AIMC Topic: Support Vector Machine

Clear Filters Showing 1591 to 1600 of 4975 articles

Prediction of Automobile Wiper Motor Noise Based on Support Vector Machine with Vibration Sensors.

Computational intelligence and neuroscience
Wiper motor noise has an important impact on vehicle comfort. Accurate prediction of wiper motor noise can obtain motor NVH performance in motor manufacturing or earlier stage and provide necessary support for NVH performance design of parts and vehi...

Data-Driven Fault Diagnosis Techniques: Non-Linear Directional Residual vs. Machine-Learning-Based Methods.

Sensors (Basel, Switzerland)
Linear dependence of variables is a commonly used assumption in most diagnostic systems for which many robust methodologies have been developed over the years. In case the system nonlinearities are relevant, fault diagnosis methods, relying on the as...

Ligand-based approaches to activity prediction for the early stage of structure-activity-relationship progression.

Journal of computer-aided molecular design
The retrospective evaluation of virtual screening approaches and activity prediction models are important for methodological development. However, for fair comparison, evaluation data sets must be carefully prepared. In this research, we compiled str...

A new intelligent bearing fault diagnosis model based on triplet network and SVM.

Scientific reports
Separating sensitive characteristic signals from original vibration data is an important challenge for rolling bearing fault diagnosis. Because it is difficult to obtain large number of damaged bearings, Rolling bearing fault datasets are often small...

Development of deep learning algorithms to discriminate giant cell tumors of bone from adjacent normal tissues by confocal Raman spectroscopy.

The Analyst
Raman spectroscopy is a non-destructive analysis technique that provides detailed information about the chemical structure of tumors. Raman spectra of 52 giant cell tumors of bone (GCTB) and 21 adjacent normal tissues of formalin-fixed paraffin embed...

EEG Feature Extraction and Data Augmentation in Emotion Recognition.

Computational intelligence and neuroscience
Emotion recognition is a challenging problem in Brain-Computer Interaction (BCI). Electroencephalogram (EEG) gives unique information about brain activities that are created due to emotional stimuli. This is one of the most substantial advantages of ...

Machine Learning-Based Software Defect Prediction for Mobile Applications: A Systematic Literature Review.

Sensors (Basel, Switzerland)
Software defect prediction studies aim to predict defect-prone components before the testing stage of the software development process. The main benefit of these prediction models is that more testing resources can be allocated to fault-prone modules...

A Novel and Effective Brain Tumor Classification Model Using Deep Feature Fusion and Famous Machine Learning Classifiers.

Computational intelligence and neuroscience
Brain tumors are difficult to treat and cause substantial fatalities worldwide. Medical professionals visually analyze the images and mark out the tumor regions to identify brain tumors, which is time-consuming and prone to error. Researchers have pr...

Differentially Private Singular Value Decomposition for Training Support Vector Machines.

Computational intelligence and neuroscience
Support vector machine (SVM) is an efficient classification method in machine learning. The traditional classification model of SVMs may pose a great threat to personal privacy, when sensitive information is included in the training datasets. Princip...

FoSSA Optimization-Based SVM Classifier for the Recognition of Partial Discharge Patterns in HV Cables.

Computational intelligence and neuroscience
In order to enhance the classification accuracy and the generalization performance of the SVM classifier in cable partial discharge (PD) pattern recognition, a firefly optimized sparrow search algorithm (FoSSA) is proposed to optimize its kernel func...