The current paper presents a novel method for reducing a multiclass confusion matrix into a $2\times 2$ version enabling the exploitation of the relevant performance metrics and methods such as the receiver operating characteristic and area under the curve for the assessment of different classification algorithms. The reduction method is based on class grouping and leads to a special type of matrix called the reduced confusion matrix. The developed method is then exploited for the assessment of state of the art machine learning algorithms applied on the net promoter score classification problem in the field of customer experience analytics indicating the value of the proposed method in real world classification problems.

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.

Hyperspectral data classification is one of the fundamental problems in remote sensing. Several algorithms based on supervised machine learning have been proposed to address it. The performance, however, of the proposed algorithms is inherently dependent on the amount and quality of annotated data. Due to recent advances in hyperspectral imaging and autonomous (unmanned) aerial vehicles collecting new hyperspectral data is an easy task. Annotating those data, however, is a tedious, time-consuming and costly task requiring the in-situ presence of human experts. One way to loosen the requirement of a large number of annotated data is the shift to semi-supervised learning combined with highly sample-efficient tensor-based neural networks. This study provides a comprehensive experimental analysis of the performance of a variety of graph-based semi-supervised learning techniques combined with tensor-based neural network embeddings for the problem of hyperspectral data classification. Experimental results suggest that the combination of tensor-based neural network embeddings with graph-based semi-supervised learning can significantly improve the classification results minimizing human annotation effort.

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.

This paper presents a novel approach to multiclass classification tasks, utilizing tensor-based embeddings for graph-based semi-supervised learning. The proposed method utilizes a tensor decomposition algorithm to create embeddings that capture the essential features of the data. These are used by various graph-based semi-supervised approach to construct a graph capable to propagate the information from labeled to unlabeled nodes, classifying available data. The proposed method was tested on hyperspectral datasets. The results demonstrate the potential of such combinatory tensor-based semi-supervised approaches.

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.