Optimal K-means Clustering Research Articles

Tuned weighted feature fusion with hybridized DNN-RNN framework for plant disease detection and classification

ABSTRACT In the world economy, agriculture is an essential part among individuals to earn money. But, the farmers face more obstacles because more diseases naturally affect the health of the plant. Hence, to overcome this limitation, automatic plant disease identification techniques are essential to monitor the growth as well as disease-affected plants and leaves. In most plants, symptoms related to plant disease can be identified by considering the leaves. Moreover, plant disease identification became more essential with deep learning architecture. Hence, it is important to enhance the solution for resource-constrained portable devices like smartphones, but it become a complicated issue as the deeply structured methods utilize extensive resources for the analysis. Hence, this paper aims to develop an automated plant disease detection framework with an advanced set of plant features. The plant images are taken from online sources. The acquired plant images undergo image enhancement using the contrast enhancement process. The enhanced images are fed in the feature retrieval phase 1, where the segmentation takes place using the Centre Optimized K-means clustering (CO-KMC) with the Modified Random Variable-based Black Widow Optimization (MRV-BWO). Later, the segmented images obtain the colour, shape, and texture features and they are considered as the first set of features. The second set of features is retrieved from the enhanced images using Multi-scale Dilated Attention CNN (MSDA-CNN). These two features are fused by the weighted feature fusion process using the developed MRV-BWO. Then, the plant diseases are detected using the Hybridized Deep Neural Network with Recurrent Neural Network (HDNN-RNN), which comprises RNN and DNN models. The parameter optimization takes place in the hybrid HDNN-RNN for improving the detection efficiency using MRV-BWO. From the verification of the findings, the recommended model obtains a 96.47 precision and accuracy rate. Therefore, the simulation results of the offered model reveal that it achieves enriched efficacy when comparing other baseline approaches.

Image Compression Scheme based on Optimized K-means Clustering and Higher-Level Decomposed DWT

Image processing is a vital research domain essential in day-to-day digital activities. Image compression reduces memory space, transmission bandwidth requirement, transmission time, and computational complexity. This article aims to construct and compare hybrid image compression schemes using DWT, PCA, and K-means clustering techniques. This research proposes an image compression scheme based on optimized K-means clustering and higher-level decomposed DWT (K-DWT). Also, we have developed the hybrid image compression schemes based on PCA and DWT (P-DWT), PCA, K-means, and DWT (P-K-DWT) and analyzed the performance. Initially, we implemented compression algorithms using K-means Clustering, PCA, and DWT. Further, using the python platform, K-DWT, P-DWT, and P-K-DWT compression models have been executed on the images of the kodak dataset. The research objective is to obtain good image quality and better compression algorithms. The image quality and compression efficiency are evaluated using Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), and Compression Ratio (CR). The K-DWT methods have achieved a higher compression ratio, i.e., 15.3870, compared with other methods. Also, the outcomes show that the K-DWT method has provided the required values of PSNR and SSIM.

A Hybrid Intelligent Clustering Model for Tackling Incomplete Mixed Data Using Heuristic Algorithm with Artificial Intelligence

Recently, in various information areas including image processing and social networks, data clustering has been vastly utilized. Thus, the clustering task is taken as successful while imputing the quality of uncertain values, whereas the traditional methods observe only poor performance. To enhance the performance, the hybrid intelligent-based clustering model is proposed by the adaptive concept. Initially, the data is collected from benchmark datasets that are composed of incomplete mixed data. The first level of clustering is processed by optimized K-Means Clustering (KMC) to acquire the optimal centroid, in which the centroid is optimized by the Adaptive Probability-assisted Water Strider Algorithm (AP-WSA). Similarly, the second clustering is done through K-Correlation-based Clustering (KCC) to obtain the clustered output. Thus, the final clustering process is named as Hybrid K-Means K-Correlation (HKMKC) clustering algorithm. Finally, the experimental results are validated with the performance measures and estimated with the traditional methods. Through the result analysis, the Average Silhouette Coefficient (AS) of the designed AP-WSA-HKMKC is 50%, 28%, 16%, and 5.45% progressed than KMC, FCM, KCC, and K-median clustering methods for dataset 2 at 15% miss rate. Thus, the designed hybrid clustering ensures the efficiency of handling incomplete data regarding accurate clustered outputs.

