The most common form of cancer for women is breast cancer.Recent advances in medical imaging technologies increase the use of digital mammograms to diagnose breast cancer.Thus,an automated computerized system with hig...The most common form of cancer for women is breast cancer.Recent advances in medical imaging technologies increase the use of digital mammograms to diagnose breast cancer.Thus,an automated computerized system with high accuracy is needed.In this study,an efficient Deep Learning Architecture(DLA)with a Support Vector Machine(SVM)is designed for breast cancer diagnosis.It combines the ideas from DLA with SVM.The state-of-the-art Visual Geometric Group(VGG)architecture with 16 layers is employed in this study as it uses the small size of 3×3 convolution filters that reduces system complexity.The softmax layer in VGG assumes that the training samples belong to exactly only one class,which is not valid in a real situation,such as in medical image diagnosis.To overcome this situation,SVM is employed instead of the softmax layer in VGG.Data augmentation is also employed as DLA usually requires a large number of samples.VGG model with different SVM kernels is built to classify the mammograms.Results show that the VGG-SVM model has good potential for the classification of Mammographic Image Analysis Society(MIAS)database images with an accuracy of 98.67%,sensitivity of 99.32%,and specificity of 98.34%.展开更多
Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularl...Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.展开更多
Background:Accurate classification of normal blood cells is a critical foundation for automated hematological analysis,including the detection of pathological conditions like leukemia.While convolutional neural networ...Background:Accurate classification of normal blood cells is a critical foundation for automated hematological analysis,including the detection of pathological conditions like leukemia.While convolutional neural networks(CNNs)excel in local feature extraction,their ability to capture global contextual relationships in complex cellular morphologies is limited.This study introduces a hybrid CNN-Transformer framework to enhance normal blood cell classification,laying the groundwork for future leukemia diagnostics.Methods:The proposed architecture integrates pre-trained CNNs(ResNet50,EfficientNetB3,InceptionV3,CustomCNN)with Vision Transformer(ViT)layers to combine local and global feature modeling.Four hybrid models were evaluated on the publicly available Blood Cell Images dataset from Kaggle,comprising 17,092 annotated normal blood cell images across eight classes.The models were trained using transfer learning,fine-tuning,and computational optimizations,including cross-model parameter sharing to reduce redundancy by reusing weights across CNN backbones and attention-guided layer pruning to eliminate low-contribution layers based on attention scores,improving efficiency without sacrificing accuracy.Results:The InceptionV3-ViT model achieved a weighted accuracy of 97.66%(accounting for class imbalance by weighting each class’s contribution),a macro F1-score of 0.98,and a ROC-AUC of 0.998.The framework excelled in distinguishing morphologically similar cell types demonstrating robustness and reliable calibration(ECE of 0.019).The framework addresses generalization challenges,including class imbalance and morphological similarities,ensuring robust performance across diverse cell types.Conclusion:The hybrid CNN-Transformer framework significantly improves normal blood cell classification by capturing multi-scale features and long-range dependencies.Its high accuracy,efficiency,and generalization position it as a strong baseline for automated hematological analysis,with potential for extension to leukemia subtype classification through future validation on pathological samples.展开更多
The co-electrolysis of CO_(2)and H_(2)O through solid oxide electrolysis cells(SOECs),powered by renewable energy sources,offers a promising pathway to achieving carbon neutrality in the chemical industry.However,the ...The co-electrolysis of CO_(2)and H_(2)O through solid oxide electrolysis cells(SOECs),powered by renewable energy sources,offers a promising pathway to achieving carbon neutrality in the chemical industry.However,the inherent intermittency of renewable energy generation,such as wind power,leads to unstable power input for electrolysis.This variability induces significant thermal stress in SOECs,potentially causing cracks or even system failure.To address this challenge,a hybrid deep learning architecture(HDLA)was developed to control the temperature gradient of SOECs.The architecture combines a convolutional neural network(CNN)and a long short-term memory(LSTM)model for wind power prediction,a multi-physics model for temperature gradient simulation,and a linear neural network regression model to simulate the temperature distribution in SOECs.Training and verification are conducted using 16 datasets from an industrial wind farm.The results demonstrate that the application of HDLA successfully reduce the temperature gradient of SOECs from±20℃ to±5℃.Additionally,the potential wind power utilization achieved near-complete wind power utilization,increasing from 18%to 99%.This real-time control strategy,which optimizes flow regulation,effectively mitigates thermal stress,thereby extending the lifespan of SOECs and ensuring continuous carbon reduction,efficient conversion,and utilization.展开更多
