An integrated machine learning model for accurate and robust prediction of superconducting critical temperature

Discovering new superconductors via traditional trial-and-error experimental approaches is apparently a time-consuming process,and the correlations between the critical temperature(Tc) and material features are still obscure.The rise of machine learning(ML) technology provides new opportunities to speed up inefficient exploration processes,and could potentially uncover new hints on the unclear correlations.In this work,we utilize open-source materials data,ML models,and data mining methods to explore the correlation between the chemical features and Tcvalues of superconducting materials.To further improve the prediction accuracy,a new model is created by integrating three basic algorithms,showing an enhanced accuracy with the coefficient of determination(R2) score of 95.9 % and root mean square error(RMSE) of 6.3 K.The average marginal contributions of material features towards Tcvalues are estimated to determine the importance of various features during prediction processes.The results suggest that the range thermal conductivity plays a critical role in Tcprediction among all element features.Furthermore,the integrated ML model is utilized to screen out potential twenty superconducting materials with Tcvalues beyond 50.0 K.This study provides insights towards Tcprediction to accelerate the exploration of potential high-Tcsuperconductors.

Federated Learning for 6G:A Survey From Perspective of Integrated Sensing,Communication and Computation

With the rapid advancements in edge computing and artificial intelligence,federated learning(FL)has gained momentum as a promising approach to collaborative data utilization across organizations and devices,while ensuring data privacy and information security.In order to further harness the energy efficiency of wireless networks,an integrated sensing,communication and computation(ISCC)framework has been proposed,which is anticipated to be a key enabler in the era of 6G networks.Although the advantages of pushing intelligence to edge devices are multi-fold,some challenges arise when incorporating FL into wireless networks under the umbrella of ISCC.This paper provides a comprehensive survey of FL,with special emphasis on the design and optimization of ISCC.We commence by introducing the background and fundamentals of FL and the ISCC framework.Subsequently,the aforementioned challenges are highlighted and the state of the art in potential solutions is reviewed.Finally,design guidelines are provided for the incorporation of FL and ISCC.Overall,this paper aims to contribute to the understanding of FL in the context of wireless networks,with a focus on the ISCC framework,and provide insights into addressing the challenges and optimizing the design for the integration of FL into future 6G networks.

Machine Learning Security Defense Algorithms Based on Metadata Correlation Features

With the popularization of the Internet and the development of technology,cyber threats are increasing day by day.Threats such as malware,hacking,and data breaches have had a serious impact on cybersecurity.The network security environment in the era of big data presents the characteristics of large amounts of data,high diversity,and high real-time requirements.Traditional security defense methods and tools have been unable to cope with the complex and changing network security threats.This paper proposes a machine-learning security defense algorithm based on metadata association features.Emphasize control over unauthorized users through privacy,integrity,and availability.The user model is established and the mapping between the user model and the metadata of the data source is generated.By analyzing the user model and its corresponding mapping relationship,the query of the user model can be decomposed into the query of various heterogeneous data sources,and the integration of heterogeneous data sources based on the metadata association characteristics can be realized.Define and classify customer information,automatically identify and perceive sensitive data,build a behavior audit and analysis platform,analyze user behavior trajectories,and complete the construction of a machine learning customer information security defense system.The experimental results show that when the data volume is 5×103 bit,the data storage integrity of the proposed method is 92%.The data accuracy is 98%,and the success rate of data intrusion is only 2.6%.It can be concluded that the data storage method in this paper is safe,the data accuracy is always at a high level,and the data disaster recovery performance is good.This method can effectively resist data intrusion and has high air traffic control security.It can not only detect all viruses in user data storage,but also realize integrated virus processing,and further optimize the security defense effect of user big data.

Automated Machine Learning Algorithm Using Recurrent Neural Network to Perform Long-Term Time Series Forecasting

Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning(AutoML).At present,forecasting,whether rooted in machine learning or statistical learning,typically relies on expert input and necessitates substantial manual involvement.This manual effort spans model development,feature engineering,hyper-parameter tuning,and the intricate construction of time series models.The complexity of these tasks renders complete automation unfeasible,as they inherently demand human intervention at multiple junctures.To surmount these challenges,this article proposes leveraging Long Short-Term Memory,which is the variant of Recurrent Neural Networks,harnessing memory cells and gating mechanisms to facilitate long-term time series prediction.However,forecasting accuracy by particular neural network and traditional models can degrade significantly,when addressing long-term time-series tasks.Therefore,our research demonstrates that this innovative approach outperforms the traditional Autoregressive Integrated Moving Average(ARIMA)method in forecasting long-term univariate time series.ARIMA is a high-quality and competitive model in time series prediction,and yet it requires significant preprocessing efforts.Using multiple accuracy metrics,we have evaluated both ARIMA and proposed method on the simulated time-series data and real data in both short and long term.Furthermore,our findings indicate its superiority over alternative network architectures,including Fully Connected Neural Networks,Convolutional Neural Networks,and Nonpooling Convolutional Neural Networks.Our AutoML approach enables non-professional to attain highly accurate and effective time series forecasting,and can be widely applied to various domains,particularly in business and finance.

