AIMC Topic: Bayes Theorem

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Using Latent Dirichlet Allocation Topic Modeling to Uncover Latent Research Topics and Trends in Renal Cell Carcinoma: Bibliometric Review.

JMIR cancer
BACKGROUND: Renal cell carcinoma (RCC) is a common, often lethal kidney cancer that originates in the renal cortex. Its incidence is rising, and major factors include smoking, obesity, and hypertension, though its etiology is uncertain. While surgery...

Pediatric diabetes prediction using machine learning.

Scientific reports
Diabetes is a chronic condition that affects a substantial portion of the global population and is linked to elevated mortality rates and a range of severe health complications. Despite its clinical importance, progress in diabetes research is often ...

Bayesian neural networks for genomic prediction: uncertainty quantification and SNP interpretation with SHAP and GWAS.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
This study presents a Bayesian neural networks framework with LASSO regularization and the GSMeSP interpretability tool, enabling accurate, uncertainty-aware, and biologically interpretable genomic prediction. Deep learning offers significant potenti...

A hybrid ensembling and autoencoder scheme for improving sensing reliability in cognitive radio networks.

PloS one
This paper proposes a hybrid ensemble classifier with denoising autoencoder (ECDAE) framework to address reliability and robustness challenges in cooperative spectrum sensing (CSS) for cognitive radio networks (CRNs). The proposed framework first emp...

A cost effective machine learning based network intrusion detection system using Raspberry Pi for real time analysis.

PloS one
In an increasingly interconnected world, the security of sensitive data and critical operations is paramount. This study presents the development of a Network Intrusion Detection System (NIDS) that analyzes both inbound and outbound network traffic t...

A Robust Gaussian Process Paradigm for Predictive Modeling on Small Data sets in Environmental Science: A Case Study in Ballasted Flocculation.

Environmental science & technology
Environmental processes including ballasted flocculation (BF) present significant optimization challenges due to complex multicomponent interactions and small, heterogeneous experimental data sets that frequently lead to overfitted machine learning (...

Enhanced machine learning and hybrid ensemble approaches for Coronary Heart Disease prediction.

PloS one
Coronary heart disease (CHD) remains the leading cause of mortality worldwide, disproportionately affecting low- and middle-income countries where diagnostic resources are limited. Traditional statistical models often fail to deliver adequate predict...

An augmented preference-based Bayesian approach for optimizing neuromodulation stimulation parameters using meta learning.

Journal of neural engineering
Electrical neuromodulation is increasingly used in the treatment of neurological disorders; however, the selection of stimulation parameters that provide optimal therapeutic benefits remains a major challenge. Moreover, identifying pathological bioma...

Towards AI-based precision rehabilitation via contextual model-based reinforcement learning.

Journal of neuroengineering and rehabilitation
BACKGROUND: Stroke is a condition marked by considerable variability in lesions, recovery trajectories, and responses to therapy. Consequently, precision medicine in rehabilitation post-stroke, which aims to deliver the "right intervention, at the ri...

Virtual Brain Inference (VBI), a flexible and integrative toolkit for efficient probabilistic inference on whole-brain models.

eLife
Network neuroscience has proven essential for understanding the principles and mechanisms underlying complex brain (dys)function and cognition. In this context, whole-brain network modeling-also known as virtual brain modeling-combines computational ...