Khalaf, L.I.Aswad, S.A.Ahmed, S.R.Makki, B.Ahmed, M.R.2024-09-292024-09-292022978-166546835-0https://doi.org/10.1109/HORA55278.2022.9800090https://hdl.handle.net/20.500.14619/94214th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2022 -- 9 June 2022 through 11 June 2022 -- Ankara -- 180434We investigated the usage of hand motion recognition using data mining approaches. In gesture detection applications, I've discovered that employing data mining to recognize hand motions has several advantages. To begin, we employ data mining to recognize bandwidth hand motions. Second, we have a high temporal resolution, which is great for data mining. A short-range, close-pulse broadcast signal is required in most cases. The frequency components of the data mining for hand gesture recognition are the same as the pulse, but the phase components are different. Our vision must be strong enough to differentiate a far hand from distant things. Data mining is equal to recognizing hand motions by heart rate divided by length in these categorized articles. Obtaining significant power for a brief period. © 2022 IEEE.eninfo:eu-repo/semantics/closedAccessClassificationClusteringData MiningDetectionGesturesHandConference Object10.1109/HORA55278.2022.98000902-s2.0-85133961030N/A