AIMC Topic: Machine Learning

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Is this real? Susceptibility to deepfakes in machines and humans.

Cognitive research: principles and implications
Deepfakes are synthetic media created by deep-generative methods to fake a person's audio-visual representation. Growing sophistication of deepfake technology poses significant challenges for both machine learning (ML) algorithms and humans. Here we ...

A Machine Learning-Driven Electrophysiological Platform for Real-Time Tumor-Neural Interaction Analysis and Modulation.

Nature communications
Neural-tumor electrophysiology-marked by pathological membrane potentials and ion channel dysregulation-emerges as actionable targets to curb tumor aggression. Yet, how neural-driven bioelectrical crosstalk dynamically regulates tumors within functio...

Machine learning-based cardiovascular risk calculator for non-cardiac surgery.

Open heart
BACKGROUND: Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least one cardiovascular risk factor. It is estimated that the 30-day mortality is between 0.5% and 2%.The main objective of this st...

Hybrid additive manufacturing and data-guided design optimization for graded anterior cruciate ligament engineering.

Biomedical materials (Bristol, England)
Interface tissues, such as the enthesis connecting ligaments to bone, present multiphasic architectures with continuous gradients in structure, composition, and mechanics. Engineering such complex transitions remains a major challenge in biofabricati...

Unbiased inference for echocardiogram urgency prediction using double machine learning.

PloS one
The increased utilization of echocardiography in clinical practice has witnessed a substantial rise, underscoring its pivotal role as a diagnostic tool for various cardiovascular conditions. However, due to the relative scarcity of echocardiography t...

Diagnostic performance of eNose technology in detecting colorectal cancer recurrence: A prospective evaluation.

PloS one
INTRODUCTION: After curative treatment for colorectal cancer (CRC), there is a 15% risk of recurrence. Early detection of an asymptomatic recurrence may lead to curative treatment options. To date, follow-up strategies do not have optimal sensitivity...

Machine learning-based prediction of the axial load capacity of UHPC strengthened reinforced concrete columns: A comparative analysis.

PloS one
This study develops and evaluates machine learning (ML) models to predict the axial load capacity (Pu) of reinforced concrete (RC) columns strengthened with ultra-high-performance concrete (UHPC) jackets. A comprehensive experimental database contain...

Applications of machine learning and natural language processing to neurocognitive outcomes in posttreatment cancer survivors: a scoping review.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
PURPOSE: This scoping review explores how machine learning (ML) and natural language processing (NLP) are used to detect, characterize, and predict neurocognitive symptoms in cancer survivors across age groups. The review had two goals: (1) to compar...

Personalized Machine Learning Intervention to Improve Sleep Quality Using Wearable Technology in Healthy Middle-Aged Adults From Mexico City: Protocol for a Pilot Randomized Controlled Trial.

JMIR research protocols
BACKGROUND: In 2019, global sleep surveys reported that 80% of adults want to improve their sleep quality, and in 2021, 45% were reported to be dissatisfied with their sleep. In 2025, among American adults, 37% reported sleep dissatisfaction and 38% ...

Probing curcumin reactive conformers in keto-enol tautomerization enhanced by clustering with t-SNE.

Journal of molecular modeling
CONTEXT: The extensive conformational space of flexible molecules poses a significant challenge for predicting chemical reactivity through quantum chemical methods. For curcumin, whose keto-enol tautomerization is crucial to its biological activity a...