AIMC Topic: Machine Learning

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Nucleic Acid Plate Culture: Label-Free and Naked-Eye-Based Digital Loop-Mediated Isothermal Amplification in Hydrogel with Machine Learning.

ACS sensors
Digital nucleic acid amplification enables the absolute quantification of single molecules. However, due to the ultrasmall reaction volume in the digital system (, short light path), most digital systems are limited to fluorescence signals, while lab...

Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review.

Sensors (Basel, Switzerland)
Reinforcement learning (RL) has emerged as a dynamic and transformative paradigm in artificial intelligence, offering the promise of intelligent decision-making in complex and dynamic environments. This unique feature enables RL to address sequential...

Machine Learning Gene Signature to Metastatic ccRCC Based on ceRNA Network.

International journal of molecular sciences
Clear-cell renal-cell carcinoma (ccRCC) is a silent-development pathology with a high rate of metastasis in patients. The activity of coding genes in metastatic progression is well known. New studies evaluate the association with non-coding genes, su...

Development of machine learning-based shelf-life prediction models for multiple marine fish species and construction of a real-time prediction platform.

Food chemistry
At least 10 million tons of seafood products are spoiled or damaged during transportation or storage every year worldwide. Monitoring the freshness of seafood in real time has become especially important. In this study, four machine learning algorith...

Towards quantifying biomarkers for respiratory distress in preterm infants: Machine learning on mid infrared spectroscopy of lipid mixtures.

Talanta
Neonatal respiratory distress syndrome (nRDS) is a challenging condition to diagnose which can lead to delays in receiving appropriate treatment. Mid infrared (IR) spectroscopy is capable of measuring the concentrations of two diagnostic nRDS biomark...

Illuminating patterns of firefly abundance using citizen science data and machine learning models.

The Science of the total environment
As insect populations decline in many regions, conservation biologists are increasingly tasked with identifying factors that threaten insect species and developing effective strategies for their conservation. One insect group of global conservation c...

Machine learning applied to gait analysis data in cerebral palsy and stroke: A systematic review.

Gait & posture
BACKGROUND: Among neurological pathologies, cerebral palsy and stroke are the main contributors to walking disorders. Machine learning methods have been proposed in the recent literature to analyze gait data from these patients. However, machine lear...

Exploring tumor heterogeneity in colorectal liver metastases by imaging: Unsupervised machine learning of preoperative CT radiomics features for prognostic stratification.

European journal of radiology
OBJECTIVES: This study aimed to investigate tumor heterogeneity of colorectal liver metastases (CRLM) and stratify the patients into different risk groups of prognoses following liver resection by applying an unsupervised radiomics machine-learning a...

Predicting adverse long-term neurocognitive outcomes after pediatric intensive care unit admission.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Critically ill children may suffer from impaired neurocognitive functions years after ICU (intensive care unit) discharge. To assess neurocognitive functions, these children are subjected to a fixed sequence of tests. Underg...