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2017计算科学与工程国际会议

作者： Xiaoqian Liu Yutao Zhao Shanyu Xu Dongdong Tian Fucheng Song Hongzong Si School of Public Health Qingdao University School of Stomatology Qingdao University Institute for Computational Science and Engineering Qingdao University

In this study, the QSAR（quantitative structure-activity relationship） model led to the discussion of a highly potent and selective ligand for the p38α MAP kinase inhibitors between the physical and chemical structure information of inhibition rate of cancer. All 62 ligands for the p38α MAP kinase inhibitors were divided randomly into training set with 48, and test set with 14. After molecular mechanics optimization with MM+ and AM1 by Hyperchem software, numerous descriptors were calculated by CODESSA software, including constitution, topolopy, geomrtry, electron, quantum chemistry descriptors. HM was used for the selection of optimal descriptor combination, which was as a group descriptors to build linear QSAR models by HM and GEP, respectively. As a significant result, HM has a precision of 0.82 and 0.76 in the training and test set, and the GEP accuracy of the training and test sets is 0.82 and 0.82. The results show that the both two quantitative constitutive relationship model has good prediction ability and two points, and further optimize the p38α MAP kinase inhibitor. The method is an effective application of carcinogenic compounds. The study can confirm some pivotal factors that influence the value of IC and draw some new drugs. It will save lots of energy and resources.

关键词： P38α MAP kinase P38α MAP kinase inhibitors Quantitative structure-activity gene expression programming Heuristic method Cancer

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Research on Music Emotion Cognition Model Based on Linguistic Value Computing

Research on Music Emotion Cognition Model Based on Linguisti...

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7th World Congress on Intelligent Control and Automation (WCICA 2008), vol.18

作者： Bin Zhu Tao Liu Yongchuan Tang College of information science of technology Zhejiang Shuren University Hangzhou China College of computer science and technology University of Zhejiang Hangzhou China

Research on music's affective computing model plays an important role in digital entertainment and harmonious human-machine interactive, and belongs to the field of artificial emotion. For modeling the music affective. There are two major insufficients in current researches such as: lack of knowledge expression and generalization. This paper presents a general model building peocess, and designs a cognizing test based on semantic difference for Chinese folk music. According to the studying about the data of experiment and relied on the GEP based on dynamic mutation operator, a model tally with the linguistic modeling theory is constructed, which is consistent with psychological consistent pattern of music emotion.

关键词： Music emotional vector Linguistic value computing Semantic difference method gene expression programming

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QSAR study on the IC50 of 6-alkenylamides of 4-anilinothieno[2,3-d] pyrimidine as epidermal growth factor receptor inhibitors in lung cancer

QSAR study on the IC50 of 6-alkenylamides of 4-anilinothieno...

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作者： Ruixue Song Fucheng Song Lianhua Cui Hongzong Si Honglin Zhai Department of Public Health Qingdao University Medical College Institute for Computational Science and Engineering Laboratory of New Fibrous Materials and Modern TextileThe Growing Base for State Key LaboratoryQingdao University Department of Chemistry Lanzhou University

In this study, a nonlinear quantitative structure–activity relationship model for the prediction of the IC of 6-alkenylamides of 4-anilinothieno [2, 3-d] pyrimidine as epidermal growth factor receptor（EGFR） inhibitors was developed by the gene expression programming（GEP）. This model is based on five descriptors which were selected from all the descriptors by heuristic method（HM）. GEP rendered a good correlation between the experimental and predicted log IC values with a correlation coefficient and a mean error of 0.87 and 0.14 for the training set, 0.71 and 0.41 for the test set, respectively. The results indicate that this QSAR model has good predictive capability and it shows that there is further study we can operate on EGFR inhibitors.

关键词： QSAR gene expression programming Epidermal growth factor receptor inhibitors 6-alkenylamides of 4-anilinothieno[2,3-d] pyrimidine

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QSAR model for the LC50 of nitrobenzenes

QSAR model for the LC50 of nitrobenzenes

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作者： Fucheng Song Huazhen Yang Hongzong Si College of Public Health Qingdao University

In the study of toxicology, animal experiments have given the toxicity of nitrobenzene compounds. Similarly, in the environment of nitrobenzene has potential harm to human body. In order to further study the characteristics of nitrobenzene,according to its physical and chemical properties, through the different method to establish QSAR model to predict the toxicity. In our study, a nonlinear quantitative structure–activity relationship model for the prediction of the LC50 of nitrobenzene was developed by the gene expression programming（GEP）. This GEP model is established basis on four descriptors which were selected from the descriptor list by heuristic method（HM）. A nonlinear, four-descriptor model based on GEP with mean-square error 0.065 and a correlation coefficient（R） 0.906 for the training set, mean-square error 0.043 and a correlation coefficient（R） 0.889 for the test set. The results indicate that this QSAR model has stable predictive capability of nitrobenzene toxicity and it provides a new method for toxicology research. The QSAR used in toxicology, will greatly reduce the harm of experimental animals.

