Detecting pavement cracks is critical for road safety and infrastructure management.Traditional methods,relying on manual inspection and basic image processing,are time-consuming and prone to errors.Recent deep-learni...Detecting pavement cracks is critical for road safety and infrastructure management.Traditional methods,relying on manual inspection and basic image processing,are time-consuming and prone to errors.Recent deep-learning(DL)methods automate crack detection,but many still struggle with variable crack patterns and environmental conditions.This study aims to address these limitations by introducing the Masker Transformer,a novel hybrid deep learning model that integrates the precise localization capabilities of Mask Region-based Convolutional Neural Network(Mask R-CNN)with the global contextual awareness of Vision Transformer(ViT).The research focuses on leveraging the strengths of both architectures to enhance segmentation accuracy and adaptability across different pavement conditions.We evaluated the performance of theMaskerTransformer against other state-of-theartmodels such asU-Net,TransformerU-Net(TransUNet),U-NetTransformer(UNETr),SwinU-NetTransformer(Swin-UNETr),You Only Look Once version 8(YoloV8),and Mask R-CNN using two benchmark datasets:Crack500 and DeepCrack.The findings reveal that the MaskerTransformer significantly outperforms the existing models,achieving the highest Dice SimilarityCoefficient(DSC),precision,recall,and F1-Score across both datasets.Specifically,the model attained a DSC of 80.04%on Crack500 and 91.37%on DeepCrack,demonstrating superior segmentation accuracy and reliability.The high precision and recall rates further substantiate its effectiveness in real-world applications,suggesting that the Masker Transformer can serve as a robust tool for automated pavement crack detection,potentially replacing more traditional methods.展开更多
This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as o...This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as other transformer-based models including Token to Token ViT,ViT withoutmemory,and Parallel ViT.Leveraging awidely-used steel surface defect dataset,the research applies data augmentation and t-distributed stochastic neighbor embedding(t-SNE)to enhance feature extraction and understanding.These techniques mitigated overfitting,stabilized training,and improved generalization capabilities.The LMViT model achieved a test accuracy of 97.22%,significantly outperforming ResNet18(88.89%)and ResNet50(88.90%),aswell as the Token to TokenViT(88.46%),ViT without memory(87.18),and Parallel ViT(91.03%).Furthermore,LMViT exhibited superior training and validation performance,attaining a validation accuracy of 98.2%compared to 91.0%for ResNet 18,96.0%for ResNet50,and 89.12%,87.51%,and 91.21%for Token to Token ViT,ViT without memory,and Parallel ViT,respectively.The findings highlight the LMViT’s ability to capture long-range dependencies in images,an areawhere CNNs struggle due to their reliance on local receptive fields and hierarchical feature extraction.The additional transformer-based models also demonstrate improved performance in capturing complex features over CNNs,with LMViT excelling particularly at detecting subtle and complex defects,which is critical for maintaining product quality and operational efficiency in industrial applications.For instance,the LMViT model successfully identified fine scratches and minor surface irregularities that CNNs often misclassify.This study not only demonstrates LMViT’s potential for real-world defect detection but also underscores the promise of other transformer-based architectures like Token to Token ViT,ViT without memory,and Parallel ViT in industrial scenarios where complex spatial relationships are key.Future research may focus on enhancing LMViT’s computational efficiency for deployment in real-time quality control systems.展开更多
Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)radiation.Early identification of skin cancer enhances the likelihood of effective treatment,as delays may lead t...Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)radiation.Early identification of skin cancer enhances the likelihood of effective treatment,as delays may lead to severe tumor advancement.This study proposes a novel hybrid deep learning strategy to address the complex issue of skin cancer diagnosis,with an architecture that integrates a Vision Transformer,a bespoke convolutional neural network(CNN),and an Xception module.They were evaluated using two benchmark datasets,HAM10000 and Skin Cancer ISIC.On the HAM10000,the model achieves a precision of 95.46%,an accuracy of 96.74%,a recall of 96.27%,specificity of 96.00%and an F1-Score of 95.86%.It obtains an accuracy of 93.19%,a precision of 93.25%,a recall of 92.80%,a specificity of 92.89%and an F1-Score of 93.19%on the Skin Cancer ISIC dataset.The findings demonstrate that the model that was proposed is robust and trustworthy when it comes to the classification of skin lesions.In addition,the utilization of Explainable AI techniques,such as Grad-CAM visualizations,assists in highlighting the most significant lesion areas that have an impact on the decisions that are made by the model.展开更多
