AI Medical Compendium Topic:
Bayes Theorem

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A Bibliometric Analysis and Benchmark of Machine Learning and AutoML in Crash Severity Prediction: The Case Study of Three Colombian Cities.

Sensors (Basel, Switzerland)
Traffic accidents are of worldwide concern, as they are one of the leading causes of death globally. One policy designed to cope with them is the design and deployment of road safety systems. These aim to predict crashes based on historical records, ...

Detection of child depression using machine learning methods.

PloS one
BACKGROUND: Mental health problems, such as depression in children have far-reaching negative effects on child, family and society as whole. It is necessary to identify the reasons that contribute to this mental illness. Detecting the appropriate sig...

Application of machine learning to the monitoring and prediction of food safety: A review.

Comprehensive reviews in food science and food safety
Machine learning (ML) has proven to be a useful technology for data analysis and modeling in a wide variety of domains, including food science and engineering. The use of ML models for the monitoring and prediction of food safety is growing in recent...

Hyperparameter Optimization for Transfer Learning of VGG16 for Disease Identification in Corn Leaves Using Bayesian Optimization.

Big data
One of the world's most widely grown crops is corn. Crop loss due to diseases has a major economic effect, putting the food supply in jeopardy. In many parts of the world, lack of infrastructure still slows disease diagnosis. In this context, effecti...

Risk Prediction by Using Artificial Neural Network in Global Software Development.

Computational intelligence and neuroscience
The demand for global software development is growing. The nonavailability of software experts at one place or a country is the reason for the increase in the scope of global software development. Software developers who are located in different part...

A Comparative Performance Assessment of Optimized Multilevel Ensemble Learning Model with Existing Classifier Models.

Big data
To predict the class level of any classification problem, predictive models are used and mostly a single predictive model is built to predict the class level of any classification problem; current research considers multiple predictive models to pred...

A study of uncertainties in groundwater vulnerability modelling using Bayesian model averaging (BMA).

Journal of environmental management
Bayesian Model Averaging (BMA) is used to study inherent uncertainties using the Basic DRASTIC Framework (BDF) for assessing the groundwater vulnerability in a study area related to Lake Urmia. BMA is naturally an Inclusive Multiple Modelling (IMM) s...

An Improved Brain MRI Classification Methodology Based on Statistical Features and Machine Learning Algorithms.

Computational and mathematical methods in medicine
In this paper, we have proposed a novel methodology based on statistical features and different machine learning algorithms. The proposed model can be divided into three main stages, namely, preprocessing, feature extraction, and classification. In t...

A Multilayer Perceptron Neural Network Model to Classify Hypertension in Adolescents Using Anthropometric Measurements: A Cross-Sectional Study in Sarawak, Malaysia.

Computational and mathematical methods in medicine
This study outlines and developed a multilayer perceptron (MLP) neural network model for adolescent hypertension classification focusing on the use of simple anthropometric and sociodemographic data collected from a cross-sectional research study in ...

Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting.

Sensors (Basel, Switzerland)
Data-driven forecasts of air quality have recently achieved more accurate short-term predictions. However, despite their success, most of the current data-driven solutions lack proper quantifications of model uncertainty that communicate how much to ...