Genetic K-means Research Articles

Methods of medical image segmentation analysis to improve the effectiveness of diagnosing lung diseases

Methods of medical image segmentation analysis to improve the effectiveness of diagnosing lung diseases. The article aims to review selected methods for segmenting lung medical images based on computed tomography (CT) image data analysis. Included in the analysis are 11 different approaches, including edge algorithms such as Canny, Prewitt, Roberts, Sobel and Log, clustering-based algorithms such as Fuzzy C-means, genetic K-means, k-nearest neighbours, and neural networks such as perceptron, neural and fuzzy sets, and edge binarization techniques, among others. Each method was evaluated for its ability to segment lung structures on CT images accurately. Finally, the contour of the heart from the CT image was determined using the maximum entropy thresholding method. The juxtaposition of different approaches to medical image segmentation is an important contribution to developing medical diagnostic techniques, which can help improve the efficiency of diagnosing lung diseases.

Delineating the mechanisms controlling groundwater salinization using chemo-isotopic data and meta-heuristic clustering algorithms (case study: Saguenay-Lac-Saint-Jean region in the Canadian Shield, Quebec, Canada).

This study employed meta-heuristic clustering algorithms to determine the source and mechanism of groundwater salinization in Quebec's Saguenay-Lac-Saint-Jean (SLSJ) region, utilizing hydrogeochemical (38 inorganic constituents, including minor, major, and trace elements) and isotopic data (δ18O and δ2H). A total of 382 groundwater and precipitation samples were examined. Among the meta-heuristic algorithms, Artificial Bee Colony K-Means (ABCKM), Differential Evolution K-Means (DEKM), Harmony Search K-Means (HSKM), Particle Swarm Optimization K-Means (PSOKM), and Genetic K-Means (GKM) were used and investigated, and finally, PSOKM displayed superior performance and was chosen for further investigation. Analysis of diverse plots and hydrogeochemical modeling unveiled the impact of the Laflamme Sea invasion on groundwater chemistry. PSOKM1, PSOKM4, and PSOKM5 exhibited notable carbonate and silicate dissolution, with PSOKM4 demonstrating predominant carbonate dissolution. Cation exchange was identified through binary plots and Chloro Alkaline Index (CAI), with reverse cation exchange predominantly observed in most PSOKM4 samples, while positive values suggested direct cation exchange in other clusters. Spatial dynamics analysis using HFE-D indicated that salinization occurs as groundwater flows through crystalline bedrock aquifers, resulting in a transition from HCO3- dominance in PSOKM4 to Cl- dominance in the remaining clusters. Interaction between groundwater and rock along this path facilitated a transformation towards a Na-Cl end-member. The closely aligned stable isotopes with the Global Meteoric Water Line (GMWL) indicated a blend of meteoric water and seawater as the groundwater source.

Dominant Gray Level-based Genetic K-means Clustering Algorithm for MRI Image Segmentation

Dominant Gray Level-based Genetic K-means Clustering Algorithm for MRI Image Segmentation

A Novel K-Means Clustering Method for Locating Urban Hotspots Based on Hybrid Heuristic Initialization

With rapid economic and demographic growth, traffic conditions in medium and large cities are becoming extremely congested. Numerous metropolitan management organizations hope to promote the coordination of traffic and urban development by formulating and improving traffic development strategies. The effectiveness of these solutions depends largely on an accurate assessment of the distribution of urban hotspots (centers of traffic activity). In recent years, many scholars have employed the K-Means clustering technique to identify urban hotspots, believing it to be efficient. K-means clustering is a sort of iterative clustering analysis. When the data dimensionality is large and the sample size is enormous, the K-Means clustering algorithm is sensitive to the initial clustering centers. To mitigate the problem, a hybrid heuristic “fuzzy system-particle swarm-genetic” algorithm, named FPSO-GAK, is employed to obtain better initial clustering centers for the K-Means clustering algorithm. The clustering results are evaluated and analyzed using three-cluster evaluation indexes (SC, SP and SSE) and two-cluster similarity indexes (CI and CSI). A taxi GPS dataset and a multi-source dataset were employed to test and validate the effectiveness of the proposed algorithm in comparison to the Random Swap clustering algorithm (RS), Genetic K-means algorithm (GAK), Particle Swarm Optimization (PSO) based K-Means, PSO based constraint K-Means, PSO based Weighted K-Means, PSO-GA based K-Means and K-Means++ algorithms. The comparison findings demonstrate that the proposed algorithm can achieve better clustering results, as well as successfully acquire urban hotspots.