Accurate plant species classification is essential for many applications,such as biodiversity conservation,ecological research,and sustainable agricultural practices.Traditional morphological classification methods ar...Accurate plant species classification is essential for many applications,such as biodiversity conservation,ecological research,and sustainable agricultural practices.Traditional morphological classification methods are inherently slow,labour-intensive,and prone to inaccuracies,especiallywhen distinguishing between species exhibiting visual similarities or high intra-species variability.To address these limitations and to overcome the constraints of imageonly approaches,we introduce a novel Artificial Intelligence-driven framework.This approach integrates robust Vision Transformer(ViT)models for advanced visual analysis with a multi-modal data fusion strategy,incorporating contextual metadata such as precise environmental conditions,geographic location,and phenological traits.This combination of visual and ecological cues significantly enhances classification accuracy and robustness,proving especially vital in complex,heterogeneous real-world environments.The proposedmodel achieves an impressive 97.27%of test accuracy,andMean Reciprocal Rank(MRR)of 0.9842 that demonstrates strong generalization capabilities.Furthermore,efficient utilization of high-performance GPU resources(RTX 3090,18 GB memory)ensures scalable processing of highdimensional data.Comparative analysis consistently confirms that ourmetadata fusion approach substantially improves classification performance,particularly formorphologically similar species,and through principled self-supervised and transfer learning from ImageNet,the model adapts efficiently to new species,ensuring enhanced generalization.This comprehensive approach holds profound practical implications for precise conservation initiatives,rigorous ecological monitoring,and advanced agricultural management.展开更多
This paper introduces a novel method for medical image retrieval and classification by integrating a multi-scale encoding mechanism with Vision Transformer(ViT)architectures and a dynamic multi-loss function.The multi...This paper introduces a novel method for medical image retrieval and classification by integrating a multi-scale encoding mechanism with Vision Transformer(ViT)architectures and a dynamic multi-loss function.The multi-scale encoding significantly enhances the model’s ability to capture both fine-grained and global features,while the dynamic loss function adapts during training to optimize classification accuracy and retrieval performance.Our approach was evaluated on the ISIC-2018 and ChestX-ray14 datasets,yielding notable improvements.Specifically,on the ISIC-2018 dataset,our method achieves an F1-Score improvement of+4.84% compared to the standard ViT,with a precision increase of+5.46% for melanoma(MEL).On the ChestX-ray14 dataset,the method delivers an F1-Score improvement of 5.3%over the conventional ViT,with precision gains of+5.0% for pneumonia(PNEU)and+5.4%for fibrosis(FIB).Experimental results demonstrate that our approach outperforms traditional CNN-based models and existing ViT variants,particularly in retrieving relevant medical cases and enhancing diagnostic accuracy.These findings highlight the potential of the proposedmethod for large-scalemedical image analysis,offering improved tools for clinical decision-making through superior classification and case comparison.展开更多
Mango farming significantly contributes to the economy,particularly in developing countries.However,mango trees are susceptible to various diseases caused by fungi,viruses,and bacteria,and diagnosing these diseases at...Mango farming significantly contributes to the economy,particularly in developing countries.However,mango trees are susceptible to various diseases caused by fungi,viruses,and bacteria,and diagnosing these diseases at an early stage is crucial to prevent their spread,which can lead to substantial losses.The development of deep learning models for detecting crop diseases is an active area of research in smart agriculture.This study focuses on mango plant diseases and employs the ConvNeXt and Vision Transformer(ViT)architectures.Two datasets were used.The first,MangoLeafBD,contains data for mango leaf diseases such as anthracnose,bacterial canker,gall midge,and powdery mildew.The second,SenMangoFruitDDS,includes data for mango fruit diseases such as Alternaria,Anthracnose,Black Mould Rot,Healthy,and Stem and Rot.Both datasets were obtained from publicly available sources.The proposed model achieved an accuracy of 99.87%on the MangoLeafBD dataset and 98.40%on the MangoFruitDDS dataset.The results demonstrate that ConvNeXt and ViT models can effectively diagnose mango diseases,enabling farmers to identify these conditions more efficiently.The system contributes to increased mango production and minimizes economic losses by reducing the time and effort needed for manual diagnostics.Additionally,the proposed system is integrated into a mobile application that utilizes the model as a backend to detect mango diseases instantly.展开更多
