Treffer 121 - 140 von 5.317

Scalable transcriptomics analysis with Dask: applications in data science and machine learning.
Moreno M ; Vilaça R ; Ferreira PG
Publisher: BioMed Central Country of Publication: England NLM ID: 100965194 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2105 (Electronic) Linking ISSN: 14712105 NLM ISO Abbreviation: BMC Bioinformatics Subsets: MEDLINE

The limited value of machine learning approach to improving predictive performance: The Ministry of Justice Case Assessment Tool.
Mori, Takemi ; Harada, Kyoko ; Yamaki, Mayuko ; et al.
Psychol Public Policy Law

Data science: fundamentals and case studies, machine learning with Python and R.
MORAND, Elisabeth
Population (1634-2941). 2018, Vol. 73 Issue 2, p386-387. 2p.

A Curriculum Study: Accounting Analytics Using Python
Namryoung Lee
Journal of Curriculum and Teaching. 13:255

Automated Lesion and Feature Extraction Pipeline for Brain MRIs with Interpretability.
Eghbali R ; Nedelec P ; Weiss D ; et al.
Publisher: Humana Press, Inc Country of Publication: United States NLM ID: 101142069 Publication Model: Electronic Cited Medium: Internet ISSN: 1559-0089 (Electronic) Linking ISSN: 15392791 NLM ISO Abbreviation: Neuroinformatics Subsets: MEDLINE

Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI.
Ankenbrand, Markus J. ; Shainberg, Liliia ; Hock, Michael ; et al.
BMC Medical Imaging; 2/15/2021, Vol. 21 Issue 1, p1-8, 8p

"In the Wild" Video Content as a Special Case of User Generated Content and a System for Its Recognition.
Leszczuk, Mikołaj ; Kobosko, Marek ; Nawała, Jakub ; et al.
Sensors (14248220). Feb2023, Vol. 23 Issue 4, p1769. 17p.

Chapter 4 - Introduction to the Python Hyperspectral Analysis Tool (PyHAT)
Laura, J.R. ; Gaddis, L.R. ; Anderson, R.B. ; et al.
In Machine Learning for Planetary Science 2022:55-90

ارزیابی قابلیت اعتماد سیستمهای قدرت - گاز طبیعی یکپارچه در حضور منابع ذخیره گاز مدل مبتنی بر یادگیری ماشین.
مصطفی شبانیان بو ; حمت الله هوشمند
Computational Intelligence in Electrical Engineering. 2024, Vol. 15 Issue 2, Preceding p15-31. 18p.

Neutron Reflectometry by Gradient Descent
Champneys, Max D. ; Parnell, Andrew J. ; Gutfreund, Philipp ; et al.

Kiberbiztonsági kihívások a mezőgazdaság digitalizációjában: Szisztematikus áttekintés Python-alapú elemzéssel
Zsanett Porkoláb-Angyalos
International Journal of Engineering and Management Sciences. 10:33-47

Fast Explicit Machine Learning-Based Model Predictive Control of Nonlinear Processes Using Input Convex Neural Networks
Wang, Wenlong ; Zhang, Haohao ; Wang, Yujia ; et al.
Industrial & Engineering Chemistry Research. 63(40):17279-17293