Research on Rapid Identification Technology of Sand and Dust Characteristic Monitoring Data Based on Optimized K-Means Clustering

The criteria-based sand and dust weather determination method has the problem ofbeing a cumbersome and time-consuming process when processing a large amount of raw data, and cannot avoid the problems of repeatability and reproducibility. On the basis of statistical analysis of the air automatic monitoring data in the cities affected by sand and dust, this paper proposes a k-means optimization algorithm (MDPD-k-means) based on maximum density and percentage distance, which can quickly filter the characteristic data of sand and dust in a short time, and identify the days affected by sand and dust. This method effectively improves the data processing efficiency, solves the problems of poor reproducibility and large artificial error of traditional methods, and can support the business application of sand and dust data elimination. This paper uses the method to identify the sand and dust data of 10 cities in Shaanxi Province from 2016 to 2022, determines a total of 1107 sand and dust days, and points out that the number of days affected by sand and dust is increasing year by year. After excluding the effect of sand and dust, the urban PM10 concentration decreases by 18.42~1.41% respectively, which provides important data information for accurately evaluating the effectiveness of air pollution prevention and control.

A Smart Solution for Tomato Leaf Disease Classification by Modified Recurrent Neural Network with Severity Computation

The artificial intelligence-assisted deep learning approach plays a significant task in identifying diseases through a large set of plant leaf images. Thus, the major aim of the designed model is to design and develop the tomato leaf disease classification model with intelligent approaches. Initially, the images are gathered from standard datasets and real time datasets. Then, the pre-processing is conducted for cleaning, shadow removal, and enhancing the images using the image enhancement approach. Then, the spot segmentation from the leaf is performed by the Optimized K-Means Clustering (OKMC) using Standard Deviation-based Grasshopper Horse Herd Optimization (SD-GHHO). Further, the deep feature extraction is extracted from the segmented spot using Convolutional Neural Network (CNN), VGG16, and Residual Networks (ResNet). The gathered features are further concatenated. Then, the optimal features are selected using a new meta-heuristic algorithm called SD-GHHO. Finally, the disease classification is performed with the help of the Modified Recurrent Neural Network (MRNN), where the weight of RNN is optimized using the same SD-GHHO technique. Once after classifying the spots, the disease severity computation is performed. The proposed method has enhanced classification efficiency in high accuracy, specificity, and sensitivity values.

Automatic crop disease recognition by improved abnormality segmentation along with heuristic-based concatenated deep learning model

Plant diseases detection based on machine learning and computer vision can produce a significant effect on the quality and production of crops. Any changes that occur in crop quality or crop productivity may greatly reduce the national economy. Thus, the detection of plant diseases should be done at the early stage before intensively affecting crop production. A new technique named smart farming is introduced to benefit in “high-ended application of modern farming” by obtaining multiple data through live streams, social media, sensors, robots, etc. The attained data from diverse sources are required to processunder amultilevel database, which becomes more challenging while detecting plant diseases in smart farming techniques. The demands for using the machine learning approaches with unsupervised or supervised methods are increased on utilizing it in real-world applications. The main intention of this paper is to focus on the development of a novel crop disease detection model using the modified deep learning architecture. The images from different datasets with several crop diseases are collected from the public benchmark sources, and it is initially subjected to pre-processing using filtering and contrast enhancement techniques. Once the image is enhanced, the novel Optimized K-means Clustering (O-KMC) is adopted for performing the abnormality segmentation. Then, the feature extraction of the abnormality segmented images is done by the edge features and texture features. These features are utilized for disease recognition, in which the Heuristic-based Convolutional Neural Network with Recurrent Neural Network (H-C-RNN) is developed. In both segmentation and classification, the parameter improvement is performed by the Adaptive Inertia Weighted-Dragonfly Algorithm (AIW-DA). The performance of the proposed model under the different datasets is evaluated with various conventional methods that ensure the accurate identification of crop diseases in the proposed model.

