• Journal of Information Processing Systems
• /
• v.18 no.1
• /
• pp.146-158
• /
• 2022

With the success of the digital economy and the rapid development of its technology, network security has received increasing attention. Intrusion detection technology has always been a focus and hotspot of research. A hybrid model that combines particle swarm optimization (PSO) and kernel extreme learning machine (KELM) is presented in this work. Continuous-valued PSO and binary PSO (BPSO) are adopted together to determine the parameter combination and the feature subset. A fitness function based on the detection rate and the number of selected features is proposed. The results show that the method can simultaneously determine the parameter values and select features. Furthermore, competitive or better accuracy can be obtained using approximately one quarter of the raw input features. Experiments proved that our method is slightly better than the genetic algorithm-based KELM model.

• Journal of the Korea Computer Industry Society
• /
• v.7 no.1
• /
• pp.47-56
• /
• 2006

Proteome patterns reflect the underlying pathological state of a human organ. It is believed that the anomalies or diseases of human organs are identified by the analysis of the pattern. There are many ways to analysis these patterns. <중략> (colon cancer and leukemia dataset) indicates that the proposed method shows better classification performance and more stable results than other single kernel functions.

• Journal of Korean Institute of Industrial Engineers
• /
• v.39 no.5
• /
• pp.361-366
• /
• 2013

Prediction of process parameters is very important in parameter design. If predictions are fairly accurate, the quality improvement process will be useful to save time and reduce cost. The concept of dual response approach based on response surface methodology has widely been investigated. Dual response approach may take advantages of optimization modeling for finding optimum setting of input factor by separately modeling mean and variance responses. This study proposes an alternative dual response approach based on machine learning techniques instead of statistical analysis tools. A hybrid neural network-genetic algorithm has been proposed for the purpose of parameter design. A neural network is first constructed to model the relationship between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Using empirical process data, process parameters can be predicted without performing real experimentations. A genetic algorithm is then applied to find the optimum settings of input factors, where the neural network is used to evaluate the mean and variance response. A drug formulation example from pharmaceutical industry has been studied to demonstrate the procedures and applicability of the proposed approach.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.27 no.2
• /
• pp.189-194
• /
• 2017

An compressor that is combined with the rotor and pneumatic technology has been researching for the performance of pressure. However, the control of operations, an accurate diagnosis and the maintenance of compressor system are limited though the simple structure of compressor and compression are advantaged to reduce the energy. In this paper, the characteristic of the compressor operating under the normal or abnormal condition is realized. and the efficient diagnosis method is proposed through feature based analysis. Also, by using the GA (genetic algorithm) and SVM (support vector machine) of machine learning, the performance of feature analysis is conducted. Different misalignment mode of learning data for compressor is evaluated using the fault simulator. Therefore, feature based analysis is conducted considering misalignment mode of the compressor and the possibility of a diagnosis of misalignment is evaluated.

• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.54 no.4
• /
• pp.489-497
• /
• 2021

Climate change is affecting the evolutionary trajectories of individual species and ecological communities, partly through the creation of new species groups. As population shift geographically and temporally as a result of climate change, reproductive interactions between previously isolated species are inevitable and it could potentially lead to invasion, speciation, or even extinction. Four species of abalone, genus Haliotis are present along the Korean coastline and these species are important for commercial and fisheries resources management. In this study, genetic markers for fisheries resources management were discovered based on genomic information, as part of the management of endemic species in response to climate change. Two thousand one hundred and sixty one single nucleotide polymorphisms (SNPs) were discovered using genotyping-by-sequencing (GBS) method. Forty-one SNPs were selected based on their features for species classification. Machine learning analysis using these SNPs makes it possible to differentiate four Haliotis species and hybrids. In conclusion, the proposed machine learning method has potentials for species classification of the genus Haliotis. Our results will provide valuable data for biodiversity conservation and management of abalone population in Korea.

