[HTML][HTML] Air quality prediction by machine learning models: A predictive study on the indian coastal city of Visakhapatnam

G Ravindiran, G Hayder, K Kanagarathinam… - Chemosphere, 2023 - Elsevier
Clean air is critical component for health and survival of human and wildlife, as atmospheric
pollution is associated with a number of significant diseases including cancer. However, due …

Machine learning metamodels for thermo-mechanical analysis of friction stir welding

DV Burande, K Kalita, R Gupta, A Kumar… - International Journal on …, 2024 - Springer
This study explores the development and application of machine learning (ML) metamodels
for the thermo-mechanical analysis of Friction Stir Welding (FSW). The main objective is to …

[HTML][HTML] An effective and secure mechanism for phishing attacks using a machine learning approach

G Mohamed, J Visumathi, M Mahdal, J Anand… - Processes, 2022 - mdpi.com
Phishing is one of the biggest crimes in the world and involves the theft of the user's
sensitive data. Usually, phishing websites target individuals' websites, organizations, sites …

[HTML][HTML] Accurate estimation of tensile strength of 3D printed parts using machine learning algorithms

M Jayasudha, M Elangovan, M Mahdal, J Priyadarshini - Processes, 2022 - mdpi.com
Manufacturing processes need optimization. Three-dimensional (3D) printing is not an
exception. Consequently, 3D printing process parameters must be accurately calibrated to …

[HTML][HTML] Evaluation of methods for estimating lake surface water temperature using Landsat 8

K Dyba, S Ermida, M Ptak, J Piekarczyk, M Sojka - Remote Sensing, 2022 - mdpi.com
Changes in lake water temperature, observed with the greatest intensity during the last two
decades, may significantly affect the functioning of these unique ecosystems. Currently, in …

Aerodynamic optimization of airfoil based on deep reinforcement learning

J Lou, R Chen, J Liu, Y Bao, Y You, Z Chen - Physics of Fluids, 2023 - pubs.aip.org
The traditional optimization of airfoils relies on, and is limited by, the knowledge and
experience of the designer. As a method of intelligent decision-making, reinforcement …

Optimal prediction for patch design using YUKI-RANDOM-FOREST in a cracked pipeline repaired with CFRP

A Oulad Brahim, R Capozucca, S Khatir… - Arabian Journal for …, 2024 - Springer
This paper presents the effectiveness of a hybrid YUKI-RANDOM-FOREST, Particle Swarm
Optimization-YUKI (PSO-YUKI), and balancing composite motion optimization algorithm …

Predicting dynamic contact angle in immiscible fluid displacement: a machine learning approach for subsurface flow applications

N Suetrong, T Tosuai, H Vo Thanh… - Energy & …, 2024 - ACS Publications
Immiscible fluid–fluid displacement dynamics is a crucial element to understanding and
engineering many subsurface flow applications, including enhanced oil recovery and …

Machine learning algorithm for mid-term projection of the EU member states' indebtedness

S Zarkova, D Kostov, P Angelov, T Pavlov, A Zahariev - Risks, 2023 - mdpi.com
The main research question addressed in the paper is related to the possibility of medium-
term forecasting of the public debts of the EU member states. The analysis focuses on a …

[HTML][HTML] Well performance classification and prediction: deep learning and machine learning long term regression experiments on oil, gas, and water production

NM Ibrahim, AA Alharbi, TA Alzahrani, AM Abdulkarim… - Sensors, 2022 - mdpi.com
In the oil and gas industries, predicting and classifying oil and gas production for
hydrocarbon wells is difficult. Most oil and gas companies use reservoir simulation software …