AIMC Topic: Machine Learning

Clear Filters Showing 28191 to 28200 of 34417 articles

Quantifying regional transport contributions to PM-bound trace elements in a southeast coastal island of China: Insights from a machine learning approach.

Environmental pollution (Barking, Essex : 1987)
Identifying and quantifying pollution sources and their associated health risks are essential for formulating effective pollution control policies. This study analyzed PM-bound trace elements based on one year of sampling data collected from a low-PM...

Soil type and content of macro-elements determine hotspots of Cu and Ni accumulation in soils of subarctic industrial barren: inference from a cascade machine learning.

Environmental pollution (Barking, Essex : 1987)
Aerial technogenic pollution from the activity of ferrous and non-ferrous metallurgy resulting in degradation of vulnerable natural ecosystems is a principal environmental problem in Russian Arctic. The industrial barren in the vicinity of Monchegors...

A Machine Learning-Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia.

Cancer research
UNLABELLED: Combination therapies are one potential approach to improve the outcomes of patients with relapsed/refractory (R/R) disease. However, comprehensive testing in scarce primary patient material is hampered by the many drug combination possib...

Predictive modeling and interpretability analysis of bioconcentration factors for organic chemicals in fish using machine learning.

Environmental pollution (Barking, Essex : 1987)
Chemicals are misused and released into the environment, causing adverse effects on people and ecosystems. Assessing the potential environmental risks of these chemicals before their use is crucial. The bioconcentration factor (BCF) is a key paramete...

Plastics detection and sorting using hyperspectral sensing and machine learning algorithms.

Waste management (New York, N.Y.)
Plastic waste second life management requires effective detection (and sorting if necessary) techniques to tackle the environmental challenge it poses. This research explores the application of hyperspectral imaging in the spectral range 900-1700 nm ...

Machine learning-driven risk prediction and feature identification for major depressive disorder and its progression: an exploratory study based on five years of longitudinal data from the US national health survey.

Journal of affective disorders
BACKGROUND: Major depressive disorder (MDD) presents significant public health challenges due to its increasing prevalence and complex risk factors. This study systematically analyzed data from 2019 to 2023 to explore trends in MDD incidence, symptom...

Artificial intelligence in bacterial diagnostics and antimicrobial susceptibility testing: Current advances and future prospects.

Biosensors & bioelectronics
Recently, artificial intelligence (AI) has emerged as a transformative tool, enhancing the speed, accuracy, and scalability of bacterial diagnostics. This review explores the role of AI in revolutionizing bacterial detection and antimicrobial suscept...

Data integrity of food and machine learning: Strategies, advances and prospective.

Food chemistry
Data integrity is an emerging concept aimed at recording real food properties in the form of data throughout the food lifecycle. However, due to the one-sided nature of current food control data, the comprehensive implementation of data integrity has...

Artificial Intelligence to Improve Blood Pressure Control: A State-of-the-Art Review.

American journal of hypertension
Hypertension remains a major global health challenge, contributing to significant morbidity and mortality. Advances in artificial intelligence (AI) and machine learning (ML) are transforming hypertension care by enhancing blood pressure (BP) measurem...