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Background: Handwriting is an acquired complex cognitive and motor skill resulting from the activation of a widespread brain network. Handwriting therefore may provide biologically relevant information on health status. Also, handwriting can be collected easily in an ecological scenario, through safe, cheap, and largely available tools. Hence, objective handwriting analysis through artificial intelligence would represent an innovative strategy for telemedicine purposes in healthy subjects and people affected by neurological disorders. Materials and Methods: One-hundred and fifty-six healthy subjects (61 males; 49.6 ± 20.4 years) were enrolled and divided according to age into three subgroups: Younger adults (YA), middle-aged adults (MA), and older adults (OA). Participants performed an ecological handwriting task that was digitalized through smartphones. Data underwent the DBNet algorithm for measuring and comparing the average stroke sizes in the three groups. A convolutional neural network (CNN) was also used to classify handwriting samples. Lastly, receiver operating characteristic (ROC) curves and sensitivity, specificity, positive, negative predictive values (PPV, NPV), accuracy and area under the curve (AUC) were calculated to report the performance of the algorithm. Results: Stroke sizes were significantly smaller in OA than in MA and YA. The CNN classifier objectively discriminated YA vs. OA (sensitivity = 82%, specificity = 80%, PPV = 78%, NPV = 79%, accuracy = 77%, and AUC = 0.84), MA vs. OA (sensitivity = 84%, specificity = 56%, PPV = 78%, NPV = 73%, accuracy = 74%, and AUC = 0.7), and YA vs. MA (sensitivity = 75%, specificity = 82%, PPV = 79%, NPV = 83%, accuracy = 79%, and AUC = 0.83). Discussion: Handwriting progressively declines with human aging. The effect of physiological aging on handwriting abilities can be detected remotely and objectively by using machine learning algorithms. [ABSTRACT FROM AUTHOR]

A method for retrieving both agricultural biomass and soil moisture starting from simultaneous observations at L and X band is described in this paper. The research was carried out within the framework of the SIASGE (Italian-Argentine System of Satellites for Emergency Management) project, funded by the Italian Space Agency (ASI), for the definition and development of X and L products oriented towards applications for risk and emergency management. SAR data at L (ALOS/PALSAR) and X (COSMO/SkyMed) bands were used for producing maps of Soil Moisture Content (SMC) and Plant Water Content (PWC) using inversion agorithms based on Artificial Neural Networks and on empirical relationships based on previous experiments. SMC obtained in an agricultural area in Lombardy showed average values in agreement with monthly rainfall. The corresponding maps of PWC were produced for the same area, and in this case the result was in agreement with the season, the classification, and the related agricultural practices. [ABSTRACT FROM AUTHOR]

In a power market, the price of electricity is the most important signal to all market participants. However, electricity price forecast is a complex task due to non-linearity, non-stationarity and volatility of the price signal. In spite of all performed research on this area in the recent years, there is still an essential need for more accurate price forecast methods. Besides, there is a lack of robust feature selection technique for designing the input vector of electricity price forecast, which can consider the non-linearities of the price signal. In this study, a new price forecast method is proposed, which is composed of a modified version of Relief algorithm for feature selection and a hybrid neural network for prediction. The proposed approach is examined on Ontario, New England and Italian electricity markets and compared with some of the most recently published price forecast methods. [ABSTRACT FROM AUTHOR]

In a previous work, the hailpad data collected over the plain of the Friuli Venezia Giulia region in northeast Italy during the April-September 1992-2009 period were studied through a bivariate analysis with 52 sounding-derived indices from the Udine-Campoformido station (WMO code 16044). The results showed statistically significant relations but, nevertheless, were not completely satisfactory from a practical point of view. In the current work, a prognostic multivariate analysis is performed, using linear and nonlinear approaches, finding the best results with an ensemble of neural networks. For the hail occurrence-classification problem, a novel method for combining binary classifiers (a variant of the Mojirsheibani major voting algorithm) is introduced. For the hail extension-regression problem the ensemble is built by choosing the members with a bagging algorithm, but combining them with a linear multiregression, in order to increase the forecast variability. [ABSTRACT FROM AUTHOR]

Background: To investigate the potential use of Artificial Neural Network (ANN) in the evaluation of serum protein electrophoresis, we set up a multicenter study involving six Italian laboratories. For this purpose, we developed an algorithm named CASPER (Computer Assisted Serum Protein Electrophoresis Recognizer). Methods: A total of 59,516 samples from the six centers were divided into three groups. Training and validation sets were used to develop the neural network, whereas evaluation set was used to test the performance of CASPER in recognizing abnormal electrophoretic profiles. Results: CASPER showed 93.0% sensitivity and 47.4% specificity. CASPER sensitivity and specificity ranged in the six sites from 88% (site 3) to 97% (site 5) and from 36% (site 6) to 53% (site 3), respectively. Sensitivity for γ zone was 94.6%, for β zone 89.7% and for oligoclonal patterns 92.0%. Conclusions: The sensitivity of the CASPER algorithm does not allow us to recommend its use as a replacement for the visual inspection, but it could be helpful in avoiding accidental misclassifications by the operator. Moreover, the CASPER algorithm may be a useful tool for training operators and students. This study evidenced a high inter-observer variability, which should be addressed in a dedicated study. Data set to train and validate ANNs should contain a huge range and an adequate number of different abnormalities. Clin Chem Lab Med 2008;46:1183–8. [ABSTRACT FROM AUTHOR]