Short-term displacement prediction for newly established monitoring slopes based on transfer learning

This study makes a significant progress in addressing the challenges of short-term slope displacement prediction in the Universal Landslide Monitoring Program,an unprecedented disaster mitigation program in China,where lots of newly established monitoring slopes lack sufficient historical deformation data,making it difficult to extract deformation patterns and provide effective predictions which plays a crucial role in the early warning and forecasting of landslide hazards.A slope displacement prediction method based on transfer learning is therefore proposed.Initially,the method transfers the deformation patterns learned from slopes with relatively rich deformation data by a pre-trained model based on a multi-slope integrated dataset to newly established monitoring slopes with limited or even no useful data,thus enabling rapid and efficient predictions for these slopes.Subsequently,as time goes on and monitoring data accumulates,fine-tuning of the pre-trained model for individual slopes can further improve prediction accuracy,enabling continuous optimization of prediction results.A case study indicates that,after being trained on a multi-slope integrated dataset,the TCN-Transformer model can efficiently serve as a pretrained model for displacement prediction at newly established monitoring slopes.The three-day average RMSE is significantly reduced by 34.6%compared to models trained only on individual slope data,and it also successfully predicts the majority of deformation peaks.The fine-tuned model based on accumulated data on the target newly established monitoring slope further reduced the three-day RMSE by 37.2%,demonstrating a considerable predictive accuracy.In conclusion,taking advantage of transfer learning,the proposed slope displacement prediction method effectively utilizes the available data,which enables the rapid deployment and continual refinement of displacement predictions on newly established monitoring slopes.

On the Integrated Learning of English and Law

This paper centers on the integrated learning of English and law in China.Firstly,it outlines the importance of English in the solution of the ever increasing legal disputes between China and the outside world,which inevitably involves an integrated learning of English and law.Secondly,it points out that the content of legal English reflects a combination of legal knowledge and English skills.Thirdly,it expounds on the difficulties that Chinese English majors are facing in the process of learning English and law simultaneously and furnishes some practical suggestions.

Utilizing Machine Learning with Unique Pentaplet Data Structure to Enhance Data Integrity

Data protection in databases is critical for any organization,as unauthorized access or manipulation can have severe negative consequences.Intrusion detection systems are essential for keeping databases secure.Advancements in technology will lead to significant changes in the medical field,improving healthcare services through real-time information sharing.However,reliability and consistency still need to be solved.Safeguards against cyber-attacks are necessary due to the risk of unauthorized access to sensitive information and potential data corruption.Dis-ruptions to data items can propagate throughout the database,making it crucial to reverse fraudulent transactions without delay,especially in the healthcare industry,where real-time data access is vital.This research presents a role-based access control architecture for an anomaly detection technique.Additionally,the Structured Query Language(SQL)queries are stored in a new data structure called Pentaplet.These pentaplets allow us to maintain the correlation between SQL statements within the same transaction by employing the transaction-log entry information,thereby increasing detection accuracy,particularly for individuals within the company exhibiting unusual behavior.To identify anomalous queries,this system employs a supervised machine learning technique called Support Vector Machine(SVM).According to experimental findings,the proposed model performed well in terms of detection accuracy,achieving 99.92%through SVM with One Hot Encoding and Principal Component Analysis(PCA).

Improvement the Accuracy of Six Applied Classification Algorithms through Integrated Supervised and Unsupervised Learning Approach

We have presented an integrated approach based on supervised and unsupervised learning tech- nique to improve the accuracy of six predictive models. They are developed to predict outcome of tuberculosis treatment course and their accuracy needs to be improved as they are not precise as much as necessary. The integrated supervised and unsupervised learning method (ISULM) has been proposed as a new way to improve model accuracy. The dataset of 6450 Iranian TB patients under DOTS therapy was applied to initially select the significant predictors and then develop six predictive models using decision tree, Bayesian network, logistic regression, multilayer perceptron, radial basis function, and support vector machine algorithms. Developed models have integrated with k-mean clustering analysis to calculate more accurate predicted outcome of tuberculosis treatment course. Obtained results, then, have been evaluated to compare prediction accuracy before and after ISULM application. Recall, Precision, F-measure, and ROC area are other criteria used to assess the models validity as well as change percentage to show how different are models before and after ISULM. ISULM led to improve the prediction accuracy for all applied classifiers ranging between 4% and 10%. The most and least improvement for prediction accuracy were shown by logistic regression and support vector machine respectively. Pre-learning by k- mean clustering to relocate the objects and put similar cases in the same group can improve the classification accuracy in the process of integrating supervised and unsupervised learning.

