Two-Way Neural Network Performance PredictionModel Based onKnowledge Evolution and Individual Similarity

Predicting students’academic achievements is an essential issue in education,which can benefit many stakeholders,for instance,students,teachers,managers,etc.Compared with online courses such asMOOCs,students’academicrelateddata in the face-to-face physical teaching environment is usually sparsity,and the sample size is relativelysmall.It makes building models to predict students’performance accurately in such an environment even morechallenging.This paper proposes a Two-WayNeuralNetwork(TWNN)model based on the bidirectional recurrentneural network and graph neural network to predict students’next semester’s course performance using only theirprevious course achievements.Extensive experiments on a real dataset show that our model performs better thanthe baselines in many indicators.

Hybrid data-driven framework for shale gas production performance analysis via game theory, machine learning, and optimization approaches

A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential,designing a field development plan,and making investment decisions.However,quantitative analysis can be challenging because production performance is dominated by the complex interaction among a series of geological and engineering factors.In fact,each factor can be viewed as a player who makes cooperative contributions to the production payoff within the constraints of physical laws and models.Inspired by the idea,we propose a hybrid data-driven analysis framework in this study,where the contributions of dominant factors are quantitatively evaluated,the productions are precisely forecasted,and the development optimization suggestions are comprehensively generated.More specifically,game theory and machine learning models are coupled to determine the dominating geological and engineering factors.The Shapley value with definite physical meaning is employed to quantitatively measure the effects of individual factors.A multi-model-fused stacked model is trained for production forecast,which provides the basis for derivative-free optimization algorithms to optimize the development plan.The complete workflow is validated with actual production data collected from the Fuling shale gas field,Sichuan Basin,China.The validation results show that the proposed procedure can draw rigorous conclusions with quantified evidence and thereby provide specific and reliable suggestions for development plan optimization.Comparing with traditional and experience-based approaches,the hybrid data-driven procedure is advanced in terms of both efficiency and accuracy.

Optimal Machine Learning Enabled Performance Monitoring for Learning Management Systems

Learning Management System(LMS)is an application software that is used in automation,delivery,administration,tracking,and reporting of courses and programs in educational sector.The LMS which exploits machine learning(ML)has the ability of accessing user data and exploit it for improving the learning experience.The recently developed artificial intelligence(AI)and ML models helps to accomplish effective performance monitoring for LMS.Among the different processes involved in ML based LMS,feature selection and classification processesfind beneficial.In this motivation,this study introduces Glowworm-based Feature Selection with Machine Learning Enabled Performance Monitoring(GSO-MFWELM)technique for LMS.The key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in LMS.The pro-posed GSO-MFWELM technique involves GSO-based feature selection techni-que to select the optimal features.Besides,Weighted Extreme Learning Machine(WELM)model is applied for classification process whereas the parameters involved in WELM model are optimallyfine-tuned with the help of May-fly Optimization(MFO)algorithm.The design of GSO and MFO techniques result in reduced computation complexity and improved classification performance.The presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several aspects.The simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.

Prediction of fuel cell performance degradation using a combined approach of machine learning and impedance spectroscopy

Accurate prediction of performance degradation in complex systems such as solid oxide fuel cells is crucial for expediting technological advancements.However,significant challenges still persist due to limited comprehension of degradation mechanisms and difficulties in acquiring in-situ features.In this study,we propose an effective approach that integrates long short-term memory(LSTM) neural network and dynamic electrochemical impedance spectroscopy(DEIS).This integrated approach enables precise prediction of future evolutions in both current-voltage and EIS features using historical testing data,without prior knowledge of degradation mechanisms.For short-term predictions spanning hundreds of hours,our approach achieves a prediction accuracy exceeding 0.99,showcasing promising prospects for diagnostic applications.Additionally,for long-term predictions spanning thousands of hours,we quantitatively determine the significance of each degradation mechanism,which is crucial for enhancing cell durability.Moreover,our proposed approach demonstrates satisfactory predictive ability in both time and frequency domains,offering the potential to reduce EIS testing time by more than half.

Machine Learning Assisted Design of Natural Rubber Composites with Multi⁃Performance Optimization

Multi⁃performance optimization of tread rubber composites is a key issue of great concern in automotive industry.Traditional experimental design approach via“trial and error”or intuition is ineffective due to mutual inhibition among multiple properties.A“Uniform design⁃Machine learning”strategy for performance prediction and multi⁃performance optimization of tread rubber composites was proposed.The wear resistance,rolling resistance,tensile strength and wet skid resistance were simultaneously optimized.A series of feasible optimization designs were screened via statistical analysis and machine learning analysis,and were experimentally prepared.The verification experiments demonstrate that the optimization design via machine learning analysis meets the optimization requirements of all target performance,especially for Akron abrasion and 60℃tanδ(about 21%and 9%lower than the design targets,respectively)due to the inhibition of mechanical degradation and good dispersion of fillers.

