web service classification
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Normally web services are classified by the quality of services; however, the term quality is not absolute and defined relatively. The quality of web services is measured or derived using various parameters like reliability, scalability, flexibility, and availability. The limitation of the methods employing these parameters is that sometimes they are producing similar web services in recommendation lists. To address this research problem, the novel improved clustering-based web service recommendation method is proposed in this paper. This approach is mainly dealing with producing diversity in the results of web service recommendations. In this method, functional interest, quality of service (QoS) preference, and diversity features are combined to produce a unique recommendation list of web services to end-users. To produce the unique recommendation results, we propose a varied web service classification order that is clustering-based on web services’ functional relevance such as non-useful pertinence, recorded client intrigue importance, and potential client intrigue significance. Additionally, to further improve the performance of this approach, we designed web service graph construction, an algorithm of various widths clustering. This approach serves to enhance the exceptional quality, that is, the accuracy of web service recommendation outcomes. The performance of this method was implemented and evaluated against existing systems for precision, and f-score performance metrics, using the research datasets.

In software technology, over the diversified environment, services can be rendered using an innovative mechanism of a novel paradigm called web services. In a business environment, rapid changes and requirements from various customers can be adapted using this service. For service management and discovery, the classification of Web services having the same functions is an efficient technique. However, there will be short lengthened Web services functional description documents, having less information, and sparse features. This makes difficulties in modelling short text in various topic models and leads to make an effect in the classification of Web services. A Mixed Wide and PSO-Bi-LSTM-CNN model (MW-PSO-Bi-LSTM-CNN) is proposed in this work for solving this issue. In this technique, the Web service category‟s breadth prediction is performed by combining Web services description document‟s discrete features, which exploits the wide learning model. In the next stage, the PSO-Bi-LSTM-CNN model is used for mining Web services description document word‟s context information and word order, for performing the Web service category‟s depth prediction. Here, particle swarm optimization (PSO) is integrated with the Bi-LSTM-CNN network for computing various hyper-parameters in an automatic manner. In third stage, Web service categories, results of depth, and breadth prediction are integrated using a linear regression model as final service classification result. At last, MW-PSO-Bi-LSTM-CNN, Wide&Bi-LSTM, and Wide&Deep web service classification techniques are compared and a better result with respect to web service classification accuracy is obtained using the proposed technique as shown in experimental results.