The integration of artificial intelligence(AI)into medical robotics has emerged as a cornerstone of modern healthcare,driving transformative advancements in precision,adaptability and patient outcomes.Although computa...The integration of artificial intelligence(AI)into medical robotics has emerged as a cornerstone of modern healthcare,driving transformative advancements in precision,adaptability and patient outcomes.Although computational tools have long supported diagnostic processes,their role is evolving beyond passive assistance to become active collaborators in therapeutic decision-making.In this paradigm,knowledge-driven deep learning systems are redefining possibilities-enabling robots to interpret complex data,adapt to dynamic clinical environments and execute tasks with human-like contextual awareness.展开更多
In modern wireless communication network, the increased consumer demands for multi-type applications and high quality services have become a prominent trend, and put considerable pressure on the wireless network. In t...In modern wireless communication network, the increased consumer demands for multi-type applications and high quality services have become a prominent trend, and put considerable pressure on the wireless network. In that case, the Quality of Experience(Qo E) has received much attention and has become a key performance measurement for the application and service. In order to meet the users' expectations, the management of the resource is crucial in wireless network, especially the Qo E based resource allocation. One of the effective way for resource allocation management is accurate application identification. In this paper, we propose a novel deep learning based method for application identification. We first analyse the requirement of managing Qo E for wireless communication, and review the limitation of the traditional identification methods. After that, a deep learning based method is proposed for automatically extracting the features and identifying the type of application. The proposed method is evaluated by using the practical wireless traffic data, and the experiments verify the effectiveness of our method.展开更多
Osteoarthritis is the most common class of arthritis that involves tears down the soft cartilage between the joints of the knee.The regeneration of this cartilage tissue is not possible,and thus physicians typically s...Osteoarthritis is the most common class of arthritis that involves tears down the soft cartilage between the joints of the knee.The regeneration of this cartilage tissue is not possible,and thus physicians typically suggest therapeutic measures to prevent further deterioration over time.Normally,bringing about joint replacement is a remedial course of action.Expose itself in joint pain recog-nized with a normal X-ray.Deep learning plays a vital role in predicting the early stages of osteoarthritis by using the MRI pictures of muscles of the knee muscle.It can be used to accurately measure the shape and texture of biological structures can be measured consistently from X-ray images.Moreover,deep learning-based computation can be used to design framework to predict whether a given patient will develop osteoarthritis.Such a framework can identify clear biochemical changes in the focal point of ligaments of the knees of patients who have exhibit pre-indications in standard imaging.This study proposes framework to identify cases of osteoarthritis by using deep learning and reinforcement learning.It can be used as a clinical mechanism to predict the occurrence of osteoarthritis so that patients can benefit from early intervention.展开更多
Educational institutions showing interest to find the opinion of the students about their course and the instructors to enhance the teaching-learning process.For this,most research uses sentiment analysis to track stu...Educational institutions showing interest to find the opinion of the students about their course and the instructors to enhance the teaching-learning process.For this,most research uses sentiment analysis to track students’behavior.Traditional sentence-level sentiment analysis focuses on the whole sentence sentiment.Previous studies show that the sentiments alone are not enough to observe the feeling of the students because different words express different sentiments in a sentence.There is a need to extract the targets in a given sentence which helps to find the sentiment towards those targets.Target extraction is the subtask of targeted sentiment analysis.In this paper,we proposed the innovative model to find the targets of the given sentence using Bi-Integrated Conditional Random Fields(CRF).A Parallel fusion neural network model is designed to perform this task.We evaluate the model using the Michigan dataset and we build a dataset for target extraction from student reviews.The experimental results show that our proposed fusion model achieves better results compared to baseline models.展开更多
