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Unsupervised Machine Learning

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Identification and Classification of Images in e-Cigarette-Related Content on TikTok: Unsupervised Machine Learning Image Clustering Approach.

Substance use & misuse
BACKGROUND: Previous studies identified e-cigarette content on popular video and image-based social media platforms such as TikTok. While machine learning approaches have been increasingly used with text-based social media data, image-based analysis ...

Analyzing Demographic Grocery Purchase Patterns in Kenyan Supermarkets Through Unsupervised Learning Techniques.

Inquiry : a journal of medical care organization, provision and financing
Kenya is experiencing a significant increase in the prevalence of non-communicable diseases (NCDs) such as cardiovascular diseases, hypertension, Type 2 diabetes, and certain cancers (bowel, lung, prostate, and uterine). This case is not unique to Ke...

AutoDPS: An unsupervised diffusion model based method for multiple degradation removal in MRI.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Diffusion models have demonstrated their ability in image generation and solving inverse problems like restoration. Unlike most existing deep-learning based image restoration techniques which rely on unpaired or paired data ...

Unsupervised learning from EEG data for epilepsy: A systematic literature review.

Artificial intelligence in medicine
BACKGROUND AND OBJECTIVES: Epilepsy is a neurological disorder characterized by recurrent epileptic seizures, whose neurophysiological signature is altered electroencephalographic (EEG) activity. The use of artificial intelligence (AI) methods on EEG...

Structural Similarity, Activity, and Toxicity of Mycotoxins: Combining Insights from Unsupervised and Supervised Machine Learning Algorithms.

Journal of agricultural and food chemistry
A large number of mycotoxins and related fungal metabolites have not been assessed in terms of their toxicological impacts. Current methodologies often prioritize specific target families, neglecting the complexity and presence of co-occurring compou...

Supervised and unsupervised deep learning-based approaches for studying DNA replication spatiotemporal dynamics.

Communications biology
In eukaryotic cells, DNA replication is organised both spatially and temporally, as evidenced by the stage-specific spatial distribution of replication foci in the nucleus. Despite the genetic association of aberrant DNA replication with numerous hum...

Unsupervised Neural Decoding to Predict Dexterous Multi-Finger Flexion and Extension Forces.

IEEE journal of biomedical and health informatics
Accurate control over individual fingers of robotic hands is essential for the progression of human-robot interactions. Accurate prediction of finger forces becomes imperative in this context. The state-of-the-art neural decoders can extract neural s...

CGNet: A Correlation-Guided Registration Network for Unsupervised Deformable Image Registration.

IEEE transactions on medical imaging
Deformable medical image registration plays a significant role in medical image analysis. With the advancement of deep neural networks, learning-based deformable registration methods have made great strides due to their ability to perform fast end-to...

Tracking the spatiotemporal evolution of groundwater chemistry in the Quaternary aquifer system of Debrecen area, Hungary: integration of classical and unsupervised learning methods.

Environmental science and pollution research international
Monitoring changes in groundwater quality over time helps identify time-dependent factors influencing water safety and supports the development of effective management strategies. This study investigates the spatiotemporal evolution of groundwater ch...

CorrMorph: Unsupervised Deformable Brain MRI Registration Based on Correlation Mining.

IEEE journal of biomedical and health informatics
Deformable image registration, as a fundamental prerequisite for many medical image analysis tasks, has received considerable attention. However, existing methods suffer from two key issues: 1) single-stream methods that stack moving and fixed images...