Forecasting water quality index in groundwater using artificial neural network M Kulisz, J Kujawska, B Przysucha, W Cel Energies 14 (18), 5875, 2021 | 70 | 2021 |
Trochoidal milling and neural networks simulation of magnesium alloys I Zagórski, M Kulisz, M Kłonica, J Matuszak Materials 12 (13), 2070, 2019 | 44 | 2019 |
Prediction of municipal waste generation in Poland using neural network modeling M Kulisz, J Kujawska Sustainability 12 (23), 10088, 2020 | 40 | 2020 |
Effect of the AWJM method on the machined surface layer of AZ91D magnesium alloy and simulation of roughness parameters using neural networks I Zagórski, M Kłonica, M Kulisz, K Łoza Materials 11 (11), 2111, 2018 | 34 | 2018 |
Machine learning methods to forecast the concentration of PM10 in Lublin, Poland J Kujawska, M Kulisz, P Oleszczuk, W Cel Energies 15 (17), 6428, 2022 | 30 | 2022 |
Using an LSTM network to monitor industrial reactors using electrical capacitance and impedance tomography-a hybrid approach G Kłosowski, T Rymarczyk, K Niderla, M Kulisz, Ł Skowron, M Soleimani Eksploatacja i Niezawodność 25 (1), 2023 | 29 | 2023 |
Properties of the surface layer after trochoidal milling and brushing: experimental study and artificial neural network simulation M Kulisz, I Zagórski, J Matuszak, M Kłonica Applied Sciences 10 (1), 75, 2019 | 28 | 2019 |
Application of artificial neural network (ANN) for water quality index (WQI) prediction for the river Warta, Poland M Kulisz, J Kujawska Journal of Physics: conference series 2130 (1), 012028, 2021 | 25 | 2021 |
The effect of abrasive waterjet machining parameters on the condition of Al-Si alloy M Kulisz, I Zagórski, J Korpysa Materials 13 (14), 3122, 2020 | 24 | 2020 |
Artificial neural network modelling of vibration in the milling of AZ91D alloy I Zagórski, M Kulisz, A Semeniuk, A Malec Advances in Science and Technology. Research Journal 11 (3), 261-269, 2017 | 23 | 2017 |
Polish consumers’ response to social media eco-marketing techniques A Bojanowska, M Kulisz Sustainability 12 (21), 8925, 2020 | 18 | 2020 |
Analysis of Vibration, Deflection Angle and Surface Roughness in Water-Jet Cutting of AZ91D Magnesium Alloy and Simulation of Selected Surface Roughness Parameters Using ANN K Biruk-Urban, I Zagórski, M Kulisz, M Leleń Materials 16 (9), 3384, 2023 | 17 | 2023 |
Consumer‘s behaviour regarding cashless payments during the COVID-19 pandemic M Kulisz, A Bojanowska, K Toborek University of Piraeus. International Strategic Management Association, 2021 | 17 | 2021 |
Matrix profile implementation perspective in Industrial Internet of Things production maintenance application J Pizoń, M Kulisz, J Lipski Journal of Physics: Conference Series 1736 (1), 012036, 2021 | 13 | 2021 |
Analysis and prediction of the impact of technological parameters on cutting force components in rough milling of AZ31 magnesium alloy M Kulisz, I Zagórski, A Weremczuk, R Rusinek, J Korpysa Archives of Civil and Mechanical Engineering 22 (1), 1, 2021 | 12 | 2021 |
Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste M Kulisz, J Kujawska, M Cioch, W Cel, J Pizoń Applied Sciences 14 (7), 2997, 2024 | 11 | 2024 |
Prediction of buckling behaviour of composite plate element using artificial neural networks K Falkowicz, M Kulisz Advances in Science and Technology. Research Journal 18 (1), 2024 | 11 | 2024 |
Artificial neural network modelling of cutting force components in milling I Zagórski, M Kulisz, A Semeniuk ITM Web of Conferences 15, 02001, 2017 | 11 | 2017 |
Optimizing the neural network loss function in electrical tomography to increase energy efficiency in industrial reactors M Kulisz, G Kłosowski, T Rymarczyk, J Słoniec, K Gauda, W Cwynar Energies 17 (3), 681, 2024 | 8 | 2024 |
Improved prediction of the higher heating value of biomass using an artificial neural network model based on the selection of input parameters J Kujawska, M Kulisz, P Oleszczuk, W Cel Energies 16 (10), 4162, 2023 | 8 | 2023 |