810 KB0 KB199920252023Adeodu, AdefemiBasitere, EmmanuelDaniyan, IlesanmiMpofu, Khumbulaništevilka:1/2letnik:69str. 61-72ISSN:0039-2480DOI:10.5545/sv-jme.2022.238COBISSID_HOST:146543619URN:URN:NBN:SI:doc-X5YSRHP9enZveza strojnih inženirjev in tehnikov Slovenije etc.Strojniški vestnikalgoritem TensorFlowartificial neural networkmodular arrangementmodularna ureditevpredetermined time standardsTensorFlow algorithmumetna nevronska mrežavnaprej določeni časovni normativiThe Application of neural networks to modular arrangements of predetermined time standards|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 measurementTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaUniverza v Ljubljani, Fakulteta za strojništvo