The early and precise identification of Alzheimer’s Disease(AD)continues to pose considerable clinical difficulty due to subtle structural alterations and overlapping symptoms across the disease phases.This study pre...The early and precise identification of Alzheimer’s Disease(AD)continues to pose considerable clinical difficulty due to subtle structural alterations and overlapping symptoms across the disease phases.This study presents a novel Deformable Attention Vision Transformer(DA-ViT)architecture that integrates deformable Multi-Head Self-Attention(MHSA)with a Multi-Layer Perceptron(MLP)block for efficient classification of Alzheimer’s disease(AD)using Magnetic resonance imaging(MRI)scans.In contrast to traditional vision transformers,our deformable MHSA module preferentially concentrates on spatially pertinent patches through learned offset predictions,markedly diminishing processing demands while improving localized feature representation.DA-ViT contains only 0.93 million parameters,making it exceptionally suitable for implementation in resource-limited settings.We evaluate the model using a class-imbalanced Alzheimer’s MRI dataset comprising 6400 images across four categories,achieving a test accuracy of 80.31%,a macro F1-score of 0.80,and an area under the receiver operating characteristic curve(AUC)of 1.00 for the Mild Demented category.Thorough ablation studies validate the ideal configuration of transformer depth,headcount,and embedding dimensions.Moreover,comparison research indicates that DA-ViT surpasses state-of-theart pre-trained Convolutional Neural Network(CNN)models in terms of accuracy and parameter efficiency.展开更多
Tropical cyclone(TC)intensity estimation is a fundamental aspect of TC monitoring and forecasting.Deep learning models have recently been employed to estimate TC intensity from satellite images and yield precise resul...Tropical cyclone(TC)intensity estimation is a fundamental aspect of TC monitoring and forecasting.Deep learning models have recently been employed to estimate TC intensity from satellite images and yield precise results.This work proposes the ViT-TC model based on the Vision Transformer(ViT)architecture.Satellite images of TCs,including infrared(IR),water vapor(WV),and passive microwave(PMW),are used as inputs for intensity estimation.Experiments indicate that combining IR,WV,and PMW as inputs yields more accurate estimations than other channel combinations.The ensemble mean technique is applied to enhance the model's estimations,reducing the root-mean-square error to 9.32 kt(knots,1 kt≈0.51 m s^(-1))and the mean absolute error to 6.49 kt,which outperforms traditional methods and is comparable to existing deep learning models.The model assigns high attention weights to areas with high PMW,indicating that PMW magnitude is essential information for the model's estimation.The model also allocates significance to the cloud-cover region,suggesting that the model utilizes the whole TC cloud structure and TC eye to determine TC intensity.展开更多
The identification of ore grades is a critical step in mineral resource exploration and mining.Prompt gamma neutron activation analysis(PGNAA)technology employs gamma rays generated by the nuclear reactions between ne...The identification of ore grades is a critical step in mineral resource exploration and mining.Prompt gamma neutron activation analysis(PGNAA)technology employs gamma rays generated by the nuclear reactions between neutrons and samples to achieve the qualitative and quantitative detection of sample components.In this study,we present a novel method for identifying copper grade by combining the vision transformer(ViT)model with the PGNAA technique.First,a Monte Carlo simulation is employed to determine the optimal sizes of the neutron moderator,thermal neutron absorption material,and dimensions of the device.Subsequently,based on the parameters obtained through optimization,a PGNAA copper ore measurement model is established.The gamma spectrum of the copper ore is analyzed using the ViT model.The ViT model is optimized for hyperparameters using a grid search.To ensure the reliability of the identification results,the test results are obtained through five repeated tenfold cross-validations.Long short-term memory and convolutional neural network models are compared with the ViT method.These results indicate that the ViT method is efficient in identifying copper ore grades with average accuracy,precision,recall,F_(1)score,and F_(1)(-)score values of 0.9795,0.9637,0.9614,0.9625,and 0.9942,respectively.When identifying associated minerals,the ViT model can identify Pb,Zn,Fe,and Co minerals with identification accuracies of 0.9215,0.9396,0.9966,and 0.8311,respectively.展开更多