Feature extraction plays an important role in constructing artificial intel-ligence(AI)models of industrial control systems(ICSs).Three challenges in this field are learning effective representation from high-dimensio...Feature extraction plays an important role in constructing artificial intel-ligence(AI)models of industrial control systems(ICSs).Three challenges in this field are learning effective representation from high-dimensional features,data heterogeneity,and data noise due to the diversity of data dimensions,formats and noise of sensors,controllers and actuators.Hence,a novel unsupervised learn-ing autoencoder model is proposed for ICS data in this paper.Although traditional methods only capture the linear correlations of ICS features,our deep industrial representation learning model(DIRL)based on a convolutional neural network can mine high-order features,thus solving the problem of high-dimensional and heterogeneous ICS data.In addition,an unsupervised denoising autoencoder is introduced for noisy ICS data in DIRL.Training the denoising autoencoder allows the model to better mitigate the sensor noise problem.In this way,the represen-tative features learned by DIRL could help to evaluate the safety state of ICSs more effectively.We tested our model with absolute and relative accuracy experi-ments on two large-scale ICS datasets.Compared with other popular methods,DIRL showed advantages in four common indicators of AI algorithms:accuracy,precision,recall,and F1-score.This study contributes to the effective analysis of large-scale ICS data,which promotes the stable operation of ICSs.展开更多
The monitoring system designed in this paper is on account of YOLOv5(You Only Look Once)to monitor foreign objects on railway tracks and can broadcast the monitoring information to the locomotive in real time.First,th...The monitoring system designed in this paper is on account of YOLOv5(You Only Look Once)to monitor foreign objects on railway tracks and can broadcast the monitoring information to the locomotive in real time.First,the general structure of the system is determined through demand analysis and feasibility analysis,the foreign object intrusion recognition algorithm is designed,and the data set required for foreign object intrusion recognition is made.Secondly,according to the functional demands,the system selects a suitable neural web,and the programming is reasonable.At last,the system is simulated to validate its functionality(identification and classification of track intrusion and determination of a safe operating zone).展开更多
Accurate prediction of electricity price(EP)is crucial for energy utilities and gridoperators for enhancing the energy trading,grid stability studies,resource allocationsand pricing strategies,thereby improving the ov...Accurate prediction of electricity price(EP)is crucial for energy utilities and gridoperators for enhancing the energy trading,grid stability studies,resource allocationsand pricing strategies,thereby improving the overall grid reliability,efficiency,and cost-effectiveness.This study introduces a novel D3Net model for half-hourly EP prediction,integrating Seasonal-Trend decomposition using LOESS(STL)and Variational ModeDecomposition(VMD)with Multi-Layer Perceptron(MLP),Random Forest Regression(RFR),and Tabular Neural Network(TabNet).The methodology involves applying STL tothe EP time-series to extract trend,seasonal,and residual components.The trend ispredicted using an MLP model,the seasonal component is further decomposed withVMD into 20 Variational Mode Functions(VMFs)and predicted using an RFR model,andthe residual component is decomposed with VMD and predicted using the TabNet model.Input features are identified using the Partial Autocorrelation Function,and models areoptimized using the Optuna algorithm.The final prediction combines the trend,seasonal,and residual components'predictions.Explainable Artificial Intelligence(xAI)methodswere used to enhance model interpretability and trustworthiness,with optimization viathe Optuna algorithm.Comparative analysis with seven standalone and seven decomposition-based models confirmed the superior performance and statisticalsignificance of the D3Net model.The D3Net achieved the highest global performanceindicator for South Australia(GPI≈11.068)and Tasmania(GPI≈12.206).Theseresults validate the efficacy and statistical significance of the D3Net model,demonstrating the viability of integrating STL and VMD decomposition approaches withMLP,RFR,and TabNet for EP prediction.展开更多
Artificial intelligence-based automatic systems can reduce time,human error and post-harvest operations.By using such systems,food items can be successfully classified and graded based on defects.For this context,a ma...Artificial intelligence-based automatic systems can reduce time,human error and post-harvest operations.By using such systems,food items can be successfully classified and graded based on defects.For this context,a machine vision system was developed for fruit grading based on defects.The prototype consisted of defective fruit detection and mechanical sorting systems.Image processing algorithms and deep learning frameworks were used for detection of defective fruit.Different image processing algorithms including preprocessing,thresholding,morphological and bitwise operations combined with a deep leaning algorithm,i.e.,convolutional neural network(CNN),were applied to fruit images for the detection of defective fruit.The data set used for training CNN model consisted of fruit images collected from a publiclyavailable data set and captured fruit images:1799 and 1017 for mangoes and tomatoes,respectively.Subsequent to defective fruit detection,the information obtained was communicated to microcontroller that further actuated the mechanical sorting system accordingly.In addition,the system was evaluated experimentally in terms of detection accuracy,sorting accuracy and computational time.For the image processing algorithms scheme,the detection accuracy for mango and tomato was 89% and 92%,respectively,and for CNN architecture used,the validation accuracy for mangoes and tomatoes was 95% and 94%,respectively.展开更多