NPIPVis:A visualization system involving NBA visual analysis and integrated learning model prediction

Background Data-driven event analysis has gradually become the backbone of modern competitive sports analysis. Competitive sports data analysis tasks increasingly use computer vision and machine-learning models for intelligent data analysis. Existing sports visualization systems focus on the player–team data visualization, which is not intuitive enough for team season win–loss data and game time-series data visualization and neglects the prediction of all-star players. Methods This study used an interactive visualization system designed with parallel aggregated ordered hypergraph dynamic hypergraphs, Calliope visualization data story technology,and i Storyline narrative visualization technology to visualize the regular statistics and game time data of players and teams. NPIPVis includes dynamic hypergraphs of a team’s wins and losses and game plot narrative visualization components. In addition, an integrated learning-based all-star player prediction model, SRR-voting, which starts from the existing minority and majority samples, was proposed using the synthetic minority oversampling technique and Random Under Sampler methods to generate and eliminate samples of a certain size to balance the number of allstar and average players in the datasets. Next, a random forest algorithm was introduced to extract and construct the features of players and combined with the voting integrated model to predict the all-star players, using GridSearch CV, to optimize the hyperparameters of each model in integrated learning and then combined with five-fold cross-validation to improve the generalization ability of the model. Finally, the SHapley Additive ex Planations(SHAP) model was introduced to enhance the interpretability of the model. Results The experimental results of comparing the SRR-voting model with six common models show that the accuracy, F1-score, and recall metrics are significantly improved, which verifies the effectiveness and practicality of the SRR-voting model. Conclusions This study combines data visualization and machine learning to design a National Basketball Association data visualization system to help the general audience visualize game data and predict all-star players;this can also be extended to other sports events or related fields.

Blended Learning Mode in Undergraduate Integrated English Course

With the digital revolution,blended learning is widely adopted in Chinese higher education.The mode has been applied in our undergraduate integrated English course since April.2020.Based on the teaching practice and a questionnaire survey,this paper analyzes the characteristics of blended learning mode,discusses the new changes and the challenges of it,and aims to forward optimized proposals to explore an integrated blended learning mode in undergraduate integrated English course for English majors.

Towards Integrated Testing Approach:An Application of Cognitive Science and Deep Learning Principle

The use of multiple-choice(MC)question types has been one of the most contentious issues in language testing.Much has been said and written about the use of MC over the years.However,no attempt has ever been made to introduce any innovation in test item types.The researchers proposed a jumbled words test item(JW)based on cognitive science and deep learning principles,and addressed the feasibility of replacing the type of multiple-choice(MC)question with JW to meet the ongoing rapid development of language testing practice.Two research questions were proposed ad hoc,focusing on the co-relationship between JW and MC scores.RASCH-GZ was used to perform item analyses(Rasch,1960).The item difficulty parameters thus obtained were used to compare the two different test items.The sample data metric includes 40 Chinese participants.The findings revealed that correlation analysis revealed that the performance of the same group of subjects taking both JW and MC was not relevant(Pearson Corr=0).This is primarily due to the total elimination of guessing factors inherent in test-takers during JW test performance.Three factors were specified for the design of the JW test:compute program,test difficulty,and score acceptability.These all have three dimensions.Data collected through questionnaires were analyzed using EFA in SPSS V.24.0.KMOs(=0.867)were found to be approximately one and significance at 0.000(0.05),indicating that the construct of the􀁔uestionnaire thus designed has better validity for factor analysis.Three important conclusions were obtained,the implications of which could provide impetus for our testing counterparts to practice more precisely and correctly,potentially reshaping our overall language testing practice.Limitations and recommendations for future research were also discussed.

