• Suche: Python 3 for Machine Learning

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221

Step 1: Getting Started in Python 3
Swamynathan, Manohar
Mastering Machine Learning with Python in Six Steps ; page 1-64 ; ISBN 9781484249468 9781484249475

Buch
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222

Overcoming Barriers in Computational Catalysis through High-throughput Quantum Simulations and Machine Learning Techniques
Guo, Jiawei

Chemical engineering Density Functional Theor... Heterogeneous Catalysis Machine Learning Potenti... Metal/Electrolyte Interf... Zeolites
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223

direpack: A Python 3 package for state-of-the-art statistical dimension reduction methods
Menvouta, Emmanuel Jordy ; Serneels, Sven ; Verdonck, Tim

Statistics - Computation 62H20 62H12 62H25 62P99 stat
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224

PREDICTING SIGNS AND SYMPTOMS IN WORKERS EXPOSED TO SURGICAL SMOKE: A MACHINE LEARNING APPROACH
Lopes, Larissa Padoin ; Angelini, Carlos Eduardo ; Rocha, Aline Franco da ; et al.
Texto & Contexto - Enfermagem, Volume: 34, Article number: e20250004, Published: 01 DEC 2025. December 2025

Machine learning Perioperative nursing Occupational health Signs and symptoms Aprendizaje automático Enfermería perioperatori... …
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225

Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands
Irvin, Jeremy ; Zhou, Sharon ; McNicol, Gavin ; et al.
Agricultural and Forest Meteorology. 308

37 Earth Sciences (for-2... 30 Agricultural Veterinary and Food Scie... 31 Biological Sciences (... Networking and Informati... Machine Learning and Art... …
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226

Análisis de datos con Python 3
Javier Gamboa Cruzado ; Jorge Nolasco Valenzuela Luz Nolasco Valenzuela ; Javier Gamboa Cruzado ; et al.
2024

E-Ressource
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227

Physiological Signal-Based Affective Computing System: Device, Toolkit and Models
Fang, Ruijie

Computer engineering Health sciences Artificial intelligence affective computer artificial intelligence emotion detection …
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228

CrySPY: a crystal structure prediction tool accelerated by machine learning.
Yamashita, Tomoki ; Kanehira, Shinichi ; Sato, Nobuya ; et al.
Science & Technology of Advanced Materials. Dec2021, Vol. 22 Issue 1, p87-97. 11p.

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229

The Python Workshop
Bird, Andrew ; Han, Dr Lau Cher ; Jimenez, Mario Corchero ; et al.

E-Ressource
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230

Short-term forecasting of consumption of the oil and gas enterprises using technological factors and Shapley additive explanations
A. I. Stepanova ; A. I. Khalyasmaa ; P. V. Matrenin
Известия высших учебных заведений: Проблемы энергетики, Vol 26, Iss 4, Pp 75-88 (2024)

TK1001-1841 machine learning Production of electric e... increase of energy effic... short-term forecasting o... analysis of system prope... …
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231

Spatio-temporal simulation and prediction of land-use change using conventional and machine learning models: a review.
Aburas, Maher Milad ; Ahamad, Mohd Sanusi S. ; Omar, Najat Qader
Environmental Monitoring & Assessment. Apr2019, Vol. 191 Issue 4, pN.PAG-N.PAG. 1p.

Machine learning Geographic information s... Python programming langu... Programming languages
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232