Drug discovery is costly and time consuming,and modern drug discovery endeavors are progressively reliant on computational methodologies,aiming to mitigate temporal and financial expenditures associated with the proce...Drug discovery is costly and time consuming,and modern drug discovery endeavors are progressively reliant on computational methodologies,aiming to mitigate temporal and financial expenditures associated with the process.In particular,the time required for vaccine and drug discovery is prolonged during emergency situations such as the coronavirus 2019 pandemic.Recently,the performance of deep learning methods in drug virtual screening has been particularly prominent.It has become a concern for researchers how to summarize the existing deep learning in drug virtual screening,select different models for different drug screening problems,exploit the advantages of deep learning models,and further improve the capability of deep learning in drug virtual screening.This review first introduces the basic concepts of drug virtual screening,common datasets,and data representation methods.Then,large numbers of common deep learning methods for drug virtual screening are compared and analyzed.In addition,a dataset of different sizes is constructed independently to evaluate the performance of each deep learning model for the difficult problem of large-scale ligand virtual screening.Finally,the existing challenges and future directions in the field of virtual screening are presented.展开更多
In recent years,Deep Learning(DL)technique has been widely used in Internet of Things(IoT)and Industrial Internet of Things(IIoT)for edge computing,and achieved good performances.But more and more studies have shown t...In recent years,Deep Learning(DL)technique has been widely used in Internet of Things(IoT)and Industrial Internet of Things(IIoT)for edge computing,and achieved good performances.But more and more studies have shown the vulnerability of neural networks.So,it is important to test the robustness and vulnerability of neural networks.More specifically,inspired by layer-wise relevance propagation and neural network verification,we propose a novel measurement of sensitive neurons and important neurons,and propose a novel neuron coverage criterion for robustness testing.Based on the novel criterion,we design a novel testing sample generation method,named DeepSI,which involves definitions of sensitive neurons and important neurons.Furthermore,we construct sensitive-decision paths of the neural network through selecting sensitive neurons and important neurons.Finally,we verify our idea by setting up several experiments,then results show our proposed method achieves superior performances.展开更多
Plant-parasitic nematodes cause various diseases that can be fatal to the infected plants.It causes losses to the agricultural industry,such as crop failure and poor crop quality.Developing an accurate nematode classi...Plant-parasitic nematodes cause various diseases that can be fatal to the infected plants.It causes losses to the agricultural industry,such as crop failure and poor crop quality.Developing an accurate nematode classification system is vital for pest identification and control.Deep learning classification techniques can help speed up Nematode identification as it can perform tasks directly from images.In the present study,four state-of-the-art deep learning models(ResNet101v2,CoAtNet-0,Effi-cientNetV2B0,and EfficientNetV2M)were evaluated in plantparasitic nematode classification from microscopic image.The models were trained using a combination of three different optimizers(Adam,SGD,dan RMSProp)and several data augmentation with image transformations,such as image flip,blurring,noise addition,brightness,and contrast adjustment.The performance of the trained models was varied.Regarding test accuracy,EfficientNetV2B0 and EfficientNetV2M using RMSProp and brightness augmentation give the best result of 97.94%However,the overall performance of EfficientNetV2M was superior,with 98.66%mean class accuracy,97.99%F1 score,98.26%average precision,and 97.94%average recall.展开更多
