How blockchain improves the supply chain: case study alimentary supply chain

Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges

Technology is starting to play a key role in cities’ urban sustainability plans. This is because new technologies can provide them with robust solutions that are of benefit to citizens. Cities aim to incorporate smart systems in their industrial, infrastructural, educational, and social activities. A Smart City is managed with intelligent technologies which allow improving the quality of the services offered to citizens and make all processes more efficient. However, the Smart City concept is fairly recent. The ideas that it encompasses have not yet been consolidated due to the large number of fields and technologies that fit under this concept. All of this led to confusion about the definition of a Smart City and this is evident in the literature. This article explores the literature that addresses the topic of Smart Cities; a comprehensive analysis of the concept and existing platforms is performed. We gain a clear understanding of the services that a Smart City must provide, the technology it should employ for the development of these services, and the scope that this concept covers. Moreover, the shortcomings and needs of Smart Cities are identified and a model for designing a Smart City architecture is proposed. In addition, three case studies have been proposed: the first is a simulator to study the implementation of various services and technologies, the second case study to manage incidents that occur in a Smart City, and the third case study to monitor the deployment of large-scale sensors in a Smart City.

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Tendencies of Technologies and Platforms in Smart Cities: A State-of-the-Art Review

Unmanned aerial vehicles (UAVs) play a primary role in a plethora of technical and scientific fields owing to their wide range of applications. In particular, the provision of emergency services during the occurrence of a crisis event is a vital application domain where such aerial robots can contribute, sending out valuable assistance to both distressed humans and rescue teams. Bearing in mind that time constraints constitute a crucial parameter in search and rescue (SAR) missions, the punctual and precise detection of humans in peril is of paramount importance. The paper in hand deals with real-time human detection onboard a fully autonomous rescue UAV. Using deep learning techniques, the implemented embedded system was capable of detecting open water swimmers. This allowed the UAV to provide assistance accurately in a fully unsupervised manner, thus enhancing first responder operational capabilities. The novelty of the proposed system is the combination of global navigation satellite system (GNSS) techniques and computer vision algorithms for both precise human detection and rescue apparatus release. Details about hardware configuration as well as the system’s performance evaluation are fully discussed.

Unsupervised Human Detection with an Embedded Vision System on a Fully Autonomous UAV for Search and Rescue Operations

Classic multi-criteria group decision making models that have a high amount of alternatives are unmanageable for the experts. This is because they have to provide one value per each alternative and criteria. In this paper, we focus on solving this issue by carrying out multi-criteria group decision making methods using a different novel approach. Concretely, fuzzy ontologies reasoning procedures are used in order to automatically obtain the alternatives ranking classification. Thanks to our novel methodology, experts only need to provide the importance of a small set of criteria values making it possible for experts to perform multi-criteria group decision making procedures that have a high amount of alternatives without having to directly deal with them. Furthermore, in order to allow experts to provide their preferences in a comfortable way, multi-granular fuzzy linguistic modelling is used in order to allow each expert to choose the linguistic label set that better fits him/her.

Solving Multi-Criteria Group Decision Making Problems Under Environments with a High Number of Alternatives Using Fuzzy Ontologies and Multi-Granular Linguistic Modelling Methods

Multirotor drones have been one of the most important technological advances of the last decade. Their mechanics are simple compared to other types of drones and their possibilities in flight are greater. For example, they can take-off vertically. Their capabilities have therefore brought progress to many professional activities. Moreover, advances in computing and telecommunications have also broadened the range of activities in which drones may be used. Currently, artificial intelligence and information analysis are the main areas of research in the field of computing. The case study presented in this article employed artificial intelligence techniques in the analysis of information captured by drones. More specifically, the camera installed in the drone took images which were later analyzed using Convolutional Neural Networks (CNNs) to identify the objects captured in the images. In this research, a CNN was trained to detect cattle, however the same training process could be followed to develop a CNN for the detection of any other object. This article describes the design of the platform for real-time analysis of information and its performance in the detection of cattle.

Detection of Cattle Using Drones and Convolutional Neural Networks.

Relationship recommender system in a business and employment-oriented social network

Artificial neural network analysis of the academic performance of students in virtual learning environments

One of the main research trends over the last years has focused on knowledge extraction from social networks users. One of the main difficulties of this analysis is the lack of structure of the information and the multiple formats in which it can appear. The present article focuses on the analysis of the information provided by different users in image form. The problem that is intended to be solved is the detection of equal images (although they may have minimal transformations, such as a watermark), which allows establishing links between users who publish the same images. The solution proposed in the article is based on the comparison of hashes, which allows certain transformations that can be made to an image from a computational point of view.

A Hash Based Image Matching Algorithm for Social Networks

Human communication has evolved over the last decades thanks to rapid technological advances. It has provided us with new ways of communicating with one another and made many aspects of social interaction easier. Social computing is an important area in computer science concerned with the use of computational systems for social purposes. This paper focuses on the use of social computing to simplify the process of currency exchange at airports where services have to be provided to people of all nationalities. This is a complex social scenario in which the buyer and seller must reach an agreement without speaking the same language, and in these cases, the probability of not understanding all the aspects of the transaction is high. The proposed system improves interaction between users and ensures a fast and secure operation. A multi-agent system is the base of the developed software; MAS is an important and commonly used tool in social computing. A case study was conducted with the proposed system at Sydney airport, with a Spanish currency exchange company (Global Exchange) which provides service to travelers from all continents. The Net Promoter Score metric was used to evaluate the developed system, and a score of 29.81 was obtained, indicating that customers were highly satisfied with the performance of the system. Moreover, thanks to the system, there was an increase of 34% in currency exchange operations, and the time it takes to provide service to a customer reduced by 73.67% on average.

Alberto Rivas

Papers

Detection of Cattle Using Drones and Convolutional Neural Networks.

Relationship recommender system in a business and employment-oriented social network

Artificial neural network analysis of the academic performance of students in virtual learning environments

A Hash Based Image Matching Algorithm for Social Networks

Social computing in currency exchange