Treffer 41 - 60 von 15.425

Real-time vehicular accident prevention system using deep learning architecture
Kabir, Md Faysal ; Roy, Sahadev
In Expert Systems With Applications 15 November 2022 206

PREDICTING FLIGHT DELAYS WITH ERROR CALCULATION USING MACHINE LEARNED CLASSIFIERS
Baskar, N. ; Sarika, M. ; Anjani, N. ; et al.
Turkish Journal of Computer and Mathematics Education (TURCOMAT); Vol. 15 No. 3 (2024): Vol. 15 No. 03 (2024); 286-291 ; 3048-4855

Customer Behaviour Analysis Using Machine Learning Algorithms
Krishan, Ram, author
Digital Transformation, Strategic Resilience, Cyber Security and Risk Management. 111B:133-142

Python 3 ; Python 3: Apprendre à programmer dans l'écosystème Python
Cordeau, Robert, Bob ; Pointal, Laurent ; Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI) ; et al.
https://hal.science/hal-04464977 ; Dunod, 2020, 9782100809141.

What are the differences between student and ChatGPT-generated pseudocode? Detecting AI-generated pseudocode in high school programming using explainable machine learning.
Liu, Zifeng ; Xing, Wanli ; Jiao, Xinyue ; et al.
Education & Information Technologies. Jul2025, Vol. 30 Issue 11, p14853-14892. 40p.

Predictors of Medical and Dental Clinic Closure by Machine Learning Methods: Cross-Sectional Study Using Empirical Data.
Park, Young-Taek ; Kim, Donghan ; Jeon, Ji Soo ; et al.
Journal of Medical Internet Research. 2024, Vol. 26, p1-12. 12p.

Workshop 3: Deep Learning with Python
Zeng, David
SDSU Data Science Symposium

Machine Learning-Based Model for Individualized Drug Dose Prediction for Propofol
A Machine Learning-Based Model for Individualized Drug Dose Prediction for Propofol-Induced Loss of Consciousness in Patients

AUTOMATIC ROUTING OF SUBMARINE ELECTRICAL CABLES USING MACHINE LEARNING
Damaso, Katelyn M. ; Karpenko, Mark ; Mechanical and Aerospace Engineering (MAE) ; et al.

Deep Learning Detection of Pulmonary Hypertension and Low Ejection Fraction Via Digital Stethoscope and 3-Lead ECG (PH ELEFT 2-0)
Deep Learning for Detection of Pulmonary Hypertension and Reduced Left Ventricular Ejection Fraction Using a Combined Digital Stethoscope and Three-lead Electrocardiogram
Rees PJ, Hay JG, Webb JR. Premedication for fibreoptic bronchoscopy. Thorax. 1983 Aug;38(8):624-7. doi: 10.1136/thx.38.8.624.
Maron BA, Hess E, Maddox TM, Opotowsky AR, Tedford RJ, Lahm T, Joynt KE, Kass DJ, Stephens T, Stanislawski MA, Swenson ER, Goldstein RH, Leopold JA, Zamanian RT, Elwing JM, Plomondon ME, Grunwald GK, Baron AE, Rumsfeld JS, Choudhary G. Association of Borderline Pulmonary Hypertension With Mortality and Hospitalization in a Large Patient Cohort: Insights From the Veterans Affairs Clinical Assessment, Reporting, and Tracking Program. Circulation. 2016 Mar 29;133(13):1240-8. doi: 10.1161/CIRCULATIONAHA.115.020207. Epub 2016 Feb 12.
Assad TR, Maron BA, Robbins IM, Xu M, Huang S, Harrell FE, Farber-Eger EH, Wells QS, Choudhary G, Hemnes AR, Brittain EL. Prognostic Effect and Longitudinal Hemodynamic Assessment of Borderline Pulmonary Hypertension. JAMA Cardiol. 2017 Dec 1;2(12):1361-1368. doi: 10.1001/jamacardio.2017.3882.
Strange G, Stewart S, Celermajer DS, Prior D, Scalia GM, Marwick TH, Gabbay E, Ilton M, Joseph M, Codde J, Playford D; NEDA Contributing Sites. Threshold of Pulmonary Hypertension Associated With Increased Mortality. J Am Coll Cardiol. 2019 Jun 4;73(21):2660-2672. doi: 10.1016/j.jacc.2019.03.482.
Choudhary G, Jankowich M, Wu WC. Elevated pulmonary artery systolic pressure predicts heart failure admissions in African Americans: Jackson Heart Study. Circ Heart Fail. 2014 Jul;7(4):558-64. doi: 10.1161/CIRCHEARTFAILURE.114.001366. Epub 2014 Jun 5.
Maron BA, Choudhary G, Khan UA, Jankowich MD, McChesney H, Ferrazzani SJ, Gaddam S, Sharma S, Opotowsky AR, Bhatt DL, Rocco TP, Aragam JR. Clinical profile and underdiagnosis of pulmonary hypertension in US veteran patients. Circ Heart Fail. 2013 Sep 1;6(5):906-12. doi: 10.1161/CIRCHEARTFAILURE.112.000091. Epub 2013 Jun 27.
Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, Pellikka PA, Enriquez-Sarano M, Noseworthy PA, Munger TM, Asirvatham SJ, Scott CG, Carter RE, Friedman PA. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med. 2019 Jan;25(1):70-74. doi: 10.1038/s41591-018-0240-2. Epub 2019 Jan 7.
Guo L, Khobragade N, Kieu S, Ilyas S, Nicely PN, Asiedu EK, Lima FV, Currie C, Lastowski E, Choudhary G. Development and Evaluation of a Deep Learning-Based Pulmonary Hypertension Screening Algorithm Using a Digital Stethoscope. J Am Heart Assoc. 2025 Feb 4;14(3):e036882. doi: 10.1161/JAHA.124.036882. Epub 2025 Feb 3.
Colman R, Whittingham H, Tomlinson G, Granton J. Utility of the physical examination in detecting pulmonary hypertension. A mixed methods study. PLoS One. 2014 Oct 24;9(10):e108499. doi: 10.1371/journal.pone.0108499. eCollection 2014.
Guo L, Pressman GS, Kieu SN, Marrus SB, Mathew G, Prince J, Lastowski E, McDonough RV, Currie C, Tiwari U, Maidens JN, Al-Sudani H, Friend E, Padmanabhan D, Kumar P, Kersh E, Venkatraman S, Qamruddin S. Automated Detection of Reduced Ejection Fraction Using an ECG-Enabled Digital Stethoscope: A Large Cohort Validation. JACC Adv. 2025 Mar;4(3):101619. doi: 10.1016/j.jacadv.2025.101619. Epub 2025 Feb 20.
Marcus GM, Vessey J, Jordan MV, Huddleston M, McKeown B, Gerber IL, Foster E, Chatterjee K, McCulloch CE, Michaels AD. Relationship between accurate auscultation of a clinically useful third heart sound and level of experience. Arch Intern Med. 2006 Mar 27;166(6):617-22. doi: 10.1001/archinte.166.6.617.

