提出了一种基于三维卷积和卷积长短期记忆(convolutional long short-term memory,CLSTM)神经网络的水产养殖溶解氧预测模型。首先,将输入向量及其转置相乘形成一个单通道矩阵,把一定时间段内的单通道矩阵堆叠成一个立方体作为输入数据...提出了一种基于三维卷积和卷积长短期记忆(convolutional long short-term memory,CLSTM)神经网络的水产养殖溶解氧预测模型。首先,将输入向量及其转置相乘形成一个单通道矩阵,把一定时间段内的单通道矩阵堆叠成一个立方体作为输入数据;然后,将输入数据进行连续两次三维卷积来细化溶解氧相关因素的特征,并删除池化层以简化计算;最后,将三维卷积抽取的特征结果输入CLSTM模型以提取时间维度的信息,在全连接层根据梯度下降算法将数据反向更新。采集湖北省襄阳市某家特种水产养殖有限公司的实际数据进行实验。结果表明:相比于传统BP神经网络模型、Conv3D、Conv2D,所提出的模型具有更快的训练收敛速度、更高的预测精度和更好的预测稳定性,可以满足实际生产的需要。展开更多
Sleep plays a vital role in optimum working of the brain and the body.Numerous people suffer from sleep-oriented illnesses like apnea,insomnia,etc.Sleep stage classification is a primary process in the quantitative ex...Sleep plays a vital role in optimum working of the brain and the body.Numerous people suffer from sleep-oriented illnesses like apnea,insomnia,etc.Sleep stage classification is a primary process in the quantitative examination of polysomnographic recording.Sleep stage scoring is mainly based on experts’knowledge which is laborious and time consuming.Hence,it can be essential to design automated sleep stage classification model using machine learning(ML)and deep learning(DL)approaches.In this view,this study focuses on the design of Competitive Multi-verse Optimization with Deep Learning Based Sleep Stage Classification(CMVODL-SSC)model using Electroencephalogram(EEG)signals.The proposed CMVODL-SSC model intends to effectively categorize different sleep stages on EEG signals.Primarily,data pre-processing is performed to convert the actual data into useful format.Besides,a cascaded long short term memory(CLSTM)model is employed to perform classification process.At last,the CMVO algorithm is utilized for optimally tuning the hyperparameters involved in the CLSTM model.In order to report the enhancements of the CMVODL-SSC model,a wide range of simulations was carried out and the results ensured the better performance of the CMVODL-SSC model with average accuracy of 96.90%.展开更多
文摘提出了一种基于三维卷积和卷积长短期记忆(convolutional long short-term memory,CLSTM)神经网络的水产养殖溶解氧预测模型。首先,将输入向量及其转置相乘形成一个单通道矩阵,把一定时间段内的单通道矩阵堆叠成一个立方体作为输入数据;然后,将输入数据进行连续两次三维卷积来细化溶解氧相关因素的特征,并删除池化层以简化计算;最后,将三维卷积抽取的特征结果输入CLSTM模型以提取时间维度的信息,在全连接层根据梯度下降算法将数据反向更新。采集湖北省襄阳市某家特种水产养殖有限公司的实际数据进行实验。结果表明:相比于传统BP神经网络模型、Conv3D、Conv2D,所提出的模型具有更快的训练收敛速度、更高的预测精度和更好的预测稳定性,可以满足实际生产的需要。
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/158/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R235)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4340237DSR10).
文摘Sleep plays a vital role in optimum working of the brain and the body.Numerous people suffer from sleep-oriented illnesses like apnea,insomnia,etc.Sleep stage classification is a primary process in the quantitative examination of polysomnographic recording.Sleep stage scoring is mainly based on experts’knowledge which is laborious and time consuming.Hence,it can be essential to design automated sleep stage classification model using machine learning(ML)and deep learning(DL)approaches.In this view,this study focuses on the design of Competitive Multi-verse Optimization with Deep Learning Based Sleep Stage Classification(CMVODL-SSC)model using Electroencephalogram(EEG)signals.The proposed CMVODL-SSC model intends to effectively categorize different sleep stages on EEG signals.Primarily,data pre-processing is performed to convert the actual data into useful format.Besides,a cascaded long short term memory(CLSTM)model is employed to perform classification process.At last,the CMVO algorithm is utilized for optimally tuning the hyperparameters involved in the CLSTM model.In order to report the enhancements of the CMVODL-SSC model,a wide range of simulations was carried out and the results ensured the better performance of the CMVODL-SSC model with average accuracy of 96.90%.