The concept of deep learning has been applied to many domains, but the definition of a suitable problem depth has not been sufficiently explored. In this study, we propose a new Hierarchical Covering Algorithm （HCA）...The concept of deep learning has been applied to many domains, but the definition of a suitable problem depth has not been sufficiently explored. In this study, we propose a new Hierarchical Covering Algorithm （HCA） method to determine the levels of a hierarchical structure based on the Covering Algorithm （CA）. The CA constructs neural networks based on samples＇ own characteristics, and can effectively handle multi-category classification and large-scale data. Further, we abstract characters based on the CA to automatically embody the feature of a deep structure. We apply CA to construct hidden nodes at the lower level, and define a fuzzy equivalence relation R on upper spaces to form a hierarchical architecture based on fuzzy quotient space theory. The covering tree naturally becomes from R. HCA experiments performed on MNIST dataset show that the covering tree embodies the deep architecture of the problem, and the effects of a deep structure are shown to be better than having a single level.展开更多
Respiratory motion-induced vertebral movements can adversely impact intraoperative spine surgery,resulting in inaccurate positional information of the target region and unexpected damage during the operation.In this p...Respiratory motion-induced vertebral movements can adversely impact intraoperative spine surgery,resulting in inaccurate positional information of the target region and unexpected damage during the operation.In this paper,we propose a novel deep learning architecture for respiratory motion prediction,which can adapt to different patients.The proposed method utilizes an LSTM-AE with attention mechanism network that can be trained using few-shot datasets during operation.To ensure real-time performance,a dimension reduction method based on the respiration-induced physical movement of spine vertebral bodies is introduced.The experiment collected data from prone-positioned patients under general anaesthesia to validate the prediction accuracy and time efficiency of the LSTM-AE-based motion prediction method.The experimental results demonstrate that the presented method(RMSE:4.39%)outperforms other methods in terms of accuracy within a learning time of 2 min.The maximum predictive errors under the latency of 333 ms with respect to the x,y,and z axes of the optical camera system were 0.13,0.07,and 0.10 mm,respectively,within a motion range of 2 mm.展开更多
文摘The most common form of cancer for women is breast cancer.Recent advances in medical imaging technologies increase the use of digital mammograms to diagnose breast cancer.Thus,an automated computerized system with high accuracy is needed.In this study,an efficient Deep Learning Architecture(DLA)with a Support Vector Machine(SVM)is designed for breast cancer diagnosis.It combines the ideas from DLA with SVM.The state-of-the-art Visual Geometric Group(VGG)architecture with 16 layers is employed in this study as it uses the small size of 3×3 convolution filters that reduces system complexity.The softmax layer in VGG assumes that the training samples belong to exactly only one class,which is not valid in a real situation,such as in medical image diagnosis.To overcome this situation,SVM is employed instead of the softmax layer in VGG.Data augmentation is also employed as DLA usually requires a large number of samples.VGG model with different SVM kernels is built to classify the mammograms.Results show that the VGG-SVM model has good potential for the classification of Mammographic Image Analysis Society(MIAS)database images with an accuracy of 98.67%,sensitivity of 99.32%,and specificity of 98.34%.
文摘Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.
基金the Deanship of Graduate Studies and Scientific Research at Najran University,Saudi Arabia,for their financial support through the Easy Track Research program,grant code(NU/EFP/MRC/13).
文摘Background:Accurate classification of normal blood cells is a critical foundation for automated hematological analysis,including the detection of pathological conditions like leukemia.While convolutional neural networks(CNNs)excel in local feature extraction,their ability to capture global contextual relationships in complex cellular morphologies is limited.This study introduces a hybrid CNN-Transformer framework to enhance normal blood cell classification,laying the groundwork for future leukemia diagnostics.Methods:The proposed architecture integrates pre-trained CNNs(ResNet50,EfficientNetB3,InceptionV3,CustomCNN)with Vision Transformer(ViT)layers to combine local and global feature modeling.Four hybrid models were evaluated on the publicly available Blood Cell Images dataset from Kaggle,comprising 17,092 annotated normal blood cell images across eight classes.The models were trained using transfer learning,fine-tuning,and computational optimizations,including cross-model parameter sharing to reduce redundancy by reusing weights across CNN backbones and attention-guided layer pruning to eliminate low-contribution layers based on attention scores,improving efficiency without sacrificing accuracy.Results:The InceptionV3-ViT model achieved a weighted accuracy of 97.66%(accounting for class imbalance by weighting each class’s contribution),a macro F1-score of 0.98,and a ROC-AUC of 0.998.The framework excelled in distinguishing morphologically similar cell types demonstrating robustness and reliable calibration(ECE of 0.019).The framework addresses generalization challenges,including class imbalance and morphological similarities,ensuring robust performance across diverse cell types.Conclusion:The hybrid CNN-Transformer framework significantly improves normal blood cell classification by capturing multi-scale features and long-range dependencies.Its high accuracy,efficiency,and generalization position it as a strong baseline for automated hematological analysis,with potential for extension to leukemia subtype classification through future validation on pathological samples.
