Enhanced Clustering Algorithm Research Articles

Enhancing Residential Electricity Safety and Management: A Novel Non-Intrusive Load Monitoring-Based Methodology for Accurate Appliance Operational State Identification

Non-intrusive load monitoring (NILM) technology, crucial for intelligent electricity management, has gained considerable attention in residential electricity usage studies. NILM enables monitoring of total electrical current and voltage in homes, offering insights vital for enhancing safety and preventing domestic electrical accidents. Despite its importance, accurately discerning the operational status of appliances using non-intrusive methods remains a challenging area within this field. This paper presents a novel methodology that integrates an advanced clustering algorithm with a Bayesian network for the identification of appliance operational states. The approach involves capturing the electrical current signals during appliance operation via NILM, followed by their decomposition into odd harmonics. An enhanced clustering algorithm is then employed to ascertain the central coordinates of the signal clusters. Building upon this, a three-layer Bayesian network inference model, incorporating leak nodes, is developed. Within this model, harmonic signals are used as conditions for node activation. The operational states of the appliances are subsequently determined through probabilistic reasoning. The proposed method’s effectiveness is validated through a series of simulation experiments conducted in a laboratory environment. The results of these experiments (low mode 89.1%, medium mode 94.4%, high mode 92.0%, and 98.4% for combination) provide strong evidence of the method’s accuracy in inferring the operational status of household electrical appliances based on NILM technology.

Cultivating the Spirit of Aesthetic Education in Vocational Undergraduate Colleges in the Context of Artificial Intelligence

Abstract The fusion of aesthetic education with vocational training constitutes a necessary evolution dictated by both societal progression and the demands of contemporary times, with each element enhancing the other. This study proposes a pathway for the construction of aesthetic education courses tailored to the current educational objectives of vocational undergraduate institutions. It also introduces a teaching model designed to foster aesthetic sensibilities structured around the ADDIE instructional design framework. Moreover, this research employs the 2023 student cohort from HZ Vocational College as subjects for a comparative experimental study to empirically analyze the effectiveness of the aesthetic education teaching approach. An enhanced clustering algorithm, which integrates multi-point optimization, was applied to assess the development of students’ aesthetic spirit. Additionally, a regression analysis model was used to investigate the various factors influencing the cultivation of this aesthetic spirit, providing a comprehensive analysis of the impact of the proposed educational interventions. The results show that after the teaching, the mean values of student evaluation, self-evaluation, parent evaluation, and teacher evaluation in the five aspects of recognizing beauty, experiencing beauty, feeling beauty, appreciating beauty, and creating the beauty of the students in Class A of the sample are higher than those of Class B by 4.90, 4.08, 3.40 and 4.13. Their p-values are all statistically significant at 0.000<0.05. The ADDIE-based aesthetic education teaching model can effectively cultivate students’ aesthetic spirit and provide important value references for the reform and development of aesthetic education.

Enhanced Clustering Based OSN Privacy Preservation to Ensure k-Anonymity, t-Closeness, l-Diversity, and Balanced Privacy Utility

Online Social Networks (OSN) sites allow end-users to share a great deal of information, which may also contain sensitive information, that may be subject to commercial or non-commercial privacy attacks. As a result, guaranteeing various levels of privacy is critical while publishing data by OSNs. The clustering-based solutions proved an effective mechanism to achieve the privacy notions in OSNs. But fixed clustering limits the performance and scalability. Data utility degrades with increased privacy, so balancing the privacy utility trade-off is an open research issue. The research has proposed a novel privacy preservation model using the enhanced clustering mechanism to overcome this issue. The proposed model includes phases like pre-processing, enhanced clustering, and ensuring privacy preservation. The enhanced clustering algorithm is the second phase where authors modified the existing fixed k-means clustering using the threshold approach. The threshold value is determined based on the supplied OSN data of edges, nodes, and user attributes. Clusters are k-anonymized with multiple graph properties by a novel one-pass algorithm. After achieving the k-anonymity of clusters, optimization was performed to achieve all privacy models, such as k-anonymity, t-closeness, and l-diversity. The proposed privacy framework achieves privacy of all three network components, i.e., link, node, and user attributes, with improved utility. The authors compare the proposed technique to underlying methods using OSN Yelp and Facebook datasets. The proposed approach outperformed the underlying state of art methods for Degree of Anonymization, computational efficiency, and information loss.

