Support Vector Clustering Algorithm Research Articles

Research on Visual Inspection Method of Tree Whitening Quality based on Multi-level Feature Fusion

Background: At present, the traditional manual whitewashing method is used in tree whitewashing in China. The quality of tree whitening can only be judged by the naked eye. Objective: Up to now, the quality of tree whitewashing is still judged manually. In order to improve work efficiency, an automatic evaluation method of tree whitewashing quality based on multi-level feature fusion is proposed. Methods: The images extract texture features from white-washed trees (gray-level co-occurrence matrix) and shape features (gradient direction histogram) from pixel-level fusion to obtain a global characteristic matrix. Using a support vector machine (SVM), random forests, and clustering algorithm (KNN), three classifiers were selected to identify the integration characteristics of the training test. In order to reduce the correlation of feature information and improve the execution efficiency of the classifier, the global feature matrix was optimized by combining principal component analysis (PCA) with pixel-level fusion and feature-level fusion. Results: The experiment results showed that the classification accuracy of the support vector machine is 94.00%, which is higher than that of the random forest classifier (92.67) and KNN classifier (92.67%). Meanwhile, the support vector machine is superior to the other two classification algorithms in recall rate, accuracy rate, and algorithm execution efficiency. The results showed that the execution efficiency of each classification algorithm was improved after the optimization of the feature data. The support vector machine classification algorithm is more stable than the other two algorithms. Conclusion: The feature fusion method combined with PCA can improve the execution efficiency and recognition precision of classifiers to a certain extent. For the feature matrix obtained by different data processing methods, the SVM classifier performs more stably and reliably than the random forest classifier and KNN in the inspection of tree whitening quality.

Customer Segmentation Marketing Strategy Based on Big Data Analysis and Clustering Algorithm

Traditional customer segmentation methods cannot obtain more effective information from massive customer data, which affects the formulation of marketing strategies. Based on this, this study constructs a customer segmentation marketing strategy model that integrates support vector machines and clustering algorithms. This model first utilizes support vector machines to segment existing customer data, and then integrates support vector machines and clustering algorithms to construct a customer segmentation model. Finally, simulation experiments are conducted using the dataset. The results show that the model algorithm obtains the optimal solution when the quantity of iterations is 50. Meanwhile, the average error rate of the model algorithm in the customer segmentation process is 6.82%, the average recall rate is 91.28%, and the average profit predicted by the impact strategy developed by the segmentation model is 29.88%, which is 2.53% different from the true value.

Municipal Solid Waste Management: A Review of Machine Learning Applications

This study comprises of an analysis of various Machine Learning (ML) algorithms for municipal solid waste management to enhance waste management procedures and reduce the adverse environmental effects. The increasing population has resulted in substantial environmental hazards due to increased waste generation. Therefore, an effective waste management system with much more efficient and innovative waste management techniques is required to reduce the adverse effects that would occur due to the generation of massive waste. This study reviews various ML algorithms to automate and optimize garbage generation, collection, transportation, treatment, and disposal. To deliver and predict effective and precise waste generation, segregation, and collection forecasts, the system integrates multiple ML methods including decision trees (DT), k-nearest neighbours (KNN), support vector machines (SVM), random forests (RF), and clustering algorithms.

Mechanical Motion Trajectory Control Tracking System Based on Machine Learning Algorithm

Machine learning is a key problem in the field of artificial intelligence. It is the study of statistical learning methods. It enables computers to simulate the learning behavior of humans, accumulate experience, and continuously improve and perfect their performances. Mechanical motion has always been an important subject in the field of automation, and trajectory tracking control is an important technology for mobile robots. Therefore, its research has important theoretical and practical significance. This study proposes a research based on the machine learning algorithm applied to the control and tracking system of mechanical motion trajectory. It expounds the neural network model, support vector machine algorithm, clustering algorithm, and K-means algorithm. Zeroing in on the issue of mechanical movement direction following control, this study concentrates on the plan of the mechanical movement direction following regulator in view of the BP brain network in the AI calculation and checks the accuracy and plausibility of the regulator plan. The exploratory outcomes demonstrate the way that the ideal following direction can be acquired by taking ε = 5. It makes the tracking trajectory more accurate, and the error convergence speed is faster.

