre autres à des personnes ayant une déficience motrice grave d’interagir, uniquement à l’aide de leur activité cérébrale, avec différents types d’applications, tels qu’un système d’écriture, une neuro-prothèse, un fauteuil roulant motorisé ou un bras robotique. Une technique répandue au sein des BCI pour enregistrer l’activité cérébrale est l’électroencéphalograp
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Guillon, Jérémy. "Multilayer Approach to Brain Connectivity in Alzheimer’s Disease." Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS305.

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La maladie d’Alzheimer provoque des altérations de la structure des réseaux cérébraux et des fonctions cérébrales qui peuvent être modélisées par une analyse de la connectivité cérébrale. Nous avons proposé une approche en réseaux multi-niveaux pour analyser les réseaux cérébraux multi-fréquentiels et multimodaux construits à partir des enregistrements de magnétoencéphalographie (MEG), d’imagerie par résonance magnétique fonctionnelle (IRMf) et de diffusion (IRMd). Les principaux résultats de ces travaux ont démontré l’existence d’un type de hubs non définis précédemment que sont les hubs inte
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D'Aguanno, A. "A multilayer approach to music features' extraction and their synchronization." Doctoral thesis, Università degli Studi di Milano, 2009. http://hdl.handle.net/2434/56009.

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Kim, Min Soo. "Template-assisted multilayer electrodeposition: An approach to top-down designable, surface/volumetric hierarchical nanostructures." Diss., Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/54930.

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Driven by the emerging interest in the design and realization of structures with co-existing micro- and nanoscale features, various nanofabrication approaches are being developed. We show that the selective, conformal growth of a multilayer structure is a promising route for the controlled realization of various structures with size-hierarchy, including both surface (i.e., the structures of which functionalities are characterized by the interaction between their surface, and external systems, such as self-cleaning, superhydrophic substrates with dual-scale topography), and volumetric (i.e. com
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Evans, Barry. "A multilayered approach to two-dimensional urban flood modelling." Thesis, University of Exeter, 2010. http://hdl.handle.net/10036/117891.

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With urbanisation continuing to encroach upon flood plains, the constant replacement of permeable land with impermeable surfaces and with the changes in global climate, the need for improved flood modelling is ever more apparent. A wide range of methods exist that simulate surface flow; most commonly in one-dimensional (1D) or twodimensional (2D), and more recently on smaller scales in three-dimensional (3D) models. In urban flood modelling, 2D models are often the preferred choice as they can simulate surface flow more accurately than their 1D model counterparts; they are, however, more compu
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Walz, Andrew R. "A multilayer network approach to quantifying biologically-derived systematic risk in biomedical finance." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/107356.

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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, School of Engineering, System Design and Management Program, Engineering and Management Program, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 100-106).<br>Sharply rising disease prevalence and associated healthcare costs are placing an increasingly significant economic burden on society. Biomedical research and industry have struggled to adequately address this challenge, as evidenced by the stagnation and even decline of new therapeutics development success rates.
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Li, Han. "Transfer Matrix Approach to Propagation of Angular Plane Wave Spectra Through Metamaterial Multilayer Structures." University of Dayton / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1324508726.

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Dumkum, Chaiya. "A multilayer approach to adherent diamond-like carbon coatings on commercially pure titanium (CP-Ti) and titanium alloy (Ti6A14V)." Thesis, University of Nottingham, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263411.

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FICARA, Annamaria. "Social network analysis approaches to study crime." Doctoral thesis, Università degli Studi di Palermo, 2022. http://hdl.handle.net/10447/537005.

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Social Network Analysis (SNA) studies groups of individuals and can be applied in a lot of areas such us organizational studies, psychology, economics, information science and criminology. One of the most important results of SNA has been the definition of a set of centrality measures (e.g., degree, closeness, betweenness, or clustering coefficient) which can be used to identify the most influential people with respect to their network of relationships. The main problem with computing centrality metrics on social networks is the typical big size of the data. From the computational point o
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Books on the topic "Multilabel approach"

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NEC, Symposium on Fundamental Approaches to New Material Phases (5th 1994 Karuizawa Japan). Proceedings of the 5th NEC Symposium onFundamental Approaches to New Material Phases: Spin-sependent phenomena in multilayer systems, 16-20 October 1994, Karuizawa, Japan. Elsevier, 1995.