An adaptive RNN algorithm to detect shilling attacks for online products in hybrid recommender system

Abstract Recommender system (RS) depends on the thoughts of numerous users to predict the favourites of potential consumers. RS is vulnerable to malicious information. Unsuitable products can be offered to the user by injecting a few unscrupulous “shilling” profiles like push and nuke attacks into the RS. Injection of these attacks results in the wrong recommendation for a product. The aim of this research is to develop a framework that can be widely utilized to make excellent recommendations for sales growth. This study uses the methodology that presents an enhanced clustering algorithm named as modified density peak clustering algorithm on the consumer review dataset to ensure a well-formed cluster. An improved recurrent neural network algorithm is proposed to detect these attacks in hybrid RS, which uses the content-based RS and collaborative filtering RS. The results are compared with other state of the art algorithms. The proposed method is more suitable for E-commerce applications where the number of customers and products grows rapidly.

A PCA-EEMD-CNN-Attention-GRU-Encoder-Decoder Accurate Prediction Model for Key Parameters of Seawater Quality in Zhanjiang Bay.

In order to effectively solve the problem of low accuracy of seawater water quality prediction, an optimized water quality parameter prediction model is constructed in this paper. The model first screened the key factors of water quality data with the principal component analysis (PCA) algorithm, then realized the de-noising of the key factors of water quality data with an ensemble empirical mode decomposition (EEMD) algorithm, and the data were input into the two-dimensional convolutional neural network (2D-CNN) module to extract features, which were used for training and learning by attention, gated recurrent unit, and an encoder–decoder (attention–GRU–encoder–decoder, attention–GED) integrated module. The trained prediction model was used to predict the content of key parameters of water quality. In this paper, the water quality data of six typical online monitoring stations from 2017 to 2021 were used to verify the proposed model. The experimental results show that, based on short-term series prediction, the root mean square error (RMSE), mean absolute percentage error (MAPE), and decision coefficient (R2) were 0.246, 0.307, and 97.80%, respectively. Based on the long-term series prediction, RMSE, MAPE, and R2 were 0.878, 0.594, and 92.23%, respectively, which were all better than the prediction model based on an enhanced clustering algorithm and adam with a radial basis function neural network (ECA–Adam–RBFNN), a prediction model based on a softplus extreme learning machine method with partial least squares and particle swarm optimization (PSO–SELM–PLS), and a wavelet transform-depth Bi–S–SRU (Bi-directional Stacked Simple Recurrent Unit) prediction model. The PCA–EEMD–CNN–attention–GED prediction model not only has high prediction accuracy but can also provide a decision-making basis for the water quality control and management of aquaculture in the waters around Zhanjiang Bay.

A Rapid Integration Method of Wild Ornamental Plant Resources Based on Improved Clustering Algorithm

Wild ornamental plants are beneficial as well as dangerous for the environment. Because the introduction of attractive plants that are not suited to the local ecosystem can result in significant environmental damage, a quick integration strategy based on an enhanced clustering algorithm is proposed for wild ornamental plant resources. The technique is enhanced with density stratification by integrating the k-means distance measurement formula and establishing the objective function of clustering optimization. The cluster termination condition is controlled by the number of clusters k, and the wild plant data categories are continually merged. Uneven density distribution is used to deal with the wild plant distribution dataset. To obtain the distribution of wild ornamental plants in different regions, to estimate the optimal parameters of wild plant samples, to combine with maximum likelihood classification to obtain the plant flora differentiation degree, and to complete the resource integration, remote sensing images were used. Comprehensive survey and systematic sampling were used to conduct a complete survey of the protected area. The heat map of the plant size distribution shows that there is a clear negative correlation between the spatial scale difference and the overall density difference of the plant distribution, that is, it appears spatially. From the experimental analysis, it is observed that the high-density small-scale and low-density large-scale agglomeration distribution characteristics delay is 1.96 s.

