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 order to reduce defects. - Rajesh S. Ransing; Cinzia Giannetti; Meghana R. Ransing; M. W. James (Comput. Ind., 2013)

A novel variable selection approach based on co-linearity index to discover optimal process settings by analysing mixed data. - Cinzia Giannetti; Rajesh S. Ransing; Meghana R. Ransing; David C. Bould; David T. Gethin; Johann Sienz (Comput. Ind. Eng., 2014)

Organisational Knowledge Management for Defect Reduction and Sustainable Development in Foundries. - Cinzia Giannetti; Meghana R. Ransing; Rajesh S. Ransing; David C. Bould; David T. Gethin; Johann Sienz (Int. J. Knowl. Syst. Sci., 2015)

Risk based uncertainty quantification to improve robustness of manufacturing operations. - Cinzia Giannetti; Rajesh S. Ransing (Comput. Ind. Eng., 2016)

A quality correlation algorithm for tolerance synthesis in manufacturing operations. - Rajesh S. Ransing; Raed S. Batbooti; Cinzia Giannetti; Meghana R. Ransing (Comput. Ind. Eng., 2016)

A framework for improving process robustness with quantification of uncertainties in Industry 4.0. - Cinzia Giannetti (INISTA, 2017)

A Scalable Deep Convolutional LSTM Neural Network for Large-Scale Urban Traffic Flow Prediction using Recurrence Plots. - Aniekan Essien; Godwin Chukwkelu; Cinzia Giannetti (AFRICON, 2019)

A Deep Learning Framework for Univariate Time Series Prediction Using Convolutional LSTM Stacked Autoencoders. - Aniekan Essien; Cinzia Giannetti (INISTA, 2019)

Segmentation and generalisation for writing skills transfer from humans to robots. - Chunxu Li; Chenguang Yang 001; Cinzia Giannetti (Cogn. Comput. Syst., 2019)

A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders. - Aniekan Essien; Cinzia Giannetti (IEEE Trans. Ind. Informatics, 2020)

A Novel Deep Learning Power Quality Disturbance Classification Method using Autoencoders. - Callum O'Donovan; Cinzia Giannetti; Grazia Todeschini (ICAART (2), 2021)

Making Green Transport a Reality: A Classification Based Data Analysis Method to Identify Properties Suitable for Electric Vehicle Charging Point Installation. - James Flynn; Eleanor Brealy; Cinzia Giannetti (IGARSS, 2021)

Root Cause Classification of Temperature-related Failure Modes in a Hot Strip Mill. - Samuel Latham; Cinzia Giannetti (IN4PL, 2022)

Data modelling and Remaining Useful Life estimation of rolls in a steel making cold rolling process. - Kayal Lakshmanan; Eugenio Borghini; Arnold Beckmann; Cameron Pleydell-Pearce; Cinzia Giannetti (KES, 2022)

Towards scalable and reusable predictive models for cyber twins in manufacturing systems. - Cinzia Giannetti; Aniekan Essien (J. Intell. Manuf., 2022)

KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0. - Qiushi Cao; Cecilia Zanni-Merk; Ahmed Samet; Christoph Reich; François de Bertrand de Beuvron; Arnold Beckmann; Cinzia Giannetti (Robotics Comput. Integr. Manuf., 2022)

Comparison of a Bat and Genetic Algorithm Generated Sequence Against Lead Through Programming When Assembling a PCB Using a Six-Axis Robot With Multiple Motions and Speeds. - Christian Andrew Griffiths; Cinzia Giannetti; Kamil T. Andrzejewski; Adam Morgan (IEEE Trans. Ind. Informatics, 2022)

Revolutionising the Sustainability of Steel Manufacturing Using Computer Vision. - Callum O'Donovan; Cinzia Giannetti; Cameron Pleydell-Pearce (ISM, 2023)

A Hybrid Modelling Approach Based on Deep Learning for the Prediction of the Silicon Content in the Blast Furnace. - Kayal Lakshmanan; Aurash Karimi; Alex Carr; Philippe Wauters; Michael Auinger; Cameron Pleydell-Pearce; Cinzia Giannetti (KES, 2023)

A Tool to Combine Expert Knowledge and Machine Learning for Defect Detection and Root Cause Analysis in a Hot Strip Mill. - Samuel Latham; Cinzia Giannetti (SN Comput. Sci., 2023)

Leveraging Machine Learning Insights at the Hot Strip Rolling Mill, Tata Steel, Port Talbot. - Robert Gibbs; Cinzia Giannetti; Thomas Baynes; Cameron Pleydell-Pearce (I2MTC, 2024)

A Novel LSTM Pipeline to Detect Anomalies in Manufacturing Production (Datasets). - James Flynn; Cinzia Giannetti; Hessel van Dijk (IEEE DataPort, 2024)

Real-time monitoring of molten zinc splatter using machine learning-based computer vision. - Callum O'Donovan; Cinzia Giannetti; Cameron Pleydell-Pearce (J. Intell. Manuf., 2025)