AIMC Topic: Hydrocarbons

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Prediction of bioavailability and toxicity of complex chemical mixtures through machine learning models.

Chemosphere Oct 11, 2018
Empirical data from a 6-month mesocosms experiment were used to assess the ability and performance of two machine learning (ML) models, including artificial neural network (NN) and random forest (RF), to predict temporal bioavailability changes of co...
Environmental Pollution Biological Availability Soil Pollutants Machine Learning Charcoal Soil Metals, Heavy Neural Networks, Computer Risk Assessment Petroleum Soil Microbiology Hydrocarbons Complex Mixtures
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Moisture Damage Modeling in Lime and Chemically Modified Asphalt at Nanolevel Using Ensemble Computational Intelligence.

Computational intelligence and neuroscience Apr 18, 2018
This paper measures the adhesion/cohesion force among asphalt molecules at nanoscale level using an Atomic Force Microscopy (AFM) and models the moisture damage by applying state-of-the-art Computational Intelligence (CI) techniques (e.g., artificial...
Artificial Intelligence Water Signal Processing, Computer-Assisted Microscopy, Atomic Force Regression Analysis Oxides Mechanical Phenomena Hydrocarbons Calcium Compounds
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Adult fly age estimations using cuticular hydrocarbons and Artificial Neural Networks in forensically important Calliphoridae species.

Forensic science international Oct 8, 2017
Blowflies (Diptera: Calliphoridae) are forensically important as they are known to be one of the first to colonise human remains. The larval stage is typically used to assist a forensic entomologists with adult flies rarely used as they are difficult...
Feeding Behavior Forensic Sciences Gas Chromatography-Mass Spectrometry Diptera Postmortem Changes Hydrocarbons Time Factors Principal Component Analysis Neural Networks, Computer Animals
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A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks.

Sensors (Basel, Switzerland) Oct 25, 2016
Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior ...
Algorithms Exercise Humans Neural Networks, Computer Hydrocarbons Biosensing Techniques Principal Component Analysis
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Genetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices.

Journal of chromatography. A Oct 1, 2015
The development of quantitative structure-retention relationships (QSRR) aims at constructing an appropriate linear/nonlinear model for the prediction of the retention behavior (such as Kovats retention index) of a solute on a chromatographic column....
Nonlinear Dynamics Linear Models Hydrocarbons Chromatography, Gas Databases, Factual Adamantane Humans Neural Networks, Computer
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Pavement type and wear condition classification from tire cavity acoustic measurements with artificial neural networks.

The Journal of the Acoustical Society of America Jun 1, 2017
Tire road noise is the major contributor to traffic noise, which leads to general annoyance, speech interference, and sleep disturbances. Standardized methods to measure tire road noise are expensive, sophisticated to use, and they cannot be applied ...
Acoustics Motion Sound Sound Spectrography Porosity Signal Processing, Computer-Assisted Time Factors Automobiles Fourier Analysis Friction Transducers, Pressure Noise, Transportation Neural Networks, Computer Surface Properties Pressure Pattern Recognition, Automated Hydrocarbons
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