Correction to: An integrated approach using growing self-organizing map-based genetic K-means clustering and tolerance rough set in occupational risk analysis

Correction to: An integrated approach using growing self-organizing map-based genetic K-means clustering and tolerance rough set in occupational risk analysis

Classification of Acute Pathology for Vocal Cord Using Advanced Multi-Resolution Algorithm

Vocal fold, a significant body structure, is accountable for phonation, which regulates air motion within and out of the lungs. The disorders in the vocal fold influence the quality of life. Thus, diagnosis of vocal fold disorders has a significant need, and CT of the neck is employed for an effective imaging scheme. Accordingly, this paper proposes an advanced multi-resolution algorithm (MRA) that optimally identifies and classifies pathologies. The vocal regions are acquired using the genetic k-means algorithm. The pathology features are generated using the local directional pattern (LDP) fed to pathology classification using moth search-rider optimization-based deep convolutional neural networks (MRA-based DCNN). The hybrid optimization (MRA), integrates the standard rider optimization algorithm (ROA) and moth search algorithm (MS) that trains deep learning classifier (DCNN). The analysis using the real databases regarding the performance metrics divulge that the proposed pathology detection module obtained the accuracy, specificity, and sensitivity of 97.020%, 91.698%, and 96.624%.

An integrated approach using growing self-organizing map-based genetic K-means clustering and tolerance rough set in occupational risk analysis

An integrated approach using growing self-organizing map-based genetic K-means clustering and tolerance rough set in occupational risk analysis

Delineation of isotopic and hydrochemical evolution of karstic aquifers with different cluster-based (HCA, KM, FCM and GKM) methods

In this work, a combination of isotopic and hydrogeochemical data of a karstic region was clustered with four distinct clustering analysis (CA) methods to study water evolution in a vulnerable karstic region to improve protection, sustainability, and enhanced water resource management. Four CA methods, including hierarchical cluster analysis (HCA), K-means (KM), and fuzzy logic CA methods, fuzzy C-mean (FCM), and genetic K‐means (GKM), have been utilized to analyze hydrochemical, chemical, and isotopic datasets, including dissolved inorganic carbon (DIC), δ13C-DIC, δ18O, and δ2H datasets of water resources of Paveh-Javanrud (PV-JR) karstic region, located at the western border of Iran and Iraq countries. The utilized dataset contains 34 water samples with varied origination to evaluate the performance of each model and find the best method based on a meaningful categorization of geological, hydrogeochemical, and isotopic characteristics. Finally, the best model results were matched graphically with developed geospatial graphs to visualize the correlation between the region's water resources. Accordingly, the FCM and GKM methods represent the same, yet meaningful results and have the best performance among the four methods. It was also identified that the PV-JR water resources could be generally categorized into five distinct clusters, including FC1 to FC5 and GK1 to GK5, of which two clusters that have mixing, two clusters with solo-origination and no sign of mixing, and finally, a seasonal spring which is categorized as a separate cluster. Potentially, studying water resources via theoretical methods combined with considering isotope hydrology is of particular interest since solving the environmental issues related to karstic regions and their water resource management are shared concerns in most arid and semi-arid countries, especially in the Middle East as this study, thus could lay a basis for the following scientific attempts involving hydrogeochemical studies and advanced statistical analysis.

Multi-Mode Active Suspension Control Based on a Genetic K-Means Clustering Linear Quadratic Algorithm

The traditional Linear quadratic regulator (LQR) control algorithm depends too much on expert experience during the selection of weighting coefficients. To solve this problem, we proposed a Genetic K-means clustering Linear quadratic (GKL) algorithm. Firstly, a 2-DOF 1/4 vehicle model and road input model are established. The weights of an LQR controller are optimized using a genetic algorithm. Then, a possible weighting space is constructed based on this optimal solution. Random weighting coefficients of each performance index are generated in this space. Next, LQR control for the 1/4 vehicle model is performed, and the simulation data are recorded automatically, with these random weighting values, different road classes, and driving speed. A machine learning dataset is built from these simulations. Finally, a K-means clustering algorithm is used to classify the LQR control active suspension into three performance modes: safety mode, comprehensive mode, and comfort mode. The optimal weighting matrix of each performance mode is determined to satisfy requirements for different types of drivers. The results show that the new GKL algorithm not only improves the suspension control effect but also realizes different performance modes. It can better adapt to the changes in driving conditions and drivers.