Automated Market Analysis by RFMx Encoding Based Customer Segmentation using Initial Centroid Selection Optimized K-means Clustering Algorithm

Automated Market Analysis by RFMx Encoding Based Customer Segmentation using Initial Centroid Selection Optimized K-means Clustering Algorithm

Automated heart disease prediction model by hybrid heuristic-based feature optimization and enhanced clustering

The main intent of this paper is to implement a novel clustering model for heart disease prediction with numerical data and ECG signals using an optimal feature extraction approach. Rather than the direct use of numerical data to clustering, the Electrocardiogram (ECG) signals are initially subjected for the signal decomposition using Discrete Wavelet Transform (DWT), and dimensionality reduction is performed through Principal Component Analysis (PCA). Both the data are processed for the optimized feature extraction stage. Here, the hybrid meta-heuristic concept is adopted for the optimized feature extraction based on Jaya Algorithm with Red Deer Algorithm (J-RDA). Once the feature optimization is done, the hybrid clustering is formed by integrating the optimized Density-based Spatial Clustering of Applications with Noise (DBSCAN) and optimized K-Means Clustering (KMC), in which the proposed J-RDA is used for tuning the significant parameters. Moreover, the objective model for feature optimization and optimized hybrid clustering for proposed heart disease prediction tries to solve the multi-objective function. The results reveal that the proposed model achieves good performance in rectifying the problems in heart disease prediction for dual data types.

An Optimized K-means Clustering for Improving Accuracy in Traffic Classification

With the explosive grown network traffic, the traditional port- and payload-based methods are insatiable for the requirements of privacy protection as well as the fast real-time classification for the today traffic classification. Here, a network traffic classification model based on both the Self-Organizing Maps (SOM) and the K-means fusion algorithm is proposed. In which, the traffic data is initially clustered by the SOM network to derive the cluster number and each cluster center value. Then those values are taken as the initial parameters to run the K-means algorithm, achieving optimal classification. As results compared with the traditional K-means algorithm, the initially clustering done by using the SOM network not only inherits its advantages of simple method and efficient processing, but also reduces time cost. Moreover, a significant improvement in coossification accuracy is achieved with our proposed algorithm.

Hierarchy based firefly optimized K-means clustering for complex question answering

Complex Question Answering (CQA) is commonly used for answering community questions which requires human knowledge for answering them. It is essential to find complex question answering system for avoiding the complexities behind the question answering system. In the present work, we proposed Hierarchy based Firefly Optimized k-means Clustering (HFO-KC) method for complex question answering. Initially, the given input query is preprocessed. It eliminates the way of misclassification when comparing the strings. In order to enhance the answer selection process, the obtained keywords are mapped into the candidate solution. After mapping, the obtained keywords are segmented. Each segmentation forms a new query for answer selection and various number of answers selected for each query. Okapi-25 similarity computation is utilized for the process of document retrieval. Then the answers selected are classified with K means clustering which forms the hierarchy for each answer. Finally the firefly optimization algorithm is used for selecting the best quality of answer from the hierarchy.

Spectral rotation for deep one-step clustering

Previous spectral clustering methods sequentially conduct three steps, i.e., similarity matrix learning from original data, spectral representation learning, and K-means clustering on spectral representation, respectively, to difficultly output robust clustering result even though each of three steps achieves individual optimization. The reason is that each goal of former two steps is not focused on achieving optimal clustering result. Moreover, original data usually contains noise to affect the clustering result, as well as has high-dimensional representation to easily result in the curse of dimensionality. In this paper, we propose a deep spectral clustering method which embeds four parts (i.e., similarity matrix learning, spectral representation learning, optimized K-means clustering, and transformation matrix learning) in a unified framework with the following advantages: 1) similarity matrix is obtained from the low-dimensional feature space of original data where the influence of both noise and high-dimensional data are considered; 2) optimized K-means clustering rotates original result of K-means clustering to search optimized clustering hyperplane which partitions data points into clusters; and 3) each of four parts is iteratively updated so that the clustering result is obtained based on the feedback of other three parts. As a result, our proposed framework develops a two-task deep clustering model with linear activation functions to output effective clustering result. Experimental results on real data sets show the effectiveness of our method in terms of four clustering evaluation metrics, compared to state-of-the-art clustering methods.

