20.500.12556/RUL-144951 The Application of neural networks to modular arrangements of predetermined time standards Uporaba nevronske mreže pri modularni ureditvi vnaprej določenih časovnih normativov Modular arrangements of predetermined time standards (MODAPTS) is an effective and efficient method to measure work and the activities associated with it. The time standard is used all over the world in different industries, but the method is old, slow, and difficult for first-time users to work with. This study applied neural networks (NN) to MODAPTS. Primary training data in the form of MODAPTS keywords were employed. The training data were acquired as raw data in the form of MODAPTS time studies. These data however was then broken and processed to extract the keywords for the training of the NN. The NN was also trained with the data collected using the TensorFlow algorithm assisted by the Keras library. This was achieved by first learning the fundamentals of creating a NN. Thereafter, consolidating several tools, such as the Python programming language and the Keras library, were used to implement the artificial neural network (ANN). The results obtained indicated that 94.7 % of successful predictions were made by the NN while only 5.3 % were manually entered codes to correct the ANN chatbot. The mean difference between the two methods is 0.25 minutes; the t-test was calculated at 95 % confidence level (0.05) and produced a P-value of 0.9663. The computed P-value was greater than 0.05, showing that there is no significant difference between the two generated studies. The MODAPTS-ANN technique demonstrated in this study possesses great potential to improve and refine work measurement. artificial neural network modular arrangement predetermined time standards TensorFlow algorithm umetna nevronska mreža modularna ureditev vnaprej določeni časovni normativi algoritem TensorFlow true false true Angleški jezik Slovenski jezik Članek v reviji 2023-03-24 14:00:14 2023-03-24 14:00:18 2023-03-25 03:35:58 0000-00-00 00:00:00 2023 0 0 Str. 61-72 no. 1/2 Vol. 69 Jan./Feb. 2023 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 681.5:004.021 0039-2480 10.5545/sv-jme.2022.238 146543619 Basitere_..._-_The_Application_of_Neural_Networks_to_Modular_Arrangements.pdf Basitere_..._-_The_Application_of_Neural_Networks_to_Modular_Arrangements.pdf 1 588B18CA639672D5AE2056169379FE0E 44653386728fb2c1a6bc3e15f8e9a2d0b99d7d2447efa8ad21a49e7d13297358 5200e142-ca44-11ed-a314-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=167537 https://www.sv-jme.eu/sl/article/application-of-neural-network-to-modular-arrangement-of-predetermined-time-standards/ 1 c396ff24-ca43-11ed-a314-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=167536 Fakulteta za strojništvo Strojniški vestnik 0 0 0