Biogenic amines(BAs)are important biomarkers for monitoring food quality and assisting in the diagnosis of disease.Facial,portable,accurate and high-throughput BAs detection is still challenging by the specific sensor...Biogenic amines(BAs)are important biomarkers for monitoring food quality and assisting in the diagnosis of disease.Facial,portable,accurate and high-throughput BAs detection is still challenging by the specific sensor compounds development or the complicated instrument operation.Deep learning(DL)algorithms are blooming for their superiority on the nonlinear and multidimensional data analysis,which endow the great advantage for the artificial intelligence assisted large sample analysis of the environmental or daily health monitoring.In this work,we developed a deep learning-assisted visualized fluorometric array-based sensing method.Two commercial fluorescent dyes were selected and combined into sensor arrays.Variation in the alkalinity of BAs causes significant and distinct fluorescence changes of the dyes.In conjunction with pattern recognition by the pretrained CNN models,the sensor array clearly differentiates seven BAs with 99.29%prediction accuracy and allows rapid single and multi-component quantification with a volume fraction range from 200 cm^(3)/m^(3)to 2500 cm^(3)/m^(3).This method also provides a new way for meat freshness monitoring.We envision that this novel analytical method for BAs can be used as an alternative and promising tool for the detection of a wider variety of analytes.展开更多
Breast cancer is the most common malignant tumor and the leading cause of cancer-related deaths in women worldwide.Effective means of predicting the prognosis of breast cancer are very helpful in guiding treatment and...Breast cancer is the most common malignant tumor and the leading cause of cancer-related deaths in women worldwide.Effective means of predicting the prognosis of breast cancer are very helpful in guiding treatment and improving patients'survival.Features extracted by radiomics reflect the genetic and molecular characteristics of a tumor and are related to its biological behavior and the patient's prognosis.Thus,radiomics provides a new approach to noninvasive assessment of breast cancer prognosis.Ultrasound is one of the commonest clinical means of examining breast cancer.In recent years,some results of research into ultrasound radiomics for diagnosing breast cancer,predicting lymph node status,treatment response,recurrence and survival times,and other aspects,have been published.In this article,we review the current research status and technical challenges of ultrasound radiomics for predicting breast cancer prognosis.We aim to provide a reference for radiomics researchers,promote the development of ultrasound radiomics,and advance its clinical application.展开更多
Deep learning methods are applied into structured data and in typical methods,low-order features are discarded after combining with high-order featuresfor prediction tasks.However,in structured data,ignorance of low-o...Deep learning methods are applied into structured data and in typical methods,low-order features are discarded after combining with high-order featuresfor prediction tasks.However,in structured data,ignorance of low-order features may cause the low prediction rate.To address this issue,in this paper,deeper attention-based network(DAN)is proposed.With DAN method,to keep both low-and high-order features,attention average pooling layer was utilized to aggregate features of each order.Furthermore,by shortcut connections from each layer to attention average pooling layer,DAN can be built extremely deep to obtain enough capacity.Experimental results show DAN has good performance and works effectively.展开更多
文摘The integration of artificial intelligence(AI)into medical robotics has emerged as a cornerstone of modern healthcare,driving transformative advancements in precision,adaptability and patient outcomes.Although computational tools have long supported diagnostic processes,their role is evolving beyond passive assistance to become active collaborators in therapeutic decision-making.In this paradigm,knowledge-driven deep learning systems are redefining possibilities-enabling robots to interpret complex data,adapt to dynamic clinical environments and execute tasks with human-like contextual awareness.
基金supported by NSAF under Grant(No.U1530117)National Natural Science Foundation of China(No.61471022 and No.61201156)
文摘In modern wireless communication network, the increased consumer demands for multi-type applications and high quality services have become a prominent trend, and put considerable pressure on the wireless network. In that case, the Quality of Experience(Qo E) has received much attention and has become a key performance measurement for the application and service. In order to meet the users' expectations, the management of the resource is crucial in wireless network, especially the Qo E based resource allocation. One of the effective way for resource allocation management is accurate application identification. In this paper, we propose a novel deep learning based method for application identification. We first analyse the requirement of managing Qo E for wireless communication, and review the limitation of the traditional identification methods. After that, a deep learning based method is proposed for automatically extracting the features and identifying the type of application. The proposed method is evaluated by using the practical wireless traffic data, and the experiments verify the effectiveness of our method.
基金supported by King Khalid University,Abha,Kingdom of Saudi Arabia through a General Research Project under Grant Number GRP 119/42.