Ensemble Learning Based Collaborative Filtering with Instance Selection and Enhanced Clustering

Recommender system is a tool to suggest items to the users from the extensive history of the user's feedback. Though, it is an emerging research area concerning academics and industries, where it suffers from sparsity, scalability, and cold start problems. This paper addresses sparsity, and scalability problems of model-based collaborative recommender system based on ensemble learning approach and enhanced clustering algorithm for movie recommendations. In this paper, an effective movie recommendation system is proposed by Classification and Regression Tree (CART) algorithm, enhanced Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm and truncation method. In this research paper, a new hyper parameters tuning is added in BIRCH algorithm to enhance the cluster formation process, where the proposed algorithm is named as enhanced BIRCH. The proposed model yields quality movie recommendation to the new user using Gradient boost classification with broad coverage. In this paper, the proposed model is tested on Movielens dataset, and the performance is evaluated by means of Mean Absolute Error (MAE), precision, recall and f-measure. The experimental results showed the superiority of proposed model in movie recommendation compared to the existing models. The proposed model obtained 0.52 and 0.57 MAE value on Movielens 100k and 1M datasets. Further, the proposed model obtained 0.83 of precision, 0.86 of recall and 0.86 of f-measure on Movielens 100k dataset, which are effective compared to the existing models in movie recommendation.

Secure energy aware routing protocol for IEEE 802.15.4 wireless sensor networks

PurposeThe purpose of this paper is to investigate the latest research related to secure routing protocols in Wireless Sensor Network (WSN) and propose a new approach that can achieve a higher security level compared to the existing one. One of the main security issues in WSNs is the security of routing protocols. A typical WSN consists of a large number of small size, low-power, low-cost sensor devices. These devices are very resource-constrained and usually use cheap short-range radios to communicate with each other in an ad hoc fashion thus, achieving security in these networks is a big challenge, which is open for research.Design/methodology/approachThe route updates and data messages of the protocol are authenticated using Edwards-curves Digital Signature Algorithm (EdDSA). Routing protocols play an essential role in WSNs, they ensure the delivery of the sensed data from the remote sensor nodes to back-end systems via a data sink. Routing protocols depend on route updates received from neighboring nodes to determine the best path to the sink. Manipulating these updates by inserting rouge nodes in the network that advertise false updates can lead to a catastrophic impact on the compromised WSN performance.FindingsAs a result, a new secure energy-aware routing protocol (SEARP) is proposed, which uses security enhanced clustering algorithm and EdDSA to authenticate route advertisements and messages. A secure clustering algorithm is also used as part of the proposed protocol to conserve energy, prolong network lifetime and counteract wormhole attacks.Originality/valueIn this paper, a SEARP is proposed to address network layer security attacks in WSNs. A secure clustering algorithm is also used as part of the proposed protocol to conserve energy, prolong network lifetime and counteract wormhole attacks. A simulation has been carried out using Sensoria Simulator and the performance evaluation has been discussed.

Machine learning for fluid flow reconstruction from limited measurements

This paper investigates the use of data-driven methods for the reconstruction of unsteady fluid flow fields. The proposed framework is based on the combination of machine learning tools: dimensionality reduction to extract dominant spatial directions from data, reconstruction algorithm to recover encoded data by limited measurements and cross-validation for hyperparameter optimization. For the encoding part, linear and nonlinear extraction of patterns are considered: proper orthogonal decomposition (POD), linear autoencoder (LAE) and variational autoencoder (VAE). For the reconstruction part, regressive reconstruction (neural network, linear, support vector, gradient boosting) and library-based reconstruction are compared, each method being cross-validated to ensure good generalization on testing data. The position of sensors is optimized using an enhanced clustering algorithm. The robustness of regressive reconstructions to noise measurements is also addressed, showing the benefits of variational approaches in the reduction phase. The strategy is tested for three increasing complexity flows: 2D vortex shedding (Re=200), 2D spatial mixing layer and 3D vortex shedding (Re=20000). The results suggest that proper machine learning approaches to fluid flow data can lead to effective reconstruction models that can be used for the rapid estimation of complex flows.

