Best Paper Award at the International Symposium on Ambient Intelligence in Ávila, Spain
By: Andjela Djurovic
Tags: Conference, Ambient Intelligence, Spain, Competition, Artificial Intelligence

Our colleague Fabio Cassano awarded with the ‘’Ambient Intelligence Best Award’’

10th International Symposium on Ambient Intelligence

The conference has brought together researchers from various disciplines that constitute the scientific field of Ambient Intelligence (AmI), a recent phenomenon in the area of Artificial Intelligence (AI), where computers are used as proactive tools assisting people with their day-to-day activities, making everyone’s life more comfortable.

Ambient Intelligence is focusing on human-computer interaction and ways to interact with systems trough user friendly interfaces. The aim is to connect users and systems in a smooth way, without people even noticing it. Such a connection should bring more safety and well-being in a natural way.

‘’Best paper’’ Award

Our company was represented by a group of team members who participated at the conference held at the University of Salamanca, in Ávila, Spain. We won the Best Paper Award on the topic “A Recurrent Neural Network Approach to Improve the Air Quality Index Prediction’’.

The team members who got involved in the project are: Fabio Cassano, Antonio Casale, Paola Regina, Luana Spadafina, Petar Sekulic.

Best Paper Award Universidad Salamanca

This is the part of paper abstract which clearly point out the topic issue and the main problems to be solved:

‘’Every year, cities all over the world face the problem of the air pollution. In particular seasons, such as the winter, the levels of bad particles coming from the industrial and domestic heating systems increase the risk of pulmonary diseases.

Thus, for both the city majors and citizens, it is important to understand and predict the air pollution levels in advance to safeguard the health. Modern forecasting systems are able to alert the population in advance only about the general weather condition, while the air quality information are almost not considered at all.

The reasons are manifold and they mostly depend by the difficult that the modern systems have to generalize the problem and correct elaborate the data coming from the sensors. In this paper we address the problem of forecasting the bands of the different air pollutants according to the Air Quality Index in the Apulia region.

Using two different Recurrent Neural Network models, we have performed two tests to prove that it is possible to predict the level of pollutants in a specific area by using the data coming from the surrounding area. By using this approach on both the weather and air stations on the territory it is possible to have alerts many days ahead on the pollution levels.’’

Fabio’s impressions, Researcher

Participate to ISAmI is always a great experience. Meeting high level researcher from all over the world and talking about Artificial Intelligence gave me a great opportunity to grow and improve my knowledge. This is the third year I participated to this conference and, for the first time me and the other co-authors won the best paper award presenting an innovative approach to predict the Air Quality Index. It is for me a pride being part of such a great team and I would like to thank you all the people participated in this research.

Recurrent Neural Network Approach

Petar’s impressions, Machine Learning Scientist

“The main objective of the paper was to create a system that is going to predict the value of the air pollutants in the period of the two following days. The idea was to make a system that will be independent of the specific geographical location.

We took the data for the paper from the website ARPA Puglia.

Before creating the recurrent neural network (RNN) algorithm, we carried out data pre-processing. Data set was divided into three parts: training, validation and test. After conducting this phase, I created a neural network that is trained by batch gradient descent algorithm. Hyper-parameters of the algorithm are chosen on the validation set, while the accuracy of the algorithm is validated upon the test data set.

The overall conclusion was to get the satisfactory accuracy level about the prediction of the amount of pollutants in the air. Future research could be directed in the Graph theory implementation in the existing system.