Treffer 281 - 300 von 2.864

Hyperparameter Optimization as a Service on INFN Cloud
Barbetti, Matteo ; Anderlini, Lucio

Scaling data-intensive analytics with Heat: a Python library for massively-parallel array computing and machine learning
Comito, C. ; Gutiérrez Hermosillo Muriedas, J. P. ; Götz, M. ; et al.
Helmholtz AI Conference, Düsseldorf, Germany, 2024-06-12 - 2024-06-14

Estimation of Grain Size Distribution of Friction Stir Welded Joint by using Machine Learning Approach
Mishra, Akshansh ; Pathak, Tarushi
GREDOS. Repositorio Institucional de la Universidad de Salamanca
instname
GREDOS: Repositorio Institucional de la Universidad de Salamanca
Universidad de Salamanca (USAL)
Advances in Distributed Computing and Artificial Intelligence Journal, Vol 10, Iss 1, Pp 99-110 (2020)

ChainGuard 6G+: A Secure and Private Architecture for Wireless Communication Using Federated Learning and Blockchain in IoT Networks.
Alomari, Saleh Ali
Journal of Intelligent Systems & Internet of Things; 2026, Vol. 18 Issue 1, p12-33, 22p

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.

Hybrid-based framework for COVID-19 prediction via federated machine learning models.
Kallel, Ameni ; Rekik, Molka ; Khemakhem, Mahdi
Journal of Supercomputing; Apr2022, Vol. 78 Issue 5, p7078-7105, 28p

Modular Federated Learning: A Meta-Framework Perspective
Vicente, Frederico ; Soares, Cláudia ; Jakovetić, Dušan

A fault‐location algorithm for parallel line based on the long short‐term memory model using the distributed parameter line model.
Taheri, Behrooz ; Salehimehr, Sirus ; Sedighizadeh, Mostafa
International Transactions on Electrical Energy Systems. Nov2021, Vol. 31 Issue 11, p1-19. 19p.

Big Data analytics. Three use cases with R, Python and Spark
Apprentissage sur Données Massives; trois cas d'usage avec R, Python et Spark.
Besse, Philippe ; Guillouet, Brendan ; Loubes, Jean-Michel ; et al.
Myriam MAUMY-BERTRAND, Gilbert SAPORTA,Christine THOMAS-AGNAN. Apprentissage Statistique et Données Massives, Technip, 2017, Journées d'Etudes en Statistisque

A regression analysis method for the prediction of olive oil sensory attributes
Kottaridi, Klimentia ; Milionis, Anna ; Demopoulos, Vasilis ; et al.
In Journal of Agriculture and Food Research June 2023 12

Vamsa: Automated Provenance Tracking in Data Science Scripts
Namaki, Mohammad Hossein ; Floratou, Avrilia ; Psallidas, Fotis ; et al.

Neural Network Matrix Product Operator: A Multi-Dimensionally Integrable Machine Learning Potential
Hino, Kentaro ; Kurashige, Yuki

Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning.
LemaÎtre, Guillaume ; Nogueira, Fernando ; Aridas, Christos K.
Journal of Machine Learning Research. 2017, Vol. 18 Issue 9-17, p1-5. 5p.

Combining Serverless and High-Performance Computing Paradigms to support ML Data-Intensive Applications
Staylor, Mills ; Sarker, Arup Kumar ; von Laszewski, Gregor ; et al.

A novel evaluation methodology for supervised Feature Ranking algorithms
Overschie, Jeroen G. S.

NumS: Scalable Array Programming for the Cloud
Elibol, Melih ; Benara, Vinamra ; Yagati, Samyu ; et al.

A Machine Learning Framework for Identifying and Preventing DDoS Attacks in Real-Time
P, Dineshkumar ; S. Shinde, Vaishnavi ; R. Shinde, Amruta ; et al.
Journal of Hacking Techniques, Digital Crime Prevention and Computer Virology; Volume 1 Issue 3 (December 2024); 18-25

A review of machine learning for big data analysis
Hussien, Nadia Mahmood ; Hussain, Samira Abdul-Kader ; Abbas, Khlood Ibraheem ; et al.
International Journal Papier Advance and Scientific Review; Vol. 3 No. 2 (2022): International Journal Papier Advance and Scientific Review; 1-4 ; 2709-0248

A robust distribution network state estimation method based on enhanced clustering Algorithm: Accounting for multiple DG output modes and data loss
Yu, Yue ; Jin, Zhaoyang ; Ćetenović, Dragan ; et al.
In International Journal of Electrical Power and Energy Systems June 2024 157

RNAglib: a python package for RNA 2.5 D graphs.
Mallet V ; Oliver C ; Broadbent J ; et al.
Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print Cited Medium: Internet ISSN: 1367-4811 (Electronic) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE