Evaluating the actual price of a residential property is a critical issue in the real estate market. Real estate market practitioners gauge a property's price by considering features such as property type and residential area. Subsequently, they evaluate the property's intrinsic features, such as condition, sun exposure, scenic views, and ancillary amenities. Finally, extrinsic features such as the proximity of services and infrastructure are assessed. This paper proposes a new genetic approach for selecting residential properties that meet the purchase offer and the intrinsic and extrinsic characteristics desired by the client. Since the real estate market's changes can influence extrinsic features, the method introduces price fluctuations of properties. Extrinsic features are modelled as fuzzy partitions: each fuzzy set describes a qualitative aspect of the corresponding feature that, expressed in a linguistic term, has a human-like interpretation. Then, a deviation value (fluctuation) from the average price of the property is considered for each fuzzy set in the partition. All the property features, extrinsic and intrinsic, are encoded in the chromosome genes of the genetic algorithm. The fitness function calculates the distance between the unit price of the property and the purchase offer. Some case studies were conducted in various Italian municipalities, using the average price per square meter of residential properties the Osservatorio del Mercato Immobiliare (OMI) assigned. Depending on customer requirements and preferences, different OMI zones were selected using additional characteristics such as type, location, conservation, and proximity to various urban services. The results demonstrated the effectiveness of the proposed approach for all the case studies, showing how the optimal solution represents a good compromise between customer preferences and market offerings.

Many issues require the application of forecasting models applied to spatiotemporal data in Geographic Information Systems (GIS) to predict the spatial distribution and evolution of a specific feature. The use of soft computing techniques in the development of these forecasting models makes it possible to detect non-linear trends but has the disadvantage of increasing the computational complexity of the model. In this paper we present a GIS-based framework in which a fast soft computing forecasting model based on the multidimensional Fuzzy Transform (for short, MF-transform) is applied to evaluate the spatial distribution and the time evolution in a study area of a measurable entity (the feature). The study area is divided into homogeneous zones (the subzones) in which the feature was measured in each time frame. The time series of the feature are analyzed to assess the trend of the feature in subsequent time frames; furthermore, those sub-areas are detected in which the feature is higher than a maximum threshold (hot spots) or lower than a minimum threshold (cold spots) in this time range. A process of fuzzifying the values of the feature is carried out in order to facilitate the interpretation of the results by expert users. The framework was tested on a study area provided by the province of Naples (Italy) to predict and analyze the spatial distribution and temporal trend of the monthly rate of births compared to deaths. Furthermore, the thematic map of the hot and cold spots detected in the three months following the time period of measurements was built. The results show that our method provides reliable results both in terms of forecast error and similarity between the detected hot and cold spots and those who have really formed.

Emotion detection in the natural language text has drawn the attention of several scientific communities as well as commercial/marketing companies: analyzing human feelings expressed in the opinions and feedback of web users helps understand general moods and support market strategies for product advertising and market predictions. This paper proposes a framework for emotion-based classification from social streams, such as Twitter, according to Plutchik's wheel of emotions. An entropy-based weighted version of the fuzzy c-means (FCM) clustering algorithm, called EwFCM, to classify the data collected from streams has been proposed, improved by a fuzzy entropy method for the FCM center cluster initialization. Experimental results show that the proposed framework provides high accuracy in the classification of tweets according to Plutchik's primary emotions; moreover, the framework also allows the detection of secondary emotions, which, as defined by Plutchik, are the combination of the primary emotions. Finally, a comparative analysis with a similar fuzzy clustering-based approach for emotion classification shows that EwFCM converges more quickly with better performance in terms of accuracy, precision, and runtime. Finally, a straightforward mapping between the computed clusters and the emotion-based classes allows the assessment of the classification quality, reporting coherent and consistent results.