AIMC Topic: Support Vector Machine

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A Hybrid Method to Predict Postoperative Survival of Lung Cancer Using Improved SMOTE and Adaptive SVM.

Computational and mathematical methods in medicine
Predicting postoperative survival of lung cancer patients (LCPs) is an important problem of medical decision-making. However, the imbalanced distribution of patient survival in the dataset increases the difficulty of prediction. Although the syntheti...

Sensor Fault Diagnosis Method Based on -Grey Wolf Optimization-Support Vector Machine.

Computational intelligence and neuroscience
Aimed to address the low diagnostic accuracy caused by the similar data distribution of sensor partial faults, a sensor fault diagnosis method is proposed on the basis of Grey Wolf Optimization Support Vector Machine (-GWO-SVM) in this paper. Firstl...

Identifying indicator species in ecological habitats using Deep Optimal Feature Learning.

PloS one
Much of the current research on supervised modelling is focused on maximizing outcome prediction accuracy. However, in engineering disciplines, an arguably more important goal is that of feature extraction, the identification of relevant features ass...

RF-SVM: Identification of DNA-binding proteins based on comprehensive feature representation methods and support vector machine.

Proteins
Protein-DNA interactions play an important role in biological progress, such as DNA replication, repair, and modification processes. In order to have a better understanding of its functions, the one of the most important steps is the identification o...

Diagnostic classification of coronavirus disease 2019 (COVID-19) and other pneumonias using radiomics features in CT chest images.

Scientific reports
We propose a classification method using the radiomics features of CT chest images to identify patients with coronavirus disease 2019 (COVID-19) and other pneumonias. The chest CT images of two groups of participants (90 COVID-19 patients who were co...

Machine learning - Predicting Ames mutagenicity of small molecules.

Journal of molecular graphics & modelling
In modern drug discovery, detection of a compound's potential mutagenicity is crucial. However, the traditional method of mutagenicity detection using the Ames test is costly and time consuming as the compounds need to be synthesised and then tested ...

Prediction of Drug-Target Interactions by Combining Dual-Tree Complex Wavelet Transform with Ensemble Learning Method.

Molecules (Basel, Switzerland)
Identification of drug-target interactions (DTIs) is vital for drug discovery. However, traditional biological approaches have some unavoidable shortcomings, such as being time consuming and expensive. Therefore, there is an urgent need to develop no...

Research on Key Algorithms of the Lung CAD System Based on Cascade Feature and Hybrid Swarm Intelligence Optimization for MKL-SVM.

Computational intelligence and neuroscience
Feature selection and lung nodule recognition are the core modules of the lung computer-aided detection (Lung CAD) system. To improve the performance of the Lung CAD system, algorithmic research is carried out for the above two parts, respectively. F...

Disease type detection in lung and colon cancer images using the complement approach of inefficient sets.

Computers in biology and medicine
Lung and colon cancers are deadly diseases that can develop simultaneously in organs and adversely affect human life in some special cases. Although the frequency of simultaneous occurrence of these two types of cancer is unlikely, there is a high pr...

Enhanced Changeover Detection in Industry 4.0 Environments with Machine Learning.

Sensors (Basel, Switzerland)
Changeover times are an important element when evaluating the Overall Equipment Effectiveness (OEE) of a production machine. The article presents a machine learning (ML) approach that is based on an external sensor setup to automatically detect chang...