Výsledky vyhledávání - "Machine Learning Implementation"

The implementation challenge: Embedding ethical reasoning in modern AI Systems

Autoři: Pelluru, Prem Sai

Zdroj: World Journal of Advanced Research and Reviews. 26:2011-2023

Témata: Ethical Decision-Making, Validation Frameworks, Machine Learning Implementation, System Architecture, Artificial Intelligence Ethics

Building an AI portfolio for aspiring data scientists

Autoři: Sriram, Anjaneyulu Prabala

Zdroj: International Journal of Science and Research Archive. 14:238-247

Témata: Professional Growth, Machine Learning Implementation, echnical Documentation, Portfolio Development, Career Advancement

Harnessing the Power of ML/AI: Transforming Data into Actionable Insights

Autoři: Ankampally, Suman

Témata: Predictive analytics, Data processing infrastructure, Adoption of artificial intelligence, Machine learning implementation, Industry digital transformation

Relation: https://zenodo.org/communities/ijsrarchive/; https://zenodo.org/records/16908547; oai:zenodo.org:16908547; https://doi.org/10.5281/zenodo.16908547

Dostupnost: https://doi.org/10.5281/zenodo.16908547
https://zenodo.org/records/16908547

EXPLORING THE APPLICATIONS OF BUSINESS ANALYST SYSTEMS FOR OPTIMUM CLIENT SATISFACTION

Autoři: Researcher Tarun Teja Reddy Palyam

Přispěvatelé: Researcher Tarun Teja Reddy Palyam

Témata: Business Analyst Systems, Client Satisfaction, Requirements Gathering, Visualization Techniques, Machine Learning Implementation

Přístupová URL adresa: https://zenodo.org/records/13941620

Implementation of Machine Learning Applications in Health Care Organizations: Systematic Review of Empirical Studies

Autoři: Preti, Luigi Maria Ardito, Vittoria Compagni, Amelia a další

Zdroj: J Med Internet Res
Journal of Medical Internet Research, Vol 26, p e55897 (2024)

Témata: Machine Learning, ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, IMPLEMENTATION, HEALTHCARE ORGANIZATION, BARRIERS, FACILITATORS, Computer applications to medicine. Medical informatics, R858-859.7, Humans, Review, Public aspects of medicine, RA1-1270, Delivery of Health Care

Popis souboru: application/pdf

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/39586084
https://doaj.org/article/2112edfc98014975ac7eeac0bd6429cf

Developing machine learning systems worthy of trust for infection science: a requirement for future implementation into clinical practice

Autoři: Benjamin R. McFadden Mark Reynolds Timothy J. J. Inglis

Zdroj: Front Digit Health
Frontiers in Digital Health, Vol 5 (2023)

Témata: trustworthy machine learning, infectious disease, infection science, QA75.5-76.95, 3. Good health, clinical practice, machine learning, machine learning implementation, Electronic computers. Computer science, Medicine, Digital Health, Public aspects of medicine, RA1-1270

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/37829595
https://doaj.org/article/7667026e7aeb46f29ab4900d219d28ce

Energy Efficient System for Tactile Data Decoding Using an Ultra-Low Power Parallel Platform

Autoři: Magno, Michele MOHAMMED, IBRAHIM ALI Pullini, Antonio a další

Zdroj: 2017 New Generation of CAS (NGCAS). :17-20

Témata: Electronic skin, Low power processing, Machine learning implementation, Parallel ultra low power, Wearable devices, Computer Networks and Communications, Hardware and Architecture, Instrumentation, Electrical and Electronic Engineering, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 7. Clean energy, Parallel ultra-low power

Popis souboru: application/pdf

Přístupová URL adresa: http://ieeexplore.ieee.org/document/8052258/
https://ieeexplore.ieee.org/document/8052258/
https://dblp.uni-trier.de/db/conf/ngcas/ngcas2017.html#MagnoIPVB17
https://iris.unige.it/handle/11567/932353

