Applications of machine learning in pipeline integrity management: A state-of-the-art review
A Rachman, T Zhang, RMC Ratnayake - International journal of pressure …, 2021 - Elsevier
Despite being considered the safest means to transport oil and gas, pipelines are
susceptible to degradation. Pipeline integrity management (PIM) is implemented to lower the …
susceptible to degradation. Pipeline integrity management (PIM) is implemented to lower the …
[HTML][HTML] A comprehensive meta-analysis of emerging swarm intelligent computing techniques and their research trend
This study presents an extensive analysis of ten emerging swarm intelligence metaheuristic
techniques, namely Emperor Penguins Colony (EPC), Harris Hawks Optimizer (HHO) …
techniques, namely Emperor Penguins Colony (EPC), Harris Hawks Optimizer (HHO) …
Detection-based prioritisation: Framework of multi-laboratory characteristics for asymptomatic COVID-19 carriers based on integrated Entropy–TOPSIS methods
Context and background Corona virus (COVID) has rapidly gained a foothold and caused a
global pandemic. Particularists try their best to tackle this global crisis. New challenges …
global pandemic. Particularists try their best to tackle this global crisis. New challenges …
Particle swarm optimization feature selection for breast cancer recurrence prediction
Women who have recovered from breast cancer (BC) always fear its recurrence. The fact
that they have endured the painstaking treatment makes recurrence their greatest fear …
that they have endured the painstaking treatment makes recurrence their greatest fear …
Computational intelligence for heart disease diagnosis: A medical knowledge driven approach
This paper investigates a number of computational intelligence techniques in the detection
of heart disease. Particularly, comparison of six well known classifiers for the well used …
of heart disease. Particularly, comparison of six well known classifiers for the well used …
A novel enhanced hybrid clinical decision support system for accurate breast cancer prediction
Feature selection is one of the crucial data preprocessing techniques for improving the
performance of machine learning (ML) models. Recently, metaheuristic feature selection …
performance of machine learning (ML) models. Recently, metaheuristic feature selection …
[HTML][HTML] Comparison of feature selection methods for mapping soil organic matter in subtropical restored forests
Y Chen, L Ma, D Yu, H Zhang, K Feng, X Wang… - Ecological …, 2022 - Elsevier
Mapping Soil organic matter (SOM) over a complex forest landscape is challenging due to
the difficulty in selecting the most insightful variables from high-dimensional datasets in the …
the difficulty in selecting the most insightful variables from high-dimensional datasets in the …
Machine learning approach for risk-based inspection screening assessment
A Rachman, RMC Ratnayake - Reliability Engineering & System Safety, 2019 - Elsevier
Risk-based inspection (RBI) screening assessment is used to identify equipment that makes
a significant contribution to the system's total risk of failure (RoF), so that the RBI detailed …
a significant contribution to the system's total risk of failure (RoF), so that the RBI detailed …
R-Ensembler: A greedy rough set based ensemble attribute selection algorithm with kNN imputation for classification of medical data
Abstract Background and Objective Retrieving meaningful information from high
dimensional dataset is an important and challenging task. Normally, medical dataset suffers …
dimensional dataset is an important and challenging task. Normally, medical dataset suffers …
Machine intelligence in healthcare and medical cyber physical systems: A survey
Today, the US healthcare industry alone can save $300 B per year by using machine
intelligence to analyze a rich set of existing medical data; results from these analyses can …
intelligence to analyze a rich set of existing medical data; results from these analyses can …