• Suche: Active Machine Learning with Python

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141

Coniferest: a complete active anomaly detection framework
Kornilov, M.V ; Korolev, V.S ; Malanchev, K.L ; et al.
Astron.Comput.. 52:100960-100960

Machine learning Active learning Anomaly detection [PHYS.PHYS.PHYS-INS-DET]... Physics [physics] Instrumentation and Dete... …
Zeitschrift
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142

Large language model guided automated reaction pathway exploration.
Chen, Ruzhao ; Liu, Yubang ; Chen, Zhe ; et al.
Communications Chemistry; 8/24/2025, Vol. 8 Issue 1, p1-14, 14p

QUANTUM mechanics CATALYSTS CHEMICAL reactions MACHINE learning QUANTITATIVE research LANGUAGE models
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143

Application of Machine Learning Techniques to an Agent-Based Model of Pantoea
Serena H. Chen ; Pablo Londoño‐Larrea ; A. Stephen McGough ; et al.
Front Microbiol
Frontiers in Microbiology, Vol 12 (2021)

Artificial neural networ... 0301 basic medicine Artificial intelligence neural network Flexibility (engineering... Population …
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144

Using python libraries and k-Nearest neighbors algorithms to delineate syn-sedimentary faults in sedimentary porous media
Universidad de Alicante. Departamento de Ciencias de la Tierra y del Medio Ambiente ; Martín-Martín, Manuel ; Bullejos, Manuel ; et al.

E-Ressource
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145

LIDEB's Useful Decoys (LUDe): A freely available decoy-generation tool. Benchmarking and scope
Alberca, Lucas N. ; Prada Gori, Denis N. ; Fallico, Maximiliano J. ; et al.
In Artificial Intelligence in the Life Sciences June 2025 7

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146

Active Community Opinion Network Mining and Maximization through Social Networks Posts
Semwal, Mayank
Electronic Theses and Dissertations

Machine Learning Artificial Intelligence... Software Developer Computer Sciences Python Development Research and Development …
Dissertation
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147

Does social media activity lead to more funds? – A study on Indian start-ups
Singhal, Nidhi ; Kapur, Deepak
Journal of Entrepreneurship in Emerging Economies, 2022, Vol. 15, Issue 5, pp. 967-987.

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148

Machine learning assisted canonical sampling (MLACS)
Castellano, Aloïs ; Béjaud, Romuald ; Richard, Pauline ; et al.

Condensed Matter - Mater... Physics - Computational...
Report
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149

PyMatterSim: a Python Data Analysis Library for Computer Simulations of Materials Science, Physics, Chemistry, and Beyond
Hu, Y. -C. ; Tian, J.

Condensed Matter - Mater... Condensed Matter - Soft... Condensed Matter - Stati... Physics - Computational...
Report
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150

Beyond The Basics: A Detailed Survey of Advanced Python Applications and Innovations
Piyush D. Pitroda ; Bhavika C. Donga ; Dr. Hasmukh B. Domadiya ; et al.
International Journal for Research in Applied Science and Engineering Technology. 12:94-97

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151

Enhancing Jakarta Faces Web App with AI Data-Driven Python Data Analysis and Visualization
Bala Dhandayuthapani V.
International Journal of Information Technology and Computer Science. 16:36-51

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152

A Fully First-Order Layer for Differentiable Optimization
Zhao, Zihao ; Mo, Kai-Chia ; Ho, Shing-Hei ; et al.

Machine Learning
Report
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153

Automatically Finding Rule-Based Neurons in OthelloGPT
Singh, Aditya ; Wen, Zihang ; Medicherla, Srujananjali ; et al.

Machine Learning Artificial Intelligence
Report
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154

