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.

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.

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.

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.

Online payment fraud:from anomaly detection to risk management

Online banking fraud occurs whenever a criminal can seize accounts and transfer funds from an individual’s online bank account.Successfully preventing this requires the detection of as many fraudsters as possible,without producing too many false alarms.This is a challenge for machine learning owing to the extremely imbalanced data and complexity of fraud.In addition,classical machine learning methods must be extended,minimizing expected financial losses.Finally,fraud can only be combated systematically and economically if the risks and costs in payment channels are known.We define three models that overcome these challenges:machine learning-based fraud detection,economic optimization of machine learning results,and a risk model to predict the risk of fraud while considering countermeasures.The models were tested utilizing real data.Our machine learning model alone reduces the expected and unexpected losses in the three aggregated payment channels by 15%compared to a benchmark consisting of static if-then rules.Optimizing the machine-learning model further reduces the expected losses by 52%.These results hold with a low false positive rate of 0.4%.Thus,the risk framework of the three models is viable from a business and risk perspective.

Computational intelligence interception guidance law using online off-policy integral reinforcement learning

Missile interception problem can be regarded as a two-person zero-sum differential games problem,which depends on the solution of Hamilton-Jacobi-Isaacs(HJI)equa-tion.It has been proved impossible to obtain a closed-form solu-tion due to the nonlinearity of HJI equation,and many iterative algorithms are proposed to solve the HJI equation.Simultane-ous policy updating algorithm(SPUA)is an effective algorithm for solving HJI equation,but it is an on-policy integral reinforce-ment learning(IRL).For online implementation of SPUA,the dis-turbance signals need to be adjustable,which is unrealistic.In this paper,an off-policy IRL algorithm based on SPUA is pro-posed without making use of any knowledge of the systems dynamics.Then,a neural-network based online adaptive critic implementation scheme of the off-policy IRL algorithm is pre-sented.Based on the online off-policy IRL method,a computa-tional intelligence interception guidance(CIIG)law is developed for intercepting high-maneuvering target.As a model-free method,intercepting targets can be achieved through measur-ing system data online.The effectiveness of the CIIG is verified through two missile and target engagement scenarios.

Population amount risk assessment of extreme precipitation-induced landslides based on integrated machine learning model and scenario simulation

In this study,the future landslide population amount risk(LPAR)is assessed based on integrated machine learning models(MLMs)and scenario simulation techniques in Shuicheng County,China.Firstly,multiple MLMs were selected and hyperparameters were optimized,and the generated 11 models were crossintegrated to select the best model to calculate landslide susceptibility;by calculating precipitation for different extreme precipitation recurrence periods and combining the susceptibility results to assess the landslide hazard.Using the town as the basic unit,the exposure and vulnerability of the future landslide population under different Shared Socioeconomic Pathways(SSPs)scenarios in each town were assessed,and then combined with the hazard to estimate the LPAR in 2050.The results showed that the integrated model with the optimized random forest model as the combination strategy had the best comprehensive performance in susceptibility assessment.The distribution of hazard classes is similar to susceptibility,and with an increase in precipitation,the low-hazard area and high-hazard decrease and shift to medium-hazard and very high-hazard classes.The high-risk areas for future landslide populations in Shuicheng County are mainly concentrated in the three southwestern towns with high vulnerability,whereas the northern towns of Baohua and Qinglin are at the lowest risk class.The LPAR increased with the intensity of extreme precipitation.The LPAR differs significantly among the SSPs scenarios,with the lowest in the“fossil-fueled development(SSP5)”scenario and the highest in the“regional rivalry(SSP3)”scenario.In summary,the landslide susceptibility model based on integrated machine learning proposed in this study has a high predictive capability.The results of future LPAR assessment can provide theoretical guidance for relevant departments to cope with future socioeconomic development challenges and make corresponding disaster prevention and mitigation plans to prevent landslide risks from a developmental perspective.

