We design a new hybrid quantum-classical convolutional neural network(HQCCNN)model based on parameter quantum circuits.In this model,we use parameterized quantum circuits(PQCs)to redesign the convolutional layer in cl...We design a new hybrid quantum-classical convolutional neural network(HQCCNN)model based on parameter quantum circuits.In this model,we use parameterized quantum circuits(PQCs)to redesign the convolutional layer in classical convolutional neural networks,forming a new quantum convolutional layer to achieve unitary transformation of quantum states,enabling the model to more accurately extract hidden information from images.At the same time,we combine the classical fully connected layer with PQCs to form a new hybrid quantum-classical fully connected layer to further improve the accuracy of classification.Finally,we use the MNIST dataset to test the potential of the HQCCNN.The results indicate that the HQCCNN has good performance in solving classification problems.In binary classification tasks,the classification accuracy of numbers 5 and 7 is as high as 99.71%.In multivariate classification,the accuracy rate also reaches 98.51%.Finally,we compare the performance of the HQCCNN with other models and find that the HQCCNN has better classification performance and convergence speed.展开更多
A novel hybrid model combining a convolutional neural network(CNN)and a low-complexity Transformer network is introduced for predicting lung cancer response to neoadjuvant chemoimmunotherapy using computed tomography ...A novel hybrid model combining a convolutional neural network(CNN)and a low-complexity Transformer network is introduced for predicting lung cancer response to neoadjuvant chemoimmunotherapy using computed tomography scans.This approach is crucial as it assists clinicians in identifying patients likely to benefit from treatment and in assessing their prognosis.The model employs channel splitting to minimize parameter count.It then leverages both CNN for local feature extraction and a streamlined Transformer for global feature comprehension.To enhance efficiency,a novel self-attention mechanism is implemented,focusing on feature aggregation and element-wise multiplication.To address the different semantic meanings of features,an attention-based module is designed to seamlessly integrate features from both networks,employing a process of coarse fusion,attention computation,and fine fusion.When evaluated with data from 232 lung cancer patients who have undergone neoadjuvant chemoimmunotherapy,the model demonstrates exceptional performance,achieving a Dice score of 47.04%and a 95.00%Hausdorff distance of 25.12 mm,outperforming existing methods.Additionally,it has only 2.91×106 parameters and 52.95×109 floating point operations.Moreover,the model’s predictive accuracy in tumor diameter estimation is beneficial for treatment planning.Its robustness is further validated through its application in stroke lesion prediction,indicating its broad applicability.展开更多
With the advent of Industry 4.0(I4.0),predictive maintenance(PdM)methods have been widely adopted by businesses to deal with the condition of their machinery.With the help of I4.0,digital transformation,information te...With the advent of Industry 4.0(I4.0),predictive maintenance(PdM)methods have been widely adopted by businesses to deal with the condition of their machinery.With the help of I4.0,digital transformation,information techniques,computerised control,and communication networks,large amounts of data on operational and process conditions can be collected from multiple pieces of equipment and used to make an automated fault detection and diagnosis,all with the goal of reducing unscheduled maintenance,improving component utilisation,and lengthening the lifespan of the equipment.In this paper,we use smart approaches to create a PdM planning model.The five key steps of the created approach are as follows:(1)cleaning the data,(2)normalising the data,(3)selecting the best features,(4)making a decision about the prediction network,and(5)producing a prediction.At the outset,PdM-related data undergo data cleaning and normalisation to get everything in order and within some kind of bounds.The next step is to execute optimal feature selection in order to eliminate unnecessary data.This research presents the golden search optimization(GSO)algorithm,a powerful population-based optimization technique for efficient feature selection.The first phase of GSO is to produce a set of possible solutions or objects at random.These objects will then interact with one another using a straightforward mathematical model to find the best feasible answer.Due to the wide range over which the prediction values fall,machine learning and deep learning confront challenges in providing reliable predictions.This is why we recommend a multilayer hybrid convolution neural network(MLH-CNN).While conceptually similar to VGGNet,this approach uses fewer parameters while maintaining or improving classification correctness by adjusting the amount of network modules and channels.The projected perfect is evaluated on two datasets to show that it can accurately predict the future state of components for upkeep preparation.展开更多
基金Project supported by the Natural Science Foundation of Shandong Province,China (Grant No.ZR2021MF049)the Joint Fund of Natural Science Foundation of Shandong Province (Grant Nos.ZR2022LLZ012 and ZR2021LLZ001)。
文摘We design a new hybrid quantum-classical convolutional neural network(HQCCNN)model based on parameter quantum circuits.In this model,we use parameterized quantum circuits(PQCs)to redesign the convolutional layer in classical convolutional neural networks,forming a new quantum convolutional layer to achieve unitary transformation of quantum states,enabling the model to more accurately extract hidden information from images.At the same time,we combine the classical fully connected layer with PQCs to form a new hybrid quantum-classical fully connected layer to further improve the accuracy of classification.Finally,we use the MNIST dataset to test the potential of the HQCCNN.The results indicate that the HQCCNN has good performance in solving classification problems.In binary classification tasks,the classification accuracy of numbers 5 and 7 is as high as 99.71%.In multivariate classification,the accuracy rate also reaches 98.51%.Finally,we compare the performance of the HQCCNN with other models and find that the HQCCNN has better classification performance and convergence speed.
