A machine learning-based strategy for predicting the mechanical strength of coral reef limestone using X-ray computed tomography

Different sedimentary zones in coral reefs lead to significant anisotropy in the pore structure of coral reef limestone(CRL),making it difficult to study mechanical behaviors.With X-ray computed tomography(CT),112 CRL samples were utilized for training the support vector machine(SVM)-,random forest(RF)-,and back propagation neural network(BPNN)-based models,respectively.Simultaneously,the machine learning model was embedded into genetic algorithm(GA)for parameter optimization to effectively predict uniaxial compressive strength(UCS)of CRL.Results indicate that the BPNN model with five hidden layers presents the best training effect in the data set of CRL.The SVM-based model shows a tendency to overfitting in the training set and poor generalization ability in the testing set.The RF-based model is suitable for training CRL samples with large data.Analysis of Pearson correlation coefficient matrix and the percentage increment method of performance metrics shows that the dry density,pore structure,and porosity of CRL are strongly correlated to UCS.However,the P-wave velocity is almost uncorrelated to the UCS,which is significantly distinct from the law for homogenous geomaterials.In addition,the pore tensor proposed in this paper can effectively reflect the pore structure of coral framework limestone(CFL)and coral boulder limestone(CBL),realizing the quantitative characterization of the heterogeneity and anisotropy of pore.The pore tensor provides a feasible idea to establish the relationship between pore structure and mechanical behavior of CRL.

A Comprehensive Investigation of Machine Learning Feature Extraction and ClassificationMethods for Automated Diagnosis of COVID-19 Based on X-ray Images

The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease.In this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 vs.Normal(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases.Based on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 model.In comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.

Rapid detection and risk assessment of soil contamination at lead smelting site based on machine learning

A general prediction model for seven heavy metals was established using the heavy metal contents of 207soil samples measured by a portable X-ray fluorescence spectrometer(XRF)and six environmental factors as model correction coefficients.The eXtreme Gradient Boosting(XGBoost)model was used to fit the relationship between the content of heavy metals and environment characteristics to evaluate the soil ecological risk of the smelting site.The results demonstrated that the generalized prediction model developed for Pb,Cd,and As was highly accurate with fitted coefficients(R^(2))values of 0.911,0.950,and 0.835,respectively.Topsoil presented the highest ecological risk,and there existed high potential ecological risk at some positions with different depths due to high mobility of Cd.Generally,the application of machine learning significantly increased the accuracy of pXRF measurements,and identified key environmental factors.The adapted potential ecological risk assessment emphasized the need to focus on Pb,Cd,and As in future site remediation efforts.

Course Evaluation Based on Deep Learning and SSA Hyperparameters Optimization

Sentiment analysis attracts the attention of Egyptian Decisionmakers in the education sector.It offers a viable method to assess education quality services based on the students’feedback as well as that provides an understanding of their needs.As machine learning techniques offer automated strategies to process big data derived from social media and other digital channels,this research uses a dataset for tweets’sentiments to assess a few machine learning techniques.After dataset preprocessing to remove symbols,necessary stemming and lemmatization is performed for features extraction.This is followed by several machine learning techniques and a proposed Long Short-Term Memory(LSTM)classifier optimized by the Salp Swarm Algorithm(SSA)and measured the corresponding performance.Then,the validity and accuracy of commonly used classifiers,such as Support Vector Machine,Logistic Regression Classifier,and Naive Bayes classifier,were reviewed.Moreover,LSTM based on the SSA classification model was compared with Support Vector Machine(SVM),Logistic Regression(LR),and Naive Bayes(NB).Finally,as LSTM based SSA achieved the highest accuracy,it was applied to predict the sentiments of students’feedback and evaluate their association with the course outcome evaluations for education quality purposes.

