Generalized Linear Models (GLMs) and XGBoost are widely used in insurance risk pricing and claims prediction, with GLMs dominant in the insurance industry. The increasing prevalence of connected car data usage in insurance requires highly accurate and interpretable models. Deep learning (DL) models have outperformed traditional Machine Learning (ML) models in multiple domains; despite this, they are underutilized in insurance risk pricing. This study introduces an alternative DL architecture, TabNet, suitable for insurance telematics datasets and claim prediction. This approach compares the TabNet DL model against XGBoost and Logistic Regression on the task of claim prediction on a synthetic telematics dataset. TabNet outperformed these models, providing highly interpretable results and capturing the sparsity of the claims data with high accuracy. However, TabNet requires considerable running time and effort in hyperparameter tuning to achieve these results. Despite these limitations, TabNet provides better pricing models for interpretable models in insurance when compared to XGBoost and Logistic Regression models.

Offering seamless connectivity for Next-Generation Internet (NGI) services has become a widespread concern to achieve global sustainability goals. In this position paper, we focus on the design of a smart connectivity platform that can extend its capabilities beyond the boundaries of what today’s 5G architecture can support. The proposed platform encompasses and integrates a wide range of technologies, spanning from Non-Terrestrial Networks, NTNs (GEO/LEO satellites, and airborne) to terrestrial cellular 5G and long-range low-power Internet of Things (IoT) networks. We enhance 5G technology with novel network architectures and network intelligence to meet coverage and capacity requirements; and by strengthening infrastructure management and control components with autonomous capabilities to decouple operational costs from growth in network traffic, cloud computing, and IoT. Finally, we discuss the technical, commercial and market challenges that need to be overcome before the commercial deployment of the designed platform becomes reality.

5G technology is expected to enable a plethora of new applications with distinct requirements. Provisioning resources to accommodate such applications implies having a flexible network infrastructure that can be tailored to the specific needs of each application. This can be achieved through network slicing. Still, several applications might request network slices, but their request may not be fulfilled due to lack of resources or lack of coverage and provisioning such resources is a cumbersome task. This paper describes an architecture that facilitates the dynamic leasing of resources among network operators to support cross-domain services. The cornerstone of this architecture is a brokering layer, called DBB, that relies on a blockchain-based bidding system to request resources and evaluate resource provisioning offers. The paper also presents a simulation-based use case scenario that illustrates the need for DBB and which was used to evaluate the performance of the proposed architecture.

SUMMARY: The overarching aim of microbiome analysis is to uncover the links between microbial phylogeny and function in order to access ecosystem functioning. This can be done using several experimental strategies targeting different biomolecules, including DNA (metagenomics), RNA (metatranscriptomics) and proteins (metaproteomics). Despite the importance of linking microbial function to phylogeny there are currently no visualization tools that effectively integrate this information. Chordomics is a Shiny-based application for linked -omics data analysis, allowing users to visualize microbial function and phylogeny on a single plot and compare datasets across time and environments. AVAILABILITY AND IMPLEMENTATION: Chordomics is available on GitHub: https://github.com/kevinmcdonnell6/chordomics; software is coded in R and JavaScript and a demonstration version is available at https://kmcd.shinyapps.io/ChordomicsDemo/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.