Electronics in Advanced Research Industries. Alessandro Massaro

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СКАЧАТЬ making process model based on a multilevel control platform suitable for Industry 4.0. Proceeding of 2019 IEEE International Workshop on Metrology for Industry 4.0 and IoT, Naples, Italy (4–6 June 2019). Piscataway, NJ: IEEE.

      53 53 De Smedt, J., Hasić, F., Vanden Broucke, S.K.L.M., and Vanthienen, J. (2017). Towards a holistic discovery of decisions in process‐aware information systems. Proceedings of the International Conference on Business Process Management, Barcelona, Spain (10–15 September 2017). Cham: Springer Nature.

      54 54 Tirgul, C.S. and Naik, M.R. (2016). Artificial intelligence and robotics. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) 5 (6): 1787–1793.

      55 55 Kaura, H., Honrao, V.K., Patil, S., and Shetty, P. (2013). Gesture controlled robot using image processing. International Journal of Advanced Research in Artificial Intelligence 2 (5): 69–77.

      56 56 Moulianitis, V.C. and Aspragathos, N.A. (2015). IT and mechatronics in industrial robotic workcell design and operation. In: Encyclopedia of Information Science and Technology, 3e (ed. M. Khosrow‐Pour), 440–455. Hershey, PA: IGI Global.

      57 57 Birglen, L. (2019). Design of a partially‐coupled self‐adaptive robotic finger optimized for collaborative robots. Autonomous Robots 43 (2): 523–538.

      58 58 Jamshidi, P., Cámara, J., Schmerl, B. et al. (2019). Machine learning meets quantitative planning: enabling self‐adaptation in autonomous robots. Proceedings of the 14th International Symposium on Software Engineering for Adaptive and Self‐Managing Systems, Montreal, Canada (25–26 May 2019). Piscataway, NJ: IEEE.

      59 59 Khalid, A., Kirisci, P., Ghrairi, Z. et al. (2016). A methodology to develop collaborative robotic cyber physical systems for production environments. Logistics Research 9 (23): 1–15.

      60 60 Alcácer, V. and Cruz‐Machado, V. (2019). Scanning the Industry 4.0: a literature review on technologies for manufacturing systems. Engineering Science and Technology, an International Journal 22 (1): 899–919.

      61 61 Vidal, F., Álvarez, M., González, R. et al. (2011). Development of a flexible and adaptive robotic cell for small batch manufacturing. Contemporary Materials 2 (1): 1–12.

      62 62 Wang, S., Wan, J., Li, D., and Zhang, C. (2016). Implementing smart factory of industry 4.0: an outlook. International Journal of Distributed Sensor Networks 12 (1): 1–10.

      63 63 Mehrpouya, M., Dehghanghadikolaei, A., Fotovvati, B. et al. (2019). The potential of additive manufacturing in the smart factory industrial 4.0: a review. Applied Sciences 9 (3865): 1–34.

      64 64 Shahzad, A. and Mebarki, N. (2016). Learning dispatching rules for scheduling: a synergistic view comprising decision trees, tabu search and simulation. Computers 5 (3): 1–16.

      65 65 Zhang, X., Xiao, L., and Kan, J. (2015). Degradation prediction model based on a neural network with dynamic windows. Sensors 15 (3): 6996–7015.

      66 66 Pynam, V., Spanadna, R.R., and Srikanth, K. (2018). An extensive study of data analysis tools (Rapid Miner, Weka, R Tool, Knime, Orange). International Journal of Computer Science and Engineering 5 (9): 4–11.

      67 67 Massaro, A., Maritati, V., Savino, N. et al. (2018). A study of a health resources management platform integrating neural networks and DSS telemedicine for homecare assistance. Information 9 (176): 1–20.

      68 68 Nwankpa, C.E., Ijomah, W., Gachagan, A., and Marshall, S. (2018). Activation functions: comparison of trends in practice and research for deep learning. arXiv:1811.03378v1.

      69 69 Agostinelli, F., Hoffman, M., Sadowski, P., and Baldi, P. (2015). Learning activation functions to improve deep neural networks. arXiv:1412.6830v3.

      70 70 Wan, Y., Li, Y., Song, Y., and Rong, X. (2020). The influence of the activation function in a convolution neural network model of facial expression recognition. Applied Sciences 10 (5): 1–20.

      71 71 Harmon, M. and Klabjan, D. (2017). Activation ensembles for deep neural networks. arXiv:1702.07790v1.

      72 72 Guarnieri, S., Piazza, F., and Uncini, A. (1999). Multilayer feedforward networks with adaptive spline activation function. IEEE Transactions on Neural Networks 10 (3): 672–683.

      73 73 Biau, G. (2012). Analysis of a random forests model. Journal of Machine Learning Research 13: 1063–1095.

      74 74 Massaro, A., Maritati, V., Giannone, D. et al. (2019). LSTM DSS automatism and dataset optimization for diabetes prediction. Applied Sciences 9 (17): 1–22.

      75 75 Massaro, A., Meuli, G., and Galiano, A. (2018). Intelligent electrical multi outlets controlled and activated by a data mining engine oriented to building electrical management. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI) 7 (4): 1–20.

      76 76 Massaro, A., Lisco, P., Lombardi, A. et al. (2019). A case study of research improvements in a service industry upgrading the knowledge base of the information system and the process management: data flow automation, association rules and data mining. International Journal of Artificial Intelligence and Applications (IJAIA) 10 (1): 25–46.

      77 77 Massaro, A., Vitti, V., Lisco, P. et al. (2019). A business intelligence platform implemented in a big data system embedding data mining: a case of study. International Journal of Data Mining & Knowledge Management Process (IJDKP) 9 (1): 1–20.

      78 78 Massaro, A., Leogrande, A., Lisco, P. et al. (2019). Innovative BI approaches and methodologies implementing a multilevel analytics platform based on data mining and analytical models: a case of study in roadside assistance services. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI) 8 (1): 17–36.

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