AIMC Topic: Bayes Theorem

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Artificial intelligence-based prediction model for the elemental occurrence form of tailings and mine wastes.

Environmental research Feb 2, 2024
With the advent of the second industrial revolution, mining and metallurgical processes generate large volumes of tailings and mine wastes (TMW), which worsens global environmental pollution. Studying the occurrence of metal and metalloid elements in...
Mining Bayes Theorem Industrial Waste Artificial Intelligence Metals Environmental Monitoring
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Bayesian hypernetwork collaborates with time-difference evolutional network for temporal knowledge prediction.

Neural networks : the official journal of the International Neural Network Society Feb 1, 2024
A Temporal Knowledge Graph (TKG) is a sequence of Knowledge Graphs (KGs) attached with time information, in which each KG contains the facts that co-occur at the same timestamp. Temporal knowledge prediction (TKP) aims to predict future events given ...
Humans Neural Networks, Computer Uncertainty Algorithms Bayes Theorem Time Factors Knowledge
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Optimization of medium components for protein production by Escherichia coli with a high-throughput pipeline that uses a deep neural network.

Journal of bioscience and bioengineering Feb 1, 2024
To optimize rapidly the medium for green fluorescent protein expression by Escherichia coli with an introduced plasmid, pRSET/emGFP, a single-cycle optimization pipeline was applied. The pipeline included a deep neural network (DNN) and mathematical ...
Neural Networks, Computer Bayes Theorem Green Fluorescent Proteins Algorithms Escherichia coli
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An artificial intelligence approach to predicting personality types in dogs.

Scientific reports Jan 29, 2024
Canine personality and behavioural characteristics have a significant influence on relationships between domestic dogs and humans as well as determining the suitability of dogs for specific working roles. As a result, many researchers have attempted ...
Algorithms Animals Personality Bayes Theorem Artificial Intelligence Machine Learning Humans Dogs
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A quantum-based oversampling method for classification of highly imbalanced and overlapped data.

Experimental biology and medicine (Maywood, N.J.) Jan 28, 2024
Data imbalance is a challenging problem in classification tasks, and when combined with class overlapping, it further deteriorates classification performance. However, existing studies have rarely addressed both issues simultaneously. In this article...
Bayes Theorem Support Vector Machine Cluster Analysis Algorithms
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Data-driven approach to quantify trust in medical devices using Bayesian networks.

Experimental biology and medicine (Maywood, N.J.) Jan 27, 2024
Bayesian networks are increasingly used to quantify the uncertainty of subjective and stochastic concepts such as trust. In this article, we propose a data-driven approach to estimate Bayesian parameters in the domain of wearable medical devices. Our...
Trust Bayes Theorem
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Designing optimal behavioral experiments using machine learning.

eLife Jan 23, 2024
Computational models are powerful tools for understanding human cognition and behavior. They let us express our theories clearly and precisely and offer predictions that can be subtle and often counter-intuitive. However, this same richness and abili...
Machine Learning Cognition Humans Computer Simulation Bayes Theorem Awareness
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Can adverse childhood experiences predict chronic health conditions? Development of trauma-informed, explainable machine learning models.

Frontiers in public health Jan 15, 2024
INTRODUCTION: Decades of research have established the association between adverse childhood experiences (ACEs) and adult onset of chronic diseases, influenced by health behaviors and social determinants of health (SDoH). Machine Learning (ML) is a p...
Lung Diseases Blood Glucose Myocardial Infarction Diabetes Mellitus Adverse Childhood Experiences Chronic Disease Humans Machine Learning Bayes Theorem Adult
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Compositional diversity in visual concept learning.

Cognition Jan 14, 2024
Humans leverage compositionality to efficiently learn new concepts, understanding how familiar parts can combine together to form novel objects. In contrast, popular computer vision models struggle to make the same types of inferences, requiring more...
Spatial Learning Concept Formation Generalization, Psychological Neural Networks, Computer Bayes Theorem Humans
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An artificial intelligence approach to predict infants' health status at birth.

International journal of medical informatics Jan 5, 2024
BACKGROUND: Machine learning could be used for prognosis/diagnosis of maternal and neonates' diseases by analyzing the data sets and profiles obtained from a pregnant mother.
Female Infant, Newborn Algorithms Pregnancy Health Status Bayes Theorem Humans Artificial Intelligence Machine Learning
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