Machine Learning Enabled Early Detection of Breast Cancer by Structural Analysis of Mammograms

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.

A Study on Factors that Affect Motivation in English Learning of NonEnglish Major Cadets

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.

Deep Learning Applied to Computational Mechanics:A Comprehensive Review,State of the Art,and the Classics

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.

Image-Based Automatic Diagnostic System for Tomato Plants Using Deep Learning

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.

A Survey on Software Cost Estimation Techniques

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.

Analysis of the Online and Offline Hybrid Teaching of Traditional Chinese Medicine Classics

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.

Advances in Deep-Learning-based Precipitation Nowcasting Techniques

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.

利用E-learning服务平台有效建设高校精品课

E-Learning服务平台是现代教育一种全新的学习方式,其特有的交互性、高效性、协作性等优点,使得它在高校教育信息化建设中占有重要的地位,尤其是在高校精品课建设中更是离不开E-Learning服务平台的支撑。在E-Learning环境下有效建设精品课,提高教师认知是前提,加强网络建设是支撑,改革教学模式是关键,完善学校政策和制度是保证。

Machine learning of frustrated classical spin models （II）： Kernel principal component analysis

In this work, we apply a principal component analysis （PCA） method with a kernel trick to study the classification of phases and phase transitions in classical XY models of frustrated lattices. Compared to our previous work with the linear PCA method, the kernel PCA can capture nonlinear functions. In this case, the Z2 chiral order of the classical spins in these lattices is indeed a nonlinear function of the input spin configurations. In addition to the principal component revealed by the linear PCA, the kernel PCA can find two more principal components using the data generated by Monte Carlo simulation for various temperatures as the input. One of them is related to the strength of the U（1） order parameter, and the other directly manifests the chiral order parameter that characterizes the Z2 symmetry breaking. For a temperature-resolved study, the temperature dependence of the principal eigenvalue associated with the Z2 symmetry breaking clearly shows second-order phase transition behavior.

Cardiac Arrhythmia Disease Classication Using LSTM Deep Learning Approach

Many approaches have been tried for the classication of arrhythmia.Due to the dynamic nature of electrocardiogram(ECG)signals,it is challenging to use traditional handcrafted techniques,making a machine learning(ML)implementation attractive.Competent monitoring of cardiac arrhythmia patients can save lives.Cardiac arrhythmia prediction and classication has improved signicantly during the last few years.Arrhythmias are a group of conditions in which the electrical activity of the heart is abnormal,either faster or slower than normal.It is the most frequent cause of death for both men and women every year in the world.This paper presents a deep learning(DL)technique for the classication of arrhythmias.The proposed technique makes use of the University of California,Irvine(UCI)repository,which consists of a high-dimensional cardiac arrhythmia dataset of 279 attributes.In this research,our goal was to classify cardiac arrhythmia patients into 16 classes depending on the characteristics of the electrocardiography dataset.The DL approach in the form of long short-term memory(LSTM)is an efcient technique to deal with reduced accuracy due to vanishing and exploding gradients in traditional DL frameworks for big data analysis.The goal of this research was to categorize cardiac arrhythmia patients by developing an efcient intelligent system using the LSTM DL algorithm.This approach to arrhythmia classication includes classication algorithms along with noise removal techniques.Therefore,we utilized principal components analysis(PCA)for noise removal,and LSTM for classication.This hybrid comprehensive arrhythmia classication approach performs better than previous approaches to arrhythmia classication.We attained a highest classication accuracy of 93.5%with the DL based disease classication system,and outperformed the earlier approaches used for cardiac arrhythmia classication.

An Integrated Deep Learning Framework for Fruits Diseases Classification

:Agriculture has been an important research area in the field of image processing for the last five years.Diseases affect the quality and quantity of fruits,thereby disrupting the economy of a country.Many computerized techniques have been introduced for detecting and recognizing fruit diseases.However,some issues remain to be addressed,such as irrelevant features and the dimensionality of feature vectors,which increase the computational time of the system.Herein,we propose an integrated deep learning framework for classifying fruit diseases.We consider seven types of fruits,i.e.,apple,cherry,blueberry,grapes,peach,citrus,and strawberry.The proposed method comprises several important steps.Initially,data increase is applied,and then two different types of features are extracted.In the first feature type,texture and color features,i.e.,classical features,are extracted.In the second type,deep learning characteristics are extracted using a pretrained model.The pretrained model is reused through transfer learning.Subsequently,both types of features are merged using the maximum mean value of the serial approach.Next,the resulting fused vector is optimized using a harmonic threshold-based genetic algorithm.Finally,the selected features are classified using multiple classifiers.An evaluation is performed on the PlantVillage dataset,and an accuracy of 99%is achieved.A comparison with recent techniques indicate the superiority of the proposed method.

