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The labor education evaluation system often has problems such as strong subjectivity and a single evaluation index, which makes it difficult to comprehensively and objectively reflect the students’ labor literacy and practical ability. Therefore, a college labor education evaluation system based on big data and $K$-means is constructed. To enhance labor education, we first collect relevant data. An evaluation ratio threshold is then set to eliminate low-quality data. By leveraging big data and cloud computing technology, we build a personalized labor education and teaching evaluation system. Within this system, the $K$-means clustering algorithm is employed to classify a vast amount of college labor education data. To optimize the clustering center, particle swarm optimization is introduced. Furthermore, a multi-level evaluation system is constructed using the AHP method. This approach enables a comprehensive and systematic assessment of the effectiveness of labor education. The experimental results show that the resource utilization efficiency of the design methods is more than 90%, the loss value is the lowest 0.39, the average iteration time is 4.462s, and the evaluation time is 15s. The data clustering results show higher clustering clarity.

The development of digital pathology offers a significant opportunity to evaluate and analyze the whole slides of disease tissue effectively. In particular, the segmentation of nuclei from histopathology images plays an important role in quantitatively measuring and evaluating the acquired diseased tissue. There are many automatic methods to segment cell nuclei in histopathology images. One widely used unsupervised segmentation approach is based on standard k-means or fuzzy c-means (FCM) to process the color histopathology images to segment cell nuclei. Compared with the supervised learning method, this approach can obtain segmented nuclei without annotated nuclei labels for training, which saves a lot of labeling and training time. The color space and $k$ value among this method plays a crucial role in determining the nuclei segmentation performance. However, few works have investigated various color spaces and $k$ value selection simultaneously in unsupervised color-based nuclei segmentation with $k$-means or FCM algorithms. In this study, we will present color-based nuclei segmentation methods with standard $k$-means and FCM algorithms for histopathology images. Several color spaces of Haematoxylin and Eosin (H&E) stained histopathology data and various $k$ values among $k$-means and FCM are investigated correspondingly to explore the suitable selection for nuclei segmentation. A comprehensive nuclei dataset with 7 different organs is used to validate our proposed method. Related experimental results indicate that $L^{\ast }{a}^{\ast }{b}^{\ast }$ and the YCbCr color spaces with a $k$ of 4 are more reasonable for nuclei segmentation via $k$-means, while the $L^{\ast }{a}^{\ast }{b}^{\ast }$ color space with $k$ of 4 is useful via FCM.

We introduce a set of clustering algorithms whose performance function is such that the algorithms overcome one of the weaknesses of K-means, its sensitivity to initial conditions which leads it to converge to a local optimum rather than the global optimum. We derive online learning algorithms and illustrate their convergence to optimal solutions which K-means fails to find. We then extend the algorithm by underpinning it with a latent space which enables a topology preserving mapping to be found. We show visualisation results on some standard data sets.

As a classic NP-hard problem in machine learning and computational geometry, the $k$-means problem aims to partition a data point set into $k$ clusters such that the sum of the squared distance from each point to its nearest center is minimized. The $k$-means problem with penalties, denoted by $k$-MPWP, generalizing the $k$-means problem, allows that some points can be paid some penalties instead of being clustered. In this paper, we study the seeding algorithm of $k$-MPWP and propose a parallel seeding algorithm for $k$-MPWP along with the corresponding theoretical analysis.

Clustering is one of the most important problems in the fields of data mining, machine learning, and biological population division, etc. Moreover, robust variant for $k$-means problem, which includes $k$-means with penalties and $k$-means with outliers, is also an active research branch. Most of these problems are NP-hard even the most classical problem, $k$-means problem. For the NP-hard problems, the heuristic algorithm is a powerful method. When the quality of the output can be guaranteed, the algorithm is called an approximation algorithm.

In this paper, combining two types of robust settings, we consider $k$-means problem with penalties and outliers ($k$-MPO). In the $k$-MPO, we are given an $n$-point set $U\subset {ℝ}^d$, a penalty cost $p_v\ge 0$ for each $v\in U$, an integer $k\le n$, and an integer $z\le n$. The target is to find a center subset $S\subseteq {ℝ}^d$ with $\left|S\right|\le k$, a penalty subset $P\subseteq U$ and an outlier subset $Z\subseteq U$ with $\left|Z\right|\le z$, such that the sum of the total costs, including the connection cost and the penalty cost, is minimized. We offer an approximation algorithm using a heuristic local search scheme. Based on a single-swap manipulation, we obtain $274$-approximation algorithm.

