Related references
Note: Only part of the references are listed.Evaluation of different machine learning frameworks to predict CNL-FDC-PEF logs via hyperparameters optimization and feature selection
Auref Rostamian et al.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING (2022)
A novel approach to the role of iridium and titanium oxide in deactivation mechanisms of a Ti/(36 RuO2-x IrO2-(64-x) TiO2) coating in sodium chloride solution
Seyedeh Forough Mirseyed et al.
CORROSION SCIENCE (2022)
Interpretability in healthcare: A comparative study of local machine learning interpretability techniques
Radwa ElShawi et al.
COMPUTATIONAL INTELLIGENCE (2021)
Explaining the black-box model: A survey of local interpretation methods for deep neural networks
Yu Liang et al.
NEUROCOMPUTING (2021)
Towards the prediction of hydrogen-induced crack growth in high-graded strength steels
B. Sobhaniaragh et al.
THIN-WALLED STRUCTURES (2021)
Deep Learning and Time-Series Analysis for the Early Detection of Lost Circulation Incidents During Drilling Operations
Mohammad Aljubran et al.
IEEE ACCESS (2021)
Application of machine learning to accidents detection at directional drilling
Ekaterina Gurina et al.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING (2020)
A comparison of instance-level counterfactual explanation algorithms for behavioral and textual data: SEDC, LIME-C and SHAP-C
Yanou Ramon et al.
ADVANCES IN DATA ANALYSIS AND CLASSIFICATION (2020)
Evaluation of CTOD resistance curves in clamped SE(T) specimens with weld centerline cracks
Seyed Hamidreza Afzalimir et al.
ENGINEERING FRACTURE MECHANICS (2020)
A Survey of Methods for Explaining Black Box Models
Riccardo Guidotti et al.
ACM COMPUTING SURVEYS (2019)
Data-driven model for the identification of the rock type at a drilling bit
Nikita Klyuchnikov et al.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING (2019)
Prediction of Porosity and Permeability Alteration Based on Machine Learning Algorithms
Andrei Erofeev et al.
TRANSPORT IN POROUS MEDIA (2019)
Machine Learning Interpretability: A Survey on Methods and Metrics
Diogo Carvalho et al.
ELECTRONICS (2019)
The development of a novel multi-objective optimization framework for non-vertical well placement based on a modified non-dominated sorting genetic algorithm-II
Auref Rostamian et al.
COMPUTATIONAL GEOSCIENCES (2019)
Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets
Anna C. Belkina et al.
NATURE COMMUNICATIONS (2019)
Gradient boosting to boost the efficiency of hydraulic fracturing
Ivan Makhotin et al.
JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY (2019)
Explaining Black Box Models by means of Local Rules
Eliana Pastor et al.
SAC '19: PROCEEDINGS OF THE 34TH ACM/SIGAPP SYMPOSIUM ON APPLIED COMPUTING (2019)
Visual interpretability for deep learning: a survey
Quan-shi Zhang et al.
FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING (2018)
Opening the black box of neural networks: methods for interpreting neural network models in clinical applications
Zhongheng Zhang et al.
ANNALS OF TRANSLATIONAL MEDICINE (2018)
Explaining prediction models and individual predictions with feature contributions
Erik Strumbelj et al.
KNOWLEDGE AND INFORMATION SYSTEMS (2014)