文摘Osteoarthritis is the most common class of arthritis that involves tears down the soft cartilage between the joints of the knee.The regeneration of this cartilage tissue is not possible,and thus physicians typically suggest therapeutic measures to prevent further deterioration over time.Normally,bringing about joint replacement is a remedial course of action.Expose itself in joint pain recog-nized with a normal X-ray.Deep learning plays a vital role in predicting the early stages of osteoarthritis by using the MRI pictures of muscles of the knee muscle.It can be used to accurately measure the shape and texture of biological structures can be measured consistently from X-ray images.Moreover,deep learning-based computation can be used to design framework to predict whether a given patient will develop osteoarthritis.Such a framework can identify clear biochemical changes in the focal point of ligaments of the knees of patients who have exhibit pre-indications in standard imaging.This study proposes framework to identify cases of osteoarthritis by using deep learning and reinforcement learning.It can be used as a clinical mechanism to predict the occurrence of osteoarthritis so that patients can benefit from early intervention.
文摘Educational institutions showing interest to find the opinion of the students about their course and the instructors to enhance the teaching-learning process.For this,most research uses sentiment analysis to track students’behavior.Traditional sentence-level sentiment analysis focuses on the whole sentence sentiment.Previous studies show that the sentiments alone are not enough to observe the feeling of the students because different words express different sentiments in a sentence.There is a need to extract the targets in a given sentence which helps to find the sentiment towards those targets.Target extraction is the subtask of targeted sentiment analysis.In this paper,we proposed the innovative model to find the targets of the given sentence using Bi-Integrated Conditional Random Fields(CRF).A Parallel fusion neural network model is designed to perform this task.We evaluate the model using the Michigan dataset and we build a dataset for target extraction from student reviews.The experimental results show that our proposed fusion model achieves better results compared to baseline models.
基金This study is supported by The National Key Research and Development Program of China:“Key measurement and control equipment with built-in information security functions”(Grant No.2018YFB2004200)Independent Subject of State Key Laboratory of Robotics“Research on security industry network construction technology for 5G communication”(No.2022-Z13).
文摘Feature extraction plays an important role in constructing artificial intel-ligence(AI)models of industrial control systems(ICSs).Three challenges in this field are learning effective representation from high-dimensional features,data heterogeneity,and data noise due to the diversity of data dimensions,formats and noise of sensors,controllers and actuators.Hence,a novel unsupervised learn-ing autoencoder model is proposed for ICS data in this paper.Although traditional methods only capture the linear correlations of ICS features,our deep industrial representation learning model(DIRL)based on a convolutional neural network can mine high-order features,thus solving the problem of high-dimensional and heterogeneous ICS data.In addition,an unsupervised denoising autoencoder is introduced for noisy ICS data in DIRL.Training the denoising autoencoder allows the model to better mitigate the sensor noise problem.In this way,the represen-tative features learned by DIRL could help to evaluate the safety state of ICSs more effectively.We tested our model with absolute and relative accuracy experi-ments on two large-scale ICS datasets.Compared with other popular methods,DIRL showed advantages in four common indicators of AI algorithms:accuracy,precision,recall,and F1-score.This study contributes to the effective analysis of large-scale ICS data,which promotes the stable operation of ICSs.
文摘The monitoring system designed in this paper is on account of YOLOv5(You Only Look Once)to monitor foreign objects on railway tracks and can broadcast the monitoring information to the locomotive in real time.First,the general structure of the system is determined through demand analysis and feasibility analysis,the foreign object intrusion recognition algorithm is designed,and the data set required for foreign object intrusion recognition is made.Secondly,according to the functional demands,the system selects a suitable neural web,and the programming is reasonable.At last,the system is simulated to validate its functionality(identification and classification of track intrusion and determination of a safe operating zone).
