A Real Time Approach to Theft Prevention in the field of Transportation System

This paper report discusses a theft prevention system, which can prevent the theft and also can be track the object. This system is capable to tracking the vehicle as well as theft prevention. An R.F. module is use to exchange the information regarding vehicle and owner of the vehicle with police control room or SOS services. The vehicle can be track with the help of R.F. receiver. A DTMF based fuel lock has been attached in this system. A cell phone with SIM card has been attached with DTMF IC. The fuel flow in the vehicle can be controlled by give a call to this cell phone. This system has been controlled by a microcontroller which can make the system cost effective, low power consumption, effective and reliable

Towards a Standard-based Domain-specific Platform to Solve Machine Learning-based Problems

Machine learning is one of the most important subfields of computer science and can be used to solve a variety of interesting artificial intelligence problems. There are different languages, framework and tools to define the data needed to solve machine learning-based problems. However, there is a great number of very diverse alternatives which makes it difficult the intercommunication, portability and re-usability of the definitions, designs or algorithms that any developer may create. In this paper, we take the first step towards a language and a development environment independent of the underlying technologies, allowing developers to design solutions to solve machine learning-based problems in a simple and fast way, automatically generating code for other technologies. That can be considered a transparent bridge among current technologies. We rely on Model-Driven Engineering approach, focusing on the creation of models to abstract the definition of artifacts from the underlying technologies

A Review of Artificial Intelligence in the Internet of Things

Humankind has the ability of learning new things automatically due to the capacities with which we were born. We simply need to have experiences, read, study… live. For these processes, we are capable of acquiring new abilities or modifying those we already have. Another ability we possess is the faculty of thinking, imagine, create our own ideas, and dream. Nevertheless, what occurs when we extrapolate this to machines? Machines can learn. We can teach them. In the last years, considerable advances have been done and we have seen cars that can recognise pedestrians or other cars, systems that distinguish animals, and even, how some artificial intelligences have been able to dream, paint, and compose music by themselves. Despite this, the doubt is the following: Can machines think? Or, in other words, could a machine which is talking to a person and is situated in another room make them believe they are talking with another human? This is a doubt that has been present since Alan Mathison Turing contemplated it and it has not been resolved yet. In this article, we will show the beginnings of what is known as Artificial Intelligence and some branches of it such as Machine Learning, Computer Vision, Fuzzy Logic, and Natural Language Processing. We will talk about each of them, their concepts, how they work, and the related work on the Internet of Things fields

Fuzzy system to adapt web voice interfaces dynamically in a vehicle sensor tracking application definition

The Vitruvius platform is focused on vehicles and the possibility of working with their multiple sensors, and the real-time data they can provide. With Vitruvius, users can create software applications specialized for the automotive context (e.g., monitor certain vehicles, warn when a vehicle sensor exceeds a certain value, etc.), with the help of fuzzy rules to make decisions. To create applications, users are provided with a domain-specific language that greatly facilitates the process. However, drivers and some passengers cannot create applications on the fly since they need to type to accomplish such a goal. In this paper, we present an adaptive speech interface to allow users to create applications by only using their voice. In addition, the application is based on fuzzy rules to suit the level of experience of users. The application provides an interface that is balanced between the amount of work users have to do and the help the system provides based on the knowledge and ability of each potential user