AIMC Topic: Support Vector Machine

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Machine learning model for prediction of permanent stoma after anterior resection of rectal cancer: A multicenter study.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
BACKGROUND: The conversion from a temporary to a permanent stoma (PS) following rectal cancer surgery significantly impacts the quality of life of patients. However, there is currently a lack of practical preoperative tools to predict PS formation. T...

Novel candidate genes for environmental stresses response in Synechocystis sp. PCC 6803 revealed by machine learning algorithms.

Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology]
Cyanobacteria have developed acclimation strategies to adapt to harsh environments, making them a model organism. Understanding the molecular mechanisms of tolerance to abiotic stresses can help elucidate how cells change their gene expression patter...

Unveiling the potential of machine learning in cost-effective degradation of pharmaceutically active compounds: A stirred photo-reactor study.

Chemosphere
In this study, neural networks and support vector regression (SVR) were employed to predict the degradation over three pharmaceutically active compounds (PhACs): Ibuprofen (IBP), diclofenac (DCF), and caffeine (CAF) within a stirred reactor featuring...

Characterization and classification of ductal carcinoma tissue using four channel based stokes-mueller polarimetry and machine learning.

Lasers in medical science
Interaction of polarized light with healthy and abnormal regions of tissue reveals structural information associated with its pathological condition. Even a slight variation in structural alignment can induce a change in polarization property, which ...

A comprehensive assessment of machine learning algorithms for enhanced characterization and prediction in orodispersible film development.

International journal of pharmaceutics
Orodispersible films (ODFs) have emerged as innovative pharmaceutical dosage forms, offering patient-specific treatment through adjustable dosing and the combination of diverse active ingredients. This expanding field generates vast datasets, requiri...

Assessing the effectiveness of spatial PCA on SVM-based decoding of EEG data.

NeuroImage
Principal component analysis (PCA) has been widely employed for dimensionality reduction prior to multivariate pattern classification (decoding) in EEG research. The goal of the present study was to provide an evaluation of the effectiveness of PCA o...

Ensemble machine learning prediction of anaerobic co-digestion of manure and thermally pretreated harvest residues.

Bioresource technology
This study aimed to clarify the statistical accuracy assessment approaches used in recent biogas prediction studies using state-of-the-art ensemble machine learning approach according to 10-fold cross-validation in 100 repetitions. Three thermally pr...

Detecting the symptoms of Parkinson's disease with non-standard video.

Journal of neuroengineering and rehabilitation
BACKGROUND: Neurodegenerative diseases, such as Parkinson's disease (PD), necessitate frequent clinical visits and monitoring to identify changes in motor symptoms and provide appropriate care. By applying machine learning techniques to video data, a...

Maize leaf disease recognition using PRF-SVM integration: a breakthrough technique.

Scientific reports
The difficulty of collecting maize leaf lesion characteristics in an environment that undergoes frequent changes, suffers varying illumination from lighting sources, and is influenced by a variety of other factors makes detecting diseases in maize le...

Predictive modelling of transport decisions and resources optimisation in pre-hospital setting using machine learning techniques.

PloS one
BACKGROUND: The global evolution of pre-hospital care systems faces dynamic challenges, particularly in multinational settings. Machine learning (ML) techniques enable the exploration of deeply embedded data patterns for improved patient care and res...