Rough-Fuzzy Support Vector Clustering with OWA Operators

Rough-Fuzzy Support Vector Clustering (RFSVC) is a novel soft computing derivative of the classical Support Vector Clustering (SVC) algorithm, which has been used already in many real-world applications. RFSVC’s strengths are its ability to handle arbitrary cluster shapes, identify the number of clusters, and e?ectively detect outliers by the means of membership degrees. However, its current version uses only the closest support vector of each cluster to calculate outliers’ membership degrees, neglecting important information that remaining support vectors can contribute. We present a novel approach based on the ordered weighted average (OWA) operator that aggregates information from all cluster representatives when computing ?nal membership degrees and at the same time allows a better interpretation of the cluster structures found. Particularly, we propose the induced OWA using weights determined by the employed kernel function. The computational experiments show that our approach outperforms the current version of RFSVC as well as alternative techniques ?xing the weights of the OWA operator while maintaining the level of interpretability of membership degrees for detecting outliers.

An Improving Accuracy in Predicting the spread of Covid over Online Social Networks based on Geographical Location using Novel Autoregressive Algorithm comparing Support Vector Clustering Algorithm

An Improving Accuracy in Predicting the spread of Covid over Online Social Networks based on Geographical Location using Novel Autoregressive Algorithm comparing Support Vector Clustering Algorithm

Adoption of Blockchain Based Smart Application in Machine Learning

Abstract: In recent years, the emergence of blockchain technology (BT) has become a novel, most disruptive, and trending technology. The redistributed database in BT emphasizes data security and privacy. Also, the consensus mechanism makes positive that data is secured and bonafide. Still, it raises new security issues like majority attacks and double-spending. To handle the said problems, data analytics is required on blockchain-based secure knowledge. Analytics on these data raises the importance of arising technology Machine Learning (ML). ml involves the rational quantity of data to create precise selections. data reliability and its sharing are terribly crucial in ml to enhance the accuracy of results. the combination of those two technologies (ML and BT) provide give highly precise results. in this paper, present gift a detailed study on ml adoption we BTbased present applications additional resilient against attacks. There area unit varied ancient ML techniques, for example, Support Vector Machines (SVM), clustering, bagging, and Deep Learning (DL) algorithms like Convolutional Neural Network (CNN) and Long STM (LSTM) are often used to analyze the attacks on a blockchain-based network. Further, we tend to embody however each the technologies are often applied in many sensible applications like unmanned Aerial Vehicle (UAV), sensible Grid (SG), healthcare, and sensible cities. Then, future analysis problems and challenges are explored. At last, a case study is presented with a conclusion. Keywords: Blockchain, machine learning, smart grid, data security and privacy, data analytics, smart applications.

A data-driven approach for crude oil scheduling optimization under product yield uncertainty

Crude oil scheduling plays an important role in reducing production cost of refineries. However, fluctuations in operation conditions in crude distillation units (CDUs) and quality of crude oil will lead to uncertainties of product yields that may nullify the plan obtained from the deterministic model. A data-driven robust optimization (DDRO) method for crude oil scheduling is proposed in this study to address these uncertainties. Historical data of the product yield of crude oil are fully utilized by a generalized intersection kernel support vector clustering (GIKSVC) algorithm to construct uncertainty sets. A new DDRO model is then developed on the basis of derived uncertainty sets and further reformed as a solvable mixed integer nonlinear programming (MINLP) problem via dual transformation. Case studies from a refinery are performed to indicate the performance of this method and the influence of the regularization parameter ν on the optimization solution is explored to reach an acceptable balance between total production cost and robustness of crude oil scheduling.

Sparse Twin Support Vector Clustering Using Pinball Loss.

