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We consider the problem of fully automatic brain tumor segmentation in MR images containing glioblastomas. We propose a three Dimensional Convolutional Neural Network (3D MedImg-CNN) approach which achieves high performance while being extremely efficient, a balance that existing methods have struggled to achieve. Our 3D MedImg-CNN is formed directly on the raw image modalities and thus learn a characteristic representation directly from the data. We propose a new cascaded architecture with two pathways that each model normal details in tumors. Fully exploiting the convolutional nature of our model also allows us to segment a complete cerebral image in one minute. The performance of the proposed 3D MedImg-CNN with CNN segmentation method is computed using dice similarity coefficient (DSC). In experiments on the 2013, 2015 and 2017 BraTS challenges datasets; we unveil that our approach is among the most powerful methods in the literature, while also being very effective.

With the rapid development of communication technology, vehicular communications systems are evolving to Intelligent Transportation System (ITS) by providing its wireless network services with increasing demand for high data rate. However, the highly mobile feature of vehicles and varying network densities in such communication systems pose challenges for the mobility management, including frequent handovers, increased delay and failure of the handover process. In this work, we propose an optimized mobility management approach using Software-Defined Network (SDN) in the future vehicular networks. In addition, we have proposed a new handover management mechanism. This new mechanism allows vehicles to select the most optimal network based on multi-criteria metrics. The simulation results show that the proposed approach performs well and achieves an improvement in terms of handover delay and handover failure rate, compared to existing approach.

Today's mobile devices or smartphones have a revolutionary impact on how we communicate, especially after the advent of devices like smart watches and Google glasses that require new ways to interact with them. To optimise the use of mobile devices, special input and output peripherals have been designed over the years to facilitate communication with them. The well known peripherals are the multi-touch screens. Smartphones are too small to work freely using their input screens. To solve this problem, recent research has focused on contactless and natural gestural interfaces. In this context, we propose a hand gesture recognition system for mobile devices as a simple way of communication with smartphones. In this work, we describe a hand gesture recognition system for Android devices based on a combination of HOG and LBP features and the SVM classifier. An accuracy rate of about 95% is obtained on the improved 'NUS database I'. In addition, we conduct experiments on different Android devices to know the impact of using such a recognition task on embedded systems like smartphones.

As smartphones and mobile devices become ubiquitous in the daily life of human beings, facilitating interaction with them will have a positive impact on their use. This paper presents an emerging and important area for research on human-machine interaction, involving interaction with mobile devices. Hand gesture recognition is already a quite mature field and its integration on advanced technologies like smartphones makes a survey an interesting and needed contribution. The design of interactive mobile applications should differ from traditional desktop applications. For this purpose, the paper discusses some concepts that help to understand new challenges and introduces techniques that may be useful to properly exploit these devices. This paper gives also an overview of recent works done on vision-based and contact-based hand gesture recognition systems for mobile devices. Used techniques and approaches are also discussed. At the end, we conclude with some reflections.

The goal of this article is the development of an evaluation system based on the Arabic language. This article contains four parts. The first part is the corpus construction from the 7th year basic education classes' grammar book in Tunisia. Then, the second part is on the construction of the Concept Maps (CMaps) ontological for simple Arabic sentences from this corpus, where the automatic extraction of terms is completed. This extraction is based on two major approaches: linguistic and statistical. The third part in this article is the automatic instantiation. The last part is devoted to the application of the similarity measure chosen in the CMaps ontological fusion, which summarizes the various semantic links and which ends with a judgment according to the calculated score.

In this article, the authors introduce their OSSVIRI information retrieval system which composed of three modules. In the analysis module, they have proposed a statistical technique exploiting the word frequency in order to extract the simple, compound and specific terms from the documents. In the indexing module, the authors used the ontology to associate the terms with their concepts, retrieve the relations between them and disambiguate the concepts to improve the sematic content of the documents. The concepts and relations are represented as a conceptual graph. In the research module, the authors have proposed a technique of users' query reformulation based on external resources and users' profiles and a technique of pairing based on the combined expansion of the requests and the documents guided by the context of the requirement in information and the documentary contents. This system is validated using the metrics from the research information and comparisons with existing statistical approach. The authors show that their approach achieves good results.