An Enhanced Cluster based Energy Efficient Optimal Routing in WSN

In recent years, Wireless sensor networks (WSNs) have been emerged as an important research area due to its wide spread application in various domains such as military sensing and tracking, environment monitoring, patient monitoring, etc. WSN also have various advantages in gathering the data also with data transmission as well. Even though WSN has such advantages, there is certain drawback related to the energy consumption for data transmission over the network. Wireless sensor networks basically depend on the availability of nodes for transmission and if some dead nodes are available on the designated path of transmission, there will be delay in communication and also will affect the energy consumptions. Also when a particular node is transmitting any data packet with high power, it may lead to interference which will affect the proper transmission of data and wastage of power as well. For power level reduction proper methodology has to be followed starting with the clustering and designing of routing protocols. We intend to develop an enhanced clustering algorithm for initial clustering of sensor nodes for data transmission. Nodes will be clustered based on working attributes. Once nodes are clustered into different groups, transmission path will be assigned. An energy efficient optimal protocol will be designed in our approach for routing to improve the energy utilization by optimal power utilization. For optimization, we can employ multi objective optimization techniques which can enhance the optimal selection of power utilization. The proposed scheme will be then compared with some existing techniques to show the efficiency of the proposed approach.

Radial Basis Function Neural Network Model for Dissolved Oxygen Concentration Prediction Based on an Enhanced Clustering Algorithm and Adam

In fishery aquaculture, water quality directly determines the economic benefits of aquatic products, and dissolved oxygen is an important factor affecting water quality. To accurately grasp the trends of variation in dissolved oxygen, a dissolved oxygen concentration forecasting model based on an enhanced clustering algorithm and Adam with a radial basis function neural network (ECA-Adam-RBFNN) is proposed. An enhanced clustering algorithm (ECA) combining K-means with ant colony optimization is introduced in place of random selection to determine the center positions of the neural network hidden layer units. If the number of center points is too high, the neural network will be overfit, whereas if it is too low, sudden changes will appear in the results. Once the hidden layer centers have been determined, the radial basis function (RBF) width is calculated from the maximum center distance and the number of center points to avoid the two extreme cases of RBF that are too peaked or flat. The recursive least squares (RLS) algorithm is introduced to obtain the connection weights from the hidden layer to the output layer. The Adam algorithm is introduced to iteratively differentiate the objective function to adjust the center values, weights and width while adaptively varying the learning rates for these three types of parameters. Finally, the improved forecasting algorithm is applied for the prediction of the dissolved oxygen concentration in fishery aquaculture. The experimental results show that under identical conditions, compared with a long short-term memory (LSTM) network, a backpropagation neural network (BPNN), a traditional RBF neural network, a support vector regression (SVR) model, an autoregressive integrated moving average (ARIMA), K-MLPNN (K-means muhilayer perceptron neural networks), and SC-K-means-RBF model, the improved algorithm achieves significant reductions in the mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) as model evaluation indicators.

Community detection and co-author recommendation in co-author networks

With the increasing complexity of scientific research and the expanding scale of projects, scientific research cooperation is an important trend in large-scale research. The analysis of co-authorship networks is a big data problem due to the expanding scale of the literature. Without sufficient data mining, research cooperation will be limited to a similar group, namely, a “small group”, in the co-author networks. This “small group” limits the research results and openness. However, the researchers are not aware of the existence of other researchers due to insufficient big data support. Considering the importance of discovering communities and recommending potential collaborations from a large body of literature, we propose an enhanced clustering algorithm for detecting communities. It includes the selection of an initial central node and the redefinition of the distance and iteration of the central node. We also propose a method that is based on the hilltop algorithm, which is an algorithm that is used in search engines, for recommending co-authors via link analysis. The co-author candidate set is improved by screening and scoring. In screening, the expert set formation of the hilltop algorithm is added. The score is calculated from the durations and quantity of the collaborations. Via experiments, communities can be extracted, and co-authors can be recommended from the big data of the scientific research literature.