Risk Stratification of Hepatocarcinogenesis Using a Deep Learning Based Clinical, Biological and Ultrasound Model in High-risk Patients (STARHE)
Risk Stratification of Hepatocarcinogenesis Using a Deep Learning Based Clinical, Biological and Ultrasound Model in High-risk Patients
Cadier B, Bulsei J, Nahon P, Seror O, Laurent A, Rosa I, Layese R, Costentin C, Cagnot C, Durand-Zaleski I, Chevreul K; ANRS CO12 CirVir and CHANGH groups. Early detection and curative treatment of hepatocellular carcinoma: A cost-effectiveness analysis in France and in the United States. Hepatology. 2017 Apr;65(4):1237-1248. doi: 10.1002/hep.28961. Epub 2017 Feb 8.
Costentin CE, Layese R, Bourcier V, Cagnot C, Marcellin P, Guyader D, Pol S, Larrey D, De Ledinghen V, Ouzan D, Zoulim F, Roulot D, Tran A, Bronowicki JP, Zarski JP, Riachi G, Cales P, Peron JM, Alric L, Bourliere M, Mathurin P, Blanc JF, Abergel A, Serfaty L, Mallat A, Grange JD, Attali P, Bacq Y, Wartelle C, Dao T, Thabut D, Pilette C, Silvain C, Christidis C, Nguyen-Khac E, Bernard-Chabert B, Zucman D, Di Martino V, Sutton A, Letouze E, Imbeaud S, Zucman-Rossi J, Audureau E, Roudot-Thoraval F, Nahon P; ANRS CO12 CirVir Group. Compliance With Hepatocellular Carcinoma Surveillance Guidelines Associated With Increased Lead-Time Adjusted Survival of Patients With Compensated Viral Cirrhosis: A Multi-Center Cohort Study. Gastroenterology. 2018 Aug;155(2):431-442.e10. doi: 10.1053/j.gastro.2018.04.027. Epub 2018 May 3.
Ioannou GN, Green P, Kerr KF, Berry K. Models estimating risk of hepatocellular carcinoma in patients with alcohol or NAFLD-related cirrhosis for risk stratification. J Hepatol. 2019 Sep;71(3):523-533. doi: 10.1016/j.jhep.2019.05.008. Epub 2019 May 28.
Audureau E, Carrat F, Layese R, Cagnot C, Asselah T, Guyader D, Larrey D, De Ledinghen V, Ouzan D, Zoulim F, Roulot D, Tran A, Bronowicki JP, Zarski JP, Riachi G, Cales P, Peron JM, Alric L, Bourliere M, Mathurin P, Blanc JF, Abergel A, Chazouilleres O, Mallat A, Grange JD, Attali P, d'Alteroche L, Wartelle C, Dao T, Thabut D, Pilette C, Silvain C, Christidis C, Nguyen-Khac E, Bernard-Chabert B, Zucman D, Di Martino V, Sutton A, Pol S, Nahon P; ANRS CO12 CirVir group. Personalized surveillance for hepatocellular carcinoma in cirrhosis - using machine learning adapted to HCV status. J Hepatol. 2020 Dec;73(6):1434-1445. doi: 10.1016/j.jhep.2020.05.052. Epub 2020 Jun 29.
Kitamura S, Iishi H, Tatsuta M, Ishikawa H, Hiyama T, Tsukuma H, Kasugai H, Tanaka S, Kitamura T, Ishiguro S. Liver with hypoechoic nodular pattern as a risk factor for hepatocellular carcinoma. Gastroenterology. 1995 Jun;108(6):1778-84. doi: 10.1016/0016-5085(95)90140-x.
Tarao K, Hoshino H, Shimizu A, Ohkawa S, Harada M, Nakamura Y, Ito Y, Tamai S, Okamoto N. Patients with ultrasonic coarse-nodular cirrhosis who are anti-hepatitis C virus-positive are at high risk for hepatocellular carcinoma. Cancer. 1995 Mar 15;75(6):1255-62. doi: 10.1002/1097-0142(19950315)75:63.0.co;2-q.
Caturelli E, Castellano L, Fusilli S, Palmentieri B, Niro GA, del Vecchio-Blanco C, Andriulli A, de Sio I. Coarse nodular US pattern in hepatic cirrhosis: risk for hepatocellular carcinoma. Radiology. 2003 Mar;226(3):691-7. doi: 10.1148/radiol.2263011737. Epub 2003 Jan 24.
Dana J, Agnus V, Ouhmich F, Gallix B. Multimodality Imaging and Artificial Intelligence for Tumor Characterization: Current Status and Future Perspective. Semin Nucl Med. 2020 Nov;50(6):541-548. doi: 10.1053/j.semnuclmed.2020.07.003. Epub 2020 Aug 2.
Yala A, Schuster T, Miles R, Barzilay R, Lehman C. A Deep Learning Model to Triage Screening Mammograms: A Simulation Study. Radiology. 2019 Oct;293(1):38-46. doi: 10.1148/radiol.2019182908. Epub 2019 Aug 6.
Dohan A, Gallix B, Guiu B, Le Malicot K, Reinhold C, Soyer P, Bennouna J, Ghiringhelli F, Barbier E, Boige V, Taieb J, Bouche O, Francois E, Phelip JM, Borel C, Faroux R, Seitz JF, Jacquot S, Ben Abdelghani M, Khemissa-Akouz F, Genet D, Jouve JL, Rinaldi Y, Desseigne F, Texereau P, Suc E, Lepage C, Aparicio T, Hoeffel C; PRODIGE 9 Investigators and PRODIGE 20 Investigators. Early evaluation using a radiomic signature of unresectable hepatic metastases to predict outcome in patients with colorectal cancer treated with FOLFIRI and bevacizumab. Gut. 2020 Mar;69(3):531-539. doi: 10.1136/gutjnl-2018-316407. Epub 2019 May 17.
Savadjiev P, Chong J, Dohan A, Agnus V, Forghani R, Reinhold C, Gallix B. Image-based biomarkers for solid tumor quantification. Eur Radiol. 2019 Oct;29(10):5431-5440. doi: 10.1007/s00330-019-06169-w. Epub 2019 Apr 8.
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015 May 28;521(7553):436-44. doi: 10.1038/nature14539.
European Association for the Study of the Liver. Corrigendum to "EASL Clinical Practice Guidelines: Management of hepatocellular carcinoma" [J Hepatol 69 (2018) 182-236]. J Hepatol. 2019 Apr;70(4):817. doi: 10.1016/j.jhep.2019.01.020. Epub 2019 Feb 7. No abstract available.
Dana J, Meyer A, Paisant A, Rode A, Sartoris R, Seror O, Cassinotto C, Milot L, Gregory J, Coeur J, Lebigot J, Schembri V, Villeret F, Takeda AN, Ronot M, Vilgrain V, Baumert TF, Gallix B, Padoy N, Nahon P. Improving risk stratification and detection of early HCC using ultrasound-based deep learning models. JHEP Rep. 2025 Jul 5;7(10):101510. doi: 10.1016/j.jhepr.2025.101510. eCollection 2025 Oct.