基金The authors would like to acknowledge the financial support from the National Key Research and Development Program of China(No.2022YFB4500500)the National Natural Science Foundation of China(No.22250710676).
文摘The co-electrolysis of CO_(2)and H_(2)O through solid oxide electrolysis cells(SOECs),powered by renewable energy sources,offers a promising pathway to achieving carbon neutrality in the chemical industry.However,the inherent intermittency of renewable energy generation,such as wind power,leads to unstable power input for electrolysis.This variability induces significant thermal stress in SOECs,potentially causing cracks or even system failure.To address this challenge,a hybrid deep learning architecture(HDLA)was developed to control the temperature gradient of SOECs.The architecture combines a convolutional neural network(CNN)and a long short-term memory(LSTM)model for wind power prediction,a multi-physics model for temperature gradient simulation,and a linear neural network regression model to simulate the temperature distribution in SOECs.Training and verification are conducted using 16 datasets from an industrial wind farm.The results demonstrate that the application of HDLA successfully reduce the temperature gradient of SOECs from±20℃ to±5℃.Additionally,the potential wind power utilization achieved near-complete wind power utilization,increasing from 18%to 99%.This real-time control strategy,which optimizes flow regulation,effectively mitigates thermal stress,thereby extending the lifespan of SOECs and ensuring continuous carbon reduction,efficient conversion,and utilization.
基金supported by the National Key Basic Research and Development(973)Program of China(No.2007CB311003)the National Natural Science Foundation of China(Nos.61073117 and 61175046)+1 种基金the Young Science Foundation of Anhui University(No.KJQN1118)the Outstanding Young Talents Higher Education Institutions of Anhui Province(No.2011SQRL129ZD)
文摘The concept of deep learning has been applied to many domains, but the definition of a suitable problem depth has not been sufficiently explored. In this study, we propose a new Hierarchical Covering Algorithm （HCA） method to determine the levels of a hierarchical structure based on the Covering Algorithm （CA）. The CA constructs neural networks based on samples＇ own characteristics, and can effectively handle multi-category classification and large-scale data. Further, we abstract characters based on the CA to automatically embody the feature of a deep structure. We apply CA to construct hidden nodes at the lower level, and define a fuzzy equivalence relation R on upper spaces to form a hierarchical architecture based on fuzzy quotient space theory. The covering tree naturally becomes from R. HCA experiments performed on MNIST dataset show that the covering tree embodies the deep architecture of the problem, and the effects of a deep structure are shown to be better than having a single level.
基金supported in part by the National Key R&D Program of China under grant 2022YFC240014102the Beijing Municipal Natural Science Foundation-Haidian Primitive Innovation Joint Fund Project under grant L212005the National Natural Science Foundation of China under grants 62073043 and 62003045.
文摘Respiratory motion-induced vertebral movements can adversely impact intraoperative spine surgery,resulting in inaccurate positional information of the target region and unexpected damage during the operation.In this paper,we propose a novel deep learning architecture for respiratory motion prediction,which can adapt to different patients.The proposed method utilizes an LSTM-AE with attention mechanism network that can be trained using few-shot datasets during operation.To ensure real-time performance,a dimension reduction method based on the respiration-induced physical movement of spine vertebral bodies is introduced.The experiment collected data from prone-positioned patients under general anaesthesia to validate the prediction accuracy and time efficiency of the LSTM-AE-based motion prediction method.The experimental results demonstrate that the presented method(RMSE:4.39%)outperforms other methods in terms of accuracy within a learning time of 2 min.The maximum predictive errors under the latency of 333 ms with respect to the x,y,and z axes of the optical camera system were 0.13,0.07,and 0.10 mm,respectively,within a motion range of 2 mm.