文摘Detecting pavement cracks is critical for road safety and infrastructure management.Traditional methods,relying on manual inspection and basic image processing,are time-consuming and prone to errors.Recent deep-learning(DL)methods automate crack detection,but many still struggle with variable crack patterns and environmental conditions.This study aims to address these limitations by introducing the Masker Transformer,a novel hybrid deep learning model that integrates the precise localization capabilities of Mask Region-based Convolutional Neural Network(Mask R-CNN)with the global contextual awareness of Vision Transformer(ViT).The research focuses on leveraging the strengths of both architectures to enhance segmentation accuracy and adaptability across different pavement conditions.We evaluated the performance of theMaskerTransformer against other state-of-theartmodels such asU-Net,TransformerU-Net(TransUNet),U-NetTransformer(UNETr),SwinU-NetTransformer(Swin-UNETr),You Only Look Once version 8(YoloV8),and Mask R-CNN using two benchmark datasets:Crack500 and DeepCrack.The findings reveal that the MaskerTransformer significantly outperforms the existing models,achieving the highest Dice SimilarityCoefficient(DSC),precision,recall,and F1-Score across both datasets.Specifically,the model attained a DSC of 80.04%on Crack500 and 91.37%on DeepCrack,demonstrating superior segmentation accuracy and reliability.The high precision and recall rates further substantiate its effectiveness in real-world applications,suggesting that the Masker Transformer can serve as a robust tool for automated pavement crack detection,potentially replacing more traditional methods.
基金funded by Woosong University Academic Research 2024.
文摘This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as other transformer-based models including Token to Token ViT,ViT withoutmemory,and Parallel ViT.Leveraging awidely-used steel surface defect dataset,the research applies data augmentation and t-distributed stochastic neighbor embedding(t-SNE)to enhance feature extraction and understanding.These techniques mitigated overfitting,stabilized training,and improved generalization capabilities.The LMViT model achieved a test accuracy of 97.22%,significantly outperforming ResNet18(88.89%)and ResNet50(88.90%),aswell as the Token to TokenViT(88.46%),ViT without memory(87.18),and Parallel ViT(91.03%).Furthermore,LMViT exhibited superior training and validation performance,attaining a validation accuracy of 98.2%compared to 91.0%for ResNet 18,96.0%for ResNet50,and 89.12%,87.51%,and 91.21%for Token to Token ViT,ViT without memory,and Parallel ViT,respectively.The findings highlight the LMViT’s ability to capture long-range dependencies in images,an areawhere CNNs struggle due to their reliance on local receptive fields and hierarchical feature extraction.The additional transformer-based models also demonstrate improved performance in capturing complex features over CNNs,with LMViT excelling particularly at detecting subtle and complex defects,which is critical for maintaining product quality and operational efficiency in industrial applications.For instance,the LMViT model successfully identified fine scratches and minor surface irregularities that CNNs often misclassify.This study not only demonstrates LMViT’s potential for real-world defect detection but also underscores the promise of other transformer-based architectures like Token to Token ViT,ViT without memory,and Parallel ViT in industrial scenarios where complex spatial relationships are key.Future research may focus on enhancing LMViT’s computational efficiency for deployment in real-time quality control systems.
文摘Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)radiation.Early identification of skin cancer enhances the likelihood of effective treatment,as delays may lead to severe tumor advancement.This study proposes a novel hybrid deep learning strategy to address the complex issue of skin cancer diagnosis,with an architecture that integrates a Vision Transformer,a bespoke convolutional neural network(CNN),and an Xception module.They were evaluated using two benchmark datasets,HAM10000 and Skin Cancer ISIC.On the HAM10000,the model achieves a precision of 95.46%,an accuracy of 96.74%,a recall of 96.27%,specificity of 96.00%and an F1-Score of 95.86%.It obtains an accuracy of 93.19%,a precision of 93.25%,a recall of 92.80%,a specificity of 92.89%and an F1-Score of 93.19%on the Skin Cancer ISIC dataset.The findings demonstrate that the model that was proposed is robust and trustworthy when it comes to the classification of skin lesions.In addition,the utilization of Explainable AI techniques,such as Grad-CAM visualizations,assists in highlighting the most significant lesion areas that have an impact on the decisions that are made by the model.