Kiberbiztonsá gi kihí vá sok á mezo gázdásá g digitálizá cio já bán: Szisztemátikus á ttekinte s Pythonálápu elemze ssel. (Hungarian)
ZSANETT, PORKOLÁB-ANGYALOS
International Journal of Engineering & Management Sciences (2498-700X) / Műszaki és Menedzsment Tudományi Közlemények; 2025, Vol. 10 Issue 1, p33-47, 15p

CommonPower: A Framework for Safe Data-Driven Smart Grid Control
Eichelbeck, Michael ; Markgraf, Hannah ; Althoff, Matthias
IEEE Transactions on Smart Grid, 2025

Deep Learning Based System Identification and Nonlinear Model Predictive Control of pH Neutralization Process
Durairaj, Sam ; Xavier, Joanofarc ; Patnaik, Sanjib Kumar ; et al.
Industrial & Engineering Chemistry Research. 62(33):13061-13080

ASAS-NANP symposium: mathematical modeling in animal nutrition-Making sense of big data and machine learning: how open-source code can advance training of animal scientists.
Brennan JR ; Menendez HM 3rd ; Ehlert K ; et al.
Publisher: American Society of Animal Science Country of Publication: United States NLM ID: 8003002 Publication Model: Print Cited Medium: Internet ISSN: 1525-3163 (Electronic) Linking ISSN: 00218812 NLM ISO Abbreviation: J Anim Sci Subsets: MEDLINE

The Object-Oriented Approach to Problem Solving and Machine Learning with Python
Mathew, Sujith Samuel ; Kuhail, Mohammad Amin ; Hadid, Maha ; et al.
All Works

AN AUTOMATED APPROACH FOR UPDATING LAND COVER CHANGE MAPS USING SATELLITE IMAGERY.
BARTOLD, Maciej ; KLUCZEK, Marcin ; DĄBROWSKA-ZIELIŃSKA, Katarzyna
Economics & Environment / Ekonomia i Środowisko; 2025, Vol. 92 Issue 1, p1-14, 14p

Feature selection using simple and efficient machine learning models. Case studies and software tools.
Amato, Federico ; Guignard, Fabian ; Kanevski, Mikhail
Geophysical Research Abstracts. 2019, Vol. 21, p1-1. 1p.