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Evaluating house prices is crucial for various stakeholders, including homeowners, investors, and policymakers. However, traditional spatial interpolation methods have limitations in capturing the complex spatial relationships that affect property values. To address these challenges, we have developed a new method called Multi-Head Gated Attention for spatial interpolation. Our approach builds upon attention-based interpolation models and incorporates multiple attention heads and gating mechanisms to better capture spatial dependencies and contextual information. Importantly, our model produces embeddings that reduce the dimensionality of the data, enabling simpler models like linear regression to outperform complex ensembling models. We conducted extensive experiments to compare our model with baseline methods and the original attention based interpolation model. The results show a significant improvement in the accuracy of house price predictions, validating the effectiveness of our approach. This research advances the field of spatial interpolation and provides a robust tool for more precise house price evaluation. Our GitHub repository. 1 1 Final_file/tree/main/ASI-main. contains the data and code for all datasets, which are available for researchers and practitioners interested in replicating or building upon our work. • Multi-Head Gated Attention model improves spatial interpolation for house price prediction. • Geographical and structural attention mechanisms enhance spatial feature extraction. • Embedding generation reduces data complexity, boosting model performance and accuracy. • New dataset combines Italian city data with structural and geographical attributes. • Model outperforms traditional methods, validated across multiple diverse datasets. [ABSTRACT FROM AUTHOR]

Skin cancer is a major global health concern, with rising incidence rates. However, current diagnostic methods often lack objectivity, speed, and non-invasiveness. To overcome this limitation, this work proposes a novel approach combining microwave reflectometry (MR) with artificial intelligence (AI) techniques for the diagnosis of in-vivo skin cancer. In particular, MR exploits the dielectric properties of biological tissues, revealing chemical-physical differences in normal skin and benign/malignant lesions. To better improve the diagnostic performance, MR analysis is integrated with AI algorithms, particularly those based on deep learning (DL) and convolutional neural networks (CNNs), which analyze dermoscopic skin images and identify asymmetry, irregular borders, abnormal colorations, and other skin cancer indicators. Combining MR with AI image analysis provides a comprehensive diagnostic approach, as MR informs on tissue dielectric composition, while AI analyzes lesion-specific details, offering more accurate and timely assessments. This combined approach promises early skin cancer detection and could significantly impact clinical practice. [ABSTRACT FROM AUTHOR]

In this paper a system for automatic visual monitoring of welding process, in dry stainless steel kegs for food storage, is proposed. In the considered manufacturing process the upper and lower skirts are welded to the vessel by means of Tungsten Inert Gas (TIG) welding. During the process several problems can arise: 1) residuals on the bottom 2) darker weld 3) excessive/poor penetration and 4) outgrowths. The proposed system deals with all the four aforementioned problems and its inspection performances have been evaluated by using a large set of kegs demonstrating both the reliability in terms of defect detection and the suitability to be introduced in the manufacturing system in terms of computational costs. [ABSTRACT FROM AUTHOR]