Insulin-like signaling pathway regulates integration and learning in C. elegans

Animals can sense many environment stimuli simultaneously and integrate these signals within the nervous system. However, the neural system and molecular

Bionic Muscle Control with Adaptive Stiffness for Bionic Parallel Mechanism

As the torso is critical to the coordinated movement and flexibility of vertebrates,a 6-(Degree of Freedom)DOF bionic parallel torso with noteworthy motion space was designed in our previous work.To improve the compliance of the parallel mechanism,a pair of virtual muscle models is constructed on both sides of the rotating joints of each link of the mechanism,and a bionic muscle control algorithm is introduced.By analyzing the control parameters of the muscle model,dynamic characteristics similar to those of biological muscle are obtained.An adaptive stiffness control is proposed to adaptively adjust the stiffness coefficient with the change in the external load of the parallel mechanism.The attitude closed-loop control can effectively keep the attitude angle unchanged when the position of the moving platform changes.The simulations and experiments are undertaken to validate compliant movements and the flexibility and adaptability of the parallel mechanism.

Automatic Requirement Dependency Extraction Based on Integrated Active Learning Strategies

Since requirement dependency extraction is a cognitively challenging and error-prone task,this paper proposes an automatic requirement dependency extraction method based on integrated active learning strategies.In this paper,the coefficient of variation method was used to determine the corresponding weight of the impact factors from three different angles:uncertainty probability,text similarity difference degree and active learning variant prediction divergence degree.By combining the three factors with the proposed calculation formula to measure the information value of dependency pairs,the top K dependency pairs with the highest comprehensive evaluation value are selected as the optimal samples.As the optimal samples are continuously added into the initial training set,the performance of the active learning model using different dependency features for requirement dependency extraction is rapidly improved.Therefore,compared with other active learning strategies,a higher evaluation measure of requirement dependency extraction can be achieved by using the same number of samples.Finally,the proposed method using the PV-DM dependency feature improves the weight-F1 by 2.71%,the weight-recall by 2.45%,and the weight-precision by 2.64%in comparison with other strategies,saving approximately 46%of the labelled data compared with the machine learning approach.

Adaptive Human-Robot Collaboration Control Based on Optimal Admittance Parameters

In order to help the operator perform the human-robot collaboration task and optimize the task performance,an adaptive control method based on optimal admittance parameters is proposed.The overall control structure with the inner loop and outer loop is first established.The tasks of the inner loop and outer loop are robot control and task optimization,respectively.An inner-loop robot controller integrated with barrier Lyapunov function and radial basis function neural networks is then proposed,which makes the robot with unknown dynamics securely behave like a prescribed robot admittance model sensed by the operator.Subsequently,the optimal parameters of the robot admittance model are obtained in the outer loop to minimize the task tracking error and interaction force.The optimization problem of the robot admittance model is transformed into a linear quadratic regulator problem by constructing the human-robot collaboration system model.The model includes the unknown dynamics of the operator and the task performance details.To relax the requirement of the system model,the integral reinforcement learning is employed to solve the linear quadratic regulator problem.Besides,an auxiliary force is designed to help the operator complete the specific task better.Compared with the traditional control scheme,the security performance and interaction performance of the human-robot collaboration system are improved.The effectiveness of the proposed method is verified through two numerical simulations.In addition,a practical human-robot collaboration experiment is carried out to demonstrate the performance of the proposed method.

Transdisciplinary Convergence:Intelligent Infrastructure for Sustainable Development

The fast-developing intelligent infrastructure landscape catalyzes transformative new relationships of human,technology,and environment and requires new socio-technical configurations of information practice and knowledge work.With a focus on data as the source of intelligence,this paper aims to explore the shifting scenarios and indicative features of data science solutions for intelligent system applications and identify the evolving knowledge spaces and integrative learning practices in the“smart”landscape.It projects and discusses the democratization of data science platforms,the distribution of data intelligence on the edge,and the transition from vertical to horizontal data solutions in solving intelligent system problems.Through mapping the changing data research landscape,this work further reveals essential new roles of knowledge architects and social engineers in enabling dynamic data linking,interaction,and exploration for transdisciplinary data convergence.