Deep Learning and Improved Particle Swarm Optimization Based Multimodal Brain Tumor Classicatio

Background:A brain tumor reects abnormal cell growth.Challenges:Surgery,radiation therapy,and chemotherapy are used to treat brain tumors,but these procedures are painful and costly.Magnetic resonance imaging(MRI)is a non-invasive modality for diagnosing tumors,but scans must be interpretated by an expert radiologist.Methodology:We used deep learning and improved particle swarm optimization(IPSO)to automate brain tumor classication.MRI scan contrast is enhanced by ant colony optimization(ACO);the scans are then used to further train a pretrained deep learning model,via transfer learning(TL),and to extract features from two dense layers.We fused the features of both layers into a single,more informative vector.An IPSO algorithm selected the optimal features,which were classied using a support vector machine.Results:We analyzed high-and low-grade glioma images from the BRATS 2018 dataset;the identication accuracies were 99.9%and 99.3%,respectively.Impact:The accuracy of our method is signicantly higher than existing techniques;thus,it will help radiologists to make diagnoses,by providing a“second opinion.”

Deep Learning Multimodal for Unstructured and Semi-Structured Textual Documents Classicatio

Due to the availability of a huge number of electronic text documents from a variety of sources representing unstructured and semi-structured information,the document classication task becomes an interesting area for controlling data behavior.This paper presents a document classication multimodal for categorizing textual semi-structured and unstructured documents.The multimodal implements several individual deep learning models such as Deep Neural Networks(DNN),Recurrent Convolutional Neural Networks(RCNN)and Bidirectional-LSTM(Bi-LSTM).The Stacked Ensemble based meta-model technique is used to combine the results of the individual classiers to produce better results,compared to those reached by any of the above mentioned models individually.A series of textual preprocessing steps are executed to normalize the input corpus followed by text vectorization techniques.These techniques include using Term Frequency Inverse Term Frequency(TFIDF)or Continuous Bag of Word(CBOW)to convert text data into the corresponding suitable numeric form acceptable to be manipulated by deep learning models.Moreover,this proposed model is validated using a dataset collected from several spaces with a huge number of documents in every class.In addition,the experimental results prove that the proposed model has achieved effective performance.Besides,upon investigating the PDF Documents classication,the proposed model has achieved accuracy up to 0.9045 and 0.959 for the TFIDF and CBOW features,respectively.Moreover,concerning the JSON Documents classication,the proposed model has achieved accuracy up to 0.914 and 0.956 for the TFIDF and CBOW features,respectively.Furthermore,as for the XML Documents classication,the proposed model has achieved accuracy values up to 0.92 and 0.959 for the TFIDF and CBOW features,respectively.

Internet Multimedia Traffic Classification from QoS Perspective Using Semi-Supervised Dictionary Learning Models

To address the issue of finegrained classification of Internet multimedia traffic from a Quality of Service(QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modified K-Singular Value Decomposition(K-SVD) method for multimedia identification. After analyzing several instances of typical Internet multimedia traffic captured in a campus network, this paper defines a new set of QoS classes according to the difference in downstream/upstream rates and proposes a modified K-SVD method that can automatically search for underlying structural patterns in the QoS characteristic space. We define bagQoS-words as the set of specific QoS local patterns, which can be expressed by core QoS characteristics. After the dictionary is constructed with an excess quantity of bag-QoSwords, Locality Constrained Feature Coding(LCFC) features of QoS classes are extracted. By associating a set of characteristics with a percentage of error, an objective function is formulated. In accordance with the modified K-SVD, Internet multimedia traffic can be classified into a corresponding QoS class with a linear Support Vector Machines(SVM) clas-sifier. Our experimental results demonstrate the feasibility of the proposed classification method.