Understanding the spatial correlation of urban traffic state is essential for identifying the evolution patterns of urban traffic state. However, the distribution of traffic state always has characteristics of large spatial span and heterogeneity. This paper adapts the concept of community detection to the correlation network of urban traffic state and proposes a new perspective to identify the spatial correlation patterns of traffic state. In the proposed urban traffic network, the nodes represent road segments, and an edge between a pair of nodes is added depending on the result of significance test for the corresponding correlation of traffic state. Further, the process of community detection in the urban traffic network (named GWPA-K-means) is applied to analyze the spatial dependency of traffic state. The proposed method extends the traditional K-means algorithm in two steps: (i) redefines the initial cluster centers by two properties of nodes (the GWPA value and the minimum shortest path length); (ii) utilizes the weight signal propagation process to transfer the topological information of the urban traffic network into a node similarity matrix. Finally, numerical experiments are conducted on a simple network and a real urban road network in Beijing. The results show that GWPA-K-means algorithm is valid in spatial correlation analysis of traffic state. The network science and community structure analysis perform well in describing the spatial heterogeneity of traffic state on a large spatial scale.

In order to solve the low efficiency of public opinion influence analysis of social media, a new public opinion influence algorithm K-adaboost has been proposed in this paper according to adaboost and K-means algorithms. We first group the training samples and calculate the clustering center of all types of users in the group using the K-means algorithm, and then train the weak classifier of public opinion data and confirm the influence of public opinion on all types of users using the adaboost algorithm, so as to get the total influence of public opinions. Finally, we compare and analyze the performance of K-adaboost, K-means and adaboost algorithms through simulation experiments. The results show that K-adaboost has good adaptability in convergence time and accuracy.

Flying Ad-hoc Networks (FANETs) and Unmanned Aerial Vehicles (UAVs) are widely utilized in various rescues, disaster management and military operations nowadays. The limited battery power and high mobility of UAVs create problems like small flight duration and unproductive routing. In this paper, these problems will be reduced by using efficient hybrid K-Means-Fruit Fly Optimization Clustering Algorithm (KFFOCA). The performance and efficiency of K-Means clustering is improved by utilizing the Fruit Fly Optimization Algorithm (FFOA) and the results are analyzed against other optimization techniques like CLPSO, CACONET, GWOCNET and ECRNET on the basis of several performance parameters. The simulation results show that the KFFOCA has obtained better performance than CLPSO, CACONET, GWOCNET and ECRNET based on Packet Delivery Ratio (PDR), throughput, cluster building time, cluster head lifetime, number of clusters, end-to-end delay and consumed energy.

A dynamic counterpropagation network based on the forward only counterpropagation network (CPN) is applied as the classifier for face detection. The network, called the dynamic supervised forward-propagation network (DSFPN) trains using a supervised algorithm that grows dynamically during training allowing subclasses in the training data to be learnt. The network is trained using a reduced dimensionality categorized wavelet coefficients of the image data. Experimental results obtained show that a 94% correct detection rate can be achieved with less than 6% false positives.

The K-means algorithm is very popular in the machine learning community due to its inherent simplicity. However, in its basic form, it is not suitable for use in problems which contain periodic attributes, such as oscillator phase, hour of day or directional heading. A commonly used technique of trigonometrically encoding periodic input attributes to artificially generate the required topology introduces a systematic error. In this paper, a metric which induces a conceptually correct topology for periodic attributes is embedded into the K-means algorithm. This requires solving a non-convex minimization problem in the maximization step. Results of numerical experiments comparing the proposed algorithm to K-means with trigonometric encoding on synthetically generated data are reported. The advantage of using the proposed K-means algorithm is also shown on a real example using gas load data to build simple predictive models.

