AIMC Topic: Support Vector Machine

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Machine learning algorithms to identify cluster randomized trials from MEDLINE and EMBASE.

Systematic reviews
BACKGROUND: Cluster randomized trials (CRTs) are becoming an increasingly important design. However, authors of CRTs do not always adhere to requirements to explicitly identify the design as cluster randomized in titles and abstracts, making retrieva...

Joint modeling strategy for using electronic medical records data to build machine learning models: an example of intracerebral hemorrhage.

BMC medical informatics and decision making
BACKGROUND: Outliers and class imbalance in medical data could affect the accuracy of machine learning models. For physicians who want to apply predictive models, how to use the data at hand to build a model and what model to choose are very thorny p...

Monocular Camera Viewpoint-Invariant Vehicular Traffic Segmentation and Classification Utilizing Small Datasets.

Sensors (Basel, Switzerland)
The work presented here develops a computer vision framework that is view angle independent for vehicle segmentation and classification from roadway traffic systems installed by the Virginia Department of Transportation (VDOT). An automated technique...

Maximum Decentral Projection Margin Classifier for High Dimension and Low Sample Size problems.

Neural networks : the official journal of the International Neural Network Society
Compared with relatively easy feature creation or generation in data analysis, manual data labeling needs a lot of time and effort in most cases. Even if automated data labeling​ seems to make it better in some cases, the labeling results still need ...

Machine Learning Models Identify New Inhibitors for Human OATP1B1.

Molecular pharmaceutics
The uptake transporter OATP1B1 (SLC01B1) is largely localized to the sinusoidal membrane of hepatocytes and is a known victim of unwanted drug-drug interactions. Computational models are useful for identifying potential substrates and/or inhibitors o...

Advancing molecular graphs with descriptors for the prediction of chemical reaction yields.

Journal of computational chemistry
Chemical yield is the percentage of the reactants converted to the desired products. Chemists use predictive algorithms to select high-yielding reactions and score synthesis routes, saving time and reagents. This study suggests a novel graph neural n...

Explainable machine learning methods and respiratory oscillometry for the diagnosis of respiratory abnormalities in sarcoidosis.

BMC medical informatics and decision making
BACKGROUND: In this work, we developed many machine learning classifiers to assist in diagnosing respiratory changes associated with sarcoidosis, based on results from the Forced Oscillation Technique (FOT), a non-invasive method used to assess pulmo...

Hyperspectral imaging for chemicals identification: a human-inspired machine learning approach.

Scientific reports
Data analysis has increasingly relied on machine learning in recent years. Since machines implement mathematical algorithms without knowing the physical nature of the problem, they may be accurate but lack the flexibility to move across different dom...

Use of support vector machine and cellular automata methods to evaluate impact of irrigation project on LULC.

Environmental monitoring and assessment
Land use and land cover (LULC) both define the earth's surface both anthropogenically and naturally. It helps maintain global balance but changes in land use create inequality. The LULC modification adversely affects physical parameters such as infil...

Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction.

Sensors (Basel, Switzerland)
BACKGROUND: Gait recognition has been applied in the prediction of the probability of elderly flat ground fall, functional evaluation during rehabilitation, and the training of patients with lower extremity motor dysfunction. Gait distinguishing betw...