Data to intelligence: The role of data-driven models in wastewater treatment
Increasing energy efficiency in wastewater treatment plants (WWTPs) is becoming more
important. An emerging approach to addressing this issue is to exploit development in data …
important. An emerging approach to addressing this issue is to exploit development in data …
Integrity assessment of corroded oil and gas pipelines using machine learning: A systematic review
Hydrocarbon fluid integrity evaluation in oil and gas pipelines is important for anticipating
HSE measures. Ignoring corrosion is unavoidable and may have severe personal …
HSE measures. Ignoring corrosion is unavoidable and may have severe personal …
Application of gradient boosting regression model for the evaluation of feature selection techniques in improving reservoir characterisation predictions
DA Otchere, TOA Ganat, JO Ojero… - Journal of Petroleum …, 2022 - Elsevier
Feature Selection, a critical data preprocessing step in machine learning, is an effective way
in removing irrelevant variables, thus reducing the dimensionality of input features …
in removing irrelevant variables, thus reducing the dimensionality of input features …
Prevalence and early prediction of diabetes using machine learning in North Kashmir: a case study of district bandipora
Diabetes is one of the biggest health problems that affect millions of people across the
world. Uncontrolled diabetes can increase the risk of heart attack, cancer, kidney damage …
world. Uncontrolled diabetes can increase the risk of heart attack, cancer, kidney damage …
[HTML][HTML] Removal of congo red from water by adsorption onto activated carbon derived from waste black cardamom peels and machine learning modeling
The present work utilizes waste black cardamom (BC) as an inexpensive and
environmentally friendly adsorbent for sequestering the Congo Red (CR) dye from aqueous …
environmentally friendly adsorbent for sequestering the Congo Red (CR) dye from aqueous …
Multitask Neural Network for Mapping the Glass Transition and Melting Temperature Space of Homo- and Co-Polyhydroxyalkanoates Using σProfiles Molecular …
Polyhydroxyalkanoates (PHAs) are an emerging type of bioplastic that have the potential to
replace petroleum-based plastics. They are biosynthetizable, biodegradable, and …
replace petroleum-based plastics. They are biosynthetizable, biodegradable, and …
Novel Genetic Algorithm (GA) based hybrid machine learning-pedotransfer Function (ML-PTF) for prediction of spatial pattern of saturated hydraulic conductivity
Saturated hydraulic conductivity (Ks) is an important soil characteristic that controls water
moves through the soil. On the other hand, its measurement is difficult, time-consuming, and …
moves through the soil. On the other hand, its measurement is difficult, time-consuming, and …
[HTML][HTML] Machine learning assisted prediction of mechanical properties of graphene/aluminium nanocomposite based on molecular dynamics simulation
Predicting mechanical properties of graphene-reinforced metal matrix nanocomposites
(GRMMNCs) usually requires atomistic simulations that are computationally expensive …
(GRMMNCs) usually requires atomistic simulations that are computationally expensive …
District heating planning with focus on solar energy and heat pump using GIS and the supervised learning method: Case study of Gaziantep, Turkey
S Eslami, Y Noorollahi, M Marzband… - Energy Conversion and …, 2022 - Elsevier
In the present context, the global concern on energy consumption and management have
been significantly increased due to the environmental issues, such as global warming and …
been significantly increased due to the environmental issues, such as global warming and …
Machine learning assisted probabilistic creep-fatigue damage assessment
In order to investigate the probabilistic damage distribution under creep-fatigue interaction,
machine learning framework with the divide-and-conquer methodology is proposed to …
machine learning framework with the divide-and-conquer methodology is proposed to …