关键词： gene expression programming LC50 Toxicology Quantitative structure activity relationship Nitrobenzene

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GEP-basedclassifiers with drift-detection

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EXPERT SYSTEMS 2021年第7期38卷 e12571-e12571页

作者： Jedrzejowicz, Joanna Jedrzejowicz, Piotr Univ Gdansk Inst Informat Fac Math Phys & Informat PL-80308 Gdansk Poland Gdynia Maritime Univ Dept Informat Syst Gdynia Poland

In the paper, we propose two gene expression programming (GEP)-based ensemble classifiers with different drift detection mechanisms. In the related work section, we briefly review GEP as a classification tool, incremental classifiers, and concept drift detectors. Next, the structure of our two-level GEP ensemble with metagenes is described. Further on, two integrated classifiers with drift detection algorithm and Wilcoxon rank sum test drift detector are proposed. The approach is validated in the computational experiment in which several real-life and artificial datasets with concept drift have been used. Experiment confirmed that the proposed approach can be competitive to existing solutions. In the conclusion section, we briefly outline directions for future research.

关键词： classification algorithms concept drift detection ensemble classifiers gene expression programming

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Optimization of cesium adsorption by Prussian blue using experiments and gene expression modeling

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JOURNAL OF WATER PROCESS ENGINEERING 2021年 41卷

作者： Nguyen, Mai Ngoc Yaqub, Muhammad Kim, Seongbeom Lee, Wontae Kumoh Natl Inst Technol Dept Environm Engn 61 Daehak Ro Gumi 39177 South Korea

Water-soluble and insoluble Prussian blue (PB) named PB-sol and PB-insol are synthesized to investigate the removal of cesium (Cs+) from an aqueous solution. During synthesis, different molar ratios of potassium hexacyanoferrate and ferric chloride are tested for adsorption of Cs+. The effect of synthesized PBs on the pH of deionized water (DI) and surface water is also studied. The effect of aqueous solution pH, competitive ions, and initial concentration of Cs+ on adsorption by PB-sol and PB-insol are explored separately as well as in combination with zeolite. Moreover, the adsorption kinetics are discussed by applying pseudo 1st and pseudo 2nd order kinetic models, and gene expression programming (GEP) model is developed for the first time to optimize the process. The PB-sol and PB-insol led to a lower pH of the aqueous solution. PB-insol/zeolite showed maximum adsorption of 91.8 mg g (-1) at an initial Cs+ concentration of 100 ppb in the surface water, whereas PB-sol, PBinsol, and PB-sol/zeolite demonstrated comparatively lower results. An increasing trend with higher initial concentrations is observed, while high pH and the absence of competing ions also favored the Cs+ adsorption. A significant decrease is noticed in presence of both monovalent and divalent ions (K+, Na+, Ca2+, and Mg2+). The kinetics are better described by the pseudo 2nd order kinetics model because of the physiochemical adsorption. Moreover, the developed GEP model presented reasonably good results for PB-sol and PB-insol by tracing the input-output relationship with R-2 = 0.799 and 0.886, respectively, for the testing dataset.

关键词： Adsorption Cesium gene expression programming Kinetics Prussian blue

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Predictive energy control for grid-connected industrial PV-battery systems using GEP-ANFIS

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 9卷

作者： Bakare, Mutiu Shola Abdulkarim, Abubakar Shuaibu, Aliyu Nuhu Muhamad, Mundu Mustafa Department of Electrical Engineering Kampala International University Ishaka 20000 Uganda

Rising energy costs in Uganda's industrial sector have significantly increased manufacturing expenses and led to the closure of potential enterprises, underscoring the urgent need for efficient energy management solutions. The study presents an optimization-based predictive energy control model that integrates gene expression programming (GEP) and Adaptive Network-based Fuzzy Inference Systems (ANFIS) for grid-connected solar PV-battery systems. The hybrid GEP-ANFIS model leverages GEP's strength in complex pattern recognition and ANFIS's adaptive learning capability to manage nonlinear data, resulting in more accurate and reliable energy predictions. The model utilizes historical load patterns, grid data, and solar PV/battery inputs to forecast load demand and solar power generation, optimizing energy usage based on Time of Use (TOU) pricing. The approach addresses the limitations of existing energy management techniques by enhancing predictive accuracy and ensuring seamless integration with industrial applications. The present work demonstrates that the proposed GEP-ANFIS double diode model consistently outperforms its counterparts, achieving a mean absolute percentage error (MAPE) of 7.25 %, a mean absolute deviation (MAD) of 2.95 %, and a root mean square error (RMSE) of 8.42 %. Moreover, the model exhibits superior accuracy in predicting short-term load demand, with a MAPE of 2.24 %, a MAD of 0.13 %, and an RMSE of 0.18 %. Additionally, the model results in an average energy cost reduction of 6.7 % for hybrid grid-connected solar PV/battery configurations, highlighting its economic benefits. The study's key contributions include the creation of a novel hybrid predictive model, significant cost savings, improved load prediction accuracy, and the integration of renewable energy sources into industrial energy management. The model's inferences can effectively address the challenges of rising energy costs, providing a robust framework for sustainable and efficient

