Research and Discussion on Flipped Classroom Combined with Case-Based Learning in Pharmacoeconomics Teaching

Objective:To explore the application effect of flipped classroom combined with case-based learning teaching methods in pharmacoeconomics teaching.Methods:The students majoring in clinical pharmacy in 2019 were selected as the study subjects,and the cost-effectiveness analysis of different dosage forms of Yinzhihuang in the treatment of neonatal jaundice was selected as the teaching case.The flipped classroom combined with case-based learning teaching method was used to carry out theoretical teaching to the students.After the course,questionnaires were distributed through the Sojump platform to evaluate the teaching effect.Results:The results of the questionnaire showed that 85.71%of the students believed that the flipped classroom combined with case-based learning teaching method was helpful in mobilizing the learning enthusiasm and initiative,and improving the comprehensive application ability of the knowledge of pharmacoeconomics.92.86%of the students think that it is conducive to the understanding and memorization of learning content,as well as the cultivation of teamwork,communication,etc.Conclusion:Flipped classroom combined with case-based learning teaching method can improve students’knowledge mastery,thinking skills,and practical application skills,as well as optimize and improve teachers’teaching levels.

Integration of Theory and Practice in Medical Morphology Curriculum in Postgraduate Training:A Flipped Classroom and Case-based Learning Exercise

Objective:The integration of training in theory and practice across the medical education spectrum is being encouraged to increase student understanding and skills in the sciences.This study aimed to determine the deciding factors that drive students'perceived advantages in class to improve precision education and the teaching model.Methods:A mixed strategy of an existing flipped classroom(FC)and a case-based learning(CBL)model was conducted in a medical morphology curriculum for 575 postgraduate students.The subjective learning evaluation of the individuals(learning time,engagement,study interest and concentration,and professional integration)was collected and analyzed after FC-CBL model learning.Results:The results from the general evaluation showed promising results of the medical morphology in the FC-CBL model.Students felt more engaged by instructors in person and benefited in terms of time-saving,flexible arrangements,and professional improvement.Our study contributed to the FC-CBL model in Research Design in postgraduate training in 4 categories:1)advancing a guideline of precision teaching according to individual characteristics;2)revealing whether a learning background is needed for a Research Design course to guide setting up a preliminary course;3)understanding the perceived advantages and their interfaces;and 4)barriers and/or improvement to implement the FC-CBL model in the Research Design class,such as a richer description of e-learning and hands-on practice.Conclusion:Undertaking a FC-CBL combined model could be a useful addition to pedagogy for medical morphology learning in postgraduate training.

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.

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.

Hydraulic Circuit Design and Dynamic Learning Using Case-based Reasoning

This paper describes the design and implementation of a hydraulic circuit design system using case-based reasoning (CBR) paradigm from AI community The domain of hydraulic circuit design and case-based reasoning are briefly reviewed Then a proposed methodology in compuer-aided circuit design and dynamic leaning with the use of CBR is described Finally an application example is selected to illustrate the ussfulness of applying CBR in hydraulic circuit design with leaming.

State of the art in applications of machine learning in steelmaking process modeling

With the development of automation and informatization in the steelmaking industry,the human brain gradually fails to cope with an increasing amount of data generated during the steelmaking process.Machine learning technology provides a new method other than production experience and metallurgical principles in dealing with large amounts of data.The application of machine learning in the steelmaking process has become a research hotspot in recent years.This paper provides an overview of the applications of machine learning in the steelmaking process modeling involving hot metal pretreatment,primary steelmaking,secondary refining,and some other aspects.The three most frequently used machine learning algorithms in steelmaking process modeling are the artificial neural network,support vector machine,and case-based reasoning,demonstrating proportions of 56%,14%,and 10%,respectively.Collected data in the steelmaking plants are frequently faulty.Thus,data processing,especially data cleaning,is crucially important to the performance of machine learning models.The detection of variable importance can be used to optimize the process parameters and guide production.Machine learning is used in hot metal pretreatment modeling mainly for endpoint S content prediction.The predictions of the endpoints of element compositions and the process parameters are widely investigated in primary steelmaking.Machine learning is used in secondary refining modeling mainly for ladle furnaces,Ruhrstahl–Heraeus,vacuum degassing,argon oxygen decarburization,and vacuum oxygen decarburization processes.Further development of machine learning in the steelmaking process modeling can be realized through additional efforts in the construction of the data platform,the industrial transformation of the research achievements to the practical steelmaking process,and the improvement of the universality of the machine learning models.

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.

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.

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 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.

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.

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.

Learning and competence development via clinical cases-what elements should be investigated to best train good medical doctors?

