AIMC Topic: Bayes Theorem

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Predicting Coronary Heart Disease Using Data Mining and Machine Learning Solutions.

Anais da Academia Brasileira de Ciencias
This research focuses on predicting cardiovascular disease using machine learning classification strategies. The study presents a unique approach by integrating multiple machine learning techniques, leveraging the strengths of Random Forest and Gradi...

Toward a general framework for AI-enabled prediction in crop improvement.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
A theoretical framework for AI and ensembled prediction for crop improvement is introduced and demonstrated using the logistic map. Symbolic/sub-symbolic AI-based prediction can increase predictive skill with increase in system complexity. The curse ...

Bayesian optimization and machine learning for vaccine formulation development.

PloS one
Developing vaccines with a better stability is an area of improvement to meet the global health needs of preventing infectious diseases. With the advancement of data science and artificial intelligence, innovative approaches have emerged. This manusc...

Robust Bayesian brain extraction by integrating structural subspace-based spatial prior into deep neural networks.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurate and robust brain extraction, or skull stripping, is essential for studying brain development, aging, and neurological disorders. However, brain images exhibit substantial data heterogeneity due to differences in contrast and geometric charac...

Prediction of air temperature and humidity in greenhouses via artificial neural network.

PloS one
Accurate prediction of greenhouse temperature and relative humidity is critical for developing environmental control systems. Effective regulation strategies can help improve crop yields while reducing energy consumption. In this study, Multilayer Pe...

Comparative analysis of AI algorithms on real medical data for chronic pain detection.

International journal of medical informatics
BACKGROUND AND OBJECTIVE: Chronic pain is a pervasive healthcare challenge with profound implications for patient well-being, clinical decision-making, and resource allocation. Traditional detection methods often rely on subjective assessments and ma...

Assessing simulation-based supervised machine learning for demographic parameter inference from genomic data.

Heredity
The ever-increasing availability of high-throughput DNA sequences and the development of numerous computational methods have led to considerable advances in our understanding of the evolutionary and demographic history of populations. Several demogra...

Optimization of hemocompatibility metrics in ventricular assist device design using machine learning and CFD-based response surface analysis.

The International journal of artificial organs
Ventricular assist devices (VADs) are essential for end-stage heart failure patients, but their design must balance hydraulic efficiency and hemocompatibility to minimize blood damage. This study presents a multi-objective optimization framework inte...

Long-term exposure to PM and liver cancer mortality: Insights into the role of smaller particulate fractions.

Ecotoxicology and environmental safety
Particulate matter (PM) is a recognized carcinogen, but the effects of PM on liver cancer remain underexplored. This study investigates the long-term association between PM and liver cancer mortality, as well as the contribution of smaller particles ...

Recognition of flight cadets brain functional magnetic resonance imaging data based on machine learning analysis.

PloS one
The rapid advancement of the civil aviation industry has attracted significant attention to research on pilots. However, the brain changes experienced by flight cadets following their training remain, to some extent, an unexplored territory compared ...