WITHDRAWN: An efficient detection of micro aneurysms from fundus images

Diabetic Retinopathy (DR) is used for elongated occurring Diabetes, and the foremost reason for visual damage is the discrepancies in the blood vessels of the retina. As the disease is asymptomatic, it is just being diagnosed through an ophthalmologist. Nevertheless, defensive and premature diagnosis is problematic owing to the absence of changes in terms of period and price. Throughout this phase, there is an injury on the retinal image such as Microaneurysms (MA), which is one of the initially noticed medical symptoms that specify the illness. Detection of MA in the early stage is one of the Challenging and an open issue in modern Literature. An Automatic System is introduced to assist the ophthalmologist for early detection. The retinal images need to pre-process prior to performing the segmentation using the clustering approach. The segmentation attained employing Enhanced Genetic K-Means (EGK-Means) Clustering algorithm from which the features are minimized through Support Vector Machines and, finally, the categorization of the retinal images is done through the usage of region descriptors employing Decision tree algorithm. The experimental results are carried out using DiaRetDB1 and Retinopathy Online Challenge (ROC) database, and it is observed that the specificity and sensitivity of the proposed methodology are higher when matched with the existing approaches.

Multiattribute Group Decision-Making Method Using a Genetic K-Means Clustering Algorithm

Multiattribute group decision-making (MAGDM) problems are characterized by the large number, uneven levels, and bounded rationality of decision-makers; multiple attributes and fuzziness of decision problems; and complex group behaviours. Considering these characteristics, we propose a MAGDM method using a genetic K-means clustering algorithm. First, we briefly review the traditional multiattribute decision-making method based on prospect theory (PT) and trapezoidal intuitionistic fuzzy numbers (TrIFNs) under the premise of human bounded rationality and uncertain decision environment. Then, the aggregation model of decision information given by decision-makers is established using the genetic K-means algorithm in order to determine optimal clustering results. Each clustering center represents decision information given by decision-makers in each cluster, and the weight of each clustering center is determined by considering the tightness of decision information within a cluster and the count of decision-makers in each cluster. Finally, the ranking of schemes is obtained according to the comparison rules of TrIFNs. We design comparison simulation experiments to test the proposed method and the simulation results demonstrate that the proposed method is apprehensible and feasible to solve MAGDM problems.

GKS Algorithmic Technique for Early Defect Prediction (GKS: A Genetic Feature K-means Clustering with Support Vector Machines)

GKS Algorithmic Technique for Early Defect Prediction (GKS: A Genetic Feature K-means Clustering with Support Vector Machines)

Optimization of geochemical anomaly detection using a novel genetic K-means clustering (GKMC) algorithm

Due to the complicated characteristics of regional geochemical data from stream sediments as a result of the complexity of geological features, detection of multi-elemental geochemical footprints of mineral deposits of interest is a challenging task. As a way to address this, a hybrid genetic algorithm-based technique, namely genetic K-means clustering (GKMC) algorithm, is proposed here for optimum delineation of multi-elemental patterns (both anomaly and background) in stream sediment geochemical data. To do so, factor analysis and sample catchment basin modeling were coupled with GKMC and traditional K-means clustering (TKMC) methods for identification of anomalous multi-elemental geochemical footprints of deposits of porphyry copper and skarn copper in the 1:100,000 scale Varzaghan map sheet, northwest Iran. Based on higher prediction rate, it can be inferred that the model derived by GKMC is superior to the one derived by TKMC. In addition, the strong anomaly classes of the GKMC and TKMC models predict, respectively, ~83% and ~66% of the porphyry/skarn Cu deposits in ~22% and ~36% of the study district. Thus, the geochemical anomaly targets derived by the GKMC method are more reliable than those generated by the TKMC method. This revealed that the GKMC algorithm is an efficient and robust tool for recognizing multi-element geochemical anomalies for mineral exploration.

An intelligent computing technique based on a dynamic-size subpopulations for unit commitment problem

A new intelligent computing based approach for solving multi-objective unit commitment problem (MOUCP) and its fuzzy model is presented in this paper. The proposed intelligent approach combines binary-real-coded genetic algorithm (BRCGA) and K-means clustering technique to find the optimal schedule of the generation units in MOUCP. BRCGA is used in order to tackle both the unit scheduling and load dispatch problems. While, K-means clustering technique is used to divide the population into a specific number of subpopulation with-dynamic-sizes. In this way, different genetic algorithm (GA) operators can apply to each sub-population, instead of using the same GA operators for all population. The proposed intelligent algorithm has been tested on standard systems of MOUCPs. The results showed the efficiency of the proposed approach to solve (MOUCP) and its fuzzy model.

