AIMC Topic: Support Vector Machine

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Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction.

Scientific reports
Breast cancer is a heterogeneous disease. To guide proper treatment decisions for each patient, robust prognostic biomarkers, which allow reliable prognosis prediction, are necessary. Gene feature selection based on microarray data is an approach to ...

MRI Image Segmentation Model with Support Vector Machine Algorithm in Diagnosis of Solitary Pulmonary Nodule.

Contrast media & molecular imaging
This study focused on the application value of MRI images processed by a Support Vector Machine (SVM) algorithm-based model in diagnosis of benign and malignant solitary pulmonary nodule (SPN). The SVM algorithm was constrained by a self-paced regula...

Support Vector Machine as a Supervised Learning for the Prioritization of Novel Potential SARS-CoV-2 Main Protease Inhibitors.

International journal of molecular sciences
In the last year, the COVID-19 pandemic has highly affected the lifestyle of the world population, encouraging the scientific community towards a great effort on studying the infection molecular mechanisms. Several vaccine formulations are nowadays a...

Forecast of E-Commerce Transactions Trend Using Integration of Enhanced Whale Optimization Algorithm and Support Vector Machine.

Computational intelligence and neuroscience
E-commerce has become a crucial business model through the Internet around the world. Therefore, its transaction trend forecast can provide important information for the market planning and development in advance. For this purpose, the integrated mod...

Exploring Factors for Predicting Anxiety Disorders of the Elderly Living Alone in South Korea Using Interpretable Machine Learning: A Population-Based Study.

International journal of environmental research and public health
This epidemiological study aimed to develop an X-AI that could explain groups with a high anxiety disorder risk in old age. To achieve this objective, (1) this study explored the predictors of senile anxiety using base models and meta models. (2) Thi...

Skin Cancer Detection Using Kernel Fuzzy C-Means and Improved Neural Network Optimization Algorithm.

Computational intelligence and neuroscience
Early diagnosis of malignant skin cancer from images is a significant part of the cancer treatment process. One of the principal purposes of this research is to propose a pipeline methodology for an optimum computer-aided diagnosis of skin cancers. T...

Identifying mislabelled samples: Machine learning models exceed human performance.

Annals of clinical biochemistry
BACKGROUND: It is difficult for clinical laboratories to identify samples that are labelled with the details of an incorrect patient. Many laboratories screen for these errors with delta checks, with final decision-making based on manual review of re...

SVM classification of facial functions based on facial landmarks and animation Units.

Biomedical physics & engineering express
Quantitative assessment and classification of facial paralysis (FP) are essential for treatment selection and progress evaluation of the condition. As part of a comprehensive framework towards this goal, this study aims to classify five normal facial...

Automatic classification of autism spectrum disorder in children using cortical thickness and support vector machine.

Brain and behavior
OBJECTIVE: Autism spectrum disorder (ASD) is a neurodevelopmental condition with a heterogeneous phenotype. The role of biomarkers in ASD diagnosis has been highlighted; cortical thickness has proved to be involved in the etiopathogenesis of ASD core...