Distribution Properties of Internal Air Voids in Ultrathin Asphalt Friction Course

The distribution characteristics of air voids in ultrathin asphalt friction course(UAFC) samples with different gradations and compaction methods were statistically analyzed using X-ray computed tomography(CT) and image analysis techniques. Based on the results, compared with the AC-5 sample, the OGFC-5mixture has a higher air void ratio, a larger air void size and a greater number of air voids, with the distribution of internal air voids being more uniform and their shapes being more rounded. The two-parameter Weibull function was applied to fit the gradation of air voids. The fitting results is good, and the function parameters are sensitive to changes in both mineral gradation and compaction method. Moreover, two homogeneity indices were proposed to evaluate the compaction uniformity of UAFC samples. Compared with the Marshall method,the SGC method is more conducive to improve the compaction uniformity of UAFC samples. The compaction method significantly influences the air void distribution characteristics and compaction uniformity of AC-5sample, but has a less significant impact on OGFC-5 sample. The experimental results in the study provides a solid foundation for further explorations on the internal structure and mixture design of UAFC.

X-ray image distortion correction based on SVR

X-ray image has been widely used in many fields such as medical diagnosis,industrial inspection,and so on.Unfortunately,due to the physical characteristics of X-ray and imaging system,distortion of the projected image will happen,which restrict the application of X-ray image,especially in high accuracy fields.Distortion correction can be performed using algorithms that can be classified as global or local according to the method used,both having specific advantages and disadvantages.In this paper,a new global method based on support vector regression（SVR）machine for distortion correction is proposed.In order to test the presented method,a calibration phantom is specially designed for this purpose.A comparison of the proposed method with the traditional global distortion correction techniques is performed.The experimental results show that the proposed correction method performs better than the traditional global one.

Covid-19 Detection from Chest X-Ray Images Using Advanced Deep Learning Techniques

Like the Covid-19 pandemic,smallpox virus infection broke out in the last century,wherein 500 million deaths were reported along with enormous economic loss.But unlike smallpox,the Covid-19 recorded a low exponential infection rate and mortality rate due to advancement inmedical aid and diagnostics.Data analytics,machine learning,and automation techniques can help in early diagnostics and supporting treatments of many reported patients.This paper proposes a robust and efficient methodology for the early detection of COVID-19 from Chest X-Ray scans utilizing enhanced deep learning techniques.Our study suggests that using the Prediction and Deconvolutional Modules in combination with the SSD architecture can improve the performance of the model trained at this task.We used a publicly open CXR image dataset and implemented the detectionmodelwith task-specific pre-processing and near 80:20 split.This achieved a competitive specificity of 0.9474 and a sensibility/accuracy of 0.9597,which shall help better decision-making for various aspects of identification and treat the infection.

Multi-Label Chest X-Ray Classification via Deep Learning

In this era of pandemic, the future of healthcare industry has never been more exciting. Artificial intelligence and machine learning (AI & ML) present opportunities to develop solutions that cater for very specific needs within the industry. Deep learning in healthcare had become incredibly powerful for supporting clinics and in transforming patient care in general. Deep learning is increasingly being applied for the detection of clinically important features in the images beyond what can be perceived by the naked human eye. Chest X-ray images are one of the most common clinical method for diagnosing a number of diseases such as pneumonia, lung cancer and many other abnormalities like lesions and fractures. Proper diagnosis of a disease from X-ray images is often challenging task for even expert radiologists and there is a growing need for computerized support systems due to the large amount of information encoded in X-Ray images. The goal of this paper is to develop a lightweight solution to detect 14 different chest conditions from an X ray image. Given an X-ray image as input, our classifier outputs a label vector indicating which of 14 disease classes does the image fall into. Along with the image features, we are also going to use non-image features available in the data such as X-ray view type, age, gender etc. The original study conducted Stanford ML Group is our base line. Original study focuses on predicting 5 diseases. Our aim is to improve upon previous work, expand prediction to 14 diseases and provide insight for future chest radiography research.

