Treffer 61 - 80 von 15.425

Educational data mining for predicting students’ academic performance using machine learning algorithms
Dabhade, Pranav ; Agarwal, Ravina ; Alameen, K.P. ; et al.
In Materials Today: Proceedings 2021 47 Part 15:5260-5267

Preprocessing and Analyzing Raman Spectra Using Python †.
Pavlou, Eleftherios ; Kourkoumelis, Nikolaos
Engineering Proceedings; 2023, Vol. 56, p28, 5p

Shopping trends: a machine learning approach to recommendation systems to improve customer loyalty
Lacerda, Paulo Henrique Alves de ; Fernandes, João M. ; Universidade do Minho
urn:tid:203231384

Chapter 3: Classifiers in Machine Learning
Python 3 and Machine Learning Using ChatGPT/GPT-4. :65-86

Local mean suppression filter for effective background identification in fluorescence images
Kochetov, Bogdan ; Uttam, Shikhar
In Computers in Biology and Medicine June 2025 192 Part B

[¬Re] Reproducing 'Fair Selective Classification via Sufficiency'
Peters, Nils ; Crosbie, Joy ; Hull, Rachel Van\'T ; et al.

Comparison of machine learning-based algorithms using corneal asymmetry vs. single-metric parameters for keratoconus detection.
Prakash G ; Perera C ; Jhanji V
Publisher: Springer-Verlag Country of Publication: Germany NLM ID: 8205248 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1435-702X (Electronic) Linking ISSN: 0721832X NLM ISO Abbreviation: Graefes Arch Clin Exp Ophthalmol Subsets: MEDLINE

Deep-Learning Application to in silico Drug Design
Gomes, António José Preto Martins

Toolbox for Multimodal Learn (scikit-multimodallearn)
Benielli, Dominique ; Capponi, Cécile ; Koço, Sokol ; et al.

A Python library for agnostic feature selection and explainability of Machine Learning spectroscopic problems
Grau-Luque, Enric ; orcid:0000-0002-8357- ; Becerril-Romero, Ignacio ; et al.

A Deep Learning Model for Detection Cancer in Breast
T.V. Ramana
Journal of Nursing Research,Patient Safety and Practise. :1-7

Python Data Driven framework for acceleration of Phase-Field simulations[Formula presented]
Fetni, Seifallah ; Delahaye, Jocelyn ; Habraken, Anne ; et al.
Software Impacts, 17, 100563 (2023-09)

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.

Pyeo: A Python package for near-real-time forest cover change detection from Earth observation using machine learning
JF Roberts ; R Mwangi ; F Mukabi ; et al.

direpack : a Python 3 package for state-of-the-art statistical dimensionality reduction methods
Menvouta, Emmanuel Jordy ; Serneels, Sven ; Verdonck, Tim
23527110 ; SoftwareX

Predicting major adverse cardiovascular events after orthotopic liver transplantation using a supervised machine learning model: A cohort study.
Soldera J ; Corso LL ; Rech MM ; et al.
Publisher: Baishideng Publishing Group Country of Publication: United States NLM ID: 101532469 Publication Model: Print Cited Medium: Print ISSN: 1948-5182 (Print) NLM ISO Abbreviation: World J Hepatol Subsets: PubMed not MEDLINE

Implementation of machine learning models as a quantitative evaluation tool for preclinical studies in dental education.
Oguzhan, Aybeniz ; Peskersoy, Cem ; Devrimci, Elif Ercan ; et al.
Journal of Dental Education. Mar2025, Vol. 89 Issue 3, p383-397. 15p.

Chapter AI and Machine Learning to extend Meteo-Marine Station Observations into the Future
Azzopardi, Joel

Representing DNA for Machine Learning Algorithms: A Primer on One-Hot, Binary, and Integer Encodings
Yash Munnalal Gupta ; Satwika Nindya Kirana ; Somjit Homchan
5

Optimizing Credit Scoring Models in Face of Global Economic Uncertainty: A Comprehensive Risk Analysis in Banking loans
Susana, David Manuel Pereira ; Bravo, Jorge Miguel Ventura ; RUN