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A novel variable selection approach based on co-linearity index to discover optimal process settings by analysing mixed data., , , , , and . Comput. Ind. Eng., (2014)Machine Learning as a universal tool for quantitative investigations of phase transition, , and . (2018)cite arxiv:1812.06726Comment: 32 pages, 8 figures. References, more technical details and some clarifications added, typos fixed, numerical results and conclusions unchanged. Version accepted for publication in Nucl. Phys. B.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., , , and . Comput. Ind., 64 (5): 514-523 (2013)Organisational Knowledge Management for Defect Reduction and Sustainable Development in Foundries., , , , , and . IJKSS, 6 (3): 18-37 (2015)Data modelling and Remaining Useful Life estimation of rolls in a steel making cold rolling process., , , , and . KES, volume 207 of Procedia Computer Science, page 1057-1066. Elsevier, (2022)Making Green Transport a Reality: A Classification Based Data Analysis Method to Identify Properties Suitable for Electric Vehicle Charging Point Installation., , and . IGARSS, page 6229-6232. IEEE, (2021)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., , , and . IEEE Trans. Ind. Informatics, 18 (2): 1102-1110 (2022)A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders., and . IEEE Trans. Ind. Informatics, 16 (9): 6069-6078 (2020)KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0., , , , , , and . Robotics Comput. Integr. Manuf., (2022)A Tool to Combine Expert Knowledge and Machine Learning for Defect Detection and Root Cause Analysis in a Hot Strip Mill., and . SN Comput. Sci., 4 (5): 628 (September 2023)