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
Clinical image processing plays a signicant role in healthcare systems and is currently a widely used methodology.In carcinogenic diseases,time is crucial;thus,an image’s accurate analysis can help treat disease at a...Clinical image processing plays a signicant role in healthcare systems and is currently a widely used methodology.In carcinogenic diseases,time is crucial;thus,an image’s accurate analysis can help treat disease at an early stage.Ductal carcinoma in situ(DCIS)and lobular carcinoma in situ(LCIS)are common types of malignancies that affect both women and men.The number of cases of DCIS and LCIS has increased every year since 2002,while it still takes a considerable amount of time to recommend a controlling technique.Image processing is a powerful technique to analyze preprocessed images to retrieve useful information by using some remarkable processing operations.In this paper,we used a dataset from the Mammographic Image Analysis Society and MATLAB 2019b software from MathWorks to simulate and extract our results.In this proposed study,mammograms are primarily used to diagnose,more precisely,the breast’s tumor component.The detection of DCIS and LCIS on breast mammograms is done by preprocessing the images using contrast-limited adaptive histogram equalization.The resulting images’tumor portions are then isolated by a segmentation process,such as threshold detection.Furthermore,morphological operations,such as erosion and dilation,are applied to the images,then a gray-level co-occurrence matrix texture features,Harlick texture features,and shape features are extracted from the regions of interest.For classication purposes,a support vector machine(SVM)classier is used to categorize normal and abnormal patterns.Finally,the adaptive neuro-fuzzy inference system is deployed for the amputation of fuzziness due to overlapping features of patterns within the images,and the exact categorization of prior patterns is gained through the SVM.Early detection of DCIS and LCIS can save lives and help physicians and surgeons todiagnose and treat these diseases.Substantial results are obtained through cubic support vector machine(CSVM),respectively,showing 98.95%and 98.01%accuracies for normal and abnormal mammograms.Through ANFIS,promising results of mean square error(MSE)0.01866,0.18397,and 0.19640 for DCIS and LCIS differentiation during the training,testing,and checking phases.展开更多
Precipitation nowcasting,as a crucial component of weather forecasting,focuses on predicting very short-range precipitation,typically within six hours.This approach relies heavily on real-time observations rather than...Precipitation nowcasting,as a crucial component of weather forecasting,focuses on predicting very short-range precipitation,typically within six hours.This approach relies heavily on real-time observations rather than numerical weather models.The core concept involves the spatio-temporal extrapolation of current precipitation fields derived from ground radar echoes and/or satellite images,which was generally actualized by employing computer image or vision techniques.Recently,with stirring breakthroughs in artificial intelligence(AI)techniques,deep learning(DL)methods have been used as the basis for developing novel approaches to precipitation nowcasting.Notable progress has been obtained in recent years,manifesting the strong potential of DL-based nowcasting models for their advantages in both prediction accuracy and computational cost.This paper provides an overview of these precipitation nowcasting approaches,from which two stages along the advancing in this field emerge.Classic models that were established on an elementary neural network dominated in the first stage,while large meteorological models that were based on complex network architectures prevailed in the second.In particular,the nowcasting accuracy of such data-driven models has been greatly increased by imposing suitable physical constraints.The integration of AI models and physical models seems to be a promising way to improve precipitation nowcasting techniques further.展开更多
Motivation is one of the critical factors that affect foreign language learning. This study attempts to explore the factors that affect motivation in English learning of non-English major cadets. An investigation is c...Motivation is one of the critical factors that affect foreign language learning. This study attempts to explore the factors that affect motivation in English learning of non-English major cadets. An investigation is conducted in a military academy in southwest China, in which a Likert scale questionnaire is adopted. Through data analysis, eight factors are found to affect cadets' English learning motivation. Moreover, suggestions are provided to shed light on English teaching and learning in military academies.展开更多
Distance education had sufficient technical capabilities before the novel coronavirus outbreak,but its advantages were not reflected in the normalized school running model.In the early stage of the pandemic,many stude...Distance education had sufficient technical capabilities before the novel coronavirus outbreak,but its advantages were not reflected in the normalized school running model.In the early stage of the pandemic,many students were affected and could not return to school.Many schools implemented online teaching to avoid delaying classes.After the alleviation of the pandemic,several colleges and universities taught students with a combination of online and offline methods after returning to school.The integration of online and offline teaching is conducive to the overall improvement of teaching quality in colleges and universities.This paper summarizes the shortcomings of the existing online and offline integrated education model in the teaching of traditional Chinese medicine classics in hope to further optimize the modem education system of traditional Chinese medicine courses.展开更多