文摘Accurate plant species classification is essential for many applications,such as biodiversity conservation,ecological research,and sustainable agricultural practices.Traditional morphological classification methods are inherently slow,labour-intensive,and prone to inaccuracies,especiallywhen distinguishing between species exhibiting visual similarities or high intra-species variability.To address these limitations and to overcome the constraints of imageonly approaches,we introduce a novel Artificial Intelligence-driven framework.This approach integrates robust Vision Transformer(ViT)models for advanced visual analysis with a multi-modal data fusion strategy,incorporating contextual metadata such as precise environmental conditions,geographic location,and phenological traits.This combination of visual and ecological cues significantly enhances classification accuracy and robustness,proving especially vital in complex,heterogeneous real-world environments.The proposedmodel achieves an impressive 97.27%of test accuracy,andMean Reciprocal Rank(MRR)of 0.9842 that demonstrates strong generalization capabilities.Furthermore,efficient utilization of high-performance GPU resources(RTX 3090,18 GB memory)ensures scalable processing of highdimensional data.Comparative analysis consistently confirms that ourmetadata fusion approach substantially improves classification performance,particularly formorphologically similar species,and through principled self-supervised and transfer learning from ImageNet,the model adapts efficiently to new species,ensuring enhanced generalization.This comprehensive approach holds profound practical implications for precise conservation initiatives,rigorous ecological monitoring,and advanced agricultural management.
基金funded by the Deanship of Research and Graduate Studies at King Khalid University through small group research under grant number RGP1/278/45.
文摘This paper introduces a novel method for medical image retrieval and classification by integrating a multi-scale encoding mechanism with Vision Transformer(ViT)architectures and a dynamic multi-loss function.The multi-scale encoding significantly enhances the model’s ability to capture both fine-grained and global features,while the dynamic loss function adapts during training to optimize classification accuracy and retrieval performance.Our approach was evaluated on the ISIC-2018 and ChestX-ray14 datasets,yielding notable improvements.Specifically,on the ISIC-2018 dataset,our method achieves an F1-Score improvement of+4.84% compared to the standard ViT,with a precision increase of+5.46% for melanoma(MEL).On the ChestX-ray14 dataset,the method delivers an F1-Score improvement of 5.3%over the conventional ViT,with precision gains of+5.0% for pneumonia(PNEU)and+5.4%for fibrosis(FIB).Experimental results demonstrate that our approach outperforms traditional CNN-based models and existing ViT variants,particularly in retrieving relevant medical cases and enhancing diagnostic accuracy.These findings highlight the potential of the proposedmethod for large-scalemedical image analysis,offering improved tools for clinical decision-making through superior classification and case comparison.
基金funded by Princess Nourah bint Abdulrahman University and Researchers Supporting Project number(PNURSP2025R346)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Mango farming significantly contributes to the economy,particularly in developing countries.However,mango trees are susceptible to various diseases caused by fungi,viruses,and bacteria,and diagnosing these diseases at an early stage is crucial to prevent their spread,which can lead to substantial losses.The development of deep learning models for detecting crop diseases is an active area of research in smart agriculture.This study focuses on mango plant diseases and employs the ConvNeXt and Vision Transformer(ViT)architectures.Two datasets were used.The first,MangoLeafBD,contains data for mango leaf diseases such as anthracnose,bacterial canker,gall midge,and powdery mildew.The second,SenMangoFruitDDS,includes data for mango fruit diseases such as Alternaria,Anthracnose,Black Mould Rot,Healthy,and Stem and Rot.Both datasets were obtained from publicly available sources.The proposed model achieved an accuracy of 99.87%on the MangoLeafBD dataset and 98.40%on the MangoFruitDDS dataset.The results demonstrate that ConvNeXt and ViT models can effectively diagnose mango diseases,enabling farmers to identify these conditions more efficiently.The system contributes to increased mango production and minimizes economic losses by reducing the time and effort needed for manual diagnostics.Additionally,the proposed system is integrated into a mobile application that utilizes the model as a backend to detect mango diseases instantly.
基金Prince Sattambin Abdulaziz University for funding this research work through the project number(PSAU/2025/R/1446).