In European higher education,application of information technology,concentration on the learning-processes,consistent implementation,transfer learning,case-based learning,autonomous learning has been extensively studied in the last decade.Educational sciences based on neuroscientific findings use brain-based learning and teaching,including integrated thematic instructions and emotion-theory.Elements essential to this strategy,such as theory and methods for learning,competencies,attitudes,social reality,and a metadiscourse are described herein.Research on learning tends to focus on declarative knowledge,associative learning with conditional stimuli,and procedural knowledge with polythematic/crosslinking thinking.Research on competencies:In research on competencies(e.g.,for clinical reasoning,decision-making),intuitive and analytical components are studied.As repeated presentation and exercising of clinical cases is crucial for an efficient learning process,the implementation of interactive scenarios including affectively involving didactics is considered.For competence-development observational methods,questionnaires/item sets or factors have to be targeted and empirically validated.Attitudes and social reality:Clinical decision-making,identification processes and attitudes(“Hidden curriculum”),as well as secondary socialization processes(integration of social norms,values,preparation of role-acquisition,occupational role)are studied via process research,conceptual research,and observational methods.With respect to social reality research,conscious and unconscious bargaining processes have to be taken into account.Methodology:Neuroscience-memory,neuronal,molecular biology,and computer science(Neurocircuits)are integrated into observational process research(e.g.,affective-cognitive interface,identification processes)and conceptual research is added and studied on the meta-level,including discussion of research paradigms.This discussion provides ongoing feedback to projects in a hermeneutic circle.

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.

IoTFLiP： IoT-based flipped learning platform for medical education

Case-Based Learning （CBL） has become an effective pedagogy for student-centered learning in medical education, which is founded on persistent patient cases. Flippped learning and Internet of Things （IoTs） concepts have gained significant attention in recent years. Using these concepts in conjunction with CBL can improve learning ability by providing real evolutionary medical eases. It also enables students to build confidence in their decision making, and efficiently enhances teamwork in the learning environment. We propose an IoT-based Flip Learning Platform, called IoTFLiP, where an IoT infrastrneture is exploited to support flipped case-based learning in a cloud environment with state of the art security and privacy measures for personalized medical data. It also provides support for application delivery in private, public, and hybrid approaches. The proposed platform is an extension of our Interactive Case-Based Flipped Learning Tool （ICBFLT）, which has been developed based on current CBL practices. ICBFLT formulates summaries of CBL cases through synergy between students＇ and medical expert knowledge. The low cost and reduced size of sensor device, support of IoTs, and recent flipped learning advancements can enhance medical students＇ academic and practical experiences. In order to demonstrate a working scenario for the proposed IoTFLiP platform, real-time data from IoTs gadgets is collected to generate a real-world case for a medical student using ICBFLT.

Case and Questions Design in Case- based Learning Used in Medical-nursing English Teaching

Case-based learning(CBL) is gradually replacing the traditional lecturing-based learning in nursing English teaching.In the process of CBL, selecting and compiling a good case is key to the success of CBL. In the meantime, designing questions is an important factor for successful CBL. In this article, we discuss how to select and compile cases and how to design questions in CBL used in Medical-nursing English Teaching.

Research of Genetic Training Algorithm for Identifying Mechanical Failure Modes within the Framework of Case-Based Reasoning

The combination of case-based reasoning （CBR） and genetic algorithm （GA） is considered in the problem of failure mode identification in aeronautical component failure analysis. Several imple- mentation issues such as matching attributes selection, similarity measure calculation, weights learning and training evaluation policies are carefully studied. The testing applications illustrate that an accuracy of 74.67 % can be achieved with 75 balanced-distributed failure cases covering 3 failure modes, and that the resulting learning weight vector can be well applied to the other 2 failure modes, achieving 73.3 % of recognition accuracy. It is also proved that its popularizing capability is good to the recognition of even more mixed failure modes.

A Requirement Driven Learning Management Architecture Based on BPEL

With the prosperity of the Intemet, e-learning has been greatly improved. By supporting multiple learners and multiple roles in a learning activity, the IMS Leaming Design （LD） specification provides a collaborative scenario for participants. However, IMS LD provides insufficient support for interaction among learning activities and can not dynamically integrate learning resources to meet the continually changing service requirements. In this paper, a Business Process Execution Language （BPEL） enhanced requirement driven learning management architecture to address the issues of personalize adaptive learning was proposed. It models the learning activity by combining IMS LD with BPEL and matches optimal learning sequence based on Case-based reasoning （CBR） method. By providing expandable secure learning sequences flexibly, it satisfies the different actual demands for personalize learning

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.