文摘Accurate prediction of electricity price(EP)is crucial for energy utilities and gridoperators for enhancing the energy trading,grid stability studies,resource allocationsand pricing strategies,thereby improving the overall grid reliability,efficiency,and cost-effectiveness.This study introduces a novel D3Net model for half-hourly EP prediction,integrating Seasonal-Trend decomposition using LOESS(STL)and Variational ModeDecomposition(VMD)with Multi-Layer Perceptron(MLP),Random Forest Regression(RFR),and Tabular Neural Network(TabNet).The methodology involves applying STL tothe EP time-series to extract trend,seasonal,and residual components.The trend ispredicted using an MLP model,the seasonal component is further decomposed withVMD into 20 Variational Mode Functions(VMFs)and predicted using an RFR model,andthe residual component is decomposed with VMD and predicted using the TabNet model.Input features are identified using the Partial Autocorrelation Function,and models areoptimized using the Optuna algorithm.The final prediction combines the trend,seasonal,and residual components'predictions.Explainable Artificial Intelligence(xAI)methodswere used to enhance model interpretability and trustworthiness,with optimization viathe Optuna algorithm.Comparative analysis with seven standalone and seven decomposition-based models confirmed the superior performance and statisticalsignificance of the D3Net model.The D3Net achieved the highest global performanceindicator for South Australia(GPI≈11.068)and Tasmania(GPI≈12.206).Theseresults validate the efficacy and statistical significance of the D3Net model,demonstrating the viability of integrating STL and VMD decomposition approaches withMLP,RFR,and TabNet for EP prediction.
文摘Artificial intelligence-based automatic systems can reduce time,human error and post-harvest operations.By using such systems,food items can be successfully classified and graded based on defects.For this context,a machine vision system was developed for fruit grading based on defects.The prototype consisted of defective fruit detection and mechanical sorting systems.Image processing algorithms and deep learning frameworks were used for detection of defective fruit.Different image processing algorithms including preprocessing,thresholding,morphological and bitwise operations combined with a deep leaning algorithm,i.e.,convolutional neural network(CNN),were applied to fruit images for the detection of defective fruit.The data set used for training CNN model consisted of fruit images collected from a publiclyavailable data set and captured fruit images:1799 and 1017 for mangoes and tomatoes,respectively.Subsequent to defective fruit detection,the information obtained was communicated to microcontroller that further actuated the mechanical sorting system accordingly.In addition,the system was evaluated experimentally in terms of detection accuracy,sorting accuracy and computational time.For the image processing algorithms scheme,the detection accuracy for mango and tomato was 89% and 92%,respectively,and for CNN architecture used,the validation accuracy for mangoes and tomatoes was 95% and 94%,respectively.
基金the National Natural Science Foundation of China(62073231,62176175,62172076)National Research Project(2020YFC2006602)+2 种基金Provincial Key Laboratory for Computer Information Processing Technology,Soochow University(KJS2166)Opening Topic Fund of Big Data Intelligent Engineering Laboratory of Jiangsu Province(SDGC2157)Postgraduate Research&Practice Innovation Program of Jiangsu Province.
文摘Drug discovery is costly and time consuming,and modern drug discovery endeavors are progressively reliant on computational methodologies,aiming to mitigate temporal and financial expenditures associated with the process.In particular,the time required for vaccine and drug discovery is prolonged during emergency situations such as the coronavirus 2019 pandemic.Recently,the performance of deep learning methods in drug virtual screening has been particularly prominent.It has become a concern for researchers how to summarize the existing deep learning in drug virtual screening,select different models for different drug screening problems,exploit the advantages of deep learning models,and further improve the capability of deep learning in drug virtual screening.This review first introduces the basic concepts of drug virtual screening,common datasets,and data representation methods.Then,large numbers of common deep learning methods for drug virtual screening are compared and analyzed.In addition,a dataset of different sizes is constructed independently to evaluate the performance of each deep learning model for the difficult problem of large-scale ligand virtual screening.Finally,the existing challenges and future directions in the field of virtual screening are presented.
基金supported by the National Key R&DProgram of China(No.2021YFF0602104-2)。
文摘In recent years,Deep Learning(DL)technique has been widely used in Internet of Things(IoT)and Industrial Internet of Things(IIoT)for edge computing,and achieved good performances.But more and more studies have shown the vulnerability of neural networks.So,it is important to test the robustness and vulnerability of neural networks.More specifically,inspired by layer-wise relevance propagation and neural network verification,we propose a novel measurement of sensitive neurons and important neurons,and propose a novel neuron coverage criterion for robustness testing.Based on the novel criterion,we design a novel testing sample generation method,named DeepSI,which involves definitions of sensitive neurons and important neurons.Furthermore,we construct sensitive-decision paths of the neural network through selecting sensitive neurons and important neurons.Finally,we verify our idea by setting up several experiments,then results show our proposed method achieves superior performances.