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Bianconi, Ginestra. Synchronization, Non-linear Dynamics and Control. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0015.

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This chapter is entirely devoted to characterizing non-linear dynamics on multilayer networks. Special attention is given to recent results on the stability of synchronization that extend the Master Stability Function approach to the multilayer networks scenario. Discontinous synchronization transitions on multiplex networks recently reported in the literature are also discussed, and their application discussed in the context of brain networks. This chapter also presents an overview of the major results regarding pattern formation in multilayer networks, and the proposed characterization of mu
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Bianconi, Ginestra. Complex Systems as Multilayer Networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0001.

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Chapter 1 constitutes Part I of the book: ‘Single and Multilayer Networks’. This chapter introduces multilayer networks as an important new development of Network Science that allows a more comprehensive understanding of Complex Systems. It identifies the main motivations driving the research activity in this field of multilayer networks and emphasizes the benefits of taking a multilayer network perpective to characterize network data. The main advantages of a multilayer network approach with respect to the more traditional single layer characterization of complex networks are broadly discusse
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Bianconi, Ginestra. Communities. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0008.

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Multilayer networks have a mesoscale structure organized in multilayer communities, spanning different layers and often revealing important functional properties of the network. In this chapter the major techniques proposed for detecting and characterizing the multilayer communities are described, including: generalized modularity, consensus clustering, multilayer infomaps, multilink communities, tensorial decomposition, Normalized Mutual Information, theta indicators. The main benefits and limitations of these approaches are discussed and revealed by analysing the results obtained on real dat
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Ahmed, Q. Amin. A knowledge-based system approach to work shift selection for multilane highway reconstruction and maintenance projects. 1993.

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Book chapters on the topic "Multilabel approach"

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Charte, Francisco, Antonio J. Rivera, María J. del Jesus, and Francisco Herrera. "MLeNN: A First Approach to Heuristic Multilabel Undersampling." In Intelligent Data Engineering and Automated Learning – IDEAL 2014. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10840-7_1.

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Masera, Luca, and Enrico Blanzieri. "AWX: An Integrated Approach to Hierarchical-Multilabel Classification." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10925-7_20.

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Rivera Rivas, Antonio Jesús, Francisco Charte Ojeda, Francisco Javier Pulgar, and Maria Jose del Jesus. "A Transformation Approach Towards Big Data Multilabel Decision Trees." In Advances in Computational Intelligence. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59153-7_7.

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Ekundayo, Olufisayo, and Serestina Viriri. "Facial Expression Recognition and Ordinal Intensity Estimation: A Multilabel Learning Approach." In Advances in Visual Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64559-5_46.

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Gonçalves, Teresa, and Paulo Quaresma. "A Preliminary Approach to the Multilabel Classification Problem of Portuguese Juridical Documents." In Progress in Artificial Intelligence. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24580-3_50.

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Vellmer, Jan, Peter Mandl, Tobias Bellmann, et al. "A Machine Learning Approach to Enterprise Matchmaking Using Multilabel Text Classification Based on Semi-structured Website Content." In Information Integration and Web Intelligence. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-48316-5_44.

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Zamiri Azar, Kimia, Hadi Mardani Kamali, Farimah Farahmandi, and Mark Tehranipoor. "Multilayer Approach to Logic Locking." In Understanding Logic Locking. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-37989-5_11.

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Taud, H., and J. F. Mas. "Multilayer Perceptron (MLP)." In Geomatic Approaches for Modeling Land Change Scenarios. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60801-3_27.

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Lynn, David M. "A “Multilayered” Approach to the Delivery of DNA: Exploiting the Structure of Polyelectrolyte Multilayers to Promote Surface-Mediated Cell Transfection and Multi-Agent Delivery." In Multilayer Thin Films. Wiley-VCH Verlag GmbH & Co. KGaA, 2012. http://dx.doi.org/10.1002/9783527646746.ch30.

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Korczak, Jerzy, and Emmanuel Blindauer. "An Approach to Encode Multilayer Perceptrons." In Artificial Neural Networks — ICANN 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46084-5_50.