A high-performance parallel coral reef optimization for data clustering

As a critical research topic toward the new era of big data, how to develop a high-performance data analytics system has received significant research attention from different disciplines since the 2000s. In the literature, many recent works attempted to develop a high-performance data analytics system to handle the large amount of data (i.e., volume) from different information systems (i.e., variety) that typically will be created very quickly in a short time (i.e., velocity). In particular, several recent studies have shown that metaheuristic algorithms can be applied to many data mining optimization problems to provide a better way to find a high-quality result than traditional deterministic algorithms. A high-performance clustering algorithm for big data analytics system will be presented in this paper. The proposed algorithm is designed based on a new kind of metaheuristic algorithm, coral reef optimization with substrate layers (CRO-SL), to get a better cluster result. To improve the effectiveness and efficiency, the proposed CRO-SL scheme has been applied to a cloud computing platform as well to reduce the response time of a data analytics system. The simulation results show that the proposed algorithm is able to provide a better clustering result than the other clustering algorithms compared in this research, including k-means, genetic k-means algorithm, particle swarm optimization, and simple coral reef optimization algorithm in terms of the sum of squared errors.

An Improved NSGA-III Algorithm Using Genetic K-Means Clustering Algorithm

The non-dominated sorting genetic algorithm III (NSGA-III) has recently been proposed to solve many-objective optimization problems (MaOPs). While this algorithm achieves good diversity, its convergence is unsatisfactory. In order to improve the convergence, we propose an improved NSGA-III using a genetic K-means clustering algorithm (NSGA-III-GKM), which can also ensure diversity and automatically provide the number and direction vector of the subspaces. Compared with the NSGA-III, the proposed NSGA-III-GKM has two key features. First, the initial reference points are clustered using a GKM clustering algorithm, which realizes automatic learning of the number of clusters. Second, as the reference points are replaced by cluster centers, a penalty-based boundary intersection (PBI) aggregation function is introduced to replace the perpendicular distance. The proposed NSGA-III-GKM and other similar optimization algorithms (NSGA-III, MOEA/D, U-NSGA-III, DC-NSGA-III and B-NSGA-III) are tested on DTLZ test problems and UF test problems. The simulation results demonstrate that the NSGA-III-GKM exhibits better diversity and convergence performance than the other algorithms.

Equivalent consumption minimization strategy of hybrid electric vehicle considering the impact of driving style

The impact of driving style on the fuel economy is normally unconsidered in the development process of the equivalent consumption minimization strategy for hybrid electric vehicles. This article established a real vehicle energy management strategy based on the combination of driving style recognition and optimized equivalent consumption minimization strategy. In detail, the driving style recognition algorithm is obtained by introducing the driving style identification coefficient containing driving condition type influence factor and combining the coefficient with the genetic optimization K-means driving condition recognition algorithm. The equivalent consumption minimization strategy is optimized by genetic algorithm to optimize the battery SOC penalty function and charge–discharge coefficient. Comparing the proposed energy management strategy with the traditional equivalent consumption minimization strategy in a stochastic driving condition, the simulated result shows that the fuel consumption rate is reduced by 8.49% by making the operating points of the engine closer to the engine best efficiency curve, and making battery SOC changes more smooth and maintaining it in a reasonable area. It means that the proposed energy management strategy achieves higher fuel computation efficiency and better power distribution between the integrated starter generator and the engine.

Comparative Analysis of Genetic k-means and Fuzzy k-modes Approach for Clustering Tweets

Comparative Analysis of Genetic k-means and Fuzzy k-modes Approach for Clustering Tweets

Automated Malware Detection Based on HMM and Genetic K-means

Automated Malware Detection Based on HMM and Genetic K-means

Clustering of interlaminar and intralaminar damages in laminated composites under indentation loading using Acoustic Emission

This study focuses on the clustering of the indentation-induced interlaminar and intralaminar damages in carbon/epoxy laminated composites using Acoustic Emission (AE) technique. Two quasi-isotropic specimens with layups of [60/0/-60]4S (is named dispersed specimen) and [604/04/-604]S (is named blocked specimen) were fabricated and subjected to a quasi-static indentation loading. The mechanical data, digital camera and ultrasonic C-scan images of the damaged specimens showed different damage evolution behaviors for the blocked and dispersed specimens. Then, the AE signals of the specimens were clustered for tracking the evolution behavior of different damage mechanisms. In order to select a reliable clustering method, the performance of six different clustering methods consisting of k-Means, Genetic k-Means, Fuzzy C-Means, Self-Organizing Map (SOM), Gaussian Mixture Model (GMM), and hierarchical model were compared. The results illustrated that hierarchical model has the best performance in clustering of AE signals. Finally, the evolution behavior of each damage mechanism was investigated by the clustered AE signals with hierarchical model. The results of this study show that using AE technique with an appropriate clustering method such as hierarchical model could be an applicable tool for structural health monitoring of composite structures.