K-means is undoubtedly the most widely used partitional clustering algorithm. Unfortunately, due to its gradient descent nature, this algorithm is highly sensitive to the initial placement of the cluster centers. Numerous initialization methods have been proposed to address this problem. Many of these methods, however, have superlinear complexity in the number of data points, making them impractical for large data sets. On the other hand, linear methods are often random and/or order-sensitive, which renders their results unrepeatable. Recently, Su and Dy proposed two highly successful hierarchical initialization methods named Var-Part and PCA-Part that are not only linear, but also deterministic (nonrandom) and order-invariant. In this paper, we propose a discriminant analysis based approach that addresses a common deficiency of these two methods. Experiments on a large and diverse collection of data sets from the UCI machine learning repository demonstrate that Var-Part and PCA-Part are highly competitive with one of the best random initialization methods to date, i.e. k-means++, and that the proposed approach significantly improves the performance of both hierarchical methods.

This paper presents a novel method of robust eye feature extraction from facial color images by considering the variety of iris colors. Given an eye window containing a single eye, the proposed method assesses the iris color tone based on the difference images between the red and the green channels and the red and the blue channels. A weighted scaling compensation method is then proposed for increasing the separability and homogeneity of the iris region. The extraction of the eye features is performed by an unsupervised K-means clustering on the compensated feature spaces. The eye corners are detected after eyelid fitting using a least mean square cost function. Experiments on a collection of eye images extracted from the FERET face database show evidence of promising performance from color facial images with variation in illumination, pose, eye gazing direction, and race.

In order to overcome the drawbacks of the K-means (KM) for clustering problems such as excessively depending on the initial guess values and easily getting into local optimum, a clustering algorithm of invasive weed optimization (IWO) and KM based on the cloud model has been proposed in the paper. The so-called cloud model IWO (CMIWO) is adopted to direct the search of KM algorithm to ensure that the population has a definite evolution direction in the iterative process, thus improving the performance of CMIWO K-means (CMIWOKM) algorithm in terms of convergence speed, computing precision and algorithm robustness. The experimental results show that the proposed algorithm has such advantages as higher accuracy, faster constringency, and stronger stability.

Co-saliency detection, an emerging research area in saliency detection, aims to extract the common saliency from the multi images. The extracted co-saliency map has been utilized in various applications, such as in co-segmentation, co-recognition and so on. With the rapid development of image acquisition technology, the original digital images are becoming more and more clearly. The existing co-saliency detection methods processing these images need enormous computer memory along with high computational complexity. These limitations made it hard to satisfy the demand of real-time user interaction. This paper proposes a fast co-saliency detection method based on the image block partition and sparse feature extraction method (BSFCoS). Firstly, the images are divided into several uniform blocks, and the low-level features are extracted from Lab and RGB color spaces. In order to maintain the characteristics of the original images and reduce the number of feature points as well as possible, Truncated Power for sparse principal components method are employed to extract sparse features. Furthermore, K-Means method is adopted to cluster the extracted sparse features, and calculate the three salient feature weights. Finally, the co-saliency map was acquired from the feature fusion of the saliency map for single image and multi images. The proposed method has been tested and simulated on two benchmark datasets: Co-saliency Pairs and CMU Cornell iCoseg datasets. Compared with the existing co-saliency methods, BSFCoS has a significant running time improvement in multi images processing while ensuring detection results. Lastly, the co-segmentation method based on BSFCoS is also given and has a better co-segmentation performance.

In order to improve the segmentation accuracy of plant lesion images, multi-channels segmentation algorithm of plant disease image was proposed based on linear discriminant analysis (LDA) method’s mapping and K-means’ clustering. Firstly, six color channels from RGB model and HSV model were obtained, and six channels of all pixels were laid out to six columns. Then one of these channels was regarded as label and the others were regarded as sample features. These data were grouped for linear discrimination analysis, and the mapping values of the other five channels were applied to the eigen vector space according to the first three big eigen values. Secondly, the mapping value was used as the input data for K-means and the points with minimum and maximum pixel values were used as the initial cluster center, which overcame the randomness for selecting the initial cluster center in K-means. And the segmented pixels were changed into background and foreground, so that the proposed segmentation method became the clustering of two classes for background and foreground. Finally, the experimental result showed that the segmentation effect of the proposed LDA mapping-based method is better than those of K-means, ExR and CIVE methods.