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Conference papers on the topic "Multilabel approach"

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Shapiro, Naomi, Amandalynne Paullada, and Shane Steinert-Threlkeld. "A multilabel approach to morphosyntactic probing." In Findings of the Association for Computational Linguistics: EMNLP 2021. Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.findings-emnlp.382.

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Ratadiya, Pratik, and Deepak Mishra. "An Attention Ensemble Based Approach for Multilabel Profanity Detection." In 2019 International Conference on Data Mining Workshops (ICDMW). IEEE, 2019. http://dx.doi.org/10.1109/icdmw.2019.00083.

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Manchanda, Saurav, and George Karypis. "Text Segmentation on Multilabel Documents: A Distant-Supervised Approach." In 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 2018. http://dx.doi.org/10.1109/icdm.2018.00154.

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Sharma, Gaurav, Kuruvilla Abraham, and Bagyalakshmi V. "Transfer Learning Approach for Multilabel Fine Grain Image Attribute Extraction." In 2022 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2022. http://dx.doi.org/10.1109/csci58124.2022.00269.

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Prati, Ronaldo C., Francisco Charte, and Francisco Herrera. "A first approach towards a fuzzy decision tree for multilabel classification." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015521.

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Thushari, Pahalage Dona, Sakina Niazi, and Shweta Meena. "Transfer Learning Approach to Multilabel Biomedical Literature Classification using Transformer Models." In 2023 IEEE 8th International Conference for Convergence in Technology (I2CT). IEEE, 2023. http://dx.doi.org/10.1109/i2ct57861.2023.10126262.

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Roy, Saikat, Kakuli Mishra, Ujjwal Maulik, and Srinka Basu. "A distributed multilabel classification approach towards mining appliance usage in smart homes." In 2017 IEEE Calcutta Conference (CALCON). IEEE, 2017. http://dx.doi.org/10.1109/calcon.2017.8280779.

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Saputra, Dimas Gilang, and Masayu Leylia Khodray. "An ensemble approach to handle out of vocabulary in multilabel document classification." In 2016 International Conference On Advanced Informatics: Concepts, Theory And Application (ICAICTA). IEEE, 2016. http://dx.doi.org/10.1109/icaicta.2016.7803109.

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Sadot, Arefin Abu Isha Md, Mitu Maliha Mehjabin, and Aziz Mahafuz. "A Novel Approach to Efficient Multilabel Text Classification: BERT-Federated Learning Fusion." In 2023 26th International Conference on Computer and Information Technology (ICCIT). IEEE, 2023. http://dx.doi.org/10.1109/iccit60459.2023.10441264.

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Ezemba, Jessica, James D. Cunningham, and Conrad S. Tucker. "Parameter Extraction From Images Using Multilabel Supervised Learning." In ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/detc2023-116501.

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Abstract In this work, we propose an approach to predict multiple design parameters of products using 2D images and supervised learning techniques. Fully parametric 2D or 3D vector representations have a high degree of flexibility since they are generally capable of morphing between all designs in the design space. A reverse design method is applied to parametrically designed products to extract design knowledge from existing designs and apply it to future designs. The goal of this research is to develop an accurate and efficient predictive model for multiple design parameters. The paper intro
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Reports on the topic "Multilabel approach"

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Lobbia, Marcus A. 1-D Heat Transfer in Multilayer Materials Using a Finite Volume Approach. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada594913.

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Rivera-Casillas, Peter, and Ian Dettwiller. Neural Ordinary Differential Equations for rotorcraft aerodynamics. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48420.

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High-fidelity computational simulations of aerodynamics and structural dynamics on rotorcraft are essential for helicopter design, testing, and evaluation. These simulations usually entail a high computational cost even with modern high-performance computing resources. Reduced order models can significantly reduce the computational cost of simulating rotor revolutions. However, reduced order models are less accurate than traditional numerical modeling approaches, making them unsuitable for research and design purposes. This study explores the use of a new modified Neural Ordinary Differential
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Gonczy, J., W. Boroski, and R. Niemann. Multilayer Insulation (Mli) in the Superconducting Super Collider: A Practical Engineering Approach to Physical Parameters Governing Mli Thermal Performance. Office of Scientific and Technical Information (OSTI), 1989. http://dx.doi.org/10.2172/1151491.

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