Machine-Learning Based EEG Biomarkers for Personalized Interventions (EEG-INSIGHT)
Castellers de la Vila de Gràcia ; Dolors Soler, PhD, Principal Investigator
Machine-Learning Based EEG Biomarkers for Personalized Interventions
Gewandter JS, McDermott MP, Evans S, Katz NP, Markman JD, Simon LS, Turk DC, Dworkin RH. Composite outcomes for pain clinical trials: considerations for design and interpretation. Pain. 2021 Jul 1;162(7):1899-1905. doi: 10.1097/j.pain.0000000000002188. No abstract available.
Bikson M, Grossman P, Thomas C, Zannou AL, Jiang J, Adnan T, Mourdoukoutas AP, Kronberg G, Truong D, Boggio P, Brunoni AR, Charvet L, Fregni F, Fritsch B, Gillick B, Hamilton RH, Hampstead BM, Jankord R, Kirton A, Knotkova H, Liebetanz D, Liu A, Loo C, Nitsche MA, Reis J, Richardson JD, Rotenberg A, Turkeltaub PE, Woods AJ. Safety of Transcranial Direct Current Stimulation: Evidence Based Update 2016. Brain Stimul. 2016 Sep-Oct;9(5):641-661. doi: 10.1016/j.brs.2016.06.004. Epub 2016 Jun 15.
Zhdanov A, Atluri S, Wong W, Vaghei Y, Daskalakis ZJ, Blumberger DM, Frey BN, Giacobbe P, Lam RW, Milev R, Mueller DJ, Turecki G, Parikh SV, Rotzinger S, Soares CN, Brenner CA, Vila-Rodriguez F, McAndrews MP, Kleffner K, Alonso-Prieto E, Arnott SR, Foster JA, Strother SC, Uher R, Kennedy SH, Farzan F. Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression. JAMA Netw Open. 2020 Jan 3;3(1):e1918377. doi: 10.1001/jamanetworkopen.2019.18377.
Vuckovic A, Gallardo VJF, Jarjees M, Fraser M, Purcell M. Prediction of central neuropathic pain in spinal cord injury based on EEG classifier. Clin Neurophysiol. 2018 Aug;129(8):1605-1617. doi: 10.1016/j.clinph.2018.04.750. Epub 2018 May 23.
Mussigmann T, Bardel B, Lefaucheur JP. Resting-state electroencephalography (EEG) biomarkers of chronic neuropathic pain. A systematic review. Neuroimage. 2022 Sep;258:119351. doi: 10.1016/j.neuroimage.2022.119351. Epub 2022 Jun 2.
Mari T, Henderson J, Maden M, Nevitt S, Duarte R, Fallon N. Systematic Review of the Effectiveness of Machine Learning Algorithms for Classifying Pain Intensity, Phenotype or Treatment Outcomes Using Electroencephalogram Data. J Pain. 2022 Mar;23(3):349-369. doi: 10.1016/j.jpain.2021.07.011. Epub 2021 Aug 21.

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155

Integrated use of Sentinel-1 and Sentinel-2 data and open-source machine learning algorithms for land cover mapping in a Mediterranean region.
De Luca, Giandomenico ; M. N. Silva, João ; Di Fazio, Salvatore ; et al.
European Journal of Remote Sensing; Dec2022, Vol. 55 Issue 1, p52-70, 19p

SENTINEL-1 (Artificial s... MACHINE learning PYTHON programming langu... LAND cover GREEN roofs NORMALIZED difference ve... …
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156

python-adaptive/adaptive: version 1.2.0
Nijholt, Bas ; orcid:0000-0003-0383- ; Weston, Joseph ; et al.

adaptive-learning machine-learning python live-plots parallel-computing adaptive-sampling …
E-Ressource
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157

A Deep Learning-Based Framework for Sentiment and Emotion Classification of Social Media Messages During Pandemic Periods.
Yu, Feng ; Liu, Jian Ming
Journal of Circuits, Systems & Computers; 2/15/2025, Vol. 35 Issue 3, p1-32, 32p

TWITTER (Web resource) SENTIMENT analysis EMOTION recognition MACHINE learning DEEP learning NATURAL language process... …
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158

ML4Fire-XGBv1.0: Improving North American wildfire prediction by integrating a machine-learning fire model in a land surface model.
Liu, Ye ; Huang, Huilin ; Wang, Sing-Chun ; et al.
Geoscientific Model Development Discussions. 8/30/2024, p1-23. 23p.

MACHINE learning WILDFIRE prevention VEGETATION dynamics WILDFIRES DEEP learning FIRE management …
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160

A universal tool for predicting differentially active features in single-cell and spatial genomics data
Vandenbon, Alexis ; Diez, Diego
Sci Rep
Scientific Reports, Vol 13, Iss 1, Pp 1-14 (2023)

Data Analysis Science Gene Expression Profilin... Genomics Article Computational biology an... …
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