Pancreatic Tumor Segmentation Based on Optimized K-Means Clustering and Saliency Map Model

Segmentation of positron emission tomography (PET) plays a major role in research and clinical applications. The segmentation of pancreatic tumors using PET / CT is challenging due to a significant amount of noise that may result in serious segmentation inaccuracies. The evaluation of the results of segmentation in medical imaging is due to the presence of a gold standard. Therefore, the performance evaluation of these methods would be necessary. This paper suggested a new object segmentation method that is based on K-means clustering with Saliency Maps. The K-means clustering approach restricts every pixel of the image that belongs to a single cluster. One drawback with using the K-means algorithm to segment objects is that segments are not connected and can be widely scattered. It is known that using saliency region, the approximate location of the desired object in the map can easily be identified. In this proposed method, the saliency map is used to distinguish the desired object cluster from the image from the background cluster, and then, to map the object clusters together Experimental results shows that the proposed algorithm outperforms dramatically in terms of visual plausibility and computational cost compared to state-of - the-art methods and achieves excellent performance for object segmentation.

Segmentation of Soft Tissues and Tumors from Biomedical Images using Optimized K-Means Clustering via Level Set formulation

Segmentation of Soft Tissues and Tumors from Biomedical Images using Optimized K-Means Clustering via Level Set formulation

Computational analysis of single-cell transcriptomics data elucidates the stabilization of Oct4 expression in the E3.25 mouse preimplantation embryo

Our computational analysis focuses on the 32- to 64-cell mouse embryo transition, Embryonic day (E3.25), whose study in literature is concentrated mainly on the search for an early onset of the second cell-fate decision, the specification of the inner cell mass (ICM) to primitive endoderm (PE) and epiblast (EPI). We analysed single-cell (sc) microarray transcriptomics data from E3.25 using Hierarchical Optimal k-Means (HOkM) clustering, and identified two groups of ICM cells: a group of cells from embryos with less than 34 cells (E3.25-LNCs), and another group of cells from embryos with more than 33 cells (E3.25-HNCs), corresponding to two developmental stages. Although we found massive underlying heterogeneity in the ICM cells at E3.25-HNC with over 3,800 genes with transcriptomics bifurcation, many of which are PE and EPI markers, we showed that the E3.25-HNCs are neither PE nor EPI. Importantly, analysing the differently expressed genes between the E3.25-LNCs and E3.25-HNCs, we uncovered a non-autonomous mechanism, based on a minimal number of four inner-cell contacts in the ICM, which activates Oct4 in the preimplantation embryo. Oct4 is highly expressed but unstable at E3.25-LNC, and stabilizes at high level at E3.25-HNC, with Bsg highly expressed, and the chromatin remodelling program initialised to establish an early naïve pluripotent state. Our results indicate that the pluripotent state we found to exist in the ICM at E3.25-HNC is the in vivo counterpart of a new, very early pluripotent state. We compared the transcriptomics profile of this in vivo E3.25-HNC pluripotent state, together with the profiles of E3.25-LNC, E3.5 EPI and E4.5 EPI cells, with the profiles of all embryonic stem cells (ESCs) available in the GEO database from the same platform (over 600 microarrays). The shortest distance between the set of inner cells (E3.25, E3.5 and E4.5) and the ESCs is between the E3.25-HNC cells and 2i + LIF ESCs; thus, the developmental transition from 33 to 34 cells decreases dramatically the distance with the naïve ground state of the 2i + LIF ESCs. We validated the E3.25 events through analysis of scRNA-seq data from early and late 32-cell ICM cells.

