AIMC Topic: Machine Learning

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How do machine learning models perform in the detection of depression, anxiety, and stress among undergraduate students? A systematic review.

Cadernos de saude publica
Undergraduate students are often impacted by depression, anxiety, and stress. In this context, machine learning may support mental health assessment. Based on the following research question: "How do machine learning models perform in the detection o...

Automated approach for fetal and maternal health management using light gradient boosting model with SHAP explainable AI.

Frontiers in public health
Fetal health holds paramount importance in prenatal care and obstetrics, as it directly impacts the wellbeing of mother and fetus. Monitoring fetal health through pregnancy is crucial for identifying and addressing potential risks and complications t...

Accelerating antimicrobial peptide design: Leveraging deep learning for rapid discovery.

PloS one
Antimicrobial peptides (AMPs) are excellent at fighting many different infections. This demonstrates how important it is to make new AMPs that are even better at eliminating infections. The fundamental transformation in a variety of scientific discip...

Revolutionizing cross-border e-commerce: A deep dive into AI and big data-driven innovations for the straw hat industry.

PloS one
This paper investigates the impact of artificial intelligence (AI) and big data analytics on optimizing cross-border e-commerce efficiency for straw hat manufacturers in Zhejiang Province, China. It identifies market and consumer demand trends throug...

Improving prediction of solar radiation using Cheetah Optimizer and Random Forest.

PloS one
In the contemporary context of a burgeoning energy crisis, the accurate and dependable prediction of Solar Radiation (SR) has emerged as an indispensable component within thermal systems to facilitate renewable energy generation. Machine Learning (ML...

Implementing the discontinuous-Galerkin finite element method using graph neural networks with application to diffusion equations.

Neural networks : the official journal of the International Neural Network Society
Machine learning (ML) has benefited from both software and hardware advancements, leading to increasing interest in capitalising on ML throughout academia and industry. There have been efforts in the scientific computing community to leverage this de...

Machine learning to predict the decision to perform surgery in hepatic echinococcosis.

HPB : the official journal of the International Hepato Pancreato Biliary Association
BACKGROUND: Cystic echinococcosis (CE) is a significant public health issue, primarily affecting the liver. While several management strategies exist, there is a lack of predictive tools to guide surgical decisions for hepatic CE. This study aimed to...

Network embedding: The bridge between water distribution network hydraulics and machine learning.

Water research
Machine learning has been increasingly used to solve management problems of water distribution networks (WDNs). A critical research gap, however, remains in the effective incorporation of WDN hydraulic characteristics in machine learning. Here we pre...

Design and performance analysis of multi-enzyme activity-doped nanozymes assisted by machine learning.

Colloids and surfaces. B, Biointerfaces
Traditional design approaches for nanozymes typically rely on empirical methods and trial-and-error, which hampers systematic optimization of their structure and performance, thus limiting the efficiency of developing innovative nanozymes. This study...