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In recent years, neural networks have become very popular in all kinds of prediction problems. In this paper, multiple feed-forward artificial neural networks (ANNs) with various configurations are used in the prediction of Parkinson’s disease (PD) of tested individuals, based on extracted features from 26 different voice samples per individual. Results are validated via the leave-one-subject-out (LOSO) scheme. Few feature selection procedures based on Pearson’s correlation coefficient, Kendall’s correlation coefficient, principal component analysis, and self-organizing maps, have been used for boosting the performance of algorithms and for data reduction. The best test accuracy result has been achieved with Kendall’s correlation coefficient-based feature selection, and the most relevant voice samples are recognized. Multiple ANNs have proven to be the best classification technique for diagnosis of PD without usage of the feature selection procedure (on raw data). Finally, a neural network is fine-tuned, and a test accuracy of 86.47% was achieved.

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Process planning is one of the most difficult tasks in product development caused by the large number of technical, technological, economic, environmental and other criteria. Accordingly, the selection of manufacturing processes is a complex multi-criteria decision making problem since it considers a number of possible alternative manufacturing processes in addition to a large number of specified criteria. This paper represents the computer-aided methodology for the multi-criteria evaluation and selection of manufacturing processes at the stage of conceptual process planning. The developed methodology is primarily focused on the mapping of product design and manufacturing requirements. Manufacturing processes that fail to meet the given conditions on the basis of 10 criteria such as materials, production volume, productivity, dimensional accuracy, surface finish, etc., are eliminated according to the developed rules. Then, the multi-criteria evaluation and ranking of manufacturing processes is performed based on 5 criteria: manufacturing cycle time, process flexibility, material utilization, quality and operating costs. Based on this methodology, a system is developed for the multi-criteria selection of manufacturing processes, whose implementation is presented in the case of the hip joint endoprosthesis.