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Al-Fakih AM, Algamal ZY, Qasim MK. An improved opposition-based crow search algorithm for biodegradable material classification. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2022; 33:403-415. [PMID: 35469528 DOI: 10.1080/1062936x.2022.2064546] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 04/05/2022] [Indexed: 06/14/2023]
Abstract
The development of a reliable quantitative structure-activity relationship (QSAR) classification model with a small number of molecular descriptors is a crucial step in chemometrics. In this study, an improvement of crow search algorithm (CSA) is proposed by adapting the opposite-based learning (OBL) approach, which is named as OBL-CSA, to improve the exploration and exploitation capability of the CSA in quantitative structure-biodegradation relationship (QSBR) modelling of classifying the biodegradable materials. The results reveal that the performance of OBL-CSA not only manifest in improving the classification performance, but also in reduced computational time required to complete the process when compared to the standard CSA and other four optimization algorithms tested, which are the particle swarm algorithm (PSO), black hole algorithm (BHA), grey wolf algorithm (GWA), and whale optimization algorithm (WOA). In conclusion, the OBL-CSA could be a valuable resource in the classification of biodegradable materials.
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Affiliation(s)
- A M Al-Fakih
- Department of Chemistry, Faculty of Science, Universiti Teknologi Malaysia, Johor, Malaysia and Department of Chemistry, Faculty of Science, Sana'a University, Sana'a, Yemen
| | - Z Y Algamal
- Department of Statistics and Informatics, University of Mosul, Mosul, Iraq
| | - M K Qasim
- Department of General Science, University of Mosul, Mosul, Iraq
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Tang W, Li Y, Yu Y, Wang Z, Xu T, Chen J, Lin J, Li X. Development of models predicting biodegradation rate rating with multiple linear regression and support vector machine algorithms. CHEMOSPHERE 2020; 253:126666. [PMID: 32289603 DOI: 10.1016/j.chemosphere.2020.126666] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 03/27/2020] [Accepted: 03/30/2020] [Indexed: 06/11/2023]
Abstract
Biodegradation is a significant process for removing organic chemicals from water, soil and sediment environments, and therefore biodegradability is critical to evaluate the environmental persistence of organic chemicals. In this study, based on a dataset with 171 compounds, four quantitative structure-activity relationship (QSAR) models were developed for predicting primary and ultimate biodegradation rate rating with multiple linear regression (MLR) and support vector machine (SVM) algorithms. Two MLR models were built with a dataset with carbon atom number ≤9, and two SVM models were built with a dataset with carbon atom number >9. In the MLR models, nArX (number of X on aromatic ring) is the most important descriptor governing primary and ultimate biodegradation of organic chemicals. For the SVM models, determination coefficient (R2) values, cross-validated coefficients (Q2LOO) and external validation coefficient (Q2ext) values are over 0.9, indicating the SVM models have satisfactory goodness-of-fit, robustness and external predictive abilities. The applicability domains of these models were visualized by the Williams plot. The developed models can be used as effective tools to predict biodegradability of organic chemicals.
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Affiliation(s)
- Weihao Tang
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Yanying Li
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Yang Yu
- Solid Waste and Chemicals Management Center, Ministry of Ecology and Environment (MEE), Beijing, 100029, China
| | - Zhongyu Wang
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Tong Xu
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Jingwen Chen
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China
| | - Jun Lin
- Solid Waste and Chemicals Management Center, Ministry of Ecology and Environment (MEE), Beijing, 100029, China
| | - Xuehua Li
- Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China.
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Machine Learning Modeling of Aerobic Biodegradation for Azo Dyes and Hexavalent Chromium. MATHEMATICS 2020. [DOI: 10.3390/math8060913] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The present study emphasizes the efficacy of a biosurfactant-producing bacterial strain Klebsiella sp. KOD36 in biodegradation of azo dyes and hexavalent chromium individually and in a simultaneous system. The bacterial strain has exhibited a considerable potential for biodegradation of chromium and azo dyes in single and combination systems (maximum 97%, 94% in an individual and combined system, respectively). Simultaneous aerobic biodegradation of azo dyes and hexavalent chromium (SBAHC) was modeled using machine learning programming, which includes gene expression programming, random forest, support vector regression, and support vector regression-fruit fly optimization algorithm. The correlation coefficient includes the dispersion index, and the Willmott agreement index was employed as statistical metrics to assess the performance of each model separately. In addition, the Taylor diagram was used to further investigate the methods used. The findings of the present study were that the support vector regression-fruitfly optimization algorithm (SVR-FOA) with correlation coefficient (CC) of 0.644, (scattered index) SI of 0.374, and (Willmott’s index of agreement) WI of 0.607 performed better than the autonomous support vector regression (SVR), gene expression programming (GEP), and random forest (RF) methods. In addition, the standalone SVR model with CC of 0.146, SI of 0.473, and WI of 0.408 ranked the second best. In summary, the SBAHC can be accurately estimated using the hybrid SVR-FOA method. In other words, FOA has proven to be a powerful optimization algorithm for increasing the accuracy of the SVR method.