Clustering is a widely used machine learning technique for unlabelled data. One of the recently proposed techniques is the twin support vector clustering (TWSVC) algorithm. The idea of TWSVC is to generate hyperplanes for each cluster. TWSVC utilizes the hinge loss function to penalize the misclassification. However, the hinge loss relies on shortest distance between different clusters, and is unstable for noise-corrupted datasets, and for re-sampling. In this paper, we propose a novel Sparse Pinball loss Twin Support Vector Clustering (SPTSVC). The proposed SPTSVC involves the ϵ-insensitive pinball loss function to formulate a sparse solution. Pinball loss function provides noise-insensitivity and re-sampling stability. The ϵ-insensitive zone provides sparsity to the model and improves testing time. Numerical experiments on synthetic as well as real world benchmark datasets are performed to show the efficacy of the proposed model. An analysis on the sparsity of various clustering algorithms is presented in this work. In order to show the feasibility and applicability of the proposed SPTSVC on biomedical data, experiments have been performed on epilepsy and breast cancer datasets.

A Survey on Cardiovascular Prediction using Variant Machine learning

Prediction of a cardiovascular diseases has always a tedious challenge for doctors and medical practitioners. Most of the practitioners and hospital staff offers expensive medication, care and surgeries to treat the cardiovascular patients. At early-stage of prediction of heart-oriented problems will be giving a chance of survival by taking necessary precautions. Over the years there are different types of methodologies were proposed to predict the cardiovascular diseases one of the best methodologies is a Machine learning approach. These years many scientific advancements take place in the Artificial Intelligence, Machine learning, and Deep learning which gives an extra push up to help and implement the path in the field of medical image processing and medical data analysis. By using the enormous dataset from various medical experts used to help the researchers to predict the coronary problems prior to happening. Many researchers have tried and implemented different machine learning algorithms to automate the prediction analysis using the enormous number of datasets. There are numerous algorithms and procedures to predict the cardiovascular diseases and accessible to be specific Classification methods including Artificial Neural Networks (AI), Decision tree (DT), Support vector machine (SVM), Genetic algorithm (GA), Neural network (NN), Naive Bayes (NB) and Clustering algorithms like K-NN. A few examinations have been done for creating expectation models utilizing singular procedures and additionally concatenating at least two strategies. This paper gives a speedy and simple survey and knowledge of approachable prediction models using different researchers work from 2004 to 2019. The examination indicates the precision of individual experiments done by various researchers.

DESTEK VEKTÖR ÖBEKLEME İÇİN ETKİLİ KERNEL FONKSİYONLARININ ARAŞTIRILMASI

Clustering is an effective tool that divides data into different classes to reveal internal and previously unknown data schemes. However, in conventional clustering algorithms such as the k-means, k-NN, fuzzy c tool, the selection of the appropriate number of clusters for each data set is uncertain and varies with the data sets. Furthermore, the data sets to which the clustering algorithm is applied generally have nonlinear boundaries between clusters. Determining these nonlinear boundaries in the input space causes a complex problem. To overcome these problems, kernel-based clustering methods have been developed in recent years, which automatically determine the number and boundaries of clusters. In particular, the Support Vector Clustering (SVC) algorithm has received great attention in data analysis because of its features such as automatically determining the number of clusters and recognizing nonlinear boundaries based on the Gaussian kernel parameter. The number of clusters and region boundaries produced by SVC may show variation depending on the choice of the kernel function and its parameters. Therefore, the choice of kernel function plays a significant role. In this study, for the first time, the implementation of two different kernel (Cauchy and Laplacian) functions and evaluation of their performances have been realized within the framework of SVC. It was observed that the Laplacian kernel function performed better than Gauss and Cauchy kernel functions.

