Hybrid extreme learning machine for real-time rate of penetration prediction by Abdelhamid Kenioua, Omar Djebili, Ammar Touati Brahim

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Leveraging boosting machine learning for drilling rate of penetration (ROP) prediction based on drilling and petrophysical parameters by Raed H. Allawi, Watheq J. Al-Mudhafar, Mohammed A. Abbas, David A. Wood

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Application of supervised machine learning and unsupervised data compression models for pore pressure prediction employing drilling, petrophysical, and well log data by Abu Bakker Siddique, Tanveer Alam Munshi, Nazmul Islam Rakin, Mahamudul Hashan, Sushmita Sarker Chnapa, Labiba Nusrat Jahan

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Advanced Optimization of Drilling Parameters in Composite Materials: A Topsis-Based Approach for Enhanced Manufacturing Precision by Mustapha Arslane, Mohamed Slamani, Abdelmalek Elhadi, Salah Amroune, Ugur Köklü, Madani Grine, Borhen Louhichi, Sabbah Ataya

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Optimization of Drilling Parameters in Natural Fibers Composite Plates Based on Grey Relational Analysis by Mustapha Arslane, Mohamed Slamani, Abdelmalek Elhadi, Salah Amroune, Ahmed Belaadi, Ibrahim M.H. Alshaikh, Djamel Ghernaout

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