AIMC Topic: Machine Learning

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Evaluating the change and trend of construction land in Changsha City based GeoSOS-FLUS model and machine learning methods.

Scientific reports
This study systematically analyzes the land use changes in Changsha City from 2000 to 2023. Three classification models-Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Artificial Neural Network (ANN) were employed to evaluate the accu...

Multidisciplinary characterization of embarrassment through behavioral and acoustic modeling.

Scientific reports
Embarrassment is a social emotion that shares many characteristics with social anxiety (SA). Most people experience embarrassment in their daily lives, but it is quite overlooked in research. We characterized embarrassment through an interdisciplinar...

Multimodal feature fusion machine learning for predicting chronic injury induced by engineered nanomaterials.

Nature communications
Concerns regarding chronic injuries (e.g., fibrosis and carcinogenesis) induced by nanoparticles raised public health concerns and need to be rapidly assessed in hazard identification. Although in silico analysis is commonly used for risk assessment ...

Deciphering the dark cancer phosphoproteome using machine-learned co-regulation of phosphosites.

Nature communications
Mass spectrometry-based phosphoproteomics offers a comprehensive view of protein phosphorylation, yet our limited knowledge about the regulation and function of most phosphosites hampers the extraction of meaningful biological insights. To address th...

Urban change detection: assessing biophysical drivers using machine learning and Google Earth Engine.

Environmental monitoring and assessment
Urban areas are experiencing rapid transformations, driven by population growth, economic development, and policy changes. Understanding and monitoring these dynamic changes is crucial for sustainable urban planning and management. This study leverag...

Machine learning-based habitat mapping of the invasive Prosopis juliflora in Sharjah, UAE.

Environmental monitoring and assessment
Prosopis juliflora, one of the most invasive trees, adversely affects the ecosystem and native plant communities in arid lands. This disrupts biodiversity and depletes water resources, posing significant ecological and economic challenges. Several at...

A liquid metal-based sticky conductor for wearable and real-time electromyogram monitoring with machine learning classification.

Journal of materials chemistry. B
Skin electronics face challenges related to the interface between rigid and soft materials, resulting in discomfort and signal inaccuracies. This study presents the development and characterization of a liquid metal-polydimethylsiloxane (LM-PDMS) sti...

Machine learning applications to classify and monitor medication adherence in patients with type 2 diabetes in Ethiopia.

Frontiers in endocrinology
BACKGROUND: Medication adherence plays a crucial role in determining the health outcomes of patients, particularly those with chronic conditions like type 2 diabetes. Despite its significance, there is limited evidence regarding the use of machine le...

Deciphering the Pharmacological Potential of Kouqiangjie Formula for the Treatment of Diabetic Periodontitis Based on Network Pharmacology, Machine Learning, Molecular Dynamics, and Animal Experiments.

Drug design, development and therapy
BACKGROUND: Periodontitis (PD) and type 2 diabetes mellitus (T2DM) represent interlinked global health burdens, commonly causing significant clinical complications when coincident. Therefore, managing both conditions (T2DM with periodontitis, DP) sim...

Enhanced machine learning predictive modeling for delirium in elderly ICU patients with COPD and respiratory failure: A retrospective study based on MIMIC-IV.

PloS one
BACKGROUND AND OBJECTIVE: Elderly patients with Chronic obstructive pulmonary disease (COPD) and respiratory failure admitted to the intensive care unit (ICU) have a poor prognosis, and the occurrence of delirium further worsens outcomes and increase...