AIMC Topic: Machine Learning

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Smart defense based on explainable stacked machine learning architecture for securing internet of health things with K-means clustering.

Scientific reports
The Internet of Health Things (IoHT) transformed current healthcare by facilitating real-time patient monitoring and remote diagnosis via networked medical equipment. The advanced prevalence of interconnected medical devices creates substantial vulne...

PS3N: leveraging protein sequence-structure similarity for novel drug-drug interaction discovery.

Scientific reports
Adverse drug events represent a key challenge in public health, especially concerning drug safety profiling and drug surveillance. Drug-drug interactions represent one of the most popular types of adverse drug events. Most computational approaches to...

Machine learning for early prediction of secondary cancer after radiotherapy.

Scientific reports
Secondary cancers (SCs) following radiotherapy (RT) represent a significant long-term risk of cancer survivors, necessitating accurate predictive models for early intervention. This study developed a machine learning (ML) model integrating clinical, ...

Addressing data heterogeneity in distributed medical imaging with heterosync learning.

Nature communications
Data heterogeneity critically limits distributed artificial intelligence (AI) in medical imaging. We propose HeteroSync Learning (HSL), a privacy-preserving framework that addresses heterogeneity through: (1) Shared Anchor Task (SAT) for cross-node r...

Annotated IFCB plankton images from the Mediterranean Sea.

Scientific data
The Imaging FlowCytobot (IFCB), supported by machine learning-based classifications, has revolutionized plankton research by automating plankton monitoring and considerably increasing sampling resolution. However, building a training set of labeled I...

Explore brain-inspired machine intelligence for connecting dots on graphs through holographic blueprint of oscillatory synchronization.

Nature communications
Neural coupling in both neuroscience and AI emerges dynamic oscillatory patterns that encode abstract concepts. To that end, we hypothesize that a deeper understanding of the neural mechanisms that determine brain rhythms could inspire next-generatio...

Machine Learning Applications in Population and Public Health: Guidelines for Development, Testing, and Implementation.

JMIR public health and surveillance
Machine learning (ML), a subset of artificial intelligence, uses large datasets to identify patterns between potential predictors and outcomes. ML involves iterative learning from data and is increasingly used in population and public health. Example...

Assessment of climate change impacts on arsenic contamination in groundwater through machine learning, remote sensing, and GIS: a review.

Environmental geochemistry and health
More than 50% of the world's largest countries and cities depend on groundwater for their daily needs. In particular, 80% of the largest cities in the Middle East, South Asia, and Central Asia rely on groundwater for drinking, irrigation, and industr...

Screening mild cognitive impairment using aspects of personal, social, and functional lifestyle: Machine Learning Approaches.

PloS one
OBJECTIVE: Mild cognitive impairment (MCI) signals cognitive decline beyond normal aging and increases dementia risk. Early identification enables preventative interventions, yet many patients in primary care go undetected. This study examines whethe...

MIASurviveMTP: Machine learning for immediate assessment and survival prediction after massive transfusion protocol.

PloS one
Early triage of trauma patients requiring massive transfusion (MT) may help to marshal appropriate resources and improve treatment and outcome. Artificial intelligence (AI) and machine learning (ML) offer theoretical advantages compared to convention...