The new coronavirus spreads widely through droplets, aerosols and other carriers. Wearing a mask can effectively reduce the probability of being infected by the virus. Therefore, it is necessary to monitor whether people wear masks in public to prevent the virus from spreading further. However, there is no mature general mask wearing detection algorithm. Based on tiny YOLOv3 algorithm, this paper realizes the detection of face with mask and face without mask, and proposes an improvement to the algorithm. First, the loss function of the bounding box regression is optimized, and the original loss function is optimized as the Generalized Intersection over Union (GIoU) loss. Second, the network structure is improved, the residual unit is introduced into the backbone to increase the depth of the network and the detection of two scales is expanded to three. Finally, the size of anchor boxes is clustered based on $k$-means algorithm. The experimental results on the constructed dataset show that, compared with the tiny YOLOv3 algorithm, the algorithm proposed in this paper improves the detection accuracy while maintaining high-speed inference ability.

Brain tumor is one of the most severe nervous system disorders affecting the health of humans and critically it will lead to death. The most elevated disease that causes a major death rate is Glioma, i.e. a primary intracranial tumor. One of the widely used techniques in medical imaging is Magnetic Resonance Imaging (MRI) which turned out as the principle diagnosis model for the analysis of glioma and its treatment. However, the brain tumor segmentation and classification process are more complicated problems to execute. This paper intends to introduce a novel brain tumor classification model that includes four major phases: (i) Pre-processing (ii) Segmentation (iii) Brain Feature extraction (iv) Brain tumor Classification. Initially, the input image is subjected to the pre-processing phase, in which the image is pre-processed under a certain process. The pre-processed images are then subjected to the segmentation phase, which is carried out by the k-means clustering. Subsequently, the segmented images are subjected to the brain feature extraction phase, in which the features are extracted using the hybrid Principal Component Analysis (PCA)-GIST feature extraction method. Then, these features are given as the input to the classification process, where the ensemble classifier is exploited for the same. Moreover, the proposed ensemble technique includes k-Nearest Neighbor (k-NN), Optimized Neural Network (NN), Random Forest (RF), and Support Vector Machine (SVM). To precisely detect the tumor classification, the NN training is performed using Elephant Herding Optimization with mutation operations (EHOMO) Algorithm via selecting the optimal weights.

This paper proposes a flexible example-based color transfer system, providing an automatic mode and an advanced mode, for novices and expert users, respectively. The experimental results show that the proposed color transfer system not only can introduce natural results with simple operation by novices in the first use, but also can produce various desired results by users with short-term learning.

Maize is one of the main crops in Shangluo. The maize yield and quality are adversely affected by leaf spot and rust. In order to realize the early prevention of maize leaf spot and rust and avoid problems of environmental pollution induced by the conventional chemical reagents, computer vision technology was proposed for disease detection in this research. The algorithm of $K$-means was used to process the image samples obtained from the test field. The healthy area, disease area and background area were separated. Based on the healthy area and disease area, ten parameters were extracted, including four parameters of color characteristic, four parameters of texture feature and two parameters of shape feature, which were taken as the classification criteria in the classification training by KNN algorithm. In total, 200 test image samples (100 samples of leaf spot and 100 samples of maize rust) were sent into the training model for disease identification. The results showed that the algorithm proposed can efficiently and nondestructively identify maize leaf spot and rust based on image segmentation and multi-feature fusion. The method was convenient and environmentally friendly. It can provide technical support for plant protection and can supply research ideas of precise control of crop diseases.

A Centroid Neural Network with Weighted Features (CNN-WF) is proposed and presented in this paper. The proposed CNN-WF is based on a Centroid Neural Network (CNN), an effective clustering tool that has been successfully applied to various problems. In order to evaluate the importance of each feature in a set of data, a feature weighting concept is introduced to the Centroid Neural Network in the proposed algorithm. The weight update equations for CNN-WF are derived by applying the Lagrange multiplier procedure to the objective function constructed for CNN-WF in this paper. The use of weighted features makes it possible to assess the importance of each feature and to reject features that can be considered as noise in data. Experiments on a synthetic data set and a typical image compression problem show that the proposed CNN-WF can assess the importance of each feature and the proposed CNN-WF outperforms conventional algorithms including the Self-Organizing Map (SOM) and CNN in terms of clustering accuracy.