Strengths of computational systems of techniques using artificial intelligence in machine learning
by Shashikant V. Athawale; Indrajit Patra; Amol Dattatray Dhaygude; Vaibhav Rupapara; Thanwamas Kassanuk; Khongdet Phasinam
International Journal of System of Systems Engineering (IJSSE), Vol. 14, No. 4, 2024

Abstract: As a result of technological improvements, engaging and interactive commercials frequently use short messaging services. One of the most well-known cell advertising strategies is location-based advertising. Advertisements for artificially enhanced location-based services use randomised forests, support vector machines, and synthetic neural networks. It appears sufficient for generic structures, development tools, and other field implementations. Machine learning employs algorithms and data to simulate realistic computer learning and enhance system accuracy. Machine learning algorithms can predict friction force and equipment wear to extend dry machining drill bit life. Modern cognitive computing frameworks enable optimised conventional machining process variables to increase component production productivity. Machine learning systems can predict and improve product quality to improve machine precision. Machine learning is used in self-driving cars, intelligent assistants, diagnosis, and other technologies. Machine learning predicts industrial equipment power demand and reduces milling power consumption. Future research should summarise the latest milling operations investigations on these topics. Machine tools use natural and artificial information systems in this investigation.

Online publication date: Tue, 02-Jul-2024

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