AIMC Topic: Machine Learning

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Artificial intelligence-driven label-free detection of chronic myeloid leukemia cells using ghost cytometry.

Scientific reports
Early diagnosis and treatment initiation of chronic myeloid leukemia (CML) are considered to increase the rate of deep molecular response. However, the early diagnosis of CML is challenging due to the absence of clinical symptoms and peripheral blood...

Comprehensive datasets for RNA design, machine learning, and beyond.

Scientific reports
RNA molecules are essential in regulating biological processes such as gene expression, cellular differentiation, and development. Accurately predicting RNA secondary structures and designing sequences that fold into specific configurations remain si...

Predicting PLGA nanoparticle size and zeta potential in synthesis for application of drug delivery via machine learning analysis.

Scientific reports
This study employed multiple machine learning (ML) methods to model and predict key attributes of PLGA nanoparticles, specifically particle size and zeta potential. The predictions were based on input variables, including PLGA polymer type, PLGA conc...

Potential use of saliva infrared spectra and machine learning for a minimally invasive screening test for congenital syphilis in infants.

Scientific reports
Congenital syphilis is a global public health issue, and its diagnostic complexity poses a challenge to early treatment. Fourier Transform Infrared Spectroscopy (FTIR) is a promising technological tool that facilitates the detection and diagnosis of ...

Design of Block-Scrambling-Based privacy protection mechanism in healthcare using fusion of transfer learning models with Hippopotamus optimization algorithm.

Scientific reports
In the human body, the skin is the main organ. Nearly 30-70% of individuals globally have skin-related health issues, for whom efficient and effective analysis is essential. A general method dermatologists use for analyzing skin illnesses is dermosco...

Survival analysis for sepsis patients: A machine learning approach to feature selection and predictive modeling.

Scientific reports
Sepsis is a life-threatening condition that presents substantial challenges to healthcare and pharmacological management due to its high mortality rates and complex patient responses. Accurately predicting patient outcomes is essential for optimizing...

Schizophrenia detection from electroencephalogram signals using image encoding and wrapper-based deep feature selection approach.

Scientific reports
Schizophrenia is a persistent and serious mental illness that leads to distortions in cognition, perception, emotions, speech, self-awareness, and actions. Affecting about 1% of people worldwide, schizophrenia usually emerges in late adolescence or e...

Identification and verification of immune and oxidative stress-related diagnostic indicators for malignant lung nodules through WGCNA and machine learning.

Scientific reports
Early detection of lung nodules (LNs) is critical for prevention and treatment of lung cancer. However, current noninvasive diagnostic methods face significant challenges in reliably distinguishing benign from malignant nodules. Thus, there is an urg...

Comparative analysis of machine learning approaches for heatwave event prediction in India.

Scientific reports
Heatwaves, are identified as prolonged durations of unusually high temperatures, which pose significant threats to human health, animal health and agriculture. With the increasing frequency and intensity of heatwaves driven by climate change, accurat...

Ensemble learning for biomedical signal classification: a high-accuracy framework using spectrograms from percussion and palpation.

Scientific reports
Accurate classification of biomedical signals is crucial for advancing non-invasive diagnostic methods, particularly for identifying gastrointestinal and related medical conditions where conventional techniques often fall short. An ensemble learning ...