Eleonora D'Andrea

Real-Time Detection of Traffic From Twitter Stream Analysis

Social networks have been recently employed as a source of information for event detection, with particular reference to road traffic congestion and car accidents. In this paper, we present a real-time monitoring system for traffic event detection from Twitter stream analysis. The system fetches tweets from Twitter according to several search criteria; processes tweets, by applying text mining techniques; and finally performs the classification of tweets. The aim is to assign the appropriate class label to each tweet, as related to a traffic event or not. The traffic detection system was employed for real-time monitoring of several areas of the Italian road network, allowing for detection of traffic events almost in real time, often before online traffic news web sites. We employed the support vector machine as a classification model, and we achieved an accuracy value of 95.75% by solving a binary classification problem (traffic versus nontraffic tweets). We were also able to discriminate if traffic is caused by an external event or not, by solving a multiclass classification problem and obtaining an accuracy value of 88.89%.

24-hour-ahead forecasting of energy production in solar PV systems

This paper presents a flexible approach to forecasting of energy production in solar photovoltaic (PV) installations, using time series analysis and neural networks. Our goal is to develop a one day-ahead forecasting model based on an artificial neural network with tapped delay lines. Despite some methods already exist for energy forecasting problems, the main novelty of our approach is the proposal of a tool for the technician of a PV installation to correctly configure the forecasting model according to the particular installation characteristics. The correct configuration takes into account the number of hidden neurons, the number of delay elements, and the training window width, i.e., the appropriate number of days, before the predicted day, employed for the training. The irradiation along with the sampling hour are used as input variables to predict the daily accumulated energy with a percentage error less than 5%.

One day-ahead forecasting of energy production in solar photovoltaic installations: an empirical study

This paper presents a flexible and easy-to-use methodological approach to the forecasting of energy production in solar photovoltaic (PV) installations, using time series analysis and neural networks. The aim is to develop and validate a one day-ahead forecasting model by adopting an artificial neural network with tapped delay lines. The main novelty of our approach is the proposal of a general methodology, consisting of a sequence of steps to perform in order to find, based on heuristic criteria, the optimal structure of the neural network (particularly, number of hidden neurons and number of delay elements) and the best configuration of the neural predictor (namely, the training window width and the sampling frequency). The best experimental results have been obtained using as inputs the irradiation and the sampling hour to predict the daily accumulated energy. Considering a dataset of 15-minute measurements pertinent to one year, despite the presence of 77 missing days (scattered through the whole year in correspondence with system slowdown), we achieved seasonal mean absolute percentage errors ranging from a minimum of 12.2% (Spring) to a maximum of 26% (Autumn). Moreover the achieved results are significantly better than those obtained by the persistence method, a benchmark frequently used in this kind of applications.

Providing PRTools with fuzzy rule-based classifiers

This paper first reviews the state-of-the-art of fuzzy rule-based classifiers (FRBCs), then it discusses how to implement an FRBC under the Pattern Recognition Toolbox (PRTools), the de-facto standard toolbox for classification in Matlab. Such an implementation, called frbc, allows for a straightforward comparison of frbc with other classifiers already available under the PRTools. Furthermore, frbc can easily be used and combined with any other general-purpose function already available in PRTools. In this way, e.g., it becomes really easy to perform many types of feature selection, based on the accuracy achieved by frbc on the subset of features at hand. Another useful feature is the capability to export each FRBC generated by frbc as a standard Fuzzy Inference System (FIS) structure used within the Matlab Fuzzy Logic Toolbox (FLT): this allows comparisons/validations, visual inspection of the rule base, etc. In the experimental part we first assess the correctness of the implementation, by reproducing results existing in the literature. Then we show some examples of usage of frbc, combined with existing PRTools functions.

Fuzzy forecasting of energy production in solar photovoltaic installations

In this paper we describe a fuzzy rule-based classifier applied to forecasting of energy production in solar photovoltaic installations. After adapting the available numerical data to a dataset appropriate for classification, we propose a processing method to create an efficient rule base. The aim is to build an intelligent system able to forecast the class label of the energy production from a photovoltaic installation, given the values of some environmental parameters. Despite some already existing methods for forecasting problems, the main advantages of our approach are easier interpretability and versatility, as we deal with class labels. Moreover we propose a way to extract an ad hoc training dataset, in order to perform an effective training even when we deal with non optimal data (e.g., non-uniformly sampled data, missing samples, etc.). With a fuzzy forecasting system, in place of a traditional one, even the non-expert user of a photovoltaic system may be able to make decisions more easily. The results obtained show a correct classification percentage of almost 93%.

