Identification and Suppression of Multicomponent Noise in Audio Magnetotelluric Data Based on Convolutional Block Attention Module

Audio magnetotelluric (AMT) is commonly used in mineral resource exploration. However, the weak energy of AMT signals makes them susceptible to being overwhelmed by noise, leading to erroneous geophysical interpretations. In recent years, deep learning has been applied to AMT denoising and has shown better denoising performance compared to...

DeepPick: A Deep Learning Approach to Unveil Outstanding Users Ranking with Public Attainable Features

Outstanding users (OUs) denote the influential, 'core' or 'bridge' users in online social networks. How to accurately detect and rank them is an important problem for third-party online service providers and researchers. Conventional efforts, ranging from early graph-based algorithms to recent machine learning-based approaches, typically rely on...

Application of YOLOv4 Algorithm for Foreign Object Detection on a Belt Conveyor in a Low-Illumination Environment

The most common failures of belt conveyors are runout, coal piles and longitudinal tears. The detection methods for longitudinal tearing are currently not particularly effective. A key study area for minimizing longitudinal belt tears with the advancement of machine learning is how to use machine vision technology to detect foreign items on...

Chemical and rheological properties of polymer modified bitumen incorporating bio-oil derived from waste cooking oil

The chemical and rheological properties of polymer modified bitumen incorporating bio-oil derived from waste cooking oil (WCO) were investigated in this paper. At first, the chemical composition and mixing mechanism of the experimental materials were analysed from the perspective of functional group, and the influence of bio-oil on the...

MRLR: Multi-level representation learning for personalized ranking in recommendation

Representation learning (RL) has recently proven to be effective in capturing local item relationships by modeling item co-occurrence in individual user's interaction record. However, the value of RL for recommendation has not reached the full potential due to two major drawbacks: 1) recommendation is modeled as a rating prediction problem...