文摘Plant-parasitic nematodes cause various diseases that can be fatal to the infected plants.It causes losses to the agricultural industry,such as crop failure and poor crop quality.Developing an accurate nematode classification system is vital for pest identification and control.Deep learning classification techniques can help speed up Nematode identification as it can perform tasks directly from images.In the present study,four state-of-the-art deep learning models(ResNet101v2,CoAtNet-0,Effi-cientNetV2B0,and EfficientNetV2M)were evaluated in plantparasitic nematode classification from microscopic image.The models were trained using a combination of three different optimizers(Adam,SGD,dan RMSProp)and several data augmentation with image transformations,such as image flip,blurring,noise addition,brightness,and contrast adjustment.The performance of the trained models was varied.Regarding test accuracy,EfficientNetV2B0 and EfficientNetV2M using RMSProp and brightness augmentation give the best result of 97.94%However,the overall performance of EfficientNetV2M was superior,with 98.66%mean class accuracy,97.99%F1 score,98.26%average precision,and 97.94%average recall.
基金This work is supported by the National Natural Science Foundation of China(Nos.21874056 and 52003103)the National Key R&D Program of China(No.2016YFC1100502)the Guangdong Provincial Key Laboratory of Functional Supramolecular Coordination Materials and Appications,Jinan University.
文摘Biogenic amines(BAs)are important biomarkers for monitoring food quality and assisting in the diagnosis of disease.Facial,portable,accurate and high-throughput BAs detection is still challenging by the specific sensor compounds development or the complicated instrument operation.Deep learning(DL)algorithms are blooming for their superiority on the nonlinear and multidimensional data analysis,which endow the great advantage for the artificial intelligence assisted large sample analysis of the environmental or daily health monitoring.In this work,we developed a deep learning-assisted visualized fluorometric array-based sensing method.Two commercial fluorescent dyes were selected and combined into sensor arrays.Variation in the alkalinity of BAs causes significant and distinct fluorescence changes of the dyes.In conjunction with pattern recognition by the pretrained CNN models,the sensor array clearly differentiates seven BAs with 99.29%prediction accuracy and allows rapid single and multi-component quantification with a volume fraction range from 200 cm^(3)/m^(3)to 2500 cm^(3)/m^(3).This method also provides a new way for meat freshness monitoring.We envision that this novel analytical method for BAs can be used as an alternative and promising tool for the detection of a wider variety of analytes.
基金Bejing Hope Run Special Fund of Cancer Foundation of China,G rant/Award Number:LC2019A01China Postdoctoral Science Foundation,G rant/Award Number:2017M620683National Natural Science Foundation of China,Gr ant/Award Number:81974268。
文摘Breast cancer is the most common malignant tumor and the leading cause of cancer-related deaths in women worldwide.Effective means of predicting the prognosis of breast cancer are very helpful in guiding treatment and improving patients'survival.Features extracted by radiomics reflect the genetic and molecular characteristics of a tumor and are related to its biological behavior and the patient's prognosis.Thus,radiomics provides a new approach to noninvasive assessment of breast cancer prognosis.Ultrasound is one of the commonest clinical means of examining breast cancer.In recent years,some results of research into ultrasound radiomics for diagnosing breast cancer,predicting lymph node status,treatment response,recurrence and survival times,and other aspects,have been published.In this article,we review the current research status and technical challenges of ultrasound radiomics for predicting breast cancer prognosis.We aim to provide a reference for radiomics researchers,promote the development of ultrasound radiomics,and advance its clinical application.
基金Sichuan Science and Technology Program 2018GZDZX0042,2018HH0061.
文摘Deep learning methods are applied into structured data and in typical methods,low-order features are discarded after combining with high-order featuresfor prediction tasks.However,in structured data,ignorance of low-order features may cause the low prediction rate.To address this issue,in this paper,deeper attention-based network(DAN)is proposed.With DAN method,to keep both low-and high-order features,attention average pooling layer was utilized to aggregate features of each order.Furthermore,by shortcut connections from each layer to attention average pooling layer,DAN can be built extremely deep to obtain enough capacity.Experimental results show DAN has good performance and works effectively.
