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An Automated Python Script for Data Cleaning and Labeling using Machine Learning Technique.
Matthew Abiola Oladipupo ; Princewill Chima Obuzor ; Babatunde Joseph Bamgbade ; et al.
Informatica (Slovenia). 47(6)

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3

PERC: a suite of software tools for the curation of cryoEM data with application to simulation, modeling and machine learning.
Costa-Gomes, Beatriz ; Greer, Joel ; Juraschko, Nikolai ; et al.
Acta Crystallographica: Section F, Structural Biology Communications. Oct2025, Vol. 81 Issue 10, p441-450. 10p.

DATA curation MACHINE learning PYTHON programming langu... MOLECULAR biology OPEN source software ELECTRON microscopy …
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4

Leveraging large language models to mimic domain expert labeling in unstructured text-based electronic healthcare records in non-english languages.
Akbasli IT ; Birbilen AZ ; Teksam O
Publisher: BioMed Central Country of Publication: England NLM ID: 101088682 Publication Model: Electronic Cited Medium: Internet ISSN: 1472-6947 (Electronic) Linking ISSN: 14726947 NLM ISO Abbreviation: BMC Med Inform Decis Mak Subsets: MEDLINE

Humans Large Language Models Electronic Health Record... Natural Language Process... Supervised Machine Learn...
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5

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6

Active deep learning for segmentation of industrial CT data
Michen, Markus ; Rehak, Markus ; Haßler, Ulf
tm - Technisches Messen. 90(7-8):500-511

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7

Glycowork: A Python package for glycan data science and machine learning.
Thomès, Luc ; Burkholz, Rebekka ; Bojar, Daniel
Glycobiology. Oct2021, Vol. 31 Issue 10, p1240-1244. 5p.

MACHINE learning DATA science PYTHON programming langu... GLYCANS PACKAGING design CARBOHYDRATES
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8

Comparing Facies Prediction Performance of Machine Learning Models Trained on Well and Core Data: A Case Study from Lower Indus Basin.
ul huda choudhry, Noor ; Tahir, Muhammad Ali ; Taimur, Asad
International Journal of Multiphysics. 2024, Vol. 18 Issue 4, p399-411. 13p.

MACHINE learning GAS wells OIL wells IMAGE analysis MACHINE performance
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9

Graph-based contributions to machine-learning
Contributions à base de graphes à l'apprentissage automatique
Lutz, Quentin ; Laboratoire Traitement et Communication de l'Information (LTCI) ; Institut Mines-Télécom [Paris] (IMT)-Télécom Paris ; et al.
Data Structures and Algorithms [cs.DS]. Institut Polytechnique de Paris, 2022. English. ⟨NNT : 2022IPPAT010⟩

Data labeling Graphs Machine learning Annotations de données Graphes Apprentissage automatiqu... …
Dissertation
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10

Data Quality, Semantics, and Classification Features: Assessment and Optimization of Supervised ML-AI Classification Approaches for Historical Heritage.
Cera, Valeria ; Antuono, Giuseppe ; Campi, Massimiliano ; et al.
Heritage (2571-9408). Jul2025, Vol. 8 Issue 7, p265. 33p.

DATA quality SEMANTICS ARTIFICIAL intelligence HISTORIC sites FEATURE selection CLASSIFICATION …
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11

microbeSEG: A deep learning software tool with OMERO data management for efficient and accurate cell segmentation.
Scherr, Tim ; Seiffarth, Johannes ; Wollenhaupt, Bastian ; et al.
PLoS ONE. 11/29/2022, Vol. 17 Issue 11, p1-14. 14p.

DATA management DEEP learning SOFTWARE development too... PHASE-contrast microscop... GRAPHICAL user interface... PYTHON programming langu...
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12

Real-Time Detection and Localization of Force on a Capacitive Elastomeric Sensor Array Using Image Processing and Machine Learning.
Egger, Peter Werner ; Srinivas, Gidugu Lakshmi ; Brandstötter, Mathias
Sensors (14248220). May2025, Vol. 25 Issue 10, p3011. 22p.

MACHINE learning TACTILE sensors CAPACITIVE sensors SOFT robotics SENSOR arrays STRUCTURAL health monito...
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13

The Computational Content Analyst
Vargo, Chris J.

communication studies computational social sci... mass communication big data machine learning artificial intelligence …
E-Book
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14

SeFEF: A Seizure Forecasting Evaluation Framework
Carmo, Ana Sofia ; Rodrigues, Lourenço Abrunhosa ; Peralta, Ana Rita ; et al.

Quantitative Methods Machine Learning
Report
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15

MR-Class: A Python Tool for Brain MR Image Classification Utilizing One-vs-All DCNNs to Deal with the Open-Set Recognition Problem.
Salome, Patrick ; Sforazzini, Francesco ; Grugnara, Gianluca ; et al.
Cancers. Mar2023, Vol. 15 Issue 6, p1820. 16p.

BRAIN DEEP learning SEQUENCE analysis CONFIDENCE intervals METADATA MAGNETIC resonance imagi... …
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16

TorchQL: A Programming Framework for Integrity Constraints in Machine Learning
Naik, Aaditya ; Stein, Adam ; Wu, Yinjun ; et al.

Computer Science - Datab... Computer Science - Machi... Computer Science - Softw...
Report
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17

Fluorescence Spectroscopy and a Convolutional Neural Network for High-Accuracy Japanese Green Tea Origin Identification.
Akiyama, Rikuto ; Suzuki, Kana ; Llave, Yvan ; et al.
AgriEngineering. Apr2025, Vol. 7 Issue 4, p95. 14p.

CONVOLUTIONAL neural net... GREEN tea FLUORESCENCE spectroscop... FOOD labeling MACHINE learning
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18

The assessment of fundus image quality labeling reliability among graders with different backgrounds.
Laurik-Feuerstein, Kornélia Lenke ; Sapahia, Rishav ; Cabrera DeBuc, Delia ; et al.
PLoS ONE. 7/26/2022, Vol. 17 Issue 7, p1-11. 11p.

RETINAL imaging MACHINE learning
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19

The Computational Content Analyst
Vargo, Chris J.

communication studies computational social sci... mass communication big data machine learning artificial intelligence …
E-Book
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20

Information-theoretic Classification Accuracy: A Criterion that Guides Data-driven Combination of Ambiguous Outcome Labels in Multi-class Classification
Zhang, Chihao ; Chen, Yiling Elaine ; Zhang, Shihua ; et al.

multi-class classificati... information theory noisy labels supervised learn-ing class label combination Information and Computin... …
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