Search Results - "Data labeling"

An empirical evaluation of deep semi-supervised learning

Authors: Fredriksson, Teodor Bosch, Jan Olsson, Helena H.

Source: International Journal of Data Science and Analytics Bayesian-Data-Analysis-of-Universal-Semi-Supervised-Benchmark. 20(4):4127-4148

Subject Terms: Semi-supervised learning, Software engineering, Data labeling, Bayesian data analysis

File Description: electronic

Access URL: https://research.chalmers.se/publication/544964
https://research.chalmers.se/publication/544964/file/544964_Fulltext.pdf

A machine-learning-driven data labeling pipeline for scientific analysis in MLExchange

Authors: Chavez, Tanny Zhao, Zhuowen Jiang, Runbo et al.

Source: Journal of Applied Crystallography. 58(Pt 3)

Subject Terms: 3402 Inorganic Chemistry (for-2020), 34 Chemical Sciences (for-2020), 3406 Physical Chemistry (for-2020), 51 Physical Sciences (for-2020), 5104 Condensed Matter Physics (for-2020), Machine Learning and Artificial Intelligence (rcdc), Bioengineering (rcdc), Data Science (rcdc), Networking and Information Technology R&D (NITRD) (rcdc), X-ray scattering, data labeling, machine learning, user interfaces, feature extraction, X-ray scattering, data labeling, feature extraction, machine learning, user interfaces, 01 Mathematical Sciences (for), 02 Physical Sciences (for), 09 Engineering (for), Inorganic & Nuclear Chemistry (science-metrix), 3402 Inorganic chemistry (for-2020), 3406 Physical chemistry (for-2020), 5104 Condensed matter physics (for-2020)

File Description: application/pdf

Access URL: https://escholarship.org/uc/item/38s3x62d
https://escholarship.org/content/qt38s3x62d/qt38s3x62d.pdf

DEEPAQUA: Semantic segmentation of wetland water surfaces with SAR imagery using deep neural networks without manually annotated data

Authors: Pena, Francisco J. Hübinger, Clara Payberah, Amir H. et al.

Source: International Journal of Applied Earth Observation and Geoinformation. 126

Subject Terms: Automated data labeling, CNN, Deep learning, Remote sensing, Semantic segmentation, Vegetated water, Wetland mapping

File Description: print

Access URL: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-341922
https://doi.org/10.1016/j.jag.2023.103624

Using Large Language Model to Label Data in Determining Software Quality Classes: ChatGPT

Authors: Kekül, Hakan Polatgil, Mesut

Source: 2024 Innovations in Intelligent Systems and Applications Conference (ASYU). :1-6

Subject Terms: Fleiss' Kappa, ChatGPT, data labeling, ISO/IEC 25010

Access URL: https://hdl.handle.net/20.500.12418/35021

A stepwise methodology for transport mode detection in GPS tracking data

Authors: Sadeghian, Paria Zhao, Xiaoyun Golshan, Arman et al.

Source: Travel Behaviour & Society. 26:159-167

Subject Terms: Individuals travel patterns, Unsupervised learning, Supervised learning, GIS multi-criteria process, Data labeling

File Description: print

Access URL: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-305319
https://doi.org/10.1016/j.tbs.2021.10.004

Application of Quality Management System in the Research Process: A Case Study for Plant Phenotyping Research

Authors: Galba, Alexander Kánská, Eva Mikeš, Vojtěch et al.

Source: Agris on-line Papers in Economics and Informatics. 16:79-86

Subject Terms: plant phenotyping, Research and Development/Tech Change/Emerging Technologies, root cause analysis, data labeling process, data quality, quality management system, research process

On Semi-Supervised Learning: Evaluation, Challenges and Mitigation Strategies

Authors: Fredriksson, Teodor, 1992

Subject Terms: sofrware engineering, data labeling, machine learning, active learning, semi-supervised learning

File Description: electronic

Access URL: https://research.chalmers.se/publication/546702
https://research.chalmers.se/publication/546702/file/546702_Fulltext.pdf

Development of a machine learning model for predicting abnormalities of commercial airplanes

Authors: Rossi Passarella Siti Nurmaini Muhammad Naufal Rachmatullah et al.

