The effective and timely diagnosis and treatment of ocular diseases are key to the rapid recovery of patients.Today,the mass disease that needs attention in this context is cataracts.Although deep learning has signifi...The effective and timely diagnosis and treatment of ocular diseases are key to the rapid recovery of patients.Today,the mass disease that needs attention in this context is cataracts.Although deep learning has significantly advanced the analysis of ocular disease images,there is a need for a probabilistic model to generate the distributions of potential outcomes and thusmake decisions related to uncertainty quantification.Therefore,this study implements a Bayesian Convolutional Neural Networks(BCNN)model for predicting cataracts by assigning probability values to the predictions.It prepares convolutional neural network(CNN)and BCNN models.The proposed BCNN model is CNN-based in which reparameterization is in the first and last layers of the CNN model.This study then trains them on a dataset of cataract images filtered from the ocular disease fundus images fromKaggle.The deep CNN model has an accuracy of 95%,while the BCNN model has an accuracy of 93.75% along with information on uncertainty estimation of cataracts and normal eye conditions.When compared with other methods,the proposed work reveals that it can be a promising solution for cataract prediction with uncertainty estimation.展开更多
With the rapid advancement of robotics and Artificial Intelligence(AI),aerobics training companion robots now support eco-friendly fitness by reducing reliance on nonrenewable energy.This study presents a solar-powere...With the rapid advancement of robotics and Artificial Intelligence(AI),aerobics training companion robots now support eco-friendly fitness by reducing reliance on nonrenewable energy.This study presents a solar-powered aerobics training robot featuring an adaptive energy management system designed for sustainability and efficiency.The robot integrates machine vision with an enhanced Dynamic Cheetah Optimizer and Bayesian Neural Network(DynCO-BNN)to enable precise exercise monitoring and real-time feedback.Solar tracking technology ensures optimal energy absorption,while a microcontroller-based regulator manages power distribution and robotic movement.Dual-battery switching ensures uninterrupted operation,aided by light and I/V sensors for energy optimization.Using the INSIGHT-LME IMU dataset,which includes motion data from 76 individuals performing Local Muscular Endurance(LME)exercises,the system detects activities,counts repetitions,and recognizes human movements.To minimize energy use during data processing,Min-Max normalization and two-dimensional Discrete Fourier Transform(2D-DFT)are applied,boosting computational efficiency.The robot accurately identifies upper and lower limb movements,delivering effective exercise guidance.The DynCO-BNN model achieved a high tracking accuracy of 96.8%.Results confirm improved solar utilization,ecological sustainability,and reduced dependence on fossil fuels—positioning the robot as a smart,energy-efficient solution for next-generation fitness technology.展开更多
This study utilizes the Bayesian neural network(BNN)method in machine learning to learn and predict the cross-sectional data of ^(28)Si projectile fragmentation for different targets at different energies and to quant...This study utilizes the Bayesian neural network(BNN)method in machine learning to learn and predict the cross-sectional data of ^(28)Si projectile fragmentation for different targets at different energies and to quantify the uncertainty.The detailed modeling process of the BNN is presented,and its prediction results are compared with those of the Cummings,Nilsen,EPAX2,EPAX3,and FRACS models and experimental measurement values.The results reveal that,compared with other models,the BNN method achieves the smallest root-mean-square error(RMSE)and the highest agreement with the experimental values.Only the BNN method and FRACS model show a significant odd-even staggering effect;however,the results of the BNN method are closer to the experimental values.Furthermore,the BNN method is the only model capable of reproducing data features with low cross-section values at Z=9,and the average ratio of the predicted to experimental values of the BNN is close to 1.0.These results indicate that the BNN method can accurately reproduce and predict the fragment production cross sections of ^(28)Si projectile fragmentation and demonstrate its ability to capture key data characteristics.展开更多
预测含伪结的RNA分子二级结构是生物信息学的一个研究难点。利用多分类支持向量机结合贝叶斯神经网络针对含伪结的RNA分子二级结构进行预测。利用多分类支持向量机进行预测,输出端得到相应碱基的平面伪结结构的E-NSSEL(Ex-tend New Seco...预测含伪结的RNA分子二级结构是生物信息学的一个研究难点。利用多分类支持向量机结合贝叶斯神经网络针对含伪结的RNA分子二级结构进行预测。利用多分类支持向量机进行预测,输出端得到相应碱基的平面伪结结构的E-NSSEL(Ex-tend New Secondary Structure Element Label)类别标签。使用碱基已预测的结果通过贝叶斯神经网络进行修正,并恢复RNA分子二级结构。使用该方法能有效地改善含伪结的RNA分子二级结构的预测效果。展开更多
Fragment production in spallation reactions yields key infrastructure data for various applications.Based on the empirical SPACS parameterizations,a Bayesian-neural-network(BNN)approach is established to predict the f...Fragment production in spallation reactions yields key infrastructure data for various applications.Based on the empirical SPACS parameterizations,a Bayesian-neural-network(BNN)approach is established to predict the fragment cross sections in proton-induced spallation reactions.A systematic investigation has been performed for the measured proton-induced spallation reactions of systems ranging from intermediate to heavy nuclei systems and incident energies ranging from 168 MeV/u to 1500 MeV/u.By learning the residuals between the experimental measurements and SPACS predictions,it is found that the BNN-predicted results are in good agreement with the measured results.The established method is suggested to benefit the related research on nuclear astrophysics,nuclear radioactive beam sources,accelerator driven systems,proton therapy,etc.展开更多
基金Saudi Arabia for funding this work through Small Research Group Project under Grant Number RGP.1/316/45.