应用实践导向的机器学习导论课程教学探讨

机器学习技术有很强的理论性和实用性。为提高学生对机器学习课程的兴趣和主动性,以计算机科学与技术专业机器学习导论教学为例,探究应用实践导向的机器学习导论课程教学改革,对每个机器学习算法设置对应的应用实践项目,构建1学时机器学习理论知识对应1学时项目实践的教学方式,将机器学习算法融入到实践项目中。教学实践证明,应用实践导向的机器学习导论教学模式能够明显提高理论和实践结合教学方法的效率,提高学生的积极性和主动性,培养学生解决实际问题的能力。

机械原理课程高阶教学模式探索与实践——以运动副的分析与设计为例

结合新工科、工程教育专业认证,探索适合于机械原理课程特点的高阶教学模式,并结合课程中重要的知识点——运动副的分析与设计进行实践。实践证明:高阶教学模式能够培养学生的高阶思维,契合新工科、工程教育专业认证背景下工科学生的培养要求;高阶教学模式,可以培养学生自主学习,最终达到“教为不教”的教学目的。

课外项目参与机械原理课程考核的实践探索

针对机械原理课程单一考核形式存在诸多不利影响的问题,提出了一种课外项目参与课程考核的实践方案。详细阐述了一个基于3-RRR(R表示转动副)平面并联机构杆组拆分的课外项目案例。通过项目选题、小组分工、独立计算、成果汇总、项目答辩5个环节,学生完成了项目考核的全部内容,并对平面机构的组成原理与结构分析有了更深刻的理解。这有利于提升学生的知识水平、专业技能、工程意识和科研素养,同时也是贯彻落实素质教育、创新教育理念的良好体现。

“大思政课”视域下高校网络思想政治教育的路径与机制

“大思政课”视域下,高校开展网络思想政治教育具有重要价值,但其中也存在一些困难,如教育理念需要更新、网络平台尚未完善、队伍建设有待优化、网络监管亟须加强、内容供给不够精准等。因此,高校需要从树立先进教育理念、加强网络平台建设、注重育人队伍建设、提高网络监管能力、增强供需匹配能力等维度创新网络思想政治教育路径。同时,高校需要构建科学的管理机制、创立有效的互动机制、创新动态的优化机制、建立全面的保障机制、创建合理的评估机制,培养有理想、敢担当、能吃苦、肯奋斗的新时代好青年。

基于机器学习的计算机专业课程知识点推荐系统设计与优化

计算机科学领域发展迅速,学生需要学习大量的知识点。为了帮助学生高效地掌握专业技能,文章设计一个基于机器学习的计算机专业课程知识点推荐系统。该系统的推荐准确率、响应时间、用户满意度均达到要求,能够为学生提供个性化的课程推荐,具有一定的应用价值。

新工科背景下“五融入”创新人才培养模式研究——以“机器学习”课程为例

机器学习作为培养人工智能领域人才的核心课程,其在教学内容、教学方式、课程考核等环节可进一步研究与改进。基于新工科背景,为培养顺应时代发展、符合企业需求的人才,文章以“机器学习”课程为例,探索了“五融入”创新人才培养模式,提出将科学精神、科研成果、科研实践、企业资源和SPOC微课堂融入机器学习课程,着力推进知识传授、能力培养、思想引领三位一体的育人功能,也为我国开设同类课程的高校提供有益借鉴与参考。

研究生岩土工程大数据和机器学习课程混合课程建设探索

高等教育发展任务包括适应信息化时代教育教学形态的改变,促进信息技术与教育教学的深度融合。重庆大学土木工程学院以岩土工程大数据和机器学习线上线下混合课程建设为契机,开展了以研究生培养质量提升为目标的改革与探索。针对岩土工程研究生群体新兴学科素养和信息化水平不足、高水平科研论文产出较难、科研产出单一、国际重要学术会议上学术获奖较少等问题,课程着重开展了培养模式、培养途径、培养方式、培养成果多样化四个方面的创新。课程采用国外知名教授全英文线上授课和线下授课结合的形式,结合科研项目、实践项目等加强研究生的科学创新精神和国际交流合作能力,并引入“过程测验”重点关注研究生的能力提升情况。实践证明,通过混合课程的建设,研究生科研素质与分析能力得到了明显的提升,课程建设获得了多方面的培养成果,有效推动了信息化技术在高等教育中的实施。

机器视觉在“汽车制造技术”课程中的应用

机器视觉技术因其灵敏度高、适应性强、鲁棒性好而被广泛地应用于各个领域。机器视觉技术在制造、生产类课程中具有高准确性,可以更好地体现“理论与实践相结合”的课程教学特色和要求,丰富教学资源,增强学习的沉浸感,不断提高学生的实践和创新能力。