Review of Deployment of Machine Learning in Blockchain Methodology

The evolution of blockchain methodology has been a remarkable, highly transformative and trend-setting platform in current years. BT's accessible platform reinforces data protection and confidentiality. In addition, the consensus framework in it ensures system is protected and accurate. Nevertheless, it introduces additional security challenges such as invasion by the majority and double consumption. Data analysis on encrypted data centered on blockchain is crucial to manage the existing challenges. Insights on these results elevates the value of emerging of Machine Learning technique. It covers the fair quantity of data needed to make specific choices. Consistency of data and its distribution are very critical in ML to increase findings reliability. The fusion of these two techniques will produce extremely accurate outcomes. In this article, we describe a thorough analysis on ML implementation to make smart applications based on BT further robust to threats. There are numerous standard ML approaches such as Support Vector Machines (SVM), Clustering, Bagging, and Deep Learning (DL) algorithms such as Convolutional Neural Network (CNN) and Long-Term Memory (LSTM) that can be employed to evaluate the threats on a block chain network. Finally, we discuss how two different techniques can be implemented in a number of smart applications like Unmanned Aerial Vehicle (UAV), Smart Grid (SG), medical care and Smart cities.

Classification of Multi-Spectral Images by Integrating Support Vector Machine and Clustering Algorithm

Classification of Multi-Spectral Images by Integrating Support Vector Machine and Clustering Algorithm

Least squares projection twin support vector clustering (LSPTSVC)

Clustering is a prominent unsupervised learning technique. In the literature, many plane based clustering algorithms are proposed, such as the twin support vector clustering (TWSVC) algorithm. In this work, we propose an alternative algorithm based on projection axes termed as least squares projection twin support vector clustering (LSPTSVC). The proposed LSPTSVC finds projection axis for every cluster in a manner that minimizes the within class scatter, and keeps the clusters of other classes far away. To solve the optimization problem, the concave-convex procedure (CCCP) is utilized in the proposed method. Moreover, the solution of proposed LSPTSVC involves a set of linear equations leading to very less training time. To verify the performance of the proposed algorithm, several experiments are performed on synthetic and real world benchmark datasets. Experimental results and statistical analysis show that the proposed LSPTSVC performs better than existing algorithms w.r.t. clustering accuracy as well as training time. Moreover, a comparison of the proposed method with existing algorithms is presented on biometric and biomedical applications. Better generalization performance is achieved by proposed LSPTSVC on clustering of facial images, and Alzheimer’s disease data.

An investigation on support vector clustering for big data in quantum paradigm

The support vector clustering algorithm is a well-known clustering algorithm based on support vector machines using Gaussian or polynomial kernels. The classical support vector clustering algorithm works well in general, but its performance degrades when applied on big data. In this paper, we have investigated the performance of support vector clustering algorithm implemented in a quantum paradigm for possible run-time improvements. We have developed and analyzed a quantum version of the support vector clustering algorithm. The proposed approach is based on the quantum support vector machine and quantum kernels (i.e., Gaussian and polynomial). The proposed quantum version of the SVM clustering method demonstrates a significant speed-up gain on the overall run-time complexity as compared to the classical counterpart.

Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

In recent years, the emergence of blockchain technology (BT) has become a unique, most disruptive, and trending technology. The decentralized database in BT emphasizes data security and privacy. Also, the consensus mechanism in it makes sure that data is secured and legitimate. Still, it raises new security issues such as majority attack and double-spending. To handle the aforementioned issues, data analytics is required on blockchain based secure data. Analytics on these data raises the importance of arisen technology Machine Learning (ML). ML involves the rational amount of data to make precise decisions. Data reliability and its sharing are very crucial in ML to improve the accuracy of results. The combination of these two technologies (ML and BT) can provide highly precise results. In this paper, we present a detailed study on ML adoption for making BT-based smart applications more resilient against attacks. There are various traditional ML techniques, for instance, Support Vector Machines (SVM), clustering, bagging, and Deep Learning (DL) algorithms such as Convolutional Neural Network (CNN) and Long short-term memory (LSTM) can be used to analyse the attacks on a blockchain-based network. Further, we include how both the technologies can be applied in several smart applications such as Unmanned Aerial Vehicle (UAV), Smart Grid (SG), healthcare, and smart cities. Then, future research issues and challenges are explored. At last, a case study is presented with a conclusion.