Tomato production is affected by various threats,including pests,pathogens,and nutritional deciencies during its growth process.If control is not timely,these threats affect the plant-growth,fruit-yield,or even loss o...Tomato production is affected by various threats,including pests,pathogens,and nutritional deciencies during its growth process.If control is not timely,these threats affect the plant-growth,fruit-yield,or even loss of the entire crop,which is a key danger to farmers’livelihood and food security.Traditional plant disease diagnosis methods heavily rely on plant pathologists that incur high processing time and huge cost.Rapid and cost-effective methods are essential for timely detection and early intervention of basic food threats to ensure food security and reduce substantial economic loss.Recent developments in Articial Intelligence(AI)and computer vision allow researchers to develop image-based automatic diagnostic tools to quickly and accurately detect diseases.In this work,we proposed an AI-based approach to detect diseases in tomato plants.Our goal is to develop an end-to-end system to diagnose essential crop problems in real-time,ensuring high accuracy.This paper employs various deep learning models to recognize and predict different diseases caused by pathogens,pests,and nutritional deciencies.Various Convolutional Neural Networks(CNNs)are trained on a large dataset of leaves and fruits images of tomato plants.We compared the performance of ShallowNet(a shallow network trained from scratch)and the state-of-theart deep learning network(models are ne-tuned via transfer learning).In our experiments,DenseNet consistently achieved high performance with an accuracy score of 95.31%on the test dataset.The results verify that deep learning models with the least number of parameters,reasonable complexity,and appropriate depth achieve the best performance.All experiments are implemented in Python,utilizing the Keras deep learning library backend with TensorFlow.展开更多
The ability to accurately estimate the cost needed to complete a specific project has been a challenge over the past decades. For a successful software project, accurate prediction of the cost, time and effort is a ve...The ability to accurately estimate the cost needed to complete a specific project has been a challenge over the past decades. For a successful software project, accurate prediction of the cost, time and effort is a very much essential task. This paper presents a systematic review of different models used for software cost estimation which includes algorithmic methods, non-algorithmic methods and learning-oriented methods. The models considered in this review include both the traditional and the recent approaches for software cost estimation. The main objective of this paper is to provide an overview of software cost estimation models and summarize their strengths, weakness, accuracy, amount of data needed, and validation techniques used. Our findings show, in general, neural network based models outperforms other cost estimation techniques. However, no one technique fits every problem and we recommend practitioners to search for the model that best fit their needs.展开更多
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.
文摘Clinical image processing plays a signicant role in healthcare systems and is currently a widely used methodology.In carcinogenic diseases,time is crucial;thus,an image’s accurate analysis can help treat disease at an early stage.Ductal carcinoma in situ(DCIS)and lobular carcinoma in situ(LCIS)are common types of malignancies that affect both women and men.The number of cases of DCIS and LCIS has increased every year since 2002,while it still takes a considerable amount of time to recommend a controlling technique.Image processing is a powerful technique to analyze preprocessed images to retrieve useful information by using some remarkable processing operations.In this paper,we used a dataset from the Mammographic Image Analysis Society and MATLAB 2019b software from MathWorks to simulate and extract our results.In this proposed study,mammograms are primarily used to diagnose,more precisely,the breast’s tumor component.The detection of DCIS and LCIS on breast mammograms is done by preprocessing the images using contrast-limited adaptive histogram equalization.The resulting images’tumor portions are then isolated by a segmentation process,such as threshold detection.Furthermore,morphological operations,such as erosion and dilation,are applied to the images,then a gray-level co-occurrence matrix texture features,Harlick texture features,and shape features are extracted from the regions of interest.For classication purposes,a support vector machine(SVM)classier is used to categorize normal and abnormal patterns.Finally,the adaptive neuro-fuzzy inference system is deployed for the amputation of fuzziness due to overlapping features of patterns within the images,and the exact categorization of prior patterns is gained through the SVM.Early detection of DCIS and LCIS can save lives and help physicians and surgeons todiagnose and treat these diseases.Substantial results are obtained through cubic support vector machine(CSVM),respectively,showing 98.95%and 98.01%accuracies for normal and abnormal mammograms.Through ANFIS,promising results of mean square error(MSE)0.01866,0.18397,and 0.19640 for DCIS and LCIS differentiation during the training,testing,and checking phases.