Characterization of the Convoluted 3D Internetallic Phases in a Recycled Al Alloy by Synchrotron X-ray Tomography and Machine Learning

Fe-rich intermetallic phases in recycled Al alloys often exhibit complex and 3D convoluted structures and morphologies.They are the common detrimental intermetallic phases to the mechanical properties of recycled Al alloys.In this study,we used synchrotron X-ray tomography to study the true 3D morphologies of the Ferich phases,Al_(2)Cu phases and casting defects in an ascast Al-5Cu-1.5Fe-1Si alloy.Machine learning-based image processing approach was used to recognize and segment the diff erent phases in the 3D tomography image stacks.In the studied condition,theβ-Al_(9)Fe_(2)Si_(2)andω-Al_(7)Cu_(2)Fe are found to be the main Fe-rich intermetallic phases.Theβ-Al_(9)Fe_(2)Si_(2)phases exhibit a spatially connected 3D network structure and morphology which in turn control the 3D spatial distribution of the Al_(2)Cu phases and the shrinkage cavities.The Al_(3)Fe phases formed at the early stage of solidification aff ect to a large extent the structure and morphology of the subsequently formed Fe-rich intermetallic phases.The machine learning method has been demonstrated as a powerful tool for processing big datasets in multidimensional imaging-based materials characterization work.

高职机械类专业《数控机床加工与编程》课程思政教学改革研究与实践

课程思政是当前高校课堂教学的主要方向之一,《数控机床加工与编程》是一门面向高职机械类专业的专业核心课程。根据教学内容,深挖每个项目任务思政内涵,将中国民族工业发展、工匠精神、中国传统文化等引入课堂,提出“一主线四阶段三结合”的整体课程建设思路。通过思政案例视频、思政专题案例、思政专题讨论、思政课后作业等方式,将思政教育融入课程教学的全过程,实现立德树人的根本目标。

多措并举助推数控机床故障诊断与维修课程思政建设

传统数控机床故障诊断与维修专业课程着重专业知识讲授,思政与专业各自为战,导致专业课程与思政教育形成“两张皮”。该文围绕课程、教材、教法、课堂、评价和制度保障等方面探索数控维修专业课程思政化的培养模式,明确课程思政目标、提升专业教师思政水平、优化思政教材建设、创新教学模式、完善思政评价体系及强化制度保障,多措并举助力机电类专业课程思政建设发展,并以点涉面辐射到其他专业课程中,为全课程育人提供参考。

Radiography Image Classification Using Deep Convolutional Neural Networks

Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can be used to identify each of these anomalies in the chest x-ray images. Convolutional neural networks (CNNs) have shown great success in the fields of image recognition and image classification since there are numerous large-scale annotated image datasets available. The classification of medical images, particularly radiographic images, remains one of the biggest hurdles in medical diagnosis because of the restricted availability of annotated medical images. However, such difficulty can be solved by utilizing several deep learning strategies, including data augmentation and transfer learning. The aim was to build a model that would detect abnormalities in chest x-ray images with the highest probability. To do that, different models were built with different features. While making a CNN model, one of the main tasks is to tune the model by changing the hyperparameters and layers so that the model gives out good training and testing results. In our case, three different models were built, and finally, the last one gave out the best-predicted results. From that last model, we got 98% training accuracy, 84% validation, and 81% testing accuracy. The reason behind the final model giving out the best evaluation scores is that it was a well-fitted model. There was no overfitting or underfitting issues. Our aim with this project was to make a tool using the CNN model in R language, which will help detect abnormalities in radiography images. The tool will be able to detect diseases such as Pneumonia, Covid-19, Effusions, Infiltration, Pneumothorax, and others. Because of its high accuracy, this research chose to use supervised multi-class classification techniques as well as Convolutional Neural Networks (CNNs) to classify different chest x-ray images. CNNs are extremely efficient and successful at reducing the number of parameters while maintaining the quality of the primary model. CNNs are also trained to recognize the edges of various objects in any batch of images. CNNs automatically discover the relevant aspects in labeled data and learn the distinguishing features for each class by themselves.