• Journal of Nutrition and Health
• /
• v.55 no.1
• /
• pp.1-9
• /
• 2022

In the past few decades, great progress has been made on understanding the interaction between nutrition and health status. But despite this wealth of knowledge, health problems related to nutrition continue to increase. This leads us to postulate that the continuing trend may result from a lack of consideration for intra-individual biological variation on dietary responses. Precision nutrition utilizes personal information such as age, gender, lifestyle, diet intake, environmental exposure, genetic variants, microbiome, and epigenetics to provide better dietary advices and interventions. Recent technological advances in the artificial intelligence, big data analytics, cloud computing, and machine learning, have made it possible to process data on a scale and in ways that were previously impossible. A big data platform is built by collecting numerous parameters such as meal features, medical metadata, lifestyle variation, genome diversity and microbiome composition. Sophisticated techniques based on machine learning algorithm can be used to integrate and interpret multiple factors and provide dietary guidance at a personalized or stratified level. The development of a suitable machine learning algorithm would make it possible to suggest a personalized diet or functional food based on analysis of intra-individual metabolic variation. This novel precision nutrition might become one of the most exciting and promising approaches of improving health conditions, especially in the context of non-communicable disease prevention.

• Proceedings of the KIEE Conference
• /
• 1996.07b
• /
• pp.820-822
• /
• 1996

This paper proposes a method for seeking the scheme of substation restoration by using genetic algorithm. Genetic algorithm (GA), first introduced by John Holland, is becoming an important tool in machine learning and function optimization. GA is a searching or optimization algorithm based on Darwinian biological evolution principle. As a test system, we assume a simple substation system and for the transformer fault, the result is obtained.

• Journal of Korea Society of Industrial Information Systems
• /
• v.11 no.2
• /
• pp.18-28
• /
• 2006

We consider a Direct Input Output Manufacturing System(DIOMS) which has a munber of machine centers placed along a built-in Automated Storage/Retrieval System(AS/RS). The Storage/Retrieval (S/R) machine handles parts placed on pallets for the operational aspect of DIOMS and determines the optimal operating policy by combining computer simulation and genetic algorithm. The operational problem includes: input sequencing control, dispatching rule of the S/R machine, machine center-based part type selection rule, and storage assignment policy. For each operating policy, several different policies are considered based on the known research results. In this paper, using the computer simulation and genetic algorithm we suggest a method which gives the optimal configuration of operating policies within reasonable computation time.

• Journal of the Korea Convergence Society
• /
• v.9 no.7
• /
• pp.1-7
• /
• 2018

Feature selection, one of data preprocessing techniques, is one of major research areas in many applications dealing with large dataset. It has been used in pattern recognition, machine learning and data mining, and is now widely applied in a variety of fields such as text classification, image retrieval, intrusion detection and genome analysis. The proposed method is based on a genetic algorithm which is one of meta-heuristic algorithms. There are two methods of finding feature subsets: a filter method and a wrapper method. In this study, we use a wrapper method, which evaluates feature subsets using a real classifier, to find an optimal feature subset. The training dataset used in the experiment has a severe class imbalance and it is difficult to improve classification performance for rare classes. After preprocessing the training dataset with SMOTE, we select features and evaluate them with various machine learning algorithms.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.3
• /
• pp.1202-1211
• /
• 2018

In this paper, induction machine operation efficiency and torque is improved using Machine Learning based Gene Optimization (ML-GO) Technique is introduced. Optimized Genetic Algorithm (OGA) is used to select the optimal induction machine data. In OGA, selection, crossover and mutation process is carried out to find the optimal electrical machine data for induction machine design. Initially, many number of induction machine data are given as input for OGA. Then, fitness value is calculated for all induction machine data to find whether the criterion is satisfied or not through fitness function (i.e., objective function such as starting to full load torque ratio, rotor current, power factor and maximum flux density of stator and rotor teeth). When the criterion is not satisfied, annealed selection approach in OGA is used to move the selection criteria from exploration to exploitation to attain the optimal solution (i.e., efficient machine data). After the selection process, two point crossovers is carried out to select two crossover points within a chromosomes (i.e., design variables) and then swaps two parent's chromosomes for producing two new offspring. Finally, Adaptive Levy Mutation is used in OGA to select any value in random manner and gets mutated to obtain the optimal value. This process gets iterated till finding the optimal value for induction machine design. Experimental evaluation of ML-GO technique is carried out with performance metrics such as torque, rotor current, induction machine operation efficiency and rotor power factor compared to the state-of-the-art works.