文摘The early and precise identification of Alzheimer’s Disease(AD)continues to pose considerable clinical difficulty due to subtle structural alterations and overlapping symptoms across the disease phases.This study presents a novel Deformable Attention Vision Transformer(DA-ViT)architecture that integrates deformable Multi-Head Self-Attention(MHSA)with a Multi-Layer Perceptron(MLP)block for efficient classification of Alzheimer’s disease(AD)using Magnetic resonance imaging(MRI)scans.In contrast to traditional vision transformers,our deformable MHSA module preferentially concentrates on spatially pertinent patches through learned offset predictions,markedly diminishing processing demands while improving localized feature representation.DA-ViT contains only 0.93 million parameters,making it exceptionally suitable for implementation in resource-limited settings.We evaluate the model using a class-imbalanced Alzheimer’s MRI dataset comprising 6400 images across four categories,achieving a test accuracy of 80.31%,a macro F1-score of 0.80,and an area under the receiver operating characteristic curve(AUC)of 1.00 for the Mild Demented category.Thorough ablation studies validate the ideal configuration of transformer depth,headcount,and embedding dimensions.Moreover,comparison research indicates that DA-ViT surpasses state-of-theart pre-trained Convolutional Neural Network(CNN)models in terms of accuracy and parameter efficiency.
基金Research funding for this project was provided by the National Natural Science Foundation of China(Grant Nos.42192563 and 42120104001)the Hong Kong RGC General Research Fund(Grant No.11300920)+1 种基金Anhui Provincial Natural Science Foundation(Grant Nos.2208085UQ12,2308085US01)Anhui&Huaihe River Institute of Hydraulic Research(Grant Nos.KJGG202201,KY202306)。
文摘Tropical cyclone(TC)intensity estimation is a fundamental aspect of TC monitoring and forecasting.Deep learning models have recently been employed to estimate TC intensity from satellite images and yield precise results.This work proposes the ViT-TC model based on the Vision Transformer(ViT)architecture.Satellite images of TCs,including infrared(IR),water vapor(WV),and passive microwave(PMW),are used as inputs for intensity estimation.Experiments indicate that combining IR,WV,and PMW as inputs yields more accurate estimations than other channel combinations.The ensemble mean technique is applied to enhance the model's estimations,reducing the root-mean-square error to 9.32 kt(knots,1 kt≈0.51 m s^(-1))and the mean absolute error to 6.49 kt,which outperforms traditional methods and is comparable to existing deep learning models.The model assigns high attention weights to areas with high PMW,indicating that PMW magnitude is essential information for the model's estimation.The model also allocates significance to the cloud-cover region,suggesting that the model utilizes the whole TC cloud structure and TC eye to determine TC intensity.
基金supported by the National Natural Science Foundation of China(Nos.U2BB2077 and 42374226)the Natural Science Foundation of Jiangxi Province(20232BAB201043 and 20232BCJ23006)the Nuclear energy development project of the National Defense Science and Industry Bureau(Nos.20201192-01,20201192-03).
文摘The identification of ore grades is a critical step in mineral resource exploration and mining.Prompt gamma neutron activation analysis(PGNAA)technology employs gamma rays generated by the nuclear reactions between neutrons and samples to achieve the qualitative and quantitative detection of sample components.In this study,we present a novel method for identifying copper grade by combining the vision transformer(ViT)model with the PGNAA technique.First,a Monte Carlo simulation is employed to determine the optimal sizes of the neutron moderator,thermal neutron absorption material,and dimensions of the device.Subsequently,based on the parameters obtained through optimization,a PGNAA copper ore measurement model is established.The gamma spectrum of the copper ore is analyzed using the ViT model.The ViT model is optimized for hyperparameters using a grid search.To ensure the reliability of the identification results,the test results are obtained through five repeated tenfold cross-validations.Long short-term memory and convolutional neural network models are compared with the ViT method.These results indicate that the ViT method is efficient in identifying copper ore grades with average accuracy,precision,recall,F_(1)score,and F_(1)(-)score values of 0.9795,0.9637,0.9614,0.9625,and 0.9942,respectively.When identifying associated minerals,the ViT model can identify Pb,Zn,Fe,and Co minerals with identification accuracies of 0.9215,0.9396,0.9966,and 0.8311,respectively.