Computer Vision Projects with OpenCV and Python 3
Rever, Matthew ; Rever, Matthew ; Rever, Matthew ; et al.

direpack: A Python 3 package for state-of-the-art statistical dimensionality reduction methods
Emmanuel Jordy Menvouta ; Sven Serneels ; Tim Verdonck
SoftwareX, Vol 21, Iss , Pp 101282- (2023)

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.

Machine-Learning-Based Biomechanical Feature Analysis for Orthopedic Patient Classification with Disc Hernia and Spondylolisthesis.
Nasef, Daniel ; Nasef, Demarcus ; Sawiris, Viola ; et al.
BioMedInformatics. Mar2025, Vol. 5 Issue 1, p3. 11p.

Identification of the Water Inrush Source Based on the Deep Learning Model for Mines in Shaanxi, China.
Cui, Mingyi ; Hou, Enke ; Feng, Dong ; et al.
Mine Water & the Environment. Mar2025, Vol. 44 Issue 1, p133-148. 16p.

MetaBakery: a Singularity implementation of bioBakery tools as a skeleton application for efficient HPC deconvolution of microbiome metagenomic sequencing data to machine learning ready information.
Murovec B ; Deutsch L ; Osredkar D ; et al.
Publisher: Frontiers Research Foundation Country of Publication: Switzerland NLM ID: 101548977 Publication Model: eCollection Cited Medium: Print ISSN: 1664-302X (Print) Linking ISSN: 1664302X NLM ISO Abbreviation: Front Microbiol Subsets: PubMed not MEDLINE

Optical efficiency and performance optimization of a two-stage secondary reflection hyperbolic solar concentrator using machine learning.
Wu, Shaobing ; Wang, Changmei ; Tang, Runsheng
Renewable Energy: An International Journal. Apr2022, Vol. 188, p437-449. 13p.

Textile wastewater: COD removal via Box–Behnken design, Fenton method, and machine learning integration for sustainability
International Journal of Clothing Science and Technology, 2024, Vol. 37, Issue 4, pp. 642-662.

Gestão de um sistema fotovoltaico em instalação trifásica utilizando técnicas de Machine Learning
Raimundo, Pedro Miguel Santos

A Deep Learning Model for Detection Cancer in Breast
null Sruthi Tirunagari ; null Veeramalla Nikitha ; null Jasti Padmavathi ; et al.
International Journal of Advanced Research in Science, Communication and Technology. :100-104