Customer Experience (CX) is monitored through market research surveys, based on metrics like the Net Promoter Score (NPS) and the customer satisfaction for certain experience attributes (e.g., call center, website, billing, service quality, tariff plan). The objective of companies is to maximize NPS through the improvement of the most important CX attributes. However, statistical analysis suggests that there is a lack of clear and accurate association between NPS and the CX attributes’ scores. In this paper, we address the aforementioned deficiency using a novel classification approach, which was developed based on logistic regression and tested with several state-of-the-art machine learning (ML) algorithms. The proposed method was applied on an extended data set from the telecommunication sector and the results were quite promising, showing a significant improvement in most statistical metrics.

The use of UAVs and artificial intelligence has emerged as a promising approach for monitoring road defects. This paper highlights the importance of these technologies in improving road inspection, maintenance, and safety. Traditional methods for inspecting roads are often time-consuming, expensive, and can put human inspectors in dangerous situations. However, drones equipped with high-resolution cameras and sensors can capture pavement image data quickly and safely. Deep learning algorithms can then analyze this data to identify and localize areas in need of repair. By leveraging these technologies, engineers and road construction experts can more efficiently monitor and maintain roads, reducing the costs associated with repairs and maintenance, while in parallel improving safety. To this end, this work emphasizes the potential of UAVs in conjunction with deep learning techniques to provide a more comprehensive view of road conditions, allowing for targeted repairs and more effective maintenance strategies, such as prefabrication and robotic interventions. Experimental results using objective evaluation criteria, such as precision, recall, F1-score, and IoU are promising, which entails that this study advocates for the adoption of these technologies to enhance the monitoring and maintenance of road infrastructures.

We present a deep learning framework leveraging the ResUNet-a framework for pixel-wise semantic segmentation of cracks and potholes. By integrating key components including a U-Net encoder/decoder backbone, residual connections, atrous convolutions, pyramid scene parsing pooling, and multi-tasking inference, the proposed method exhibits robustness in capturing intricate spatial details and inter-pixel contextual relationships essential for accurate road defect segmentation. Experimental results validate the efficacy of the proposed approach, with ResUNet-a consistently surpassing the conventional U-Net model, demonstrating its superior performance in crack and pothole segmentation tasks and thus providing a useful auxiliary tool for road maintenance and safety.

In recent years, there has been a notable rise in the adoption of Unmanned Aerial Vehicles (UAVs) for infrastructure inspection. This study focuses on presenting IKAROS, an integrated system that harnesses the combined capabilities of multiple UAVs and deep learning (DL) tools to improve the efficiency of road transport network inspections, facilitate the detection of road defects, and manage traffic emergencies. Specifically, we introduce the IKAROS system, outlining its key components and functionalities, with a particular emphasis on the applied technologies and the interconnections of its various systems. In addition, we provide an overview of the pilot site and address the challenges associated with real-world implementation.

This paper briefly presents the photogrammetric techniques that were used to create 3D game assets for a business simulation game named ”TRAVEL TYCOON GREECE” (TTGR). Specifically, a combination of several methods, including structure from motion, dense stereo and dense multi-view 3D techniques were applied on a significant number of drone images to reconstruct realistically 3D models for two crucial visual components of the game: 1) maps of the areas of interest, which represent the background of the TTGR game and 2) cultural heritage artefacts which, apart from the educational value, could be utilized in the game’s ticketing platform.