Intrusion Detection in 5G Cellular Network Using Machine Learning

Attacks on fully integrated servers,apps,and communication networks via the Internet of Things(IoT)are growing exponentially.Sensitive devices’effectiveness harms end users,increases cyber threats and identity theft,raises costs,and negatively impacts income as problems brought on by the Internet of Things network go unnoticed for extended periods.Attacks on Internet of Things interfaces must be closely monitored in real time for effective safety and security.Following the 1,2,3,and 4G cellular networks,the 5th generation wireless 5G network is indeed the great invasion of mankind and is known as the global advancement of cellular networks.Even to this day,experts are working on the evolution’s sixth generation(6G).It offers amazing capabilities for connecting everything,including gadgets and machines,with wavelengths ranging from 1 to 10 mm and frequencies ranging from 300 MHz to 3 GHz.It gives you the most recent information.Many countries have already established this technology within their border.Security is the most crucial aspect of using a 5G network.Because of the absence of study and network deployment,new technology first introduces new gaps for attackers and hackers.Internet Protocol(IP)attacks and intrusion will become more prevalent in this system.An efficient approach to detect intrusion in the 5G network using a Machine Learning algorithm will be provided in this research.This research will highlight the high accuracy rate by validating it for unidentified and suspicious circumstances in the 5G network,such as intruder hackers/attackers.After applying different machine learning algorithms,obtained the best result on Linear Regression Algorithm’s implementation on the dataset results in 92.12%on test data and 92.13%on train data with 92%precision.

Bloody Mahjong playing strategy based on the integration of deep learning and XGBoost

Bloody Mahjong is a kind of mahjong.It is very popular in China in recent years.It not only has the characteristics of mahjong's conventional state space,huge hidden information,complicated rules,and large randomness of hand cards but also has special rules such as Change three,Hu must lack at least one suit,and Continue playing after Hu.These rules increase the difficulty of research.These special rules are used as the input of the deep learning DenseNet model.DenseNet is used to extract the Mahjong situation features.The learned features are used as the input of the classification algorithm XGBoost,and then the XGBoost algorithm is used to derive the card strategy.Experiments show that the fusion model of deep learning and XGBoost proposed in this paper has higher accuracy than the single model using only one of them in the case of highdimensional sparse features.In the case of fewer training rounds,accuracy of the model can still reach 83%.In the games against real people,it plays like human.

Intelligent Deep Learning Enabled Wild Forest Fire Detection System

The latest advancements in computer vision and deep learning(DL)techniques pave the way to design novel tools for the detection and monitoring of forestfires.In this view,this paper presents an intelligent wild forestfire detec-tion and alarming system using deep learning(IWFFDA-DL)model.The pro-posed IWFFDA-DL technique aims to identify forestfires at earlier stages through integrated sensors.The proposed IWFFDA-DL system includes an Inte-grated sensor system(ISS)combining an array of sensors that acts as the major input source that helps to forecast thefire.Then,the attention based convolution neural network with bidirectional long short term memory(ACNN-BLSTM)model is applied to examine and identify the existence of danger.For hyperpara-meter tuning of the ACNN-BLSTM model,the bacterial foraging optimization(BFO)algorithm is employed and thereby enhances the detection performance.Finally,when thefire is detected,the Global System for Mobiles(GSM)modem transmits messages to the authorities to take required actions.An extensive set of simulations were performed and the results are investigated interms of several aspects.The obtained results highlight the betterment of the IWFFDA-DL techni-que interms of various measures.

Integrating a flipped classroom and problem-based learning into ophthalmology education

Background: Ophthalmology is an important medical science subject, but it is given with insufficient attention in undergraduate medical education. Flipped classroom(FC) and problem-based learning(PBL) are well-known education methods that can be integrated into ophthalmology education to improve students' competence level and promote active learning. Methods: We used a mixed teaching methodology that integrated a FC and PBL into a 1-week ophthalmology clerkship for 72 fourth-year medical students. The course includes two major sessions: FC session and PBL session, relying on clinical and real-patient cases. Written examinations were set up to assess students' academic performance and questionnaires were designed to evaluate their perceptions. Results: The post-course examination results were higher than the pre-course results, and many students gained ophthalmic knowledge and learning skills to varying levels. Comparison of pre-and post-course questionnaires indicated that interests in ophthalmology increased and more students expressed desires to be eye doctors. Most students were satisfied with the new method, while some suggested the process should be slower and the communication with their teacher needed to strengthen.Conclusions: FC and PBL are complementary methodologies. Utilizing the mixed teaching meth of FC and PBL was successful in enhancing academic performance, student satisfactions and promoting active learning.