Source: Data Science and Management, Vol 7, Iss 3, Pp 256-265 (2024)

Subject Terms: 0203 mechanical engineering, Prediction model, Electronic computers. Computer science, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Data-labeling, Automatic dependent surveillance–broadcast data, QA75.5-76.95, 02 engineering and technology, Commercial airplanes accident

Access URL: https://doaj.org/article/7b477a39ca6d47108703935160628791

FARPLS: A Feature-Augmented Robot Trajectory Preference Labeling System to Assist Human Labelers’ Preference Elicitation: A Feature-Augmented Robot Trajectory Preference Labeling System to Assist Human Labelers' Preference Elicitation

Authors: Hanfang Lyu Yuanchen Bai Xin Liang et al.

Source: Lyu, H, Bai, Y, Liang, X, Das, U, Shi, C, Gong, L, Li, Y, Sun, M, Ge, M & Ma, X 2024, FARPLS : A Feature-Augmented Robot Trajectory Preference Labeling System to Assist Human Labelers' Preference Elicitation. in IUI '24: : Proceedings of the 29th International Conference on Intelligent User Interfaces. Association for Computing Machinery, pp. 344-369. https://doi.org/10.1145/3640543.3645145, https://doi.org/10.1145/3640543
Lyu, H, Bai, Y, Liang, X, Das, U, Shi, C, Gong, L, Li, Y, Sun, M, Ge, M & MA, XIAOJUAN 2024 'FARPLS : A Feature-Augmented Robot Trajectory Preference Labeling System to Assist Human Labelers' Preference Elicitation' arXiv, pp. 1-26. https://doi.org/10.48550/arXiv.2403.06267

Subject Terms: FOS: Computer and information sciences, Computer Science - Artificial Intelligence, 4. Education, cs.HC, Computer Science - Human-Computer Interaction, human-robot value alignment, cs.AI, 15. Life on land, data labeling, 7. Clean energy, preference collection system, Human-Computer Interaction (cs.HC), 3. Good health, Artificial Intelligence (cs.AI), 13. Climate action, 11. Sustainability, 14. Life underwater, 10. No inequality

File Description: application/pdf

Access URL: http://arxiv.org/abs/2403.06267
https://research.manchester.ac.uk/en/publications/1e455262-8471-44fb-afa0-c8c4ed3a6d2d
https://doi.org/10.1145/3640543
https://research.manchester.ac.uk/en/publications/b8888910-4638-4b30-a119-2f99e1b6f403
https://doi.org/10.48550/arXiv.2403.06267
https://pure.manchester.ac.uk/ws/files/1563107970/2403.06267v1.pdf
https://doi.org/10.1145/3640543.3645145
https://research.manchester.ac.uk/en/publications/1e455262-8471-44fb-afa0-c8c4ed3a6d2d
https://research.manchester.ac.uk/en/publications/b8888910-4638-4b30-a119-2f99e1b6f403
https://doi.org/10.48550/arXiv.2403.06267
https://arxiv.org/abs/2403.06267

Improving drug repositioning with negative data labeling using large language models

Authors: Milan Picard Mickael Leclercq Antoine Bodein et al.

Source: Journal of Cheminformatics, Vol 17, Iss 1, Pp 1-12 (2025)

Subject Terms: AI-driven drug discovery, Computational drug scoring, Negative data labeling, Drug repurposing, Castration resistant prostate cancer, Biomedical text mining, Information technology, T58.5-58.64, Chemistry, QD1-999

File Description: electronic resource

Relation: https://doaj.org/toc/1758-2946

Access URL: https://doaj.org/article/f2b48bfe9f3942cea9a71a3a59c23c1a

A Novel Approach to Elevator Safety: Semi-Supervised Data Labeling for Function Classification and Uneven Rope Tension Detection Using FBG Sensors

Authors: Md. Rajibul Islam Linyue Lu Md. Rashedul Islam et al.