基金supported in part by the National Key Research and Development Program of China(No.2021YFF1201200)the Science and Technology Innovation Program of Hunan Province(No.2022RC1031),the Natural Science Foundation of Hunan Province(No.2023JJ50354)+1 种基金the Scientific Research Project of Hunan Education Department(No.24A0575)the High Performance Computing Center of Central South University.
文摘A novel hybrid model combining a convolutional neural network(CNN)and a low-complexity Transformer network is introduced for predicting lung cancer response to neoadjuvant chemoimmunotherapy using computed tomography scans.This approach is crucial as it assists clinicians in identifying patients likely to benefit from treatment and in assessing their prognosis.The model employs channel splitting to minimize parameter count.It then leverages both CNN for local feature extraction and a streamlined Transformer for global feature comprehension.To enhance efficiency,a novel self-attention mechanism is implemented,focusing on feature aggregation and element-wise multiplication.To address the different semantic meanings of features,an attention-based module is designed to seamlessly integrate features from both networks,employing a process of coarse fusion,attention computation,and fine fusion.When evaluated with data from 232 lung cancer patients who have undergone neoadjuvant chemoimmunotherapy,the model demonstrates exceptional performance,achieving a Dice score of 47.04%and a 95.00%Hausdorff distance of 25.12 mm,outperforming existing methods.Additionally,it has only 2.91×106 parameters and 52.95×109 floating point operations.Moreover,the model’s predictive accuracy in tumor diameter estimation is beneficial for treatment planning.Its robustness is further validated through its application in stroke lesion prediction,indicating its broad applicability.
文摘With the advent of Industry 4.0(I4.0),predictive maintenance(PdM)methods have been widely adopted by businesses to deal with the condition of their machinery.With the help of I4.0,digital transformation,information techniques,computerised control,and communication networks,large amounts of data on operational and process conditions can be collected from multiple pieces of equipment and used to make an automated fault detection and diagnosis,all with the goal of reducing unscheduled maintenance,improving component utilisation,and lengthening the lifespan of the equipment.In this paper,we use smart approaches to create a PdM planning model.The five key steps of the created approach are as follows:(1)cleaning the data,(2)normalising the data,(3)selecting the best features,(4)making a decision about the prediction network,and(5)producing a prediction.At the outset,PdM-related data undergo data cleaning and normalisation to get everything in order and within some kind of bounds.The next step is to execute optimal feature selection in order to eliminate unnecessary data.This research presents the golden search optimization(GSO)algorithm,a powerful population-based optimization technique for efficient feature selection.The first phase of GSO is to produce a set of possible solutions or objects at random.These objects will then interact with one another using a straightforward mathematical model to find the best feasible answer.Due to the wide range over which the prediction values fall,machine learning and deep learning confront challenges in providing reliable predictions.This is why we recommend a multilayer hybrid convolution neural network(MLH-CNN).While conceptually similar to VGGNet,this approach uses fewer parameters while maintaining or improving classification correctness by adjusting the amount of network modules and channels.The projected perfect is evaluated on two datasets to show that it can accurately predict the future state of components for upkeep preparation.
