Data mining in the educational field can be used to optimize the teaching and learning performance among the students.The recently developed machine learning(ML)and deep learning(DL)approaches can be utilized to mine ...Data mining in the educational field can be used to optimize the teaching and learning performance among the students.The recently developed machine learning(ML)and deep learning(DL)approaches can be utilized to mine the data effectively.This study proposes an Improved Sailfish Optimizer-based Feature SelectionwithOptimal Stacked Sparse Autoencoder(ISOFS-OSSAE)for data mining and pattern recognition in the educational sector.The proposed ISOFS-OSSAE model aims to mine the educational data and derive decisions based on the feature selection and classification process.Moreover,the ISOFS-OSSAEmodel involves the design of the ISOFS technique to choose an optimal subset of features.Moreover,the swallow swarm optimization(SSO)with the SSAE model is derived to perform the classification process.To showcase the enhanced outcomes of the ISOFSOSSAE model,a wide range of experiments were taken place on a benchmark dataset from the University of California Irvine(UCI)Machine Learning Repository.The simulation results pointed out the improved classification performance of the ISOFS-OSSAE model over the recent state of art approaches interms of different performance measures.展开更多
文摘Data mining in the educational field can be used to optimize the teaching and learning performance among the students.The recently developed machine learning(ML)and deep learning(DL)approaches can be utilized to mine the data effectively.This study proposes an Improved Sailfish Optimizer-based Feature SelectionwithOptimal Stacked Sparse Autoencoder(ISOFS-OSSAE)for data mining and pattern recognition in the educational sector.The proposed ISOFS-OSSAE model aims to mine the educational data and derive decisions based on the feature selection and classification process.Moreover,the ISOFS-OSSAEmodel involves the design of the ISOFS technique to choose an optimal subset of features.Moreover,the swallow swarm optimization(SSO)with the SSAE model is derived to perform the classification process.To showcase the enhanced outcomes of the ISOFSOSSAE model,a wide range of experiments were taken place on a benchmark dataset from the University of California Irvine(UCI)Machine Learning Repository.The simulation results pointed out the improved classification performance of the ISOFS-OSSAE model over the recent state of art approaches interms of different performance measures.
文摘精准的PM2.5小时浓度短期预测,可以有效地提高空气污染的预报预警能力.针对传统的PM2.5预测模型中存在的影响因素考虑不全面且影响因素选择方法适用性不强等问题,本文提出一种融合栈式稀疏自编码器(Stack Sparse Auto-Encoder,SSAE)和长短期记忆神经网络(Long-Short Term Memory,LSTM)的PM2.5小时浓度预测模型.SSAE-LSTM模型综合考虑了时间因素、空间因素、气象因素和空气污染物因素等多种因素对PM2.5的影响,采用SSAE以无监督方式自动提取PM2.5抽象影响特征,实现特征的压缩和降维;然后以提取的抽象特征作为LSTM模型的输入,建立PM2.5时间序列预测模型,挖掘PM2.5历史序列中的长期依赖特征.为了验证方法的有效性,本文基于2016—2018年京津冀城市群71个空气监测站点的空气数据和气象数据,建立SSAE-LSTM模型对各个站点的PM2.5浓度进行离线训练和预测实验.预测结果表明,SSAE-LSTM模型预测精度高于其它预测模型,在所有测试集上的一致性指数(IA)高达0.99,均方根误差RMSE与平均绝对误差MAE降到了13.98和7.90.此外,分析了SSAE-LSTM模型在不同季节的适用性,71个空气监测站点在春、夏、秋、冬4个季节测试集的预测值和实测值均有很好的线性关系,决定系数分别是0.86、0.92、0.96、0.93.对北京市万寿西宫站点的预测结果表明,SSAE-LSTM模型可以用于不同空气质量情况下的PM2.5小时浓度预报,且具有应用上的可行性和可靠性.
