A family-centered orthodontic screening approach using a machine learning-based mobile application.

Background/purpose: Skeletal orthodontic deformities can have functional and aesthetic consequences, making early detection critical. This study aimed to address the issue of parents bringing their children for routine orthodontic checkups after the ideal treatment age has passed. To address this, we developed a mobile application that uses machine-learning to make a preliminary diagnosis of skeletal malocclusion using just one photograph.

Dynamic Adaptation Attack Detection Model for a Distributed Multi-Access Edge Computing Smart City.

The internet of things (IoT) technology presents an intelligent way to improve our lives and contributes to many fields such as industry, communications, agriculture, etc. Unfortunately, IoT networks are exposed to many attacks that may destroy the entire network and consume network resources. This paper aims to propose intelligent process automation and an auto-configured intelligent automation detection model (IADM) to detect and prevent malicious network traffic and behaviors/events at distributed multi-access edge computing in an IoT-based smart city.

A correlation coefficient-based feature selection approach for virus-host protein-protein interaction prediction.

Prediction of virus-host protein-protein interactions (PPI) is a broad research area where various machine-learning-based classifiers are developed. Transforming biological data into machine-usable features is a preliminary step in constructing these virus-host PPI prediction tools. In this study, we have adopted a virus-host PPI dataset and a reduced amino acids alphabet to create tripeptide features and introduced a correlation coefficient-based feature selection.