Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16534
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dc.contributor.authorMathew, Sajan-
dc.contributor.authorIyyappan, M-
dc.contributor.authorModhe, Tejus-
dc.date.accessioned2024-08-29T05:41:25Z-
dc.date.available2024-08-29T05:41:25Z-
dc.date.issued2023-
dc.identifier.citationpp. 1-6en_US
dc.identifier.isbn9798350317060-
dc.identifier.urihttps://doi.org/10.1109/ICCAMS60113.2023.10525949-
dc.identifier.urihttps://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16534-
dc.description.abstractIn a proactive prognostication into exploring future scenarios of human artificial intelligence interaction among that paramount is very important to rethink and rework on our developments around narrow artificial intelligence. In the build of artificial systems that pivot safety and embed principles of collaborative intelligence. These technologies are potential to cultivated the process of transformation, optimization and predictive analytics using metrics like a cycle time, throughput time, forecasting errors, overall equipment effectiveness etc. These technologies are majorly focus in to the shop floor safety, workers wellbeing and collaborative human machine interaction. The merger of affective computing and machine learning as a whole to be focused upon is what is emphasized in this research work © 2023 IEEE.en_US
dc.language.isoenen_US
dc.publisher2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023en_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.subjectArtificial General Intelligenceen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectAutomation Systemen_US
dc.subjectCluster Densityen_US
dc.subjectK-Means Clusteringen_US
dc.subjectMachine Learningen_US
dc.titleHuman-Friendly Ai on the Shop Floor: Leveraging Affective Computing and Machine Learningen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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