AIMC Topic: Support Vector Machine

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An investigation into the deep learning approach in sentimental analysis using graph-based theories.

PloS one
Sentiment analysis is a branch of natural language analytics that aims to correlate what is expressed which comes normally within unstructured format with what is believed and learnt. Several attempts have tried to address this gap (i.e., Naive Bayes...

Application of machine learning classifiers to X-ray diffraction imaging with medically relevant phantoms.

Medical physics
PURPOSE: Recent studies have demonstrated the ability to rapidly produce large field of view X-ray diffraction (XRD) images, which provide rich new data relevant to the understanding and analysis of disease. However, work has only just begun on devel...

Prediction of PCOS and Mental Health Using Fuzzy Inference and SVM.

Frontiers in public health
Polycystic ovarian syndrome (PCOS) is a hormonal disorder found in women of reproductive age. There are different methods used for the detection of PCOS, but these methods limitedly support the integration of PCOS and mental health issues. To address...

Machine learning predicts the effect of food on orally administered medicines.

International journal of pharmaceutics
Food-mediated changes to drug absorption, termed the food effect, are hard to predict and can have significant implications for the safety and efficacy of oral drug products in patients. Mimicking the prandial states of the human gastrointestinal tra...

Mental Stress Classification Based on a Support Vector Machine and Naive Bayes Using Electrocardiogram Signals.

Sensors (Basel, Switzerland)
Examining mental health is crucial for preventing mental illnesses such as depression. This study presents a method for classifying electrocardiogram (ECG) data into four emotional states according to the stress levels using one-against-all and naive...

A graph-based gene selection method for medical diagnosis problems using a many-objective PSO algorithm.

BMC medical informatics and decision making
BACKGROUND: Gene expression data play an important role in bioinformatics applications. Although there may be a large number of features in such data, they mainly tend to contain only a few samples. This can negatively impact the performance of data ...

A Comparison among Different Machine Learning Pretest Approaches to Predict Stress-Induced Ischemia at PET/CT Myocardial Perfusion Imaging.

Computational and mathematical methods in medicine
Traditional approach for predicting coronary artery disease (CAD) is based on demographic data, symptoms such as chest pain and dyspnea, and comorbidity related to cardiovascular diseases. Usually, these variables are analyzed by logistic regression ...

Attentional load classification in multiple object tracking task using optimized support vector machine classifier: a step towards cognitive brain-computer interface.

Journal of medical engineering & technology
Cognitive brain-computer interface (cBCI) is an emerging area with applications in neurorehabilitation and performance monitoring. cBCI works on the cognitive brain signal that does not require a person to pay much effort unlike the motor brain-compu...

Prediction of functional microexons by transfer learning.

BMC genomics
BACKGROUND: Microexons are a particular kind of exon of less than 30 nucleotides in length. More than 60% of annotated human microexons were found to have high levels of sequence conservation, suggesting their potential functions. There is thus a nee...

HARTH: A Human Activity Recognition Dataset for Machine Learning.

Sensors (Basel, Switzerland)
Existing accelerometer-based human activity recognition (HAR) benchmark datasets that were recorded during free living suffer from non-fixed sensor placement, the usage of only one sensor, and unreliable annotations. We make two contributions in this...