Evolutionary Approach to Optimal Oil Skimmer Assignment for Oil Spill Response: A Case Study.

We propose a genetic algorithm for optimizing oil skimmer assignments, introducing a tailored repair operation for constrained assignments. Methods essentially involve simulation-based evaluation to ensure adherence to South Korea's regulations. Results show that the optimized assignments, compared to current ones, reduced work time on average and led to a significant reduction in total skimmer capacity.

Metaheuristic-Based Feature Selection Methods for Diagnosing Sarcopenia with Machine Learning Algorithms.

This study explores the efficacy of metaheuristic-based feature selection in improving machine learning performance for diagnosing sarcopenia. Extraction and utilization of features significantly impacting diagnosis efficacy emerge as a critical facet when applying machine learning for sarcopenia diagnosis. Using data from the 8th Korean Longitudinal Study on Aging (KLoSA), this study examines harmony search (HS) and the genetic algorithm (GA) for feature selection.

Association between Thyroid Function and Respiratory Distress Syndrome in Preterm Infants.

Thyroid hormones are known to influence the production and secretion of pulmonary surfactant. The objective of this study was to explore the relationship between respiratory distress syndrome (RDS) and thyroid hormones. This was a retrospective study of preterm infants at 24−33 weeks gestational age from April 2017 to February 2019.

Comparative Study of Classification Algorithms for Various DNA Microarray Data.

Microarrays are applications of electrical engineering and technology in biology that allow simultaneous measurement of expression of numerous genes, and they can be used to analyze specific diseases. This study undertakes classification analyses of various microarrays to compare the performances of classification algorithms over different data traits. The datasets were classified into test and control groups based on five utilized machine learning methods, including MultiLayer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and -Nearest Neighbors (KNN), and the resulting accuracies were compared.

Maximizing the Coverage of Sensor Deployments Using a Memetic Algorithm and Fast Coverage Estimation.

We derive the upper and lower bounds on the coverage of a 2-D deployment of static sensors. We use these bounds in constructing a method of estimating the coverage of deployment by assuming that there are only pairwise intersections between the disks representing the range of each sensor. The speed of this approximation allows it to be built into a local search technique, as part of a memetic algorithm (MA) that tries to deploy a given set of sensors with maximum coverage.

Optimizing taxon addition order and branch lengths in the construction of phylogenetic trees using maximum likelihood.

Taxon addition order and branch lengths are optimized by genetic algorithms (GAS) within the fastDNAml algorithm for constructing phylogenetic trees of high likelihood. Results suggest that optimizing the order in which taxa are added improves the likelihood of the resulting trees.

Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning.

We propose three quality control (QC) techniques using machine learning that depend on the type of input data used for training. These include QC based on time series of a single weather element, QC based on time series in conjunction with other weather elements, and QC using spatiotemporal characteristics. We performed machine learning-based QC on each weather element of atmospheric data, such as temperature, acquired from seven types of IoT sensors and applied machine learning algorithms, such as support vector regression, on data with errors to make meaningful estimates from them.

Machine-Learning Approach to Optimize SMOTE Ratio in Class Imbalance Dataset for Intrusion Detection.

The KDD CUP 1999 intrusion detection dataset was introduced at the third international knowledge discovery and data mining tools competition, and it has been widely used for many studies. The attack types of KDD CUP 1999 dataset are divided into four categories: user to root (U2R), remote to local (R2L), denial of service (DoS), and Probe. We use five classes by adding the normal class.

Improved Correction of Atmospheric Pressure Data Obtained by Smartphones through Machine Learning.

A correction method using machine learning aims to improve the conventional linear regression (LR) based method for correction of atmospheric pressure data obtained by smartphones. The method proposed in this study conducts clustering and regression analysis with time domain classification. Data obtained in Gyeonggi-do, one of the most populous provinces in South Korea surrounding Seoul with the size of 10,000 km(2), from July 2014 through December 2014, using smartphones were classified with respect to time of day (daytime or nighttime) as well as day of the week (weekday or weekend) and the user's mobility, prior to the expectation-maximization (EM) clustering.

A genetic filter for cancer classification on gene expression data.

We present a new genetic filter to identify a predictive gene subset for cancer-type classification on gene expression profiles. This approach pursues to not only maximize correlation between selected genes and cancer types but also minimize inter-correlation among selected genes. The proposed genetic filter was tested on well-known leukemia datasets, and significant improvement over previous work was obtained.

Charge scheduling of an energy storage system under time-of-use pricing and a demand charge.

A real-coded genetic algorithm is used to schedule the charging of an energy storage system (ESS), operated in tandem with renewable power by an electricity consumer who is subject to time-of-use pricing and a demand charge. Simulations based on load and generation profiles of typical residential customers show that an ESS scheduled by our algorithm can reduce electricity costs by approximately 17%, compared to a system without an ESS and by 8% compared to a scheduling algorithm based on net power.

An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks.

Sensor networks have a lot of applications such as battlefield surveillance, environmental monitoring, and industrial diagnostics. Coverage is one of the most important performance metrics for sensor networks since it reflects how well a sensor field is monitored. In this paper, we introduce the maximum coverage deployment problem in wireless sensor networks and analyze the properties of the problem and its solution space.

Geometric crossovers for multiway graph partitioning.

Geometric crossover is a representation-independent generalization of the traditional crossover defined using the distance of the solution space. By choosing a distance firmly rooted in the syntax of the solution representation as a basis for geometric crossover, one can design new crossovers for any representation. Using a distance tailored to the problem at hand, the formal definition of geometric crossover allows us to design new problem-specific crossovers that embed problem-knowledge in the search.

Role of Kupffer cells in the vasoregulatory gene expression during hepatic ischemia/reperfusion.

Hepatic microcirculatory failure is a major component of reperfusion injury in the liver. Recent data provided some evidence that endothelium-derived vasoconstrictors and vasodilators may be functionally important to the control of the total hepatic blood flow under these conditions of circulatory failure. Since Kupffer cells provide signals that regulate the hepatic response in ischemia/reperfusion (I/R), the aim of this study was to investigate the role of Kupffer cells in the I/R-induced imbalance of vasoregulatory gene expression.