A Feature Learning-Based Model for Analyzing Students’ Performance in Supportive Learning

Supportive learning plays a substantial role in providing a quality edu-cation system.The evaluation of students’performance impacts their deeper insight into the subject knowledge.Specifically,it is essential to maintain the baseline foundation for building a broader understanding of their careers.This research concentrates on establishing the students’knowledge relationship even in reduced samples.Here,Synthetic Minority Oversampling TEchnique(SMOTE)technique is used for pre-processing the missing value in the provided input dataset to enhance the prediction accuracy.When the initial processing is not done substantially,it leads to misleading prediction accuracy.This research concentrates on modelling an efficient classifier model to predict students’perfor-mance.Generally,the online available student dataset comprises a lesser amount of sample,and k-fold cross-validation is performed to balance the dataset.Then,the relationship among the students’performance(features)is measured using the auto-encoder.The stacked Long Short Term Memory(s-LSTM)is used to learn the previous feedback connection.The stacked model handles the provided data and the data sequence for understanding the long-term dependencies.The simula-tion is done in the MATLAB 2020a environment,and the proposed model shows a better trade-off than the existing approaches.Some evaluation metrics like pre-diction accuracy,sensitivity,specificity,AUROC,F1-score and recall are evalu-ated using the proposed model.The performance of the s?LSTM model is compared with existing approaches.The proposed model gives 89% accuracy,83% precision,86%recall,and 87%F-score.The proposed model outperforms the existing systems in terms of the earlier metrics.

Impact of Remote Learning on Student Performance and Grade: A Virtual World of Education in the COVID-19 Era

The COVID-19 pandemic has had a profound influence on education around the world, with schools and institutions shifting to remote learning to safeguard the safety of students and faculty. Concerns have been expressed about the impact of virtual learning on student performance and grades. The purpose of this study is to investigate the impact of remote learning on student performance and grades, as well as to investigate the obstacles and benefits of this new educational paradigm. The study will examine current literature on the subject, analyze data from surveys and interviews with students and educators, and investigate potential solutions to improve student performance and participation in virtual classrooms. The study’s findings will provide insights into the effectiveness of remote learning and inform ideas to improve student learning and achievement in an educational virtual world. The purpose of this article is to investigate the influence of remote learning on both students and educational institutions. The project will examine existing literature on the subject and collect data from students, instructors, and administrators through questionnaires and interviews. The paper will look at the challenges and opportunities that remote learning presents, such as the effect on student involvement, motivation, and academic achievement, as well as changes in teaching styles and technology. The outcomes of this study will provide insights into the effectiveness of remote learning and will affect future decisions about the usage of virtual learning environments in education. The research will also investigate potential solutions to improve the quality of remote education and handle any issues that occur.

Deep Learning for Multivariate Prediction of Building Energy Performance of Residential Buildings

In the quest to minimize energy waste,the energy performance of buildings(EPB)has been a focus because building appliances,such as heating,ventilation,and air conditioning,consume the highest energy.Therefore,effective design and planning for estimating heating load(HL)and cooling load(CL)for energy saving have become paramount.In this vein,efforts have been made to predict the HL and CL using a univariate approach.However,this approach necessitates two models for learning HL and CL,requiring more computational time.Moreover,the one-dimensional(1D)convolutional neural network(CNN)has gained popularity due to its nominal computa-tional complexity,high performance,and low-cost hardware requirement.In this paper,we formulate the prediction as a multivariate regression problem in which the HL and CL are simultaneously predicted using the 1D CNN.Considering the building shape characteristics,one kernel size is adopted to create the receptive fields of the 1D CNN to extract the feature maps,a dense layer to interpret the maps,and an output layer with two neurons to predict the two real-valued responses,HL and CL.As the 1D data are not affected by excessive parameters,the pooling layer is not applied in this implementation.Besides,the use of pooling has been questioned by recent studies.The performance of the proposed model displays a comparative advantage over existing models in terms of the mean squared error(MSE).Thus,the proposed model is effective for EPB prediction because it reduces computational time and significantly lowers the MSE.

Application of deep learning for informatics aided design of electrode materials in metal-ion batteries

To develop emerging electrode materials and improve the performances of batteries,the machine learning techniques can provide insights to discover,design and develop battery new materials in high-throughput way.In this paper,two deep learning models are developed and trained with two feature groups extracted from the Materials Project datasets to predict the battery electrochemical performances including average voltage,specific capacity and specific energy.The deep learning models are trained with the multilayer perceptron as the core.The Bayesian optimization and Monte Carlo methods are applied to improve the prediction accuracy of models.Based on 10 types of ion batteries,the correlation coefficients are maintained above 0.9 compared to DFT calculation results and the mean absolute error of the prediction results for voltages of two models can reach 0.41 V and 0.20 V,respectively.The electrochemical performance prediction times for the two trained models on thousands of batteries are only 72.9 ms and 75.7 ms.Besides,the two deep learning models are applied to approach the screening of emerging electrode materials for sodium-ion and potassium-ion batteries.This work can contribute to a high-throughput computational method to accelerate the rational and fast materials discovery and design.