AI-Powered Fall Risk Prediction in Nursing Care
Ahmet Ceviz, Research Assistant in the Department of Internal Medicine Nursing, Faculty of Nursing
Predicting Fall Risk With Machine Learning and Computer Vision: Development of A Clinical Decision Support System in Nursing Care
Akhtaruzzaman M, Shafie AA, Khan MR. Gait Analysis: Systems, Technologies and Significance. J Mech Med Biol. 2016;16(7).
Demir NY, Intepeler ŞS. Adaptation Of Morse Fall Scale To Turkish And Determination Of Sensitivity And Specificity. J Ege Univ Nurs Fac. 2012;28(1):57-71.
Zhao M, Chang CH, Xie W, Xie Z, Hu J. Cloud Shape Classification System Based on Multichannel CNN and Enhanced FDM. IEEE Access. 2020;8:44111-24.
Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM. 2017;60(6):84-90.
Santos GL, Endo PT, Monteiro KHC, Rocha EDS, Silva I, Lynn T. Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks. Sensors (Basel). 2019 Apr 6;19(7):1644. doi: 10.3390/s19071644.
Yunas SU, Ozanyan KB. Gait Activity Classification Using Multi-Modality Sensor Fusion: A Deep Learning Approach. IEEE Sens J. 2021;21(15):16870-9.
Manssor SAF, Sun S, Elhassan MAM. Real-Time Human Recognition at Night via Integrated Face and Gait Recognition Technologies. Sensors (Basel). 2021 Jun 24;21(13):4323. doi: 10.3390/s21134323.

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233

Features influencing surface acting of different clusters of nursing students in vocational college based on interpretable machine learning: A cross-sectional study.
Da, Chaojin ; Wu, Chen ; Ji, Zhenying ; et al.
Nurse Education in Practice. Jan2025, Vol. 82, pN.PAG-N.PAG. 1p.

Vocational education Universities & colleges Emotions Experience Students School holding power …
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234

The Python Workshop
Han, Dr Lau Cher ; Han, Dr Lau Cher ; Jimenez, Mario Corchero ; et al.

E-Ressource
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235

A Machine Learning-based Pipeline for the Classification of CTX-M in Metagenomics Samples
Diego Ceballos ; Diana López-Álvarez ; Gustavo Isaza ; et al.
Processes
Volume 7
Issue 4

0301 basic medicine metagenomics 0303 health sciences 03 medical and health sc... machine learning 9. Industry and infrastr... …
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237

STEM diffraction pattern analysis with deep learning algorithms
Wissel, Sebastian ; orcid:0000-0002-2179- ; Wissel, Sebastian ; et al.

4d-stem cnn densenets lno machine learning swin transformer
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238

Image Caption Generator Using Deep Learning
Palak Kabra ; Mihir Gharat ; Dhiraj Jha ; et al.
International Journal for Research in Applied Science and Engineering Technology. 10:621-626

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239

An operational IoT-based slope stability forecast using a digital twin.
Piciullo, Luca ; Abraham, Minu Treesa ; Drøsdal, Ida Norderhaug ; et al.
Environmental Modelling & Software. Jan2025, Vol. 183, pN.PAG-N.PAG. 1p.

Regression analysis Pore water pressure Machine learning Leaf area index Time series analysis Slope stability
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240

THE ROLE OF ARTIFICIAL INTELLIGENCE TRAINED WITH PRE-MEASURED NUMERICAL DATA IN PREDICTION OF DIFFICULT INTUBATION
Serkan TELLİ, Asistant. Prof. Dr.
THE ROLE OF ARTIFICIAL INTELLIGENCE TRAINED WITH PRE-MEASURED NUMERICAL DATA IN PREDICTION OF DIFFICULT INTUBATION
Kim JH, Jung HS, Lee SE, Hou JU, Kwon YS. Improving difficult direct laryngoscopy prediction using deep learning and minimal image analysis: a single-center prospective study. Sci Rep. 2024 Jun 20;14(1):14209. doi: 10.1038/s41598-024-65060-x.
Xia M, Jin C, Zheng Y, Wang J, Zhao M, Cao S, Xu T, Pei B, Irwin MG, Lin Z, Jiang H. Deep learning-based facial analysis for predicting difficult videolaryngoscopy: a feasibility study. Anaesthesia. 2024 Apr;79(4):399-409. doi: 10.1111/anae.16194. Epub 2023 Dec 13.
Hayasaka T, Kawano K, Kurihara K, Suzuki H, Nakane M, Kawamae K. Creation of an artificial intelligence model for intubation difficulty classification by deep learning (convolutional neural network) using face images: an observational study. J Intensive Care. 2021 May 6;9(1):38. doi: 10.1186/s40560-021-00551-x.

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