AIMC Topic: Algorithms

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Edges are all you need: Potential of medical time series analysis on complete blood count data with graph neural networks.

PloS one
PURPOSE: Machine learning is a powerful tool to develop algorithms for clinical diagnosis. However, standard machine learning algorithms are not perfectly suited for clinical data since the data are interconnected and may contain time series. As show...

Plant attribute extraction: An enhancing three-stage deep learning model for relational triple extraction.

PloS one
Various plant attributes, such as growing environment, growth cycle, and ecological distribution, can provide support to fields like agricultural production and biodiversity. This information is widely dispersed in texts. Manual extraction of this in...

Design of an evolutionary model for international trade settlement based on genetic algorithm and fuzzy neural network.

PloS one
Accurate risk assessment in international trade settlement has become increasingly critical as global financial transactions grow in scale and complexity. This study proposes a hybrid model-Genetic Algorithm-optimized Fuzzy Neural Network (GA-FNN)-to...

Predicting the Effects of Charge Mutations on the Second Osmotic Virial Coefficient for Therapeutic Antibodies via Coarse-Grained Molecular Simulations and Deep Learning Methods.

Molecular pharmaceutics
The impact of various charge mutations on the second osmotic virial coefficient was examined for three model therapeutic monoclonal antibodies (MAbs) at representative formulation pH values by using coarse-grained (CG) molecular modeling. The wild-ty...

Filter-type neural network-based counter-pulsation control in pulsatile ECMO: improving heartbeat-pulse discrimination and synchronization accuracy.

Biomedical engineering online
Implementing counter-pulsation (CP) control in pulsatile extracorporeal membrane oxygenator (p-ECMO) systems offers a refined approach to mitigate risks commonly associated with conventional ECMOs. To attain CP between the p-ECMO and heart, accurate ...

Deep learning method for cucumber disease detection in complex environments for new agricultural productivity.

BMC plant biology
Cucumber disease detection under complex agricultural conditions faces significant challenges due to multi-scale variation, background clutter, and hardware limitations. This study proposes YOLO-Cucumber, an improved lightweight detection algorithm b...

Advanced bioactuator selection for efficient human motion simulation in biohybrid robots using CSF dynamic knowledge.

Scientific reports
The selection of the most efficient actuator for biohybrid robots necessitates the implementation of precise and reliable decision-making (DM) methods. Dynamic aggregation operators (AOs) provide flexibility and consistency in DM by embracing time-de...

An enhanced fusion of transfer learning models with optimization based clinical diagnosis of lung and colon cancer using biomedical imaging.

Scientific reports
Lung and colon cancers (LCC) are among the foremost reasons for human death and disease. Early analysis of this disorder contains various tests, namely ultrasound (US), magnetic resonance imaging (MRI), and computed tomography (CT). Despite analytica...

Enhanced particle swarm optimization for feature selection in SVM-based Alzheimer's disease diagnosis.

Scientific reports
Alzheimer's Disease (AD) is a progressive neurodegenerative disorder marked by neuronal loss, leading to cognitive and behavioral decline. With the aging global population, AD incidence and its socioeconomic burden are increasing. Developing effectiv...

Development and evaluation of machine learning training strategies for neonatal mortality prediction using multicountry data.

Scientific reports
Neonatal mortality poses a critical challenge in global health, particularly in low- and middle-income countries. Leveraging advancements in technology, such as machine learning (ML) algorithms, offers the potential to improve neonatal care by enabli...