AIMC Topic: Support Vector Machine

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Texture features in the Shearlet domain for histopathological image classification.

BMC medical informatics and decision making
BACKGROUND: A various number of imaging modalities are available (e.g., magnetic resonance, x-ray, ultrasound, and biopsy) where each modality can reveal different structural aspects of tissues. However, the analysis of histological slide images that...

Imbalanced learning: Improving classification of diabetic neuropathy from magnetic resonance imaging.

PloS one
One of the fundamental challenges when dealing with medical imaging datasets is class imbalance. Class imbalance happens where an instance in the class of interest is relatively low, when compared to the rest of the data. This study aims to apply ove...

Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection.

PloS one
The coronavirus disease (COVID-19), is an ongoing global pandemic caused by severe acute respiratory syndrome. Chest Computed Tomography (CT) is an effective method for detecting lung illnesses, including COVID-19. However, the CT scan is expensive a...

Assessing the signal quality of electrocardiograms from varied acquisition sources: A generic machine learning pipeline for model generation.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: Long-term electrocardiogram monitoring comes at the expense of signal quality. During unconstrained movements, the electrocardiogram is often corrupted by motion artefacts, which can lead to inaccurate physiological informat...

Classification of Biodegradable Substances Using Balanced Random Trees and Boosted C5.0 Decision Trees.

International journal of environmental research and public health
Substances that do not degrade over time have proven to be harmful to the environment and are dangerous to living organisms. Being able to predict the biodegradability of substances without costly experiments is useful. Recently, the quantitative str...

Aligning text mining and machine learning algorithms with best practices for study selection in systematic literature reviews.

Systematic reviews
BACKGROUND: Despite existing research on text mining and machine learning for title and abstract screening, the role of machine learning within systematic literature reviews (SLRs) for health technology assessment (HTA) remains unclear given lack of ...

Identification the source of fecal contamination for geographically unassociated samples with a statistical classification model based on support vector machine.

Journal of hazardous materials
The bacterial diversity and corresponding biological significance revealed by high-throughput sequencing contribute massive information to source tracking of fecal contamination. The performances of classification models on predicting the fecal sourc...

Assessment of Machine Learning Models to Identify Port Jackson Shark Behaviours Using Tri-Axial Accelerometers.

Sensors (Basel, Switzerland)
Movement ecology has traditionally focused on the movements of animals over large time scales, but, with advancements in sensor technology, the focus can become increasingly fine scale. Accelerometers are commonly applied to quantify animal behaviour...

Computerized Classification of Prostate Cancer Gleason Scores from Whole Slide Images.

IEEE/ACM transactions on computational biology and bioinformatics
Histological Gleason grading of tumor patterns is one of the most powerful prognostic predictors in prostate cancer. However, manual analysis and grading performed by pathologists are typically subjective and time-consuming. In this paper, we present...

A Data-Driven Approach to Predict and Classify Epileptic Seizures from Brain-Wide Calcium Imaging Video Data.

IEEE/ACM transactions on computational biology and bioinformatics
The prediction of epileptic seizures has been an essential problem of epilepsy study. The calcium imaging video data images the whole brain-wide neurons activities with electrical discharge recorded by calcium fluorescence intensity (CFI). In this pa...