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Nowadays, the selection of web services with uncertain quality of service (QoS) is gaining a lot of attention in the service-oriented computing paradigm (soc). In fact, searching for a service composition that fulfills a complex user’s request is known to be NP-Complete. The search time is mainly dependent on the number of the requested tasks, the size the available services, and the size of the QoS realizations (i.e., sample size). To handle this problem, we propose a two-stage approach that reduces the search space using heuristics for ranking the tasks’ services and a bat algorithm metaheuristic for selecting the final near optimal compositions. The fitness used by the metaheuristic aims to fulfill all the global constraints of the user. The experimental study shows that the ranking heuristics, termed “fuzzy pareto dominance” and "Zero-order stochastic dominance", are highly effective than the other heuristics and most of the existing state-of-the-art methods.

Several studies are currently exploring the diagnosis of lung disorders using deep learning analysis of medical images. Deep learning is also considered to be a valuable aid to experts in the interpretation of medical images. Heuristics such as transfer learning are becoming more common; these methods (based on pretrained models) are utilized as the basis for computer vision tasks and can significantly improve various issues. This work proposes models built on Convolutional Neural Networks (CNNs) that incorporate transfer learning to identify various pneumonia infections in X-ray images. The experiments show that the model based on Xception network outperforms many existing state-ofthe- art methods and several recent backbones.

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Web service discovery is one of the most motivating issues of service-oriented computing field. Several approaches have been proposed to tackle this problem. In general, they leverage similarity measures or logic-based reasoning to perform this task, but they still present some limitations in terms of effectiveness. In this paper, we propose a probabilistic-based approach to merge a set of matching algorithms and boost the global performance. The key idea consists of learning a set of relevance probabilities; thereafter, we use them to produce a combined ranking. The conducted experiments on the real world dataset "OWL-S TC 2" demonstrate the effectiveness of our model in terms of mean averaged precision (MAP); more specifically, our solution, termed "probabilistic fusion", outperforms all the state of the art matchmakers as well as the most prominent similarity measures.