AIMC Topic: Machine Learning

Clear Filters Showing 881 to 890 of 34417 articles

Cardiovascular risk prediction in diabetes: a hybrid machine learning approach.

Biomedical physics & engineering express
Cardiovascular disease (CVD) is a major cause of morbidity and mortality in diabetic populations. Early detection of cardiovascular risk in diabetes is crucial to reduce complications, particularly in resource-limited settings. This study aimed to de...

Fractal measures as predictors of histopathological complexity in breast carcinoma mammograms.

Physical biology
This study investigates the efficacy of fractal-based global texture features for distinguishing between malignant and normal mammograms and assessing their potential for molecular subtype differentiation. Digital mammograms were analyzed using stand...

Co-cultured sensory neuron classification using extracellular electrophysiology and machine learning approaches for enhancing analgesic screening.

Journal of neural engineering
Chronic pain affects over 20% of the adult population in the United States, posing a substantial personal as well as economic burden and contributing to the ongoing opioid crisis. Effective, non-addictive chronic pain treatments are urgently needed. ...

Neural subgraph counting on stream graphs via localized updates and monotonic learning.

PloS one
Graphs are a representative type of fundamental data structures. They are capable of representing complex association relationships in diverse domains. For large-scale graph processing, the stream graphs have become efficient tools to process dynamic...

Contrastive learning-enhanced personalized interaction dual tower network for recommendation.

PloS one
Dual-tower retrieval models have become a prevalent solution in large-scale recommendation systems due to their scalability and deployment efficiency. However, they face critical limitations including insufficient modeling of user behavior sequences,...

An exploratory study on predicting HER2-positive expression status of breast cancer using ultrasound radiomics combined with machine learning models.

PloS one
OBJECTIVE: This study aimed to investigate the feasibility and potential value of predictive models for human epidermal growth factor receptor 2 (HER2)-positive status in breast cancer (BC) based on radiomics features from conventional ultrasound ima...

Identification and velocity measurement of microplastics based on machine learning.

Water research
The settling velocity of microplastics (MPs) is a critical parameter for understanding their migration and behavior in aquatic environments. Conventional methods typically focus on tracking individual MPs and often face significant challenges in capt...

Intelligent sensory technologies, NIR spectroscopy and chemometrics combined with machine learning based on multi-source data fusion for comprehensive evaluation of Sinapis Semen in different processing degrees.

Journal of pharmaceutical and biomedical analysis
Sinapis Semen, as a traditional Chinese medicine, has an unclear relationship between its stir-frying degrees and sensory characteristics. Therefore, it is essential to develop a multi-index evaluation method to classify the processing degree of Sina...

Advancement of machine learning algorithms in biosensors.

Clinica chimica acta; international journal of clinical chemistry
Biosensors have emerged as transformative tools in modern diagnostics, enabling rapid, accurate, and sensitive detection of biological markers for disease diagnosis, real-time monitoring, and personalized healthcare. However, current biosensors still...

Multimodal integration of plasma biomarkers, MRI, and genetic risk to predict cerebral amyloid burden in Alzheimer's disease.

NeuroImage
Alzheimer's disease (AD), the most prevalent neurodegenerative disorder, is marked by the accumulation of amyloid-β (Aβ) plaques. Although cerebral Aβ positron emission tomography (Aβ-PET) remains the gold standard for assessing cerebral Aβ burden, i...