AIMC Topic: Support Vector Machine

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Near-Infrared Spectral Characteristic Extraction and Qualitative Analysis Method for Complex Multi-Component Mixtures Based on TRPCA-SVM.

Sensors (Basel, Switzerland)
Quality identification of multi-component mixtures is essential for production process control. Artificial sensory evaluation is a conventional quality evaluation method of multi-component mixture, which is easily affected by human subjective factors...

IoT-based automated water pollution treatment using machine learning classifiers.

Environmental technology
Water is one of the most vital sources for the survival of life. In the globe, the accessibility of water in safe and healthy ways is a major concern. The consumption of unsafe water may lead to health risks. Therefore, it is necessary to classify an...

Machine learning models for accurate prioritization of variants of uncertain significance.

Human mutation
The growing use of next-generation sequencing technologies on genetic diagnosis has produced an exponential increase in the number of variants of uncertain significance (VUS). In this manuscript, we compare three machine learning methods to classify ...

Multi-Method Diagnosis of Blood Microscopic Sample for Early Detection of Acute Lymphoblastic Leukemia Based on Deep Learning and Hybrid Techniques.

Sensors (Basel, Switzerland)
Leukemia is one of the most dangerous types of malignancies affecting the bone marrow or blood in all age groups, both in children and adults. The most dangerous and deadly type of leukemia is acute lymphoblastic leukemia (ALL). It is diagnosed by he...

Predicting daily pore water pressure in embankment dam: Empowering Machine Learning-based modeling.

Environmental science and pollution research international
Dam safety assessment is important to implement the appropriate measures to avoid a dam break disaster as part of the water reservoirs management process. Prediction-based approaches are valuable to compare the actual measurements with the simulated ...

Hyperspectral Image Labeling and Classification Using an Ensemble Semi-Supervised Machine Learning Approach.

Sensors (Basel, Switzerland)
Hyperspectral remote sensing has tremendous potential for monitoring land cover and water bodies from the rich spatial and spectral information contained in the images. It is a time and resource consuming task to obtain groundtruth data for these ima...

An EEG Classification-Based Method for Single-Trial N170 Latency Detection and Estimation.

Computational and mathematical methods in medicine
Event-related potentials (ERPs) can reflect the high-level thinking activities of the brain. In ERP analysis, the superposition and averaging method is often used to estimate ERPs. However, the single-trial ERP estimation can provide researchers with...

A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran.

Sensors (Basel, Switzerland)
We mapped landslide susceptibility in Kamyaran city of Kurdistan Province, Iran, using a robust deep-learning (DP) model based on a combination of extreme learning machine (ELM), deep belief network (DBN), back propagation (BP), and genetic algorithm...

Integrative artificial intelligence models for Australian coastal sediment lead prediction: An investigation of in-situ measurements and meteorological parameters effects.

Journal of environmental management
Heavy metals (HMs) such as Lead (Pb) have played a vital role in increasing the sediments of the Australian bay's ecosystem. Several meteorological parameters (i.e., minimum, maximum and average temperature (T, T and TC), rainfall (R mm) and their in...

Terahertz signal analysis and substance identification via Zernike moments.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Terahertz (THz) spectra contain chemical information, along with noise and variable backgrounds. Measurement environmental changes and spectral signal differences caused by changes in the sample state can degrade the accuracy of the calibration model...