| A deep learning model for smart manufacturing using convolutional LSTM neural network autoencoders A Essien, C Giannetti IEEE Transactions on Industrial Informatics 16 (9), 6069-6078, 2020 | 319 | 2020 |
| KSPMI: a knowledge-based system for predictive maintenance in industry 4.0 Q Cao, C Zanni-Merk, A Samet, C Reich, FDB De Beuvron, A Beckmann, ... Robotics and Computer-Integrated Manufacturing 74, 102281, 2022 | 121 | 2022 |
| A deep learning framework for univariate time series prediction using convolutional LSTM stacked autoencoders A Essien, C Giannetti 2019 IEEE International Symposium on INnovations in Intelligent SysTems and …, 2019 | 67 | 2019 |
| Risk based uncertainty quantification to improve robustness of manufacturing operations C Giannetti, RS Ransing Computers & Industrial Engineering 101, 70-80, 2016 | 67 | 2016 |
| Machine learning as a universal tool for quantitative investigations of phase transitions C Giannetti, B Lucini, D Vadacchino Nuclear Physics B 944, 114639, 2019 | 55 | 2019 |
| A coupled penalty matrix approach and principal component based co-linearity index technique to discover product specific foundry process knowledge from in-process data in … RS Ransing, C Giannetti, MR Ransing, MW James Computers in Industry 64 (5), 514-523, 2013 | 32 | 2013 |
| A novel variable selection approach based on co-linearity index to discover optimal process settings by analysing mixed data C Giannetti, RS Ransing, MR Ransing, DC Bould, DT Gethin, J Sienz Computers & Industrial Engineering 72, 217-229, 2014 | 29 | 2014 |
| Data-driven energy storage scheduling to minimise peak demand on distribution systems with PV generation E Borghini, C Giannetti, J Flynn, G Todeschini Energies 14 (12), 3453, 2021 | 28 | 2021 |
| Segmentation and generalisation for writing skills transfer from humans to robots C Li, C Yang, C Giannetti Cognitive Computation and Systems 1 (1), 20-25, 2019 | 27 | 2019 |
| Optimisation process for robotic assembly of electronic components KT Andrzejewski, MP Cooper, CA Griffiths, C Giannetti The International Journal of Advanced Manufacturing Technology 99, 2523-2535, 2018 | 23 | 2018 |
| A quality correlation algorithm for tolerance synthesis in manufacturing operations RS Ransing, RS Batbooti, C Giannetti, MR Ransing Computers & Industrial Engineering 93, 1-11, 2016 | 22 | 2016 |
| Knowledge management and knowledge discovery for process improvement and sustainable manufacturing: a foundry case study C Giannetti, R Ransing, M Ransing, D Bould, D Gethin InImpact: The Journal of Innovation Impact 7 (2), 554, 2016 | 18 | 2016 |
| Towards scalable and reusable predictive models for cyber twins in manufacturing systems C Giannetti, A Essien Journal of Intelligent Manufacturing 33 (2), 441-455, 2022 | 17 | 2022 |
| An image-based deep transfer learning approach to classify power quality disturbances G Todeschini, K Kheta, C Giannetti Electric Power Systems Research 213, 108795, 2022 | 15 | 2022 |
| Short term load forecasting using TabNet: A comparative study with traditional state-of-the-art regression models E Borghini, C Giannetti Engineering Proceedings 5 (1), 6, 2021 | 15 | 2021 |
| A framework for improving process robustness with quantification of uncertainties in Industry 4.0 C Giannetti 2017 IEEE International Conference on INnovations in Intelligent SysTems and …, 2017 | 14 | 2017 |
| If only my foundry knew what it knows…”: A 7Epsilon perspective on root cause analysis and corrective action plans for ISO9001: 2008 HM Roshan, C Giannetti, MR Ransing, RS Ransing 71st World Foundry Congress, Bilbao, Spain, 19-21, 2014 | 13 | 2014 |
| A scalable deep convolutional LSTM neural network for large-scale urban traffic flow prediction using recurrence plots AE Essien, G Chukwkelu, C Giannetti 2019 IEEE AFRICON, 1-7, 2019 | 12 | 2019 |
| Organisational knowledge management for defect reduction and sustainable development in foundries C Giannetti, MR Ransing, RS Ransing, DC Bould, DT Gethin, J Sienz International Journal of Knowledge and Systems Science (IJKSS) 6 (3), 18-37, 2015 | 11 | 2015 |
| Using convolutional neural networks to map houses suitable for electric vehicle home charging J Flynn, C Giannetti Ai 2 (1), 135-149, 2021 | 10 | 2021 |