Multi Cloud Based Service Recommendation System Using DBSCAN Algorithm

Indira, K.; Devi, M. K. Kavitha

doi:10.1007/s11277-020-07609-3

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…DBSCAN can find clusters of any shape and can identify noise, which can achieve a better clustering effect for physical and chemical property data [ 12 , 41 ]. The clusters are customized according to the parameters, respectively, “eps” ( e -neighborhood with data point as center and eps as radius) and “minPts” (minimum number of data points in e -neighborhood).…”

Section: Methodsmentioning

confidence: 99%

Identifying and Classifying Enhancers by Dinucleotide-Based Auto-Cross Covariance and Attention-Based Bi-LSTM

Zhao

Pan

Zou

et al. 2022

Computational and Mathematical Methods in Medicine

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Enhancers are a class of noncoding DNA elements located near structural genes. In recent years, their identification and classification have been the focus of research in the field of bioinformatics. However, due to their high free scattering and position variability, although the performance of the prediction model has been continuously improved, there is still a lot of room for progress. In this paper, density-based spatial clustering of applications with noise (DBSCAN) was used to screen the physicochemical properties of dinucleotides to extract dinucleotide-based auto-cross covariance (DACC) features; then, the features are reduced by feature selection Python toolkit MRMD 2.0. The reduced features are input into the random forest to identify enhancers. The enhancer classification model was built by word2vec and attention-based Bi-LSTM. Finally, the accuracies of our enhancer identification and classification models were 77.25% and 73.50%, respectively, and the Matthews’ correlation coefficients (MCCs) were 0.5470 and 0.4881, respectively, which were better than the performance of most predictors.

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Section: Methodsmentioning

confidence: 99%

Identifying and Classifying Enhancers by Dinucleotide-Based Auto-Cross Covariance and Attention-Based Bi-LSTM

Zhao

Pan

Zou

et al. 2022

Computational and Mathematical Methods in Medicine

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“…Although DBSCAN clustering algorithm can determine the number of clusters center according to the classification of point cloud [36,37], in practical application, it is necessary to determine the distance between core points and the minimum number of sample points (MinPts) in the classification subset. Sometimes, due to the accuracy of point cloud computing, the actual application effect of the DBSCAN algorithm needs to be improved.…”

Section: Unsupervised Learningmentioning

confidence: 99%

AOMC: An Adaptive Point Cloud Clustering Approach for Feature Extraction

Zhang

Jin

2022

Scientific Programming

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Point cloud local feature extraction places an important part of point cloud deep learning neural networks. Accurate extraction of point cloud features is still a challenge for deep learning networks. Oversampling and feature loss of point cloud model are important problems in the accuracy of image point cloud deep learning network. In this paper, we propose an adaptive clustering method for point cloud feature extraction—adaptive optimal means clustering (AOMC)—and apply it to point cloud deep learning network tasks. This method solves the problem of determining the number of clustering centers in the process of point cloud feature extraction so that the feature points contain the whole point cloud model and avoid the problem of losing detail features. Specifically, according to the loss characteristics of point cloud clustering, AOMC selects a different number of clustering centers for various models. Moreover, in light of the density distribution of the point cloud, the radius of the clustering subset is determined. This method effectively improves the accuracy of the point cloud deep learning network on object classification and parts segmentation. Our method reaches demand on Modelnet10 and shapenetcore_partanno_segmentation_benchmark datasets. In terms of deep learning network optimization, it has good performance. Additionally, our method has high accuracy and low algorithm complexity.

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“…Indira. K, and Kavitha Devi, M.K [3] created the DBSCAN Algorithm to suggest cloud services in a multi-cloud environment. This method is mainly developed for online movie recommendations.…”

Section: Literature Reviewmentioning

confidence: 99%

Location and Time Aware Real Time Cloud Service Recommendation System Based on Multilayer Perceptron

Bose¹,

Sujatha²

2021

IJIES

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Cloud computing is the on-demand availability of internet-based computing services, especially software, large amounts of data storage, operating systems, and other computing resources. Service Level Agreement (SLA) violation is the most critical problem in cloud computing. SLA violation creates many problems for cloud service providers and cloud customers. Due to this, cloud customer gets low-quality cloud service. Thus, designing an effective cloud service recommendation algorithm is a critical research problem in cloud computing. The primary objective of this research is to determine the optimal cloud service from functionally equivalent cloud services that better fit the user's requirements (latency, throughput, response time, and cost). The efficiency of cloud services varies according to the time and location of the virtual machine. First, this proposed method determines the correlation between active user requirements and cloud services. Second, strongly correlated services are separated into two clusters based on the virtual machine's location and the cloud service's data transmission rate. For this purpose, two lightweight clustering algorithms have been proposed. A modified multilayer perceptron algorithm has been developed to recommend the optimal cloud service to the active user from the two clusters. The open-source WS-Dream dataset is used to train and validate the proposed MLP. The training efficiency, prediction accuracy, and performance of the proposed MLP-based cloud service recommendation system are experimentally compared to the existing cloud service recommendation systems analysed in the literature study [20,2,22,23]. Compared to existing cloud service recommendation approaches, the MAE and RMSE values of the proposed cloud service recommendation system are less than one. In terms of accuracy, the suggested method obtains a precession of 94 %, a recall of 97 %, and an F1-Measure of 96 %, all of these are significantly better than the existing cloud service recommendation methods. Finally, experimental results prove that the overall performance of the proposed method's throughput (increase 10 MBPS), latency (reduce 10 ms), the response time (reduce 17 ms) and service recommendation time (reduce 5ms) is more robust than existing methods.

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Multi Cloud Based Service Recommendation System Using DBSCAN Algorithm

Cited by 8 publications

References 15 publications

Identifying and Classifying Enhancers by Dinucleotide-Based Auto-Cross Covariance and Attention-Based Bi-LSTM

Identifying and Classifying Enhancers by Dinucleotide-Based Auto-Cross Covariance and Attention-Based Bi-LSTM

AOMC: An Adaptive Point Cloud Clustering Approach for Feature Extraction

Location and Time Aware Real Time Cloud Service Recommendation System Based on Multilayer Perceptron

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