A Survey on Energy Efficient Optimal Routing in Wireless Sensor Networks

In recent years, Wireless sensor networks (WSNs) have been emerged as an important research area due to its wide spread application in various domains such as military sensing and tracking, environment monitoring, patient monitoring, etc. WSN also have various advantages in gathering the data also with data transmission as well. Even though WSN has such advantages, there is certain drawback related to the energy consumption for data transmission over the network. Wireless sensor networks basically depend on the availability of nodes for transmission and if some dead nodes are available on the designated path of transmission, there will be delay in communication and also will affect the energy consumptions. Also, when a particular node is transmitting any data packet with high power, it may lead to interference which will affect the proper transmission of data and wastage of power as well. For power level reduction proper methodology has to be followed starting with the clustering and designing of routing protocols in WSNs. We intend to develop an enhanced clustering algorithm for initial clustering of sensor nodes for data transmission. Nodes will be clustered based on working attributes. Once nodes are clustered into different groups, transmission path will be assigned. An energy efficient optimal protocol will be designed in our approach for routing to improve the energy utilization by optimal power utilization. For optimization, we can employ multi objective optimization techniques which can enhance the optimal selection of power utilization. The proposed scheme will be then compared with some existing techniques to show the efficiency of the proposed approach.

Enhanced Clustering Algorithm based on Fuzzy Logic (E-CAFL) for WSN

Since longer lifetime of the network is utmost requirement of WSN, cluster formation can serve this purpose efficiently. In clustering, a node takes charge of the cluster to coordinate and receive information from the member nodes and transfer it to sink. With imbalance of energy dissipation by the sensor node, it may lead to premature failure of the network. Therefore, a robust balanced clustering algorithm can solve this issue in which a worthy candidate will play the cluster head role. In this paper, an enhanced clustering algorithm based on fuzzy logic E-CAFL is propound which is an improvement over CAFL protocol. E-CAFL takes account of the residual energy, node density in its locality and distance from sink and feed into fuzzy inference system. A rank of each node is computed for candidature of cluster coordinator. Experiments are performed for the designed protocol to validate its performance in adverse scenarios along with LEACH and CAFL protocol. The results illustrate better performance in stability period and protracted lifetime.

Improving the efficacy of clustering by using far enhanced clustering algorithm

There are several aspects on which research works are carried out on clustering. The prime focus is on finding the near optimal cluster centres and determining the best possible clusters. Hence, we have emphasised our work on finding a technique which contemplates on these facets in a way which is far more efficient than several novel approaches. In this paper, we have examined four varieties of clustering algorithms namely; K-Means, FEKM, ECM and proposed FECA implemented on varying data sets. We used few internal cluster validity indices like Dunn's index, Davies-Bouldin's index, Silhouette Coefficient, C index and Calinski index for quantitative evaluation of the clustering results obtained. The results obtained from simulation were compared, and as per our expectation it was found that, the quality of clustering produced by FECA is far more satisfactory than the others. Almost every value of validity indices used give encouraging results for FECA, implying good cluster formation. Further experiments support that the proposed algorithm also produces minimum quantisation error almost for all the data sets used.

Improving the efficacy of clustering by using far enhanced clustering algorithm

There are several aspects on which research works are carried out on clustering. The prime focus is on finding the near optimal cluster centres and determining the best possible clusters. Hence, we have emphasised our work on finding a technique which contemplates on these facets in a way which is far more efficient than several novel approaches. In this paper, we have examined four varieties of clustering algorithms namely; K-Means, FEKM, ECM and proposed FECA implemented on varying data sets. We used few internal cluster validity indices like Dunn's index, Davies-Bouldin's index, Silhouette Coefficient, C index and Calinski index for quantitative evaluation of the clustering results obtained. The results obtained from simulation were compared, and as per our expectation it was found that, the quality of clustering produced by FECA is far more satisfactory than the others. Almost every value of validity indices used give encouraging results for FECA, implying good cluster formation. Further experiments support that the proposed algorithm also produces minimum quantisation error almost for all the data sets used.

