Automated semantic web service composition is one of the critical research challenges of service-oriented computing, since it allows users to create an application simply by specifying the inputs that the application requires, the outputs it should produce, and any constraints it should respect. The composition problem has been handled using a variety of techniques, from Artificial Intelligence (AI) planning to optimization algorithms. However no approach so far has focused on handling three composition dimensions simul
The increasing interest in developing efficient and effective optimization techniques has conducted researchers to turn their attention towards biology. It has been noticed that biology offers many clues for designing novel optimization techniques, these approaches exhibit self-organizing capabilities and permit the reachability of promising solutions without the existence of a central coordinator. In this paper we handle the problem of dynamic web service composition, by using the clonal selection algorithm. In order to assess the optimality rate of a given composition, we use the QOS attributes of the services involved in the workflow as well as, the semantic similarity between these components. The experimental evaluationshows that the proposed approach has a better performance in comparison with other approaches such as the genetic algorithm.
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.
Carbon monoxide (CO) as been frequently identified as the origin of deadly domestic accidents. The heavy toll it makes every winter is often caused by using gas heaters. CO is difficult to detect without a detector because it has not particular odor or color. In this paper, we create and implement a prototype based on the Internet of Things (IoT) to combat this dangerous gas and save human lives. This prototype first allows the detection of carbon monoxide leaks and then the execution of the necessary reactions based on fuzzy logic. These responses entail turning on a ventilation system to ventilate the area where the leak occurs. The system also alerts users by making calls, emails, or notifications. In order to alert the relevant services when the gas level exceeds a predetermined threshold, this task is specifically carried out using an MQ2 sensor, calibrated to detect the level of gas present in the air, and a GMS sensor. Both of these sensors are connected to an Arduino development board.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.