Optimized K-Means Clustering Model based on Gap Statistic

Big data has become famous to process, store and manage massive volumes of data. Clustering is an essential phase in big data analysis for many real-life application areas uses clustering methodology for result analysis. The data clustered sets have become a challenging issue in the field of big data analytics. Among all clustering algorithm, the K-means algorithm is the most widely used unsupervised clustering approach as seen from past. The K-means algorithm is the best adapted for deciding similarities between objects based on distance measures with small datasets. Existing clustering algorithms require scalable solutions to manage large datasets. However, for a particular domain-specific problem the initial selection of K is still a significant concern. In this paper, an optimized clustering approach presented which is calculated the optimal number of clusters (k) for specific domain problems. The proposed approach is an optimal solution based on the cluster performance measure analysis based on gab statistic. By observation, the experimental results prove that the proposed model can efficiently enhance the speed of the clustering process and accuracy by reducing the computational complexity of the standard k-means algorithm which achieves 76.3%.

A Method of Fault Line Selection in Small Current Grounding System Based on VMD Energy Entropy and Optimized K-means Clustering

When single-phase to ground fault occurs in small current grounding system, the zero-sequence transient current contains a lot of complex fault information. In order to extract characteristic from the zero-sequence current accurately as the criterion of fault line selection, this paper puts forward a method of fault line selection based on VMD energy entropy and optimized K-means clustering. Firstly, the zero-sequence current of each line is decomposed by VMD (variational mode decomposition, VMD), and each sub mode of the original current is obtained. Secondly, the energy entropy of each sub mode is calculated as the characteristic quantity of each line state. Finally, the optimized K-means clustering algorithm is used to cluster the fault feature to realize fault line selection. In this paper, Simulink is used to simulate the single-phase to ground fault in resonant grounded system and the results show that the method that has high accuracy can provide reference for practical engineering application.

Temporally Consistent Tone Mapping of Images and Video Using Optimal K-means Clustering

The field of high dynamic range imaging addresses the problem of capturing and displaying the large range of luminance levels found in the world, using devices with limited dynamic range. In this paper we present a novel tone mapping algorithm that is based on K-means clustering. Using dynamic programming we are able to not only solve the clustering problem efficiently, but also find the global optimum. Our algorithm runs in hbox {O}(N^2K) for an image with N input luminance levels and K output levels. We show that our algorithm gives comparable results to state-of-the-art tone mapping algorithms, but with the additional large benefit of a minimum of parameters. We show how to extend the method to handle video input. We test our algorithm on a number of standard high dynamic range images and video sequences and give qualitative and quantitative comparisons to a number of state-of-the-art tone mapping algorithms.

BPSO Optimized K-means Clustering Approach for Data Analysis

BPSO Optimized K-means Clustering Approach for Data Analysis

Pure ion chromatogram extraction via optimal k-means clustering

A new method called KPIC is proposed for extracting pure ion chromatogram from raw LC-MS data accurately, which is based on optimalk-means clustering. And KPIC can reduces the number of split signals and provide higher quality chromatographic peaks.

GPU Accelerated K Means Clustering Refined using ANT Colony Optimization

Discovery of clusters using parallelization is the answer for handling computational issues invoked by large datasets in cluster analysis. The Graphics Processing Unit and Compute Unified Device Architecture form a joint platform to improvise parallel clustering algorithms. The parallel K-Means clustering algorithm improves the speedup of clustering but often faces with problems of falling into local optima. The meta-heuristic algorithm used to optimize the clustering process using pheromone updation is known as Ant Colony Optimization technique. The K-Means clustering solution is fine tuned by applying parallel implementation of Ant Colony Optimization K-Means Clustering in order to increase efficiency. The aim is to combine optimization characteristic of Ant Colony Optimization for calculation of centroids with clustering attitude of K-Means and enhance the solution using processing power of GPU. The parallelization strategy utilized exhibits variations in speedup and quality of the clusters. The Parallel Ant Colony Clustering algorithm is tested on standard datasets and performance is compared with Sequential K-Means, Parallel K-Means, Sequential Tabu K-Means, Parallel Tabu K-Means and Sequential Ant Colony Clustering algorithms. The experimental results confirm yet another parallelization technique to unravel data clustering problems.