Evaluation of Artificial Intelligence Based Models for Chemical Biodegradability Prediction

Abstract

This study presents a review of biodegradability modeling efforts including a detailed assessment of two models developed using an artificial intelligence based methodology. Validation results for these models using an independent, quality reviewed database, demonstrate that the models perform well when compared to another commonly used biodegradability model, against the same data. The ability of models induced by an artificial intelligence methodology to accommodate complex interactions in detailed systems, and the demonstrated reliability of the approach evaluated by this study, indicate that the methodology may have application in broadening the scope of biodegradability models. Given adequate data for biodegradability of chemicals under environmental conditions, this may allow for the development of future models that include such things as surface interface impacts on biodegradability for example.

Biodegradation and removal of pharmaceuticals and personal care products in treatment systems: a review

“…There are three general approaches to biodegradation prediction modeling, which are regression analysis, expert opinions, and artificial intelligence (AI) (Baker et al 2004). The regression models have been shown to possess the highest utility and are currently being adapted by the U.S. Environmental Protection Agency as EPA BIOWIN predictive models, but AI approaches have recently gained attention for their potential to greatly improve prediction accuracy (Klopman and Tu 1997;Rorije et al 1999;Baker et al 2004). The majority of current regression models rely mainly on structure activity relationships, in which statistical models (mostly regressions or Bayesian statistics) are applied based on expert knowledge regarding the biodegradability of organic compounds according to their structures (Boethling et al 2004).…”

Base Oils Biodegradability Prediction with Data Mining Techniques

“…Another Artificial Intelligence type of modeling biodegradation is an example-based learning system, instead of expert systems [7]. A third effort [8] also applied other Artificial Intelligence models for biodegradability prediction to generate predictive rules using the inductive machine learning approach with structural features as variables, and discretized biodegradability comprising two classes (slow and fast biodegradation). The IUPAC study presented a review and a critical analysis of modeling and estimating the degradability of chemicals in the environment [9].…”

“…More recently, various QSARs-based classification techniques to classify different types of base oils were tested [2]. However, available QSAR models have so far proved to be of limited effectiveness since their achieved predictive accuracy varied widely, from 40% to 90% [8].…”

Classification of Cyber-Aggression Cases Applying Machine Learning

“…AI is a discipline that emerged in the 1950s and is basically defined as the construction of automated models that can solve real problems by emulating human intelligence. Some recent applications include the following: in [25], two chemical biodegradability prediction models are evaluated against another commonly used biodegradability model; Li et al [26] applied Multiscale Sample Entropy (MSE), Multiscale Permutation Entropy (MPE), and Multiscale Fuzzy Entropy (MFE) feature extraction methods along with Support Vector Machines (SVMs) classifier to analyze Motor Imagery EEG (MI-EEG) data; Li and coworkers [27] proposed the new Temperature Sensor Clustering Method for thermal error modeling of machine tools, then the weight coefficient in the distance matrix and the number of the clusters (groups) were optimized by a genetic algorithm (GA); in [28], fuzzy theory and a genetic algorithm are combined to design a Motor Diagnosis System for rotor failures; the aim in [29] is to design a new method to predict click-through rate in Internet advertising based on a Deep Neural Network; Ocaña and coworkers [30] proposed the evolutionary algorithm TS-MBFOA (Two-Swim Modified Bacterial Foraging Optimization Algorithm) and proved a real problem that seeks to optimize the synthesis of a four-bar mechanism in a mechatronic systems.…”