In this article, a solution to the problem of perturbation in the urban transport network has been proposed. This solution is based on the multicriteria decision support method, which is an... Show More

In this article, a solution to the problem of perturbation in the urban transport network has been proposed. This solution is based on the multicriteria decision support method, which is an efficient way of identifying appropriate solutions to different perturbations situations. The proposed model should provide synthesis, evaluation and updating of available information in order to facilitate the task of the network monitoring operator. To achieve this objective, the authors propose formal modeling of the perturbation concept through an effective and above all significant decision support system exploiting the diversity of the criteria as well as the decision maker's subjectivity. This modeling makes it possible to capitalize the knowledge available within a checkpoint and to monitor the process. The authors show how, from the information available on the network, the modeling of the process represented by a perturbation makes it possible to enrich the possibilities of evaluation of the state of the network.

Most of the current smart applications are developed with basis on intelligent computing, most of which are implemented using big data analytics and much other advanced technology. With emerging... Show More

Most of the current smart applications are developed with basis on intelligent computing, most of which are implemented using big data analytics and much other advanced technology. With emerging technology, industrial and instructive improvements are causing greater changes in the lifestyle of people in smart cities and there is more chance of various health problems in urban areas. The way of life in metro urban areas with an expansive volume of people is similarly influenced by different applications and administration frameworks. In this way, the majority of the urban communities are transforming into smart urban areas by receiving mechanized frameworks in every conceivable segment. Therefore, there is more health-related issues and health hazard issues can be identified in urban areas. This article carries out a comprehensive survey of health care issues and improved solutions in automated systems using Big Data Analytics in smart cities integrated with IoT.

Instance selection and feature selection are important steps in the data mining process. They help reduce the excessive number of instances and features. The purpose of this reduction is to... Show More

Instance selection and feature selection are important steps in the data mining process. They help reduce the excessive number of instances and features. The purpose of this reduction is to eliminate the noisy and redundant instances and features in order to improve the classifiers performance. Various related works in the literature proves that metaheuristics can resolve the problem of instance and feature selection. In this article, the authors propose a new instance selection approach based on a Multi- Verse Optimizer algorithm (MVOIS), to reduce the run time and improve the performance of the one nearest neighbor classifier (1NN). This article tested the proposed approach on 31 datasets from the UCI repository and performed three more pre-process ISFS, FS and FSIS. The comparative study illustrates the efficiency of ISFS and FSIS compared to FS and IS. ISFS achieved 100% accuracy for labor and iris datasets.

In urban areas, the cost of road congestion has paid great attention to the sociological, technological and environmental aspects, such as the optimal route and fuel consumption. This step is... Show More

In urban areas, the cost of road congestion has paid great attention to the sociological, technological and environmental aspects, such as the optimal route and fuel consumption. This step is towards a smarter vehicle mobility where the travel time will be planned and dynamically adapted to changes with actual status of the traffic flow. In this article a multi-objective ACO algorithm is proposed to solve the daily carpooling problem. In particular, a set of decision variables are proposed in order to minimize three objective functions subject to a set of constraints on these objectives.