Research of intelligent traffic flow prediction algorithm

With the rapid development of transportation industry, how to carry out intelligent traffic flow prediction has become an urgent issue. In this paper, compared with the single algorithm traffic flow prediction model, we propose a new method for modelling that combines the minimum multiplication support vector machine and clustering algorithm to predict the traffic flow, which provides the basis for scientific traffic management and decision-making process.

A Comprehensive Approach for the Clustering of Similar-Performance Cells for the Design of a Lithium-Ion Battery Module for Electric Vehicles

Abstract An energy-storage system comprised of lithium-ion battery modules is considered to be a core component of new energy vehicles, as it provides the main power source for the transmission system. However, manufacturing defects in battery modules lead to variations in performance among the cells used in series or parallel configuration. This variation results in incomplete charge and discharge of batteries and non-uniform temperature distribution, which further lead to reduction of cycle life and battery capacity over time. To solve this problem, this work uses experimental and numerical methods to conduct a comprehensive investigation on the clustering of battery cells with similar performance in order to produce a battery module with improved electrochemical performance. Experiments were first performed by dismantling battery modules for the measurement of performance parameters. The k-means clustering and support vector clustering (SVC) algorithms were then employed to produce battery modules composed of 12 cells each. Experimental verification of the results obtained from the clustering analysis was performed by measuring the temperature rise in the cells over a certain period, while air cooling was provided. It was found that the SVC-clustered battery module in Category 3 exhibited the best performance, with a maximum observed temperature of 32 °C. By contrast, the maximum observed temperatures of the other battery modules were higher, at 40 °C for Category 1 (manufacturer), 36 °C for Category 2 (manufacturer), and 35 °C for Category 4 (k-means-clustered battery module).

An improved support vector clustering approach to dynamic aggregation of large wind farms

In this paper, we propose an improved support vector clustering (SVC) algorithm to cluster wind turbines (WTs) in a wind farm (WF). A boundary points (BPs) detecting method based on the grid theory and the connected subdomain (CS) are proposed. Thus the efficiency of SVC is enhanced while maintaining the accuracy of the algorithm. As for the multi-wind condition equivalent of WF, a method to determine the number and capacity of each of the aggregated wind turbines (AWTs) based on historical wind data is proposed. Only wind speed (WS) and wind direction (WD) of the WF are needed to calculate the WSs of each AWT. Results demonstrate that the algorithm proposed in this paper can cluster WTs quickly and accurately. And the dynamic aggregated models of the WFs are suitable for both the single-wind condition and multi-wind condition simulations, with high accuracy being obtained.

Fuzzy semi-supervised weighted linear loss twin support vector clustering

In this paper, we propose two variants of Weighted Linear Loss Twin Support Vector Clustering (WLL-TWSVC) algorithm for identifying cluster planes. Unlike, Twin Support Vector Clustering (TWSVC) where the solution is obtained by solving a Quadratic Programming Problem (QPP) and a system of linear equations, WLL-TWSVC needs to solve the system of linear equations only. In order to improve the clustering accuracy with the help of limited amount of labeled data, we have extended TWSVC and WLL-TWSVC in the semi-supervised framework which are termed as Laplacian TWSVC (Lap-TWSVC) and Laplacian WLL-TWSVC (Lap-WLL-TWSVC) respectively. Further, to build a robust clustering algorithm which is not sensitive to noise and outliers, we introduce a fuzzy membership matrix and thus extends Lap-WLL-TWSVC to Fuzzy Laplacian WLL-TWSVC. The experimental results on several benchmark UCI datasets indicate that our proposed formulations achieve better clustering accuracy over other state-of-the-art plane-based clustering algorithms with comparatively lesser computational time. As an application to our proposed algorithms, we also perform image segmentation over Berkeley Segmentation dataset.