Research on Stick-Slip Vibration Suppression Method of Drill String Based on Machine Learning Optimization

During the drilling process,stick-slip vibration of the drill string is mainly caused by the nonlinear friction gen-erated by the contact between the drill bit and the rock.To eliminate the fatigue wear of downhole drilling tools caused by stick-slip vibrations,the Fractional-Order Proportional-Integral-Derivative(FOPID)controller is used to suppress stick-slip vibrations in the drill string.Although the FOPID controller can effectively suppress the drill string stick-slip vibration,its structure isflexible and parameter setting is complicated,so it needs to use the cor-responding machine learning algorithm for parameter optimization.Based on the principle of torsional vibration,a simplified model of multi-degree-of-freedom drill string is established and its block diagram is designed.The continuous nonlinear friction generated by cutting rock is described by the LuGre friction model.The adaptive learning strategy of genetic algorithm(GA),particle swarm optimization(PSO)and particle swarm optimization improved(IPSO)by arithmetic optimization(AOA)is used to optimize and adjust the controller parameters,and the drill string stick-slip vibration is suppressed to the greatest extent.The results show that:When slight drill string stick-slip vibration occurs,the FOPID controller optimized by machine learning algorithm has a good effect on suppressing drill string stick-slip vibration.However,the FOPID controller cannot get the drill string system which has fallen into serious stick-slip vibration(stuck pipe)out of trouble,and the machine learning algorithm is required to mark a large amount of data on adjacent Wells to train the model.Set a reasonable range of drilling parameters(weight on bit/drive torque)in advance to avoid severe stick-slip vibration(stuck pipe)in the drill string system.

Parallel Integrated Model-Driven and Data-Driven Online Transient Stability Assessment Method for Power System

More and more uncertain factors in power systems and more and more complex operation modes of power systems put forward higher requirements for online transient stability assessment methods.The traditional modeldriven methods have clear physical mechanisms and reliable evaluation results but the calculation process is time-consuming,while the data-driven methods have the strong fitting ability and fast calculation speed but the evaluation results lack interpretation.Therefore,it is a future development trend of transient stability assessment methods to combine these two kinds of methods.In this paper,the rate of change of the kinetic energy method is used to calculate the transient stability in the model-driven stage,and the support vector machine and extreme learning machine with different internal principles are respectively used to predict the transient stability in the data-driven stage.In order to quantify the credibility level of the data-driven methods,the credibility index of the output results is proposed.Then the switching function controlling whether the rate of change of the kinetic energy method is activated or not is established based on this index.Thus,a newparallel integratedmodel-driven and datadriven online transient stability assessment method is proposed.The accuracy,efficiency,and adaptability of the proposed method are verified by numerical examples.

The Practical Path to Cultivating Innovative Talents:Implementing Cross-Disciplinary Learning Communities in Environmental Design Education

In response to the national strategy of“vigorously cultivating interdisciplinary talents and actively promoting interdisciplinary integration,”this article focuses on the nationally recognized Environmental Design program at Hezhou University’s College of Design,leveraging local industry advantages to engage in interdisciplinary integration through educational practices.Using the“Construction of the Panoramic Virtual Nature Museum of the Guizhou Crocodile Lizard at Mount Dagui”as a case study,we aim to establish a professional and interdisciplinary learning community,incorporate student-centered interactive teaching methods,boost student motivation,enhance teaching quality,nurture forward-thinking versatile innovative talents,and provide a guideline for interdisciplinary educational reform.

Under "Integration of Doing, Learning and Teaching", Research on the Project-Based Teaching Innovation of "Landscape Planning and Design"

Based on the research on the project course theory of "integration of theory and practice" in higher vocational education and the analysis of practical teaching in colleges and universities at home and abroad, combined with literature research, case analysis, system theory and other research methods, the project-based teaching goal, model, content and means of "integration of doing, learning and teaching" in higher vocational education is explored, and the project-based teaching model of "Landscape Planning and Design" is discussed combined with the application of information-based teaching methods. So as to provide references for carrying out the project-based teaching in similar courses in higher vocational colleges and really achieve docking the actual post requirements with the course to provide the basis for achieving the purpose of cultivating skilled talents in higher vocational education.

Analysis of Traffic Accidents Based on the Integration Model

To enhance the safety of road traffic operations,this paper proposed a model based on stacking integrated learning utilizing American road traffic accident statistics.Initially,the process involved data cleaning,transformation,and normalization.Subsequently,various classification models were constructed,including logistic regression,k-nearest neighbors,gradient boosting,decision trees,AdaBoost,and extra trees models.Evaluation metrics such as accuracy,precision,recall,F1 score,and Hamming loss were employed.Upon analysis,the passive-aggressive classifier model exhibited superior comprehensive indices compared to other models.Based on the model’s output results,an in-depth examination of the factors influencing traffic accidents was conducted.Additionally,measures and suggestions aimed at reducing the incidence of severe traffic accidents were presented.These findings served as a valuable reference for mitigating the occurrence of traffic accidents.