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.

Over the past five years, interest in the literature regarding the security of the Internet of Medical Things (IoMT) has increased. Due to the enhanced interconnectedness of IoMT devices, their susceptibility to cyber-attacks has proportionally escalated. Motivated by the promising potential of AI-related technologies to improve certain cybersecurity measures, we present a comprehensive review of this emerging field. In this review, we attempt to bridge the corresponding literature gap regarding modern cybersecurity technologies that deploy AI techniques to improve their performance and compensate for security and privacy vulnerabilities. In this direction, we have systematically gathered and classified the extensive research on this topic. Our findings highlight the fact that the integration of machine learning (ML) and deep learning (DL) techniques improves both the performance of cybersecurity measures and their speed, reliability, and effectiveness. This may be proven to be useful for improving the security and privacy of IoMT devices. Furthermore, by considering the numerous advantages of AI technologies as opposed to their core cybersecurity counterparts, including blockchain, anomaly detection, homomorphic encryption, differential privacy, federated learning, and so on, we provide a structured overview of the current scientific trends. We conclude with considerations for future research, emphasizing the promising potential of AI-driven cybersecurity in the IoMT landscape, especially in patient data protection and in data-driven healthcare.

The COVID-19 pandemic has affected many aspects of human life around the world, due to its tremendous outcomes on public health and socio-economic activities. Policy makers have tried to develop efficient responses based on technologies and advanced pandemic control methodologies, to limit the wide spreading of the virus in urban areas. However, techniques such as social isolation and lockdown are short-term solutions that minimize the spread of the pandemic in cities and do not invert long-term issues that derive from climate change, air pollution and urban planning challenges that enhance the spreading ability. Thus, it seems crucial to understand what kind of factors assist or prevent the wide spreading of the virus. Although AI frameworks have a very efficient predictive ability as data-driven procedures, they often struggle to identify strong correlations among multidimensional data and provide robust explanations. In this paper, we propose the fusion of a heterogeneous, spatio-temporal dataset that combine data from eight European cities spanning from 1 January 2020 to 31 December 2021 and describe atmospheric, socio-economic, health, mobility and environmental factors all related to potential links with COVID-19. Remote sensing data are the key solution to monitor the availability on public green spaces between cities in the study period. So, we evaluate the benefits of NIR and RED bands of satellite images to calculate the NDVI and locate the percentage in vegetation cover on each city for each week of our 2-year study. This novel dataset is evaluated by a tree-based machine learning algorithm that utilizes ensemble learning and is trained to make robust predictions on daily cases and deaths. Comparisons with other machine learning techniques justify its robustness on the regression metrics RMSE and MAE. Furthermore, the explainable frameworks SHAP and LIME are utilized to locate potential positive or negative influence of the factors on global and local level, with respect to our model’s predictive ability. A variation of SHAP, namely treeSHAP, is utilized for our tree-based algorithm to make fast and accurate explanations.

Targeted nature-based small-scale interventions is an approach commonly adopted by urban developers. The public acceptance of their implementation could be improved by participation, emphasizing residents or shopkeepers located close to the areas of interest. In this work, we propose a methodology that combines 3D technology, based on open data sources, user-generated content, 3D software and game engines for both minimizing the time and cost of the whole planning process and enhancing citizen participation. The proposed schemes are demonstrated in Piraeus (Greece) and Gladsaxe (Denmark). The core findings can be summarized as follows: (a) the time and cost are minimized by using online databases, (b) the gamification of the planning process enhances the decision making process and (c) the interactivity provided by the game engine inspired the participation of non-experts in the planning process (co-creation and co-evaluation), which decentralizes and democratizes the final planning solution.

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.

Serious games are receiving increasing attention in the field of Cultural Heritage (CH) applications. A special field of CH and education is Intangible Cultural Heritage and particularly dance. Machine learning (ML) tools are necessary elements for the success of a serious game platform since they introduce intelligence in processing and analysis of users' interactivity. ML provides intelligent scoring and monitoring capabilities of the user's progress in a serious game platform. In this paper, we introduce a deep learning model for motion primitive classification. The model combines a convolutional processing layer with a bi-directional analysis module. This way, RGB information is efficiently handled by the hierarchies of convolutions, while the bi-directional properties of an LSTM model are retained. The resulting Convolutionally Enhanced Bi-directional LSTM (CEBi-LSTM) architecture is less sensitive to skeleton errors, occurring using low-cost sensors, such as Kinect, while simultaneously handling the high amount of detail when using RGB visual information.