基于DBSCAN聚类算法的异常轨迹检测

Existing traditional trajectory outlier detection algorithms always focus on spatial outliers and ignore temporal outliers, and the accuracy is relatively low. To solve these problems, a simple and effective approach based on enhanced clustering algorithm was proposed to detect spatio-temporal trajectory outliers. Firstly, each original trajectory was simplified into a set of sequential line segments with the velocity-based minimum description length (VMDL) partition principle. Secondly, the distance formula between line segments was improved to enhance the clustering performance. Using DBSCAN algorithm, the line segments were classified into different groups which could represent local normal behaviors. Thirdly, outliers were detected using two-level detection algorithm which first detected spatial outliers and then detected temporal outliers. Experimental results on multiple trajectory data sets demonstrate that the proposed algorithm could successfully detect three kinds of spatio-temporal outliers, position, angle and velocity. Compared with other methods, the precision and accuracy make great improvement.

Enhanced hybrid method of divide-and-conquer and RBF neural networks for function approximation of complex problems

This paper provides an enhanced method focused on reducing the computational complexity of function approximation problems by dividing the input data vectors into small groups, which avoids the curse of dimensionality. The computational complexity and memory requirements of the approximated problem are higher when the input data dimensionality increases. A divide-and-conquer algorithm is used to distribute the input data of the complex problem to a divided radial basis function neural network (Div-RBFNN). Under this algorithm, the input data variables are typically distributed to different RBFNNs based on whether the number of the input data dimensions is odd or even. In this paper, each Div-RBFNN will be executed independently and the resulting outputs of all Div-RBFNNs will be grouped using a system of linear combination function. The parameters of each Div-RBFNN (centers, radii, and weights) will be optimized using an efficient learning algorithm, which depends on an enhanced clustering algorithm for function approximation, which clusters the centers of the RBFs. Compared to traditional RBFNNs, the proposed methodology reduces the number of executing parameters. It further outperforms traditional RBFNNs not only with respect to the execution time but also in terms of the number of executing parameters of the system, which produces better approximation error.

Cloud tracking using clusters of feature points for accurate solar irradiance nowcasting

In this work, we propose a system to track the clouds and predict relevant events based on all-sky images. To deal with the nature of variable appearance of clouds, we use clusters of feature points to perform tracking. We propose an enhanced clustering algorithm that does not require prior knowledge of number of clusters. The proposed clustering algorithm can successfully separate feature points into groups with reasonable sizes and ranges. In the tracking process, merging and splitting of clouds are handled via checking matched pairs of feature points among different clusters. Afterwards, the tracking information is utilized to predict if the sun will be covered or obscured by clouds within the prediction horizon. Features are extracted from the tracked feature points and a Markov chain model is designed to perform ramp-down event prediction. The obscuration and ramp-down events have an important impact on solar irradiance. The experiments have shown that the proposed system can substantially enhance the accuracy of solar irradiance nowcasting on a challenging dataset.

Towards parameter-independent data clustering and image segmentation

While there are a large amount of clustering algorithms proposed in the literature, the clustering results of existing algorithms usually depend on user-specified parameters heavily, and it is usually difficult to determine the optimal parameters. With the pairwise data similarity matrix as the input, dominant sets clustering has been shown to be an effective data clustering and image segmentation approach, partly due to its ability to find out the underlying data structure and determine the number of clusters automatically. However, we find that the original dominant sets algorithm is sensitive to the similarity measures used in building the similarity matrix. This means that parameter tuning is required to generate satisfactory clustering results, and dominant sets clustering results are also parameter dependent. In order to remove the dependence on the user-specified parameter, we study how the similarity measures influence the dominant sets clustering results. As a result, we propose to transform similarity matrices by histogram equalization before clustering. While this transformation is shown to remove the sensitiveness to similarity measures effectively, it also results in over-segmentation. Therefore in the next step we present a cluster extension method to overcome the over-segmentation effect and generate more reasonable clustering results. We test the enhanced clustering algorithm in both data clustering and image segmentation experiments, and comparisons with the state-of-the-art algorithms validate the effectiveness of our algorithm.