AIMC Topic: Machine Learning

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Evaluating and implementing machine learning models for personalised mobile health app recommendations.

PloS one
This paper focuses on the evaluation and recommendation of healthcare applications in the mHealth field. The increase in the use of health applications, supported by an expanding mHealth market, highlights the importance of this research. In this stu...

Weighted Multi-Modal Contrastive Learning Based Hybrid Network for Alzheimer's Disease Diagnosis.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Multiple imaging modalities and specific proteins in the cerebrospinal fluid, providing a comprehensive understanding of neurodegenerative disorders, have been widely used for computer-aided diagnosis of Alzheimer's disease (AD). Given the proven eff...

Determining IFI44 as a key lupus nephritis's biomarker through bioinformatics and immunohistochemistry.

Renal failure
BACKGROUND: Lupus nephritis (LN) emerges as a severe complication of systemic lupus erythematosus (SLE), significantly affecting patient survival. Despite improvements in treatment reducing LN's morbidity and mortality, existing therapies remain subo...

Reevaluating feature importance in machine learning: concerns regarding SHAP interpretations in the context of the EU artificial intelligence act.

Water research
This paper critically examines the analysis conducted by Maußner et al. on AI analysis, particularly their interpretation of feature importances derived from various machine learning models using SHAP (SHapley Additive exPlanations). Although SHAP ai...

Multitask learning model for predicting non-coding RNA-disease associations: Incorporating local and global context.

Methods (San Diego, Calif.)
Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are crucial non-coding RNAs involved in various diseases. Understanding these interactions is vital for advancing diagnostic, preventive, and therapeutic strategies. Existing computational methods...

Exploring emotional climate recognition in peer conversations through bispectral features and affect dynamics.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Emotion recognition in conversations using artificial intelligence (AI) has gained significant attention due to its potential to provide insights into human social behavior. This study extends AI-based emotion recognition to...

ListPred: A predictive ML tool for virulence potential and disinfectant tolerance in Listeria monocytogenes.

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases
Despite current surveillance and sanitation strategies, foodborne pathogens continue to threaten the food industry and public health. Whole genome sequencing (WGS) has reached an unprecedented resolution to analyse and compare pathogenic bacterial is...

Machine learning-assisted prediction of gas production during co-pyrolysis of biomass and waste plastics.

Waste management (New York, N.Y.)
A general method for predicting gas yield is crucial in biomass and plastics co-pyrolysis. This study employed two machine learning methods to forecast gas yield in co-pyrolysis. Comparing the predictive performance of Support Vector Regression (SVR)...

Application of machine learning in the context of reoperation, outcome and management after ACL reconstruction - A systematic review.

The Knee
INTRODUCTION: Machine learning-based tools are becoming increasingly popular in clinical practice. They offer new possibilities but are also limited in their reliability and accuracy.

Improvement of near-infrared spectroscopic assessment methods for the quality of Keemun black tea: Utilizing transfer learning.

Food research international (Ottawa, Ont.)
Keemun black tea, a renowned Chinese black tea, presents challenges in quality assessment due to variability in data across different years. To address this, we developed transfer learning algorithms using near-infrared spectral data. The qualitative...