AIMC Topic: Support Vector Machine

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Prediction of water quality in the middle area of Yangtze River using efficient machine learning model.

Environmental geochemistry and health
The Yangtze River, as the longest river in China and the third-longest in the world, holds immense significance for the country's ecological security and sustainable development. The water quality in its middle reaches directly impacts millions of pe...

Cardiovascular disease detection: A hybrid machine learning-AI framework for personalized diagnosis and risk assessment.

PloS one
Cardiovascular disease (CVD) is considered the number one killer disease in the world, underlining the importance of the application of more accurate diagnostic and therapeutic tools. Traditional screening procedures usually do not provide identifica...

Automated Classification of Sleep-Wake States and Seizures in Mice.

eNeuro
Sleep-wake states bidirectionally interact with epilepsy and seizures, but the mechanisms are unknown. A barrier to comprehensive characterization and the study of mechanisms has been the difficulty of annotating large chronic recording datasets. To ...

Automated detection of mouth opening in newborn infants.

Behavior research methods
Automated behavioral measurement using machine learning is gaining ground in psychological research. Automated approaches have the potential to reduce the labor and time associated with manual behavioral coding, and to enhance measurement objectivity...

Detection of cognitive load using EEG signal and lifting wavelet transform with specific lead selection.

Biomedical physics & engineering express
Solving an arithmetic task is a complex assignment that includes sequencing, memory, fact retrieval, and decision making. Observation of the human brain's response to such activities is quite essential as it helps in the diagnosis of various diseases...

Porosity prediction from well logging data via a hybrid MABC-LSSVM model.

PloS one
Porosity is a key parameter for evaluating reservoir performance, but high-precision prediction is highly challenging in complex shale reservoirs due to the strong heterogeneity of the formation and the highly nonlinear relationship between logging p...

Comparison of serum lactate and lactate-derived ratios as prognostic biomarkers in pediatric dengue shock syndrome using supervised machine learning models.

PloS one
BACKGROUND: Dengue shock syndrome (DSS), with critical complications encompassing mechanical ventilation (MV), dengue-associated acute liver failure (PALF), and encephalitis, is associated with high mortality in children. Although serum lactate is a ...

Artificial intelligence for predicting depression anxiety and stress using psychometric data.

Scientific reports
Mental health is a crucial aspect of overall well-being, yet it is often overlooked due to stigma and limited accessibility to care. This study investigates the ability of artificial intelligence (AI) to predict common psychological conditions, depre...

An efficient cyber-attack detection and classification in IoT networks with high-dimensional feature set using Levenberg-Marquardt optimized feedforward neural network.

PloS one
This paper examines the escalating challenge of detecting cyber-attacks within Internet of Things (IoT) networks, where conventional security measures often falter in addressing the speed and complexity of contemporary threats. In response to the nec...

Utilizing multi-level convolutional neural networks to achieve refined modeling and visual analysis of college students' mental health data.

PloS one
Early identification of students' mental health issues has become an urgent priority in education and public health. However, existing studies often rely on questionnaire-based assessments or traditional machine learning models, which are limited by ...