基金National Natural Science Foundation of China(42075075)National Key R&D Program of China(2023YFC3007700)Pre-Research Fund of USTC(YZ2082300006)。
文摘Precipitation nowcasting,as a crucial component of weather forecasting,focuses on predicting very short-range precipitation,typically within six hours.This approach relies heavily on real-time observations rather than numerical weather models.The core concept involves the spatio-temporal extrapolation of current precipitation fields derived from ground radar echoes and/or satellite images,which was generally actualized by employing computer image or vision techniques.Recently,with stirring breakthroughs in artificial intelligence(AI)techniques,deep learning(DL)methods have been used as the basis for developing novel approaches to precipitation nowcasting.Notable progress has been obtained in recent years,manifesting the strong potential of DL-based nowcasting models for their advantages in both prediction accuracy and computational cost.This paper provides an overview of these precipitation nowcasting approaches,from which two stages along the advancing in this field emerge.Classic models that were established on an elementary neural network dominated in the first stage,while large meteorological models that were based on complex network architectures prevailed in the second.In particular,the nowcasting accuracy of such data-driven models has been greatly increased by imposing suitable physical constraints.The integration of AI models and physical models seems to be a promising way to improve precipitation nowcasting techniques further.
文摘Motivation is one of the critical factors that affect foreign language learning. This study attempts to explore the factors that affect motivation in English learning of non-English major cadets. An investigation is conducted in a military academy in southwest China, in which a Likert scale questionnaire is adopted. Through data analysis, eight factors are found to affect cadets' English learning motivation. Moreover, suggestions are provided to shed light on English teaching and learning in military academies.
文摘Distance education had sufficient technical capabilities before the novel coronavirus outbreak,but its advantages were not reflected in the normalized school running model.In the early stage of the pandemic,many students were affected and could not return to school.Many schools implemented online teaching to avoid delaying classes.After the alleviation of the pandemic,several colleges and universities taught students with a combination of online and offline methods after returning to school.The integration of online and offline teaching is conducive to the overall improvement of teaching quality in colleges and universities.This paper summarizes the shortcomings of the existing online and offline integrated education model in the teaching of traditional Chinese medicine classics in hope to further optimize the modem education system of traditional Chinese medicine courses.
基金The authors extend their appreciation to the Deputyship for Research &Innovation, Ministry of Education in Saudi Arabia, for funding this research work through the Project No.IFT20065。
文摘Tomato production is affected by various threats,including pests,pathogens,and nutritional deciencies during its growth process.If control is not timely,these threats affect the plant-growth,fruit-yield,or even loss of the entire crop,which is a key danger to farmers’livelihood and food security.Traditional plant disease diagnosis methods heavily rely on plant pathologists that incur high processing time and huge cost.Rapid and cost-effective methods are essential for timely detection and early intervention of basic food threats to ensure food security and reduce substantial economic loss.Recent developments in Articial Intelligence(AI)and computer vision allow researchers to develop image-based automatic diagnostic tools to quickly and accurately detect diseases.In this work,we proposed an AI-based approach to detect diseases in tomato plants.Our goal is to develop an end-to-end system to diagnose essential crop problems in real-time,ensuring high accuracy.This paper employs various deep learning models to recognize and predict different diseases caused by pathogens,pests,and nutritional deciencies.Various Convolutional Neural Networks(CNNs)are trained on a large dataset of leaves and fruits images of tomato plants.We compared the performance of ShallowNet(a shallow network trained from scratch)and the state-of-theart deep learning network(models are ne-tuned via transfer learning).In our experiments,DenseNet consistently achieved high performance with an accuracy score of 95.31%on the test dataset.The results verify that deep learning models with the least number of parameters,reasonable complexity,and appropriate depth achieve the best performance.All experiments are implemented in Python,utilizing the Keras deep learning library backend with TensorFlow.
文摘The ability to accurately estimate the cost needed to complete a specific project has been a challenge over the past decades. For a successful software project, accurate prediction of the cost, time and effort is a very much essential task. This paper presents a systematic review of different models used for software cost estimation which includes algorithmic methods, non-algorithmic methods and learning-oriented methods. The models considered in this review include both the traditional and the recent approaches for software cost estimation. The main objective of this paper is to provide an overview of software cost estimation models and summarize their strengths, weakness, accuracy, amount of data needed, and validation techniques used. Our findings show, in general, neural network based models outperforms other cost estimation techniques. However, no one technique fits every problem and we recommend practitioners to search for the model that best fit their needs.