Source: IEEE Access, Vol 13, Pp 191673-191688 (2025)

Subject Terms: Elevator function recognition, fiber Bragg grating (FBG) sensors, K-means clustering, long short-term memory (LSTM) network, real-time tension monitoring, semi-supervised data labeling, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

File Description: electronic resource

Relation: https://ieeexplore.ieee.org/document/11224560/; https://doaj.org/toc/2169-3536

Access URL: https://doaj.org/article/5cf7391394f24c3ca8206cd026b7ca1e

Ontology-Driven Pipeline for the Automated Generation of Multimodal Datasets for Supervised Learning in Natural Hazard Models

Authors: Ion Grujdin Mihai Datcu

Source: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 18, Pp 27113-27127 (2025)

Subject Terms: Automated disaster dataset generation, explainable artificial intelligence (XAI), multimodal remote sensing datasets, ontology-based data labeling, semantic reasoning for hazard analysis, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809

File Description: electronic resource

Relation: https://ieeexplore.ieee.org/document/11205893/; https://doaj.org/toc/1939-1404; https://doaj.org/toc/2151-1535

Access URL: https://doaj.org/article/ba284afe23d840739fec8e18f65ef40d

Artificial Intelligence-Driven Diagnostics in Eye Care: A Random Forest Approach for Data Classification and Predictive Modeling

Authors: Luís F. F. M. Santos Miguel Ángel Sánchez-Tena Cristina Alvarez-Peregrina et al.

Source: Algorithms, Vol 18, Iss 10, p 647 (2025)

Subject Terms: machine learning, artificial intelligence, assisted diagnosis, data labeling, knowledge engineering, Industrial engineering. Management engineering, T55.4-60.8, Electronic computers. Computer science, QA75.5-76.95

File Description: electronic resource

Relation: https://www.mdpi.com/1999-4893/18/10/647; https://doaj.org/toc/1999-4893

Access URL: https://doaj.org/article/2930ecbd35224743af20aa66f6405c9a

Simulation-based Optimization of User Interfaces for Quality-assuring Machine Learning Model Predictions

Authors: Yu Zhang Martijn Tennekes Tim De Jong et al.

Source: Zhang, Y, Tennekes, M, de Jong, T, Curier, L, Coecke, B & Chen, M 2024, 'Simulation-based Optimization of User Interfaces for Quality-assuring Machine Learning Model Predictions', Transactions on Interactive Intelligent Systems, vol. 14, no. 1, 1. https://doi.org/10.1145/3594552
ACM Transactions on Interactive Intelligent Systems

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, 05 social sciences, Interactive machine learning, Computer Science - Human-Computer Interaction, 02 engineering and technology, Classification, Data labeling, Quality assurance, Human-Computer Interaction (cs.HC), Machine Learning (cs.LG), 0202 electrical engineering, electronic engineering, information engineering, 0501 psychology and cognitive sciences, Model-based evaluation

Access URL: http://arxiv.org/abs/2104.01129
https://research.ou.nl/en/publications/1982e7cb-4bff-404c-b0ff-e46d6d258c93
https://doi.org/10.1145/3594552
https://ora.ox.ac.uk/objects/uuid:2de0d150-64c6-49e8-9ff7-79647139a823
https://doi.org/10.1145/3594552
https://research.ou.nl/en/publications/1982e7cb-4bff-404c-b0ff-e46d6d258c93
https://doi.org/10.1145/3594552
https://www.scopus.com/pages/publications/85171549502

A K-Means-Based Interpolation Algorithm With Lp-Norm and Feature Weighting

Authors: Yipeng Miao Yenan Xu

Source: IEEE Access, Vol 12, Pp 96179-96192 (2024)

Subject Terms: feature weights, incomplete data, Lp-norm, data interpolation, 0202 electrical engineering, electronic engineering, information engineering, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, data labeling, Clustering, TK1-9971

Access URL: https://doaj.org/article/115cd0c54f024e8c8f069a6aa7419a8b

LIFEDATA - A Framework for Traceable Active Learning Projects

Authors: Stieler, Fabian Elia, Miriam Weigell, Benjamin et al.