An Energy Efficient E-Skin Embedded System for Real-Time Tactile Data Decoding

Autoři: Magno, Michele Ibrahim, Ali Pullini, Antonio a další

Zdroj: Journal of Low Power Electronics. 14:101-109

Témata: 0103 physical sciences, Electronic skin, Low power processing, Machine learning implementation, Parallel ultra-low power, Wearable devices, Electrical and Electronic Engineering, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 7. Clean energy, 01 natural sciences

Popis souboru: application/pdf

Přístupová URL adresa: https://dblp.uni-trier.de/db/journals/jolpe/jolpe14.html#MagnoIPVB18
https://www.research-collection.ethz.ch/handle/20.500.11850/257526
https://www.ingentaconnect.com/content/asp/jolpe/2018/00000014/00000001/art00012
https://core.ac.uk/display/157609200
https://hdl.handle.net/11567/906443
https://doi.org/10.1166/jolpe.2018.1537

Unsupervised Machine Learning Methoden zur Identifikation von Sicherheitsrisiken in Logfiles: Vergleich unterschiedlicher Methoden zur Anomalie-Erkennung in Zeitreihen

Autoři: Tropper, Benjamin Zugaj, Wilhelm Universitätsbibliothek

Přispěvatelé: Tropper, Benjamin Zugaj, Wilhelm Universitätsbibliothek

Témata: Implementierung von maschinellem Lernen, Isolation Forest, threat intelligence, cybersecurity, logfile analysis, Identifikation von Sicherheitsrisiken, system event logging, Prognose, IT-Sicherheitsbedrohungen, seasonal and trend decomposition, forecasting, Unüberwachtes Maschinelles Lernen, machine learning implementation, ARCS Log-Datensatz, machine learning algorithms, IT security threats, Anomalie-Erkennung, Splunk, autoencoder, password-Spraying attack, phishing attack, real-time data processing, Echtzeit-Datenverarbeitung, Cybersicherheit, Ereignisprotokollierung des Systems, isolation forest, Autoencoder, Bedrohungsintelligenz, system logs, Maschinelles Lernen Algorithmen, Systemprotokolle, anomaly detection, Datenverletzung, Logfile-Analyse, data breach, Saisonale und Trend-Zerlegung, security risk identification, Phishing-Angriff, Password-Spraying-Angriff, Kalman filter, time series, unsupervised machine learning, Zeitreihen, ARCS log data set, large-scale data analysis, Kalman-Filter, Python, Großflächige Datenanalyse

Přístupová URL adresa: https://permalink.obvsg.at/fhj/AC16899004
https://epub.fh-joanneum.at/id/8887150

Künstliche Intelligenz im Gießereiwesen

Additional Titles: Artificial Intelligence in Foundry Industry

Autoři: Volk, Wolfram (Prof. Dr.-Ing.) Volk, Wolfram (Prof. Dr.-Ing.); Schilp, Johannes (Prof. Dr.-Ing.) Dobmeier, Fabian

Témata: info:eu-repo/classification/ddc/620, Ingenieurwissenschaften, Künstliche Intelligenz; Maschinelles Lernen; Vorgehensmethode; Gießereiwesen; Qualitätssicherung, Artificial Intelligence; machine learning; implementation method; foundry industry; quality assurance, thesis, doc-type:doctoralThesis

URL: https://mediatum.ub.tum.de/1715387

Preparing for the bedside-optimizing a postpartum depression risk prediction model for clinical implementation in a health system.

Autoři: Liu Y Joly R Reading Turchioe M a další

Zdroj: Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2024 May 20; Vol. 31 (6), pp. 1258-1267.

Způsob vydávání: Journal Article

Informace o časopise: Publisher: Oxford University Press Country of Publication: England NLM ID: 9430800 Publication Model: Print Cited Medium: Internet ISSN: 1527-974X (Electronic) Linking ISSN: 10675027 NLM ISO Abbreviation: J Am Med Inform Assoc Subsets: MEDLINE

Výrazy ze slovníku MeSH: Depression, Postpartum* , Electronic Health Records* , Machine Learning*, Humans ; Female ; Risk Assessment/methods ; Decision Support Systems, Clinical