AIMC Topic: Machine Learning

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Machine learning based analysis of leucocyte cell population data by Sysmex XN series hematology analyzer for the diagnosis of bacteremia.

Scientific reports
In clinical practice, early recognition of bacteremia leads to prognostic improvement. Recently, cell population data (CPD) from the Sysmex XN-series hematology analyzer has attracted attention as a new method for the early diagnosis of bacteremia, b...

AAGP integrates physicochemical and compositional features for machine learning-based prediction of anti-aging peptides.

Scientific reports
Aging is a natural phenomenon characterized by the loss of normal morphology and physiological functioning of the body, causing wrinkles on the skin, loss of hair, and compromised immune systems. Peptide therapies have emerged as a promising approach...

Breast cancer is detectable from peripheral blood using machine learning over T cell receptor repertoires.

NPJ systems biology and applications
The immune system's defense abilities rely on the diversity of T and B lymphocytes. T Cell Receptors (TCRs) are generated through V(D)J recombination, where distinct genetic elements combine and undergo modifications, creating extensive variability. ...

Influence of sample size and machine learning algorithms on digital soil nutrient mapping accuracy.

Environmental monitoring and assessment
The objective of this study is to evaluate and compare the performance of different machine learning (ML) algorithms, viz., multi-layer perceptron (MLP), random forest (RF), extra trees regressor (ETR), CatBoost, and gradient boost (GB), considering ...

Data quality in crowdsourcing and spamming behavior detection.

Behavior research methods
As crowdsourcing emerges as an efficient and cost-effective method for obtaining labels for machine learning datasets, it is important to assess the quality of crowd-provided data to improve analysis performance and reduce biases in subsequent machin...

Federated hierarchical MARL for zero-shot cyber defense.

PloS one
Cyber defense systems face increasingly sophisticated threats that rapidly evolve and exploit vulnerabilities in complex environments. Traditional approaches which often rely on centralized monitoring and static rule-based detection, struggle to adap...

LGD_Net: Capsule network with extreme learning machine for classification of lung diseases using CT scans.

PloS one
Lung diseases (LGDs) are related to an extensive range of lung disorders, including pneumonia (PNEUM), lung cancer (LC), tuberculosis (TB), and COVID-19 etc. The diagnosis of LGDs is performed by using different medical imaging such as X-rays, CT sca...

Species discrimination and VIP-stacking quantitative models for Curcumae Rhizoma utilizing multi-modal spectra combined with machine learning algorithm.

Journal of pharmaceutical and biomedical analysis
Curcumae Rhizoma (Ezhu) is a multi-species herbal medicine with excellent medicinal value and development potential. However, challenges such as the difficulty in differentiating its varieties and the limitations of current methods for determining mi...

Near-infrared spectroscopy coupled with machine learning algorithms based on L1-norm and L21-norm to identify the geographical origins of Chinese wolfberry.

Food chemistry
The nutritional value of Chinese wolfberry varies depending on different geographical origins, even at the regional level. Therefore, a non-destructive and effective method has important implications for identifying the geographical origins of Chines...

Gastrointestinal bleeding detection on digital subtraction angiography using convolutional neural networks with and without temporal information.

Diagnostic and interventional radiology (Ankara, Turkey)
PURPOSE: Digital subtraction angiography (DSA) offers a real-time approach to locating lower gastrointestinal (GI) bleeding. However, many sources of bleeding are not easily visible on angiograms. This investigation aims to develop a machine learning...