A Hybrid Machine Learning Approach for Improvised QoE in Video Services over 5G Wireless Networks

Video streaming applications have grown considerably in recent years.As a result,this becomes one of the most significant contributors to global internet traffic.According to recent studies,the telecommunications industry loses millions of dollars due to poor video Quality of Experience(QoE)for users.Among the standard proposals for standardizing the quality of video streaming over internet service providers(ISPs)is the Mean Opinion Score(MOS).However,the accurate finding of QoE by MOS is subjective and laborious,and it varies depending on the user.A fully automated data analytics framework is required to reduce the inter-operator variability characteristic in QoE assessment.This work addresses this concern by suggesting a novel hybrid XGBStackQoE analytical model using a two-level layering technique.Level one combines multiple Machine Learning(ML)models via a layer one Hybrid XGBStackQoE-model.Individual ML models at level one are trained using the entire training data set.The level two Hybrid XGBStackQoE-Model is fitted using the outputs(meta-features)of the layer one ML models.The proposed model outperformed the conventional models,with an accuracy improvement of 4 to 5 percent,which is still higher than the current traditional models.The proposed framework could significantly improve video QoE accuracy.

An Improved Enterprise Resource Planning System Using Machine Learning Techniques

Traditional Enterprise Resource Planning (ERP) systems with relational databases take weeks to deliver predictable insights instantly. The most accurate information is provided to companies to make the best decisions through advanced analytics that examine the past and the future and capture information about the present. Integrating machine learning (ML) into financial ERP systems offers several benefits, including increased accuracy, efficiency, and cost savings. Also, ERP systems are crucial in overseeing different aspects of Human Capital Management (HCM) in organizations. The performance of the staff draws the interest of the management. In particular, to guarantee that the proper employees are assigned to the convenient task at the suitable moment, train and qualify them, and build evaluation systems to follow up their performance and an attempt to maintain the potential talents of workers. Also, predicting employee salaries correctly is necessary for the efficient distribution of resources, retaining talent, and ensuring the success of the organization as a whole. Conventional ERP system salary forecasting methods typically use static reports that only show the system’s current state, without analyzing employee data or providing recommendations. We designed and enforced a prototype to define to apply ML algorithms on Oracle EBS data to enhance employee evaluation using real-time data directly from the ERP system. Based on measurements of accuracy, the Random Forest algorithm enhanced the performance of this system. This model offers an accuracy of 90% on the balanced dataset.

Parallel Inference for Real-Time Machine Learning Applications

Hyperparameter tuning is a key step in developing high-performing machine learning models, but searching large hyperparameter spaces requires extensive computation using standard sequential methods. This work analyzes the performance gains from parallel versus sequential hyperparameter optimization. Using scikit-learn’s Randomized SearchCV, this project tuned a Random Forest classifier for fake news detection via randomized grid search. Setting n_jobs to -1 enabled full parallelization across CPU cores. Results show the parallel implementation achieved over 5× faster CPU times and 3× faster total run times compared to sequential tuning. However, test accuracy slightly dropped from 99.26% sequentially to 99.15% with parallelism, indicating a trade-off between evaluation efficiency and model performance. Still, the significant computational gains allow more extensive hyperparameter exploration within reasonable timeframes, outweighing the small accuracy decrease. Further analysis could better quantify this trade-off across different models, tuning techniques, tasks, and hardware.

Individual differences in English Learning Strategies

Some learners' personal factors, such as gender, major, motivation and language proficiency, influence the use of language learn- ing strategies. This paper reports the results of a survey study which investigates the individual differences and the employment of English learning strategies, examines the association between enjoyment and strategy use, motivation and strategy use, gender and strategy use, major and strategy use, English proficiency and strategy use.

