An evaluation model for children's foot & ankle deformity severity using sparse multi-objective feature selection algorithm.

Foot & ankle deformity is a chronic disease with high incidence and is best treated in childhood. However, the current diagnostic procedures rely on doctor's consultation and empirical judgment, and lack objective and quantitative evaluation methods, resulting in low screening rates. To solve this problem, this paper aims to construct an evaluation model for children's foot & ankle deformity through data mining and machine learning technologies. Firstly, it proposes the grading rules for children's foot & ankle deformity severity based on analyzing the existing quantitative indexes and expert experience. Then the 3D foot scanner is used to collect the sample data including 30 foot structure indexes. Finally, an advanced sparse multi-objective evolutionary algorithm (sparse MO-FS) is present for feature selection. The effectiveness of the proposed sparse MO-FS and its search efficiency are proved by comparing 8 feature selection methods and 7 search strategies. Using sparse MO-FS, foot length, arch index, ankle index, and hallux valgus index are selected, which not only simplifies the evaluation model but also improves the average classification accuracy of random forest to more than 98%.

E-Learning Performance Prediction: Mining the Feature Space of Effective Learning Behavior.

Learning analysis provides a new opportunity for the development of online education, and has received extensive attention from scholars at home and abroad. How to use data and models to predict learners' academic success or failure and give teaching feedback in a timely manner is a core problem in the field of learning analytics. At present, many scholars use key learning behaviors to improve the prediction effect by exploring the implicit relationship between learning behavior data and grades. At the same time, it is very important to explore the association between categories and prediction effects in learning behavior classification. This paper proposes a self-adaptive feature fusion strategy based on learning behavior classification, aiming to mine the effective E-learning behavior feature space and further improve the performance of the learning performance prediction model. First, a behavior classification model (E-learning Behavior Classification Model, EBC Model) based on interaction objects and learning process is constructed; second, the feature space is preliminarily reduced by entropy weight method and variance filtering method; finally, combined with EBC Model and a self-adaptive feature fusion strategy to build a learning performance predictor. The experiment uses the British Open University Learning Analysis Dataset (OULAD). Through the experimental analysis, an effective feature space is obtained, that is, the basic interactive behavior (BI) and knowledge interaction behavior (KI) of learning behavior category has the strongest correlation with learning performance.And it is proved that the self-adaptive feature fusion strategy proposed in this paper can effectively improve the performance of the learning performance predictor, and the performance index of accuracy(ACC), F1-score(F1) and kappa(K) reach 98.44%, 0.9893, 0.9600. This study constructs E-learning performance predictors and mines the effective feature space from a new perspective, and provides some auxiliary references for online learners and managers.

Predicting students' performance in e-learning using learning process and behaviour data.

E-learning is achieved by the deep integration of modern education and information technology, and plays an important role in promoting educational equity. With the continuous expansion of user groups and application areas, it has