AIMC Topic: Support Vector Machine

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A review of recent developments in the application of machine learning in solar thermal collector modelling.

Environmental science and pollution research international
Over the past few decades, the popularity of solar thermal collectors has increased dramatically because of many significant advantages like being a free, natural, environmentally friendly and permanent energy source. Today, developing and optimising...

Dendrite Net: A White-Box Module for Classification, Regression, and System Identification.

IEEE transactions on cybernetics
The simulation of biological dendrite computations is vital for the development of artificial intelligence (AI). This article presents a basic machine-learning (ML) algorithm, called Dendrite Net or DD, just like the support vector machine (SVM) or m...

Machine Learning Techniques for Arousal Classification from Electrodermal Activity: A Systematic Review.

Sensors (Basel, Switzerland)
This article introduces a systematic review on arousal classification based on electrodermal activity (EDA) and machine learning (ML). From a first set of 284 articles searched for in six scientific databases, fifty-nine were finally selected accordi...

Machine Learning Models to Predict Protein-Protein Interaction Inhibitors.

Molecules (Basel, Switzerland)
Protein-protein interaction (PPI) inhibitors have an increasing role in drug discovery. It is hypothesized that machine learning (ML) algorithms can classify or identify PPI inhibitors. This work describes the performance of different algorithms and ...

Prediction of monthly dry days with machine learning algorithms: a case study in Northern Bangladesh.

Scientific reports
Dry days at varied scale are an important topic in climate discussions. Prolonged dry days define a dry period. Dry days with a specific rainfall threshold may visualize a climate scenario of a locality. The variation of monthly dry days from station...

Research on Satellite Network Traffic Prediction Based on Improved GRU Neural Network.

Sensors (Basel, Switzerland)
The current satellite network traffic forecasting methods cannot fully exploit the long correlation between satellite traffic sequences, which leads to large network traffic forecasting errors and low forecasting accuracy. To solve these problems, we...

A Connection Between Pattern Classification by Machine Learning and Statistical Inference With the General Linear Model.

IEEE journal of biomedical and health informatics
A connection between the general linear model (GLM) with frequentist statistical testing and machine learning (MLE) inference is derived and illustrated. Initially, the estimation of GLM parameters is expressed as a Linear Regression Model (LRM) of a...

Key factors selection on adolescents with non-suicidal self-injury: A support vector machine based approach.

Frontiers in public health
Comparing a family structure to a company, one can often think of parents as leaders and adolescents as employees. Stressful family environments and anxiety levels, depression levels, personality disorders, emotional regulation difficulties, and chil...

Utilizing machine learning algorithms to predict subject genetic mutation class from in silico models of neuronal networks.

BMC medical informatics and decision making
BACKGROUND: Epilepsy is the fourth-most common neurological disorder, affecting an estimated 50 million patients globally. Nearly 40% of patients have uncontrolled seizures yet incur 80% of the cost. Anti-epileptic drugs commonly result in resistance...

Reducing Data Complexity Using Autoencoders With Class-Informed Loss Functions.

IEEE transactions on pattern analysis and machine intelligence
Available data in machine learning applications is becoming increasingly complex, due to higher dimensionality and difficult classes. There exists a wide variety of approaches to measuring complexity of labeled data, according to class overlap, separ...