文摘The effective and timely diagnosis and treatment of ocular diseases are key to the rapid recovery of patients.Today,the mass disease that needs attention in this context is cataracts.Although deep learning has significantly advanced the analysis of ocular disease images,there is a need for a probabilistic model to generate the distributions of potential outcomes and thusmake decisions related to uncertainty quantification.Therefore,this study implements a Bayesian Convolutional Neural Networks(BCNN)model for predicting cataracts by assigning probability values to the predictions.It prepares convolutional neural network(CNN)and BCNN models.The proposed BCNN model is CNN-based in which reparameterization is in the first and last layers of the CNN model.This study then trains them on a dataset of cataract images filtered from the ocular disease fundus images fromKaggle.The deep CNN model has an accuracy of 95%,while the BCNN model has an accuracy of 93.75% along with information on uncertainty estimation of cataracts and normal eye conditions.When compared with other methods,the proposed work reveals that it can be a promising solution for cataract prediction with uncertainty estimation.
文摘With the rapid advancement of robotics and Artificial Intelligence(AI),aerobics training companion robots now support eco-friendly fitness by reducing reliance on nonrenewable energy.This study presents a solar-powered aerobics training robot featuring an adaptive energy management system designed for sustainability and efficiency.The robot integrates machine vision with an enhanced Dynamic Cheetah Optimizer and Bayesian Neural Network(DynCO-BNN)to enable precise exercise monitoring and real-time feedback.Solar tracking technology ensures optimal energy absorption,while a microcontroller-based regulator manages power distribution and robotic movement.Dual-battery switching ensures uninterrupted operation,aided by light and I/V sensors for energy optimization.Using the INSIGHT-LME IMU dataset,which includes motion data from 76 individuals performing Local Muscular Endurance(LME)exercises,the system detects activities,counts repetitions,and recognizes human movements.To minimize energy use during data processing,Min-Max normalization and two-dimensional Discrete Fourier Transform(2D-DFT)are applied,boosting computational efficiency.The robot accurately identifies upper and lower limb movements,delivering effective exercise guidance.The DynCO-BNN model achieved a high tracking accuracy of 96.8%.Results confirm improved solar utilization,ecological sustainability,and reduced dependence on fossil fuels—positioning the robot as a smart,energy-efficient solution for next-generation fitness technology.
基金Supported by the National Nature Science Foundation of China(11075100,11347198,11565001)the Natural Science Foundation of Shanxi Province,China(2011011001-2)+1 种基金the Shanxi Provincial Foundation for Returned Overseas Chinese Scholars(2011-058)the Postgraduate Science and Technology Innovation Project of Shanxi Normal University(2023XBY004)。
文摘This study utilizes the Bayesian neural network(BNN)method in machine learning to learn and predict the cross-sectional data of ^(28)Si projectile fragmentation for different targets at different energies and to quantify the uncertainty.The detailed modeling process of the BNN is presented,and its prediction results are compared with those of the Cummings,Nilsen,EPAX2,EPAX3,and FRACS models and experimental measurement values.The results reveal that,compared with other models,the BNN method achieves the smallest root-mean-square error(RMSE)and the highest agreement with the experimental values.Only the BNN method and FRACS model show a significant odd-even staggering effect;however,the results of the BNN method are closer to the experimental values.Furthermore,the BNN method is the only model capable of reproducing data features with low cross-section values at Z=9,and the average ratio of the predicted to experimental values of the BNN is close to 1.0.These results indicate that the BNN method can accurately reproduce and predict the fragment production cross sections of ^(28)Si projectile fragmentation and demonstrate its ability to capture key data characteristics.
文摘预测含伪结的RNA分子二级结构是生物信息学的一个研究难点。利用多分类支持向量机结合贝叶斯神经网络针对含伪结的RNA分子二级结构进行预测。利用多分类支持向量机进行预测,输出端得到相应碱基的平面伪结结构的E-NSSEL(Ex-tend New Secondary Structure Element Label)类别标签。使用碱基已预测的结果通过贝叶斯神经网络进行修正,并恢复RNA分子二级结构。使用该方法能有效地改善含伪结的RNA分子二级结构的预测效果。
基金Supported by the National Natural Science Foundation of China(U1732135,11975091)。
文摘Fragment production in spallation reactions yields key infrastructure data for various applications.Based on the empirical SPACS parameterizations,a Bayesian-neural-network(BNN)approach is established to predict the fragment cross sections in proton-induced spallation reactions.A systematic investigation has been performed for the measured proton-induced spallation reactions of systems ranging from intermediate to heavy nuclei systems and incident energies ranging from 168 MeV/u to 1500 MeV/u.By learning the residuals between the experimental measurements and SPACS predictions,it is found that the BNN-predicted results are in good agreement with the measured results.The established method is suggested to benefit the related research on nuclear astrophysics,nuclear radioactive beam sources,accelerator driven systems,proton therapy,etc.
