AIMC Topic: Support Vector Machine

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High-Sensitivity Detection of C-Peptide Biomarker for Diabetes by Solid-State Nanopore Using Machine Learning Identification.

The journal of physical chemistry letters
Accurate and early detection of C-peptide, a stable biomarker indicative of diabetes, is crucial for disease diagnosis, treatment, and prevention. This study explores a novel detection methodology using solid-state nanopore technology coupled with ma...

Robust Multiclass Feature Selection for the Authentication of Honey Botanical Origin via Nontargeted LC-MS Analysis.

Analytical chemistry
Honey is one of the most frequently frauded foods due to the high market price of certain kinds of monofloral honey. Traditional authentication methods involving pollen or targeted analysis have limitations that can be manipulated by fraudsters. Nont...

BanglaNewsClassifier: A machine learning approach for news classification in Bangla Newspapers using hybrid stacking classifiers.

PloS one
Bangla news floods the web, and the need for smarter and more efficient classification techniques is greater than ever. Previous studies mostly focused on traditional models, overlooking the potential of hybrid techniques to handle the ever-growing c...

An EEG-based imagined speech recognition using CSP-TP feature fusion for enhanced BCI communication.

Behavioural brain research
BACKGROUND: Imagined speech has emerged as a promising paradigm for intuitive control of brain-computer interface (BCI)-based communication systems, providing a means of communication for individuals with severe brain disabilities. In this work, a no...

Development of machine learning models for gait-based classification of incomplete spinal cord injuries and cauda equina syndrome.

Scientific reports
Incomplete tetraplegia, incomplete paraplegia, and cauda equina syndrome are major neurological disorders that significantly reduce patients' quality of life, primarily due to impaired motor function and gait instability. Although conventional neurol...

Machine learning-based prediction model for cognitive impairment risk in patients with chronic kidney disease.

PloS one
BACKGROUND: The high prevalence of cognitive impairment (CI) in Chronic kidney disease (CKD) patients impacts their quality of life and prognosis, yet risk prediction models for CI in this population remain underexplored.

Recognition of flight cadets brain functional magnetic resonance imaging data based on machine learning analysis.

PloS one
The rapid advancement of the civil aviation industry has attracted significant attention to research on pilots. However, the brain changes experienced by flight cadets following their training remain, to some extent, an unexplored territory compared ...

Machine learning framework coupled with CADD for predicting sphingosine kinase 1 inhibitors.

Computers in biology and medicine
Sphingosine kinase 1 (SphK1) plays a pivotal role in cancer progression, metastasis, and chemotherapy resistance, making it a key target for therapeutic interventions in cancer, cardiovascular diseases, and inflammation. Machine learning models, incl...

Principal fitted component framework for robust support vector regression based on bounded loss: A simulation study with potential applications.

PloS one
The inferential results regarding estimates of Support Vector Regression (SVR) are highly influenced by anomalies and ill-conditioned predictors. Excessive dimensions of data also make the model complex. To improve estimation accuracy, this paper int...