AIMC Topic: Machine Learning

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Advanced smart human activity recognition system for disabled people using artificial intelligence with snake optimizer techniques.

Scientific reports
Human Activity Recognition (HAR) has become an active research area in recent years due to its applicability in various domains and the growing need for convenient facilities and intelligent homes for the elderly. Physical activity tends to decrease ...

Identifying significant features in adversarial attack detection framework using federated learning empowered medical IoT network security.

Scientific reports
The expansion of the Internet of Medical Things (IoHT) presents significant advantages for healthcare over improved data-driven insights and connectivity and offers critical cybersecurity challenges. Attacks are a serious risk for neural network secu...

Common Variable Immunodeficiency Disorder: A Decade of Insights from a Cohort of 150 Patients in India and the Use of Machine Learning Algorithms to Predict Severity.

Journal of clinical immunology
Common Variable Immunodeficiency (CVID) is a heterogeneous disorder characterized by impaired antibody production and recurrent infections. In this study we investigated the clinical and immunological features of CVID in Indian patients and develops ...

Assessment of pulse wave velocity through weighted visibility graph metrics from photoplethysmographic signals.

Scientific reports
Pulse Wave Velocity (PWV) is a widely recognized non-invasive biomarker of arterial stiffness and an independent predictor of cardiovascular risk, including atherosclerosis, hypertension, and vascular aging. Accurate, accessible estimation of PWV is,...

Identification of core genes in the extracellular matrix and the regulatory mechanisms of the immune microenvironment in idiopathic pulmonary fibrosis using WGCNA and machine learning methods.

PloS one
OBJECTIVE: This research aims to detect genes associated with the extracellular matrix (ECM) in idiopathic pulmonary fibrosis (IPF) using bioinformatics techniques and investigate their relationships with immune infiltration, with the goal of identif...

Revealing potential interfering genes between abdominal aortic aneurysm and periodontitis through machine learning and bioinformatics analysis.

PloS one
This study aimed to identify potential interacting genes between abdominal aortic aneurysm (AAA) and periodontitis. To achieve this, we obtained datasets of AAA and periodontitis from the GEO database, conducted differential analysis on the AAA datas...

Immunophenotyping identifies key immune biomarkers for coronary artery disease through machine learning.

PloS one
INTRODUCTION: The differences among immune subtypes in coronary artery disease (CAD), their interrelationships, and the associated immune biomarkers remain incompletely understood.

Nanobodies targeting cytokines for the amelioration of autoimmune diseases.

International immunopharmacology
Autoimmune diseases are driven by dysregulated cytokine networks, where excessive cytokines such as TNF-α, IL-6, IL-17, and IL-23 promote chronic inflammation and tissue damage. While monoclonal antibodies effectively neutralise these cytokines, they...

Increasing the Scale of the Mass Spectrometry Query Language Compendium with Explainable AI.

Analytical chemistry
A significant bottleneck in metabolomics data interpretation is the effective use of domain knowledge to assign structural information based on fragmentation patterns. The mass spectrometry query language (MassQL) aims to make this process accessible...

Causal effect of conventional anti-dementia drugs on economic burden: an orthogonal double/debiased machine learning approach.

BMC geriatrics
BACKGROUND: The Inflation Reduction Act (IRA) did not introduce a cap on out-of-pocket (OOP) for newly approved Alzheimer's Disease (AD) drugs, such as lecanemab which is covered under Medicare Part B. Therefore, expanding the use of conventional ant...