Source: 2023 IEEE 31st International Requirements Engineering Conference Workshops (REW). :465-474

Subject Terms: ddc:004, 0301 basic medicine, 03 medical and health sciences, Data-Centric AI, Python Framework, 0202 electrical engineering, electronic engineering, information engineering, Active Learning, Traceability, Data Labeling, 02 engineering and technology

File Description: application/pdf

Access URL: https://opus.bibliothek.uni-augsburg.de/opus4/files/108197/108197.pdf
https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/index/index/docId/108197
https://doi.org/10.1109/REW57809.2023.00088
https://nbn-resolving.org/urn:nbn:de:bvb:384-opus4-1081971

CheckMate: English Grammatical Error Correction for Native Turkish Speakers

Authors: Ersoy, Asım Savlak, O. Kart, C. et al.

Contributors: Ersoy, Asım Savlak, O. Kart, C. et al.

Source: 2023 31st Signal Processing and Communications Applications Conference (SIU). :1-4

Subject Terms: Data collection, Deep learning, Grammatical error correction, Data labeling

Access URL: http://hdl.handle.net/10679/8941
https://doi.org/10.1109/SIU59756.2023.10223761

Automated Radar Data Labeling through Computer Vision

Authors: Gabsteiger, Jasmin Maiwald, Timo Wünsche, Simon et al.

Source: 2023 IEEE Wireless and Microwave Technology Conference (WAMICON). :77-80

Subject Terms: MLE@TUHH, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, radar data labeling, 01 natural sciences, computer vision

Artificial Intelligence and Aviation: A Deep Learning Strategy for Improved Data Classification and Management

Authors: Flávio L. Lázaro Luís F. F. M. Santos Duarte Valério et al.

Source: Applied Sciences, Vol 15, Iss 17, p 9403 (2025)

Subject Terms: aviation, deep learning, data labeling, management, AI, BERT, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

File Description: electronic resource

Relation: https://www.mdpi.com/2076-3417/15/17/9403; https://doaj.org/toc/2076-3417

Access URL: https://doaj.org/article/91783a032f5c4039948b36fa620dd051

A matter of annotation: an empirical study on in situ and self-recall activity annotations from wearable sensors

Authors: Hölzemann, Alexander Van Laerhoven, Kristof Lukowicz, Paul

Contributors: Hölzemann, Alexander Van Laerhoven, Kristof Lukowicz, Paul

Source: Frontiers in Computer Science, Vol 6 (2024)

Subject Terms: ddc:004, FOS: Computer and information sciences, Computer Science - Machine Learning, data annotation ambiguity, Computer Science - Human-Computer Interaction, 02 engineering and technology, data labeling, Data labeling, 01 natural sciences, Human-Computer Interaction (cs.HC), Machine Learning (cs.LG), Datenbeschriftung, 0202 electrical engineering, electronic engineering, information engineering, dataset, annotation method, Mehrdeutigkeit von Datenkommentaren, Datensatz, deep learning, Deep learning, QA75.5-76.95, Data annotation ambiguity, Tiefes Lernen, 0104 chemical sciences, Annotation method, Electronic computers. Computer science, Annotationsmethode, Dataset

Access URL: http://arxiv.org/abs/2305.08752
https://doaj.org/article/94abbca1cfe545ab82d4540bd5a9c76d