MODELFY: A Model-driven Solution for Decision Making based on Fuzzy Information

There exist areas, such as the disease prevention or inclement weather protocols, in which the analysis of the information based on strict protocols require a high level of rigor and security. In this situation, it would be desirable to apply formal methodologies that provide these features. In this scope, recently, it has been proposed a formalism, fuzzy automaton, that captures two relevant aspects for fuzzy information analysis: imprecision and uncertainty. However, the models should be designed by domain experts, who have the required knowledge for the design of the processes, but do not have the necessary technical knowledge. To address this limitation, this paper proposes MODELFY, a novel model-driven solution for designing a decision-making process based on fuzzy automata that allows users to abstract from technical complexities. With this goal in mind, we have developed a framework for fuzzy automaton model design based on a Domain- Specific Modeling Language (DSML) and a graphical editor. To improve the interoperability and functionality of this framework, it also includes a model-to-text transformation that translates the models designed by using the graphical editor into a format that can be used by a tool for data analysis. The practical value of this proposal is also evaluated through a non-trivial medical protocol for detecting potential heart problems. The results confirm that MODELFY is useful for defining such a protocol in a user-friendly and rigorous manner, bringing fuzzy automata closer to domain expert

A specific modeling language for Fuzzy Automatons

El código del proyecto puede ser descargado en https://github.com/mariacaslop/ Environment-of-model-design-based-on-fuzzy-automaton Trabajo de Fin de Máster, Universidad Complutense, Facultad de Informática, Departamento de Sistemas Informáticos y Computación, Curso 2018/2019En la actualidad los avances médicos están ligados a los tecnológicos, pero no por ello los especialistas tienen los conocimientos necesarios para poder explotar las nuevas tecnologías. Uno de los campos en los que es necesario el uso de sistemas que traten y estudien los datos clínicos de manera precisa es la prevención de enfermedades. Los métodos formales ofrecen el rigor y la precisión necesarios para el modelado del proceso de análisis de dichos datos. En particular, los autómatas fuzzy son muy apropiados en este caso, ya que permiten representar la incertidumbre e imprecisión que se han de tener en cuenta en este tipo de análisis. En este trabajo se propone crear una herramienta gráfica que permita a los especialistas médicos, que son los que tienen el conocimiento clínico, el diseño de estos modelos de análisis de una manera sencilla e intuitiva. El objetivo principal de este trabajo es crear un entorno de diseño de modelos basados en autómatas fuzzy a través de un lenguaje de dominio específico y un editor gráfico para facilitar el diagnóstico precoz de enfermedades. El código del proyecto puede ser descargado en https://github.com/mariacaslop/ Environment-of-model-design-based-on-fuzzy-automatonCurrently, it is undeniable that medical advances are linked to technological ones, but that this does not mean that specialists have the necessary knowledge to be able to exploit the new technologies. One of the fields in which it is necessary to use systems that process and study clinical data in a precise way is disease prevention. Formal methods can offer the rigor and precision necessary for the modeling of the process of analysis of said data. In particular, the fuzzy automatons are very suitable in this case, since they allow to represent the uncertainty and inaccuracy which must be taken into account in this type of analysis. In this paper is proposed to create a graphic tool which allows medical specialists with clinical knowledge, the design of these analysis models in a simple and intuitive way. In relation to the main purpose of this paper, it could be stated that it is to create an environment of model design based on fuzzy automaton through a specific domain language and a graphic editor to facilitate the early diagnosis of diseases. The code of this proyect can be downloaded from https://github.com/mariacaslop/ Environment-of-model-design-based-on-fuzzy-automatonDepto. de Sistemas Informáticos y ComputaciónFac. de InformáticaTRUEunpu

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Intelligent Embedded Software: New Perspectives and Challenges

Intelligent embedded systems (IES) represent a novel and promising generation of embedded systems (ES). IES have the capacity of reasoning about their external environments and adapt their behavior accordingly. Such systems are situated in the intersection of two different branches that are the embedded computing and the intelligent computing. On the other hand, intelligent embedded software (IESo) is becoming a large part of the engineering cost of intelligent embedded systems. IESo can include some artificial intelligence (AI)-based systems such as expert systems, neural networks and other sophisticated artificial intelligence (AI) models to guarantee some important characteristics such as self-learning, self-optimizing and self-repairing. Despite the widespread of such systems, some design challenging issues are arising. Designing a resource-constrained software and at the same time intelligent is not a trivial task especially in a real-time context. To deal with this dilemma, embedded system researchers have profited from the progress in semiconductor technology to develop specific hardware to support well AI models and render the integration of AI with the embedded world a reality