Reinforcement Learning:A Technical Introduction–Part I

Reinforcement learning provides a cognitive science perspective to behavior and sequential decision making providedthat reinforcement learning algorithms introduce a computational concept of agency to the learning problem.Hence it addresses an abstract class of problems that can be characterized as follows: An algorithm confronted withinformation from an unknown environment is supposed to find step wise an optimal way to behave based only on somesparse, delayed or noisy feedback from some environment, that changes according to the algorithm’s behavior. Hencereinforcement learning offers an abstraction to the problem of goal-directed learning from interaction. The paper offersan opinionated introduction in the algorithmic advantages and drawbacks of several algorithmic approaches to providealgorithmic design options.

Continuous Variable Quantum MNIST Classifiers—Classical-Quantum Hybrid Quantum Neural Networks

In this paper, classical and continuous variable (CV) quantum neural network hybrid multi-classifiers are presented using the MNIST dataset. Currently available classifiers can classify only up to two classes. The proposed architecture allows networks to classify classes up to nm classes, where n represents cutoff dimension and m the number of qumodes on photonic quantum computers. The combination of cutoff dimension and probability measurement method in the CV model allows a quantum circuit to produce output vectors of size nm. They are then interpreted as one-hot encoded labels, padded with nm - 10 zeros. The total of seven different classifiers is built using 2, 3, …, 6, and 8-qumodes on photonic quantum computing simulators, based on the binary classifier architecture proposed in “Continuous variable quantum neural networks” [1]. They are composed of a classical feed-forward neural network, a quantum data encoding circuit, and a CV quantum neural network circuit. On a truncated MNIST dataset of 600 samples, a 4-qumode hybrid classifier achieves 100% training accuracy.

明清之际中国社会对欧洲文明的拒斥与接受

16-18世纪耶稣会传教士来中国传教,同时也带来了部分西方的科学知识。中国的儒家士人和士大夫群体中,有一部分人激烈反对、拒斥接受这些知识,也有一部分人乐于学习传教士带来的科学知识,接受并试图融合而后超越西方文化。拒斥的一方,有出于意识形态立场、政治安全角度考虑的,如《破邪集》的编辑者徐昌治,以及该书中收录的各色人等,如清初布衣杨光先;也有王夫之这样的大儒,从儒家经学中天文、地理知识的固有立场出发,批评利玛窦的地圆说和地球与太阳及诸行星的距离说。接受的一方表现出比较多元的立场,有徐光启的“超胜会通”说、方以智的“坐集千古之智”折衷说,也有以康熙皇帝为代表的“西学中源”说。“西学中源”说虽然最终不利于中国人虚心学习西方的科学知识,但也包含着一定的合理的文明交流互鉴思想,即在学习外来文明中的先进内容时,必须保持民族文化的主体性。

清代书院课艺:联结书院学与科举学的历史文献

在上千年的中国书院史上,多数书院都重视以考促学。从流传形态来看,清代书院课艺主要分为课艺原件、编入别集中的课艺和以书院名义选编的课艺总集三大类别;就主课艺内容而言,清代书院课艺大体包括制艺试帖、经史词章、时务西学等门类。书院课艺数量与书院数量、考课次数、书院额数和刊刻频率直接相关。尽管历史上汗牛充栋的书院课艺多已散佚,但其现存数量依旧十分巨大,无法准确估算。清代书院课艺具有一定的经学、文学、史学、书院学和科举学价值,深入挖掘其中的有用元素,可以从书院考课内容与科举考试内容、考课衡文标准与科举衡文标准、书院育人目标与科举取士目标、科举人物的思想观念与关系网络、书院生徒平时成绩与科考录取率等方面,考察书院学与科举学的共生和互动关系。

“红色经典”海外传播与国际社会对中国的认知

作为与当代社会发展结合紧密的文学艺术类型,“红色经典”的海外传播同国际社会对中国的认知有着内在的关联。“红色经典”在20世纪50—70年代集中传播到欧美和亚洲的一些国家,建构起关于“红色经典”的知识再生产;而国外知识界特别是美国和法国左翼知识分子对红色中国的认同与赞赏起到了消解彼时西方主流社会对中国负面宣传的作用,他们对毛泽东思想的热衷与“红色经典”所反映的中国革命和社会主义建设成就形成互文、互动及互鉴。新世纪以来,国外文学文化领域对我国“红色经典”改编的关注,同近年来国外经济政治领域渴望了解并热衷探讨当代中国成功发展经验的内在需求,形成某种隐在的呼应。