Innovative deep learning artificial intelligence applications for predicting relationships between individual tree height and diameter at breast height

Background:Deep Learning Algorithms(DLA)have become prominent as an application of Artificial Intelligence(Al)Techniques since 2010.This paper introduces the DLA to predict the relationships between individual tree height(ITH)and the diameter at breast height(DBH).Methods:A set of 2024 pairs of individual height and diameter at breast height measurements,originating from 150 sample plots located in stands of even aged and pure Anatolian Crimean Pine(Pinus nigra J.F.Arnold ssp.pallasiana(Lamb.)Holmboe)in Konya Forest Enterprise.The present study primarily investigated the capability and usability of DLA models for predicting the relationships between the ITH and the DBH sampled from some stands with different growth structures.The 80 different DLA models,which involve different the alternatives for the numbers of hidden layers and neuron,have been trained and compared to determine optimum and best predictive DLAs network structure.Results:It was determined that the DLA model with 9 layers and 100 neurons has been the best predictive network model compared as those by other different DLA,Artificial Neural Network,Nonlinear Regression and Nonlinear Mixed Effect models.The alternative of 100#neurons and 9#hidden layers in deep learning algorithms resulted in best predictive ITH values with root mean squared error(RMSE,0.5575),percent of the root mean squared error(RMSE%,4.9504%),Akaike information criterion(AIC,-998.9540),Bayesian information criterion(BIC,884.6591),fit index(Fl,0.9436),average absolute error(AAE,0.4077),maximum absolute error(max.AE,2.5106),Bias(0.0057)and percent Bias(Bias%,0.0502%).In addition,these predictive results with DLAs were further validated by the Equivalence tests that showed the DLA models successfully predicted the tree height in the independent dataset.Conclusion:This study has emphasized the capability of the DLA models,novel artificial intelligence technique,for predicting the relationships between individual tree height and the diameter at breast height that can be required information for the management of forests.

INDIVIDUAL DIFFERENCES IN FOREIGN LANGUAGE TEACHING AND LEARNING

Individual differences in foreign language learning have long been the concern of linguists and language teachers. Researches on this subject have been carried out in schools, universities and other educational institutions and great achievements have been made. As it is, there are many individual differences which affect the learning of foreign languages, such as intelligence, aptitude, motivation, personality, attitude,

The Effects of Individual Differences on English Vocabulary Learning

The English vocabulary study is a very complicated process,affected by lots of factors.This article will analyze several important individual differences among learners,such as,learning style,gender and learning strategy,exploring the most effective ways to expand English vocabulary.

Motivation——the Major Individual Learner Difference in Second Language Learning

As one of the major individual learner differences in second language learning,motivation is frequently used to account for differential success in learning a foreign language.When comes to the definition of motivation,Ellis(2006:537) primarily defines motivation is "the effort learners were prepared to make to learn a language and their persistence in learning." According to Dornyei(2001a:8) motivation is The choice of a particular action,the persistence with it,the effort expended on it.In other words,motivation is responsible for why people decide to do something,how long they are willing to sustain the activity,how hard they are going to pursue it.

Individual differences in English Learning Strategies

Some learners' personal factors,such as gender,major,motivation and language proficiency,influence the use of language learning strategies. This paper reports the results of a survey study which investigates the individual differences and the employment of English learning strategies,examines the association between enjoyment and strategy use,motivation and strategy use,gender and strategy use,major and strategy use,English proficiency and strategy use.

Civil Engineering Students'English Metacognitive Strategies,Autono-mous Learning Competence and ListeningPerformance

In order to improve Englishlistening more effectively, the application status of metacognitive strategies and autonomouslearning competence is exploredbased on a questionnaire investigation among 120 civil engineering major in BJUT. The methods of correlation analysis and an independent sample t-test are employed.The results indicated that 1) their metacognitivestrategies, and the planning, monitoring strategy have positive significant correlations with the students'listening performance and listening auton-omous learning competence;2) There are significant differences in the use of metacognitivestrategies,the level of autonomous learn-ing competence between different levels of listening performance;3) The more accuracy of self-judgment is, the higher level Eng-lish listening performance is.

Pre-research on Enhanced Heat Transfer Method for Special Vehicles at High Altitude Based on Machine Learning

The performance of an integrated thermal management system signi?cantly in?uences the stability of special-purpose vehicles;thus,enhancing the heat transfer of the radiator is of great signi?cance.Common research methods for radiators include?uid mechanics numerical simulations and experimental measurements,both of which are time-consuming and expensive.Applying the surrogate model to the analysis of the?ow and heat transfer in louvered?ns can effectively reduce the computational cost and obtain more data.A simpli?ed louvered-?n heat transfer unit was established,and computational?uid dynamics(CFD)simulations were conducted to obtain the?ow and heat transfer characteristics of the geometric structure.A three-factor and six-level orthogonal design was established with three structural parameters:angleθ,length a,and pitch L_p of the louvered?ns.The results of the orthogonal design were subjected to a range analysis,and the effects of the three parametersθ,a,and L_p on the j,f,and JF factors were obtained.Accordingly,a proxy model of the heat transfer performance for louvered?ns was established based onthe arti?cial neural network algorithm,and the model was trained with the data obtained by the orthogonal design.Finally,the?n structure with the largest JF factor was realized.Compared with the original model,the optimizedmodel improved the heat transfer factor j by 2.87%,decreased the friction factor f by 30.4%,and increased the comprehensive factor JF by 15.7%.