Internal organs, like lungs, are very often examined by the use of screening methods. For this purpose we present an evaluation model based on a composition of fuzzy system combined with a neural network. The input image is evaluated by means of custom rules which use type-1 fuzzy membership functions. The results are forwarded to a neural network for final evaluation. Our model was validated by using X ray images with lung nodules. The results shows the high performances of our approach with sensitivity and specificity reaching almost 95% and 90% respectively, with an accuracy of 92.56%. The new methodology lower considerably the computational demands and increases detection performances.

Rainfall estimation based on the impact of rain on electromagnetic waves is a novel methodology that has had notable advancements during the last few years. Many studies conducted on this topic in the past considered only the electromagnetic waves with frequencies greater than 10 GHz since the rainfall impact on the electromagnetic wave attenuation is reduced at lower frequencies. Over the last few years, some authors have demonstrated that there can be a non-negligible attenuation even on the signals received on a global system for mobile communications mobile terminal in presence of rain. In this paper, we propose a new classification method based on a probabilistic neural network to obtain an accurate classification between four rainfall intensities (no rain, weak rain, moderate rain, and heavy rain). The innovative rainfall classification method is based on three received signal level (RSL) local features of the 4G/LTE: the instantaneous RSL, the average RSL value, and its variance calculated by using a sliding window. The proposed method exhibits good performance, obtaining an overall correct classification rate of 96.7%. Almost all papers on this topic present in the literature focus on electromagnetic waves with frequencies greater than 10 GHz, in which the rain impact is more relevant, according to the rain attenuation model. However, only the 4G/LTE signal has such widespread geographic coverage, so the proposed classification method can provide noticeable improvements in the creation of rainfall maps with higher spatial resolution.

Integrated generation systems are increasingly considered suitable to supply remote areas, less developed countries, and small isolated communities with power. The energy management investigated in this paper concerns a smart grid encompassing a photovoltaic park. We propose a novel cloud-distributed solution to determine the best energy dispatch, i.e. where energy is going to be used and whether to change the operating points for some consumption devices. Neural networks have been used to predict both energy production and consumption, making it possible to strategically set the activation time of loading devices and to minimize energy flow changes. Moreover, cloud computing resources make it possible to have fast and distributed computation on the big amount of data gauging power production and consumption.