A new model-based Web service clustering algorithm

2016 ◽  
Author(s):  
Huan Zhao ◽  
Junhao Wen ◽  
Junhua Zhao ◽  
Fengji Luo
Keyword(s):  
Web Service ◽  
Clustering Algorithm ◽  
New Model ◽  
Model Based ◽  
Service Clustering

A New Model for Web Service Composition

2011 ◽  
Vol 219-220 ◽  
pp. 638-642
Author(s):  
Long Hao
Keyword(s):  
Web Service ◽  
Service Composition ◽  
Optimization Methods ◽  
Web Service Composition ◽  
New Model ◽  
Model Based ◽  
Multiple Input ◽  
Composite Services ◽  
Individual Service

To support automated Web service composition, it is compelling to provide a template, or model that dictates the ways in which services can be composed. In this paper, a novel composition model based on the relative vector is proposed, where individual services, composite services, and user objectives are described with the relative vectors, through a series of operators defined on the relative vector, available composite services can be found, and how much they meet user objectives can be evaluated. A significant advantage of our approach is that many existing optimization methods can be used to search optimized compositions, where parallel or choice structure is enabled, individual service with multiple input or output parameters is allowed.

Incorporating LDA With Word Embedding for Web Service Clustering

2018 ◽  
Vol 15 (4) ◽  
pp. 29-44 ◽  
Author(s):  
Yi Zhao ◽  
Chong Wang ◽  
Jian Wang ◽  
Keqing He
Keyword(s):  
Web Service ◽  
Service Discovery ◽  
Word Embedding ◽  
The Internet ◽  
Word Embeddings ◽  
Training Process ◽  
Web Service Discovery ◽  
Processing Data ◽  
Clustering Approach ◽  
Service Clustering

With the rapid growth of web services on the internet, web service discovery has become a hot topic in services computing. Faced with the heterogeneous and unstructured service descriptions, many service clustering approaches have been proposed to promote web service discovery, and many other approaches leveraged auxiliary features to enhance the classical LDA model to achieve better clustering performance. However, these extended LDA approaches still have limitations in processing data sparsity and noise words. This article proposes a novel web service clustering approach by incorporating LDA with word embedding, which leverages relevant words obtained based on word embedding to improve the performance of web service clustering. Especially, the semantically relevant words of service keywords by Word2vec were used to train the word embeddings and then incorporated into the LDA training process. Finally, experiments conducted on a real-world dataset published on ProgrammableWeb show that the authors' proposed approach can achieve better clustering performance than several classical approaches.

Bovine Serum Albumin Adsorption onto Colloidal Al2O3Particles:  A New Model Based on Zeta Potential and UV−Vis Measurements

Langmuir ◽  
2004 ◽  
Vol 20 (23) ◽  
pp. 10055-10061 ◽  
Author(s):  
Kurosch Rezwan ◽  
Lorenz P. Meier ◽  
Mandana Rezwan ◽  
Janos Vörös ◽  
Marcus Textor ◽  
...  
Keyword(s):  
Bovine Serum Albumin ◽  
Zeta Potential ◽  
Serum Albumin ◽  
Bovine Serum ◽  
New Model ◽  
Model Based ◽  
Bovine Serum Albumin Adsorption ◽  
Albumin Adsorption

scholarly journals An Improved Image Denoising Model Based on Nonlocal Means Filter

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yan Jin ◽  
Wenyu Jiang ◽  
Jianlong Shao ◽  
Jin Lu
Keyword(s):  
Image Denoising ◽  
Natural Images ◽  
Nonlocal Means ◽  
New Model ◽  
Model Based ◽  
Better Than

The nonlocal means filter plays an important role in image denoising. We propose in this paper an image denoising model which is a suitable improvement of the nonlocal means filter. We compare this model with the nonlocal means filter, both theoretically and experimentally. Experiment results show that this new model provides good results for image denoising. Particularly, it is better than the nonlocal means filter when we consider the denoising for natural images with high textures.

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