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13 13 Massaro, A. and Galiano, A. (2020). Infrared thermography for intelligent robotic systems in research industry inspections: thermography in industry processes. In: Handbook of Research on Advanced Mechatronic Systems and Intelligent Robotics (ed. M.K. Habib), 98–125. Hershey, PA: IGI Global.

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32 32 Massaro, A., Calicchio, A., Maritati, V. et al. (2018). A case study of innovation of an information communication system and upgrade of the knowledge base in industry by ESB, artificial intelligence, and big data system integration. International Journal of Artificial Intelligence and Applications (IJAIA) 9 (5): 27–43.

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41 41 Massaro, A., Manfredonia, I., Galiano, A., and Contuzzi, N. (eds.) (2019). Inline image vision Technique for tires industry 4.0: quality and defect monitoring in tires assembly. Proceedings of 2019 IEEE International Workshop on Metrology for Industry 4.0 and IoT, Naples, Italy (4–6 June 2019). Piscataway, NJ: IEEE.

42 42 Sathya, R. and Abraham, A. (2013). Comparison of supervised and unsupervised learning algorithms for pattern classification. International Journal of Advanced Research in Artificial Intelligence 2 (2): 34–38.

43 43 Massaro, A., Manfredonia, I., Galiano, A. et al. (2019). Sensing and quality monitoring facilities designed for pasta industry including traceability, image vision and predictive maintenance. Proceeding of 2019 IEEE International Workshop on Metrology for Industry 4.0 and IoT, Naples, Italy (4–6 June 2019). Piscataway, NJ: IEEE.

44 44 Massaro, A., Manfredonia, I., Galiano, A., and Xhaysa, B. (2019). Advanced process defect monitoring model and prediction improvement by artificial neural network in kitchen manufacturing industry: a case of study. Proceeding of IEEE International Workshop on Metrology for Industry 4.0 and IoT, Naples, Italy (4–6 June 2019). Piscataway, NJ: IEEE.

45 45 Massaro, A., Vitti, V., and Galiano, A. (2018). Automatic image processing engine oriented on quality control of electronic boards. Signal & Image Processing: An International Journal (SIPIJ) 9 (2): 1–14.

46 46 Kiran, B.R., Thomas, D.M., and Parakkal, R. (2018). An overview of deep learning based methods for unsupervised and semi‐supervised anomaly detection in videos. Journal of Imaging 4 (36): 1–25.

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51 51 Iskra, P. and Hernàndez, R.E. (2009). The influence of cutting parameters on the surface quality of routed paper birch and surface roughness prediction modeling. Wood and Fiber Science 41 (1): 28–37.

52 52 Contuzzi, N., Massaro, A., Manfredonia, I. et al. (2019). A decision 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.

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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.

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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.

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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.

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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.

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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.