关键词： ANFIS Energy management gene expression programming Predictive energy control Solar PV-battery systems Time of use pricing

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Predicting discharge coefficient of triangular side orifice using ANN and GEP models

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Water Science 2024年第1期38卷 1-20页

作者： Elshaarawy, Mohamed Kamel Hamed, Abdelrahman Kamal Civil Engineering Department Faculty of Engineering Horus University-Egypt New Damietta Egypt

This study utilized machine learning models to predict the discharge coefficient for a sharp-crested triangular side orifice (TSO). The chosen models were the Artificial Neural Network (ANN) and gene expression programming (GEP). Development of the models was based on 570 experimental datasets, with 70% allocated for training and the remaining 30% for testing. Five nondimensional parameters were utilized as inputs for the models, including TSO’s crest height to its height (W^*=W/H), main channel width to TSO’s base length (L*=B/L), main channel width to TSO’s height (H^*=B/H), upstream flow depth to the TSO’s height (Y^*=y₁/H), and upstream Froude number of the main channel (F_r). While the discharge coefficient (C_d) was defined as the output. Then, the developed models were evaluated by three performance metrics, violin boxplots, and Taylor diagrams to ensure their reliability and accuracy. Furthermore, a sensitivity analysis was conducted to indicate the most effective parameter affecting the C_d value. The findings revealed that both models predicted very well compared to the actual values, with the ANN model emerging as the most reliable predictor. It exhibited the highest determination coefficient (R²), nearing 1, along with the lowest Mean-Square-Error (MSE) and Mean-Absolute-Error (MAE) values, both close to zero. The sensitivity analysis highlighted that the orifice crest height and Froude number significantly impacted the C_d value, contributing to more than 36%. In addition, the predicted discharge coefficient stayed within the range of ± 5.0% of the experimental values. Finally, the developed models demonstrated a high level of equivalence compared to previous studies, especially the ANN model. Therefore, these models are recommended as accurate, robust, and rapid tools to predict the TSO’s discharge coefficient. © 2023 The Author(s). Published by Informa UK Limit

关键词： artificial neural network discharge coefficient gene expression programming prediction Side orifice

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An evolutionary approach to predict slope displacement of earth embankments unde earthquake ground motions

Journal of Engineering Research (Kuwait)

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Journal of Engineering Research (Kuwait) 2024年

作者： Jin, Zhenyang Yuan, Bingkun Fan, Siyu Javdanian, Hamed College of Public Security Management People's Public Security University of China Beijing 100000 China School of Energy Science and Engineering Central South University Hunan Changsha 410000 China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing 100000 China Department of Civil Engineering Shahrekord University Shahrekord Iran

Accurate slope stability analysis of earth embankments under ground shaking is of great importance for practical use in earthquake geotechnics. This study aims to predict soil slope displacements of earth embankments subjected to earthquake loading using evolutionary algorithms. Comprehensive real case histories of slope displacement of earth embankments under past earthquakes in different areas of the world were gathered and analyzed. A robust model was then developed to predict earthquake induced soil slope displacements using gene expression programming (GEP). Characteristics of earthquake ground motion including earthquake magnitude, earthquake predominant period, maximum earthquake acceleration and also geotechnical specifications of earth embankment including yield acceleration and fundamental period of earth embankment were taken as most influential factors on the slope displacements of earth embankments under earthquakes. Subsequently, performance of developed GEP-based predictive model was assessed using a sensitivity analysis under various effective factors. Finally, the accuracy of the predictive model was evaluated through comparison with the available relationships for estimation of seismic soil slope displacements. The results clearly indicate favorable accuracy of developed GEP-based model to predict slope displacements of earth embankments subjected to earthquake ground motions. © 2024 The Authors

关键词： Earth embankment Earthquake gene expression programming Soil slope

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基于基因表达式编程和时变强度的时间序列预测

基于基因表达式编程和时变强度的时间序列预测

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第二十二届中国数据库学术会议

作者：陈宇唐常杰钟义啸段磊乔少杰普东航四川大学计算机学院

1引言时间序列预测(Time Series Prediction)是数据挖掘中的一类典型问题,被广泛地应用在金融经济,气象水文,信号处理,灾害预警等领域。作为概率统计学科中应用性较强的一个分支,时间序列分析已经有了比较完整的理论系统,传统的AR模型,MA

关键词： gene expression programming Self-correlation coefficient Embedding dimension Sliding window

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