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Zhang M, Li J, Kang L, Zhang N, Huang C, He Y, Hu M, Zhou X, Zhang J. Machine learning-guided design and development of multifunctional flexible Ag/poly (amic acid) composites using the differential evolution algorithm. NANOSCALE 2020; 12:3988-3996. [PMID: 32016252 DOI: 10.1039/c9nr09146g] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
The development of flexible composites is of great significance in the flexible electronic field. In combination with machine learning technology, the introduction of artificial intelligence to flexible materials design, synthesis, characterization and application research will greatly promote the flexible materials research efficiency. In this study, the back propagation (BP) neural network based on the differential evolution (DE) algorithm was applied to determine the electrical properties of the flexible Ag/poly (amic acid) (PAA) composite structure and to develop flexible materials for its different applications. In the machine learning model, the concentration of PAA, the ion exchange time of AgNO3, and the concentration and reduction time of NaBH4 are set as input parameters, and the product of the sheet resistance of the Ag/PAA film and the processing time are set as output information. To overcome the situation whereby the BP neural network solution process could fall into the local optimum, the initial threshold and the weight of the BP neural network and the data import model are optimized by the DE algorithm. Utilizing 1077 learning samples and 49 predictive samples, a machine learning model with very high accuracy was established and relative errors of predictions less than 1.96% were achieved. In terms of this model, the optimized fabrication conditions of the Ag/PAA composites, which are suitable for strain sensors and electrodes, were predicted. To identify the availability and applicability of the proposed algorithm, a strain gauge sensor, a triboelectric nanogenerator (TENG) and a capacitive pressure sensor array were fabricated successfully using the optimized process parameters. This work shows that machine learning can be used to quickly optimize the process and provide guidance for material and process design, which is of significance for the development of flexible materials and devices.
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Affiliation(s)
- Mengyao Zhang
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, 500 Dongchuan Road, 200241, Shanghai, China.
| | - Jia Li
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, 500 Dongchuan Road, 200241, Shanghai, China.
| | - Ling Kang
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, 500 Dongchuan Road, 200241, Shanghai, China.
| | - Nan Zhang
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, 500 Dongchuan Road, 200241, Shanghai, China.
| | - Chun Huang
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, 500 Dongchuan Road, 200241, Shanghai, China.
| | - Yaqin He
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, 500 Dongchuan Road, 200241, Shanghai, China.
| | - Menghan Hu
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, 500 Dongchuan Road, 200241, Shanghai, China.
| | - Xiaofeng Zhou
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, 500 Dongchuan Road, 200241, Shanghai, China.
| | - Jian Zhang
- Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, 500 Dongchuan Road, 200241, Shanghai, China. and Shanghai Institute of Intelligent Electronics & Systems, Fudan University, Shanghai 200433, China
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Zhan Z, Li L, Tian S, Zhen X, Li Y. Prediction of chemical biodegradability using computational methods. MOLECULAR SIMULATION 2017. [DOI: 10.1080/08927022.2017.1328556] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Zhixiong Zhan
- Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou, P.R. China
| | - Linlang Li
- Jiangsu Key Laboratory of Translational Research and Therapy for Neuro-Psycho-Diseases and College of Pharmaceutical Sciences, Soochow University, Suzhou, P.R. China
| | - Sheng Tian
- Jiangsu Key Laboratory of Translational Research and Therapy for Neuro-Psycho-Diseases and College of Pharmaceutical Sciences, Soochow University, Suzhou, P.R. China
| | - Xuechu Zhen
- Jiangsu Key Laboratory of Translational Research and Therapy for Neuro-Psycho-Diseases and College of Pharmaceutical Sciences, Soochow University, Suzhou, P.R. China
| | - Youyong Li
- Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou, P.R. China
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