Determining the composition of bronze alloys by means of high-dimensional feature selection and Artificial Neural Networks

In this paper we exploit Artificial Neural Networks (ANN) to model the functional relationship between LIBS spectra and the corresponding composition of bronze alloys, expressed in terms of concentrations of the four elements constituting the alloy. The typical approach to Laser-Induced Breakdown Spectroscopy (LIBS) quantitative analysis uses calibration curves, suitably built based on appropriate reference standards. More recently, statistical methods relying on the principles of ANNs are increasingly used. In particular, an ANN can be used for a preliminary exploration of the LIBS spectra in order to find out the most significant areas of the spectrum, which will be used by another ANN dedicated to the calibration. In this paper we will show that the use of ANNs to deal with LIBS spectra provides a viable, fast and robust method for LIBS quantitative analysis. Actually, this approach requires a relatively limited number of reference samples for the training of the network, with respect to the current approaches, and can automatically analyze a large number of samples.

Monitoring the public opinion about the vaccination topic from tweets analysis

Abstract The paper presents an intelligent system to automatically infer trends in the public opinion regarding the stance towards the vaccination topic: it enables the detection of significant opinion shifts, which can be possibly explained with the occurrence of specific social context-related events. The Italian setting has been taken as the reference use case. The source of information exploited by the system is represented by the collection of vaccine-related tweets, fetched from Twitter according to specific criteria; subsequently, tweets undergo a textual elaboration and a final classification to detect the expressed stance towards vaccination (i.e. in favor, not in favor, and neutral). In tuning the system, we tested multiple combinations of different text representations and classification approaches: the best accuracy was achieved by the scheme that adopts the bag-of-words, with stemmed n-grams as tokens, for text representation and the support vector machine model for the classification. By presenting the results of a monitoring campaign lasting 10 months, we show that the system may be used to track and monitor the public opinion about vaccination decision making, in a low-cost, real-time, and quick fashion. Finally, we also verified that the proposed scheme for continuous tweet classification does not seem to suffer particularly from concept drift, considering the time span of the monitoring campaign.

UHF-RFID smart gate: Tag action classifier by artificial neural networks

The application of Artificial Neural Networks (ANNs) to discriminate tag actions in UHF-RFID gate is presented in this paper. By exploiting Received Signal Strength Indicator values acquired in a real experimental scenario, a multi-layer perceptron neural network is trained to distinguish among tags incoming, outgoing or passing the RFID gate. A 99% accuracy can be obtained in tag classification by employing only one reader antenna and independently from tag orientation and typology.

Path Clustering Based on a Novel Dissimilarity Function for Ride-Sharing Recommenders

Ride-sharing practice represents one of the possible answers to the traffic congestion problem in todays cities. In this scenario, recommenders aim to determine similarity among different paths with the aim of suggesting possible ride shares. In this paper, we propose a novel dissimilarity function between pairs of paths based on the construction of a shared path, which visits all points of the two paths by respecting the order of sequences within each of them. The shared path is computed as the shortest path on a directed acyclic graph with precedence constraints between the points of interest defined in the single paths. The dissimilarity function evaluates how much a user has to extend his/her path for covering the overall shared path. After computing the dissimilarity between any pair of paths, we execute a fuzzy relational clustering algorithm for determining groups of similar paths. Within these groups, the recommenders will choose users who can be invited to share rides. We show and discuss the results obtained by our approach on 45 paths.

An ensemble of learning machines for quantitative analysis of bronze alloys

We deal with the determination of the composition of bronze alloys measured through Laser-Induced Breakdown Spectroscopy (LIBS) analysis. The relation between LIBS spectra and bronze alloy composition, represented by means of the concentrations of constituting elements, is modeled by adopting an ensemble of learning machines, fed with different inputs. Then, the combiner computes the final response. The results obtained on the test set show that the ensemble model manages to determine the composition of alloy samples with mean squared error of about 6.53 10-2.