AIMC Topic: Machine Learning

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A comparative study of various statistical and machine learning models for predicting restaurant demand in Bangladesh.

PloS one
Precise demand forecasting has become crucial for merchants due to the growing complexity of client behavior and market dynamics. This allows them to enhance inventory management, minimize instances of stock outs, and enhance overall operational effi...

The Performance of Biomarkers for the Diagnosis of Parkinson Disease: A Systematic Review.

The American journal of medicine
BACKGROUND: Early diagnosis of Parkinson disease remains challenging due to the current clinical diagnostic approach. With machine learning emerging as a powerful tool for biomarker discovery, we aimed to determine whether biomarkers processed by mac...

Use of Transfer Learning for the Automated Segmentation and Detection of Swallows via Digital Cervical Auscultation in Children.

Dysphagia
Digital cervical auscultation (CA) has high diagnostic test accuracy in the detection of aspiration in children. However, the clinical application of digital CA is limited because swallow sound recordings require manual segmentation by trained expert...

Explainable AI assisted vertebral refracture diagnosis after percutaneous vertebroplasty through effective feature engineering and stacked ensemble learning.

International journal of medical informatics
BACKGROUND AND OBJECTIVE: To develop and validate a machine learning model based on stacking ensemble learning and feature selection strategies to predict vertebral refracture risk after percutaneous vertebroplasty.

AI-chemometric assisted real-time monitoring of tryptophan fermentation process using a sensor fusion strategy.

Food chemistry
Tryptophan, an essential amino acid, crucially impacts neuronal function, metabolism, immunity, and gut homeostasis. Microbial fermentation is the mainstream method for tryptophan production. The precise production process is essential for ensuring b...

Impact of e-waste pollutant exposure on renal injury and oxidative stress biomarkers: Evidence from causal machine learning.

Journal of hazardous materials
Global electronification has driven an unprecedented surge in electronic and electrical waste (e-waste), with approximately 75 % of this e-waste informally managed, releasing hazardous chemicals. Traditional association analyses have limited ability ...

Developing a CT radiomics-based model for assessing split renal function using machine learning.

Japanese journal of radiology
PURPOSE: This study aims to investigate whether non-contrast computed tomography radiomics can effectively reflect split renal function and to develop a radiomics model for its assessment.

A systematic review: Brain age gap as a promising early diagnostic biomarker for Alzheimer's disease.

Journal of the neurological sciences
Alzheimer's disease (AD) is a progressive neurodegenerative disorder for which there is currently no cure, and its incidence is on the rise. Early detection is essential for timely intervention and slowing the progression of the disease. While the br...

Artificial intelligence in bone metastasis analysis: Current advancements, opportunities and challenges.

Computers in biology and medicine
BACKGROUND: Artificial Intelligence is transforming medical imaging, particularly in the analysis of bone metastases (BM), a serious complication of advanced cancers. Machine learning and deep learning techniques offer new opportunities to improve de...

Acquired resistance in cancer: towards targeted therapeutic strategies.

Nature reviews. Cancer
Development of acquired therapeutic resistance limits the efficacy of cancer treatments and accounts for therapeutic failure in most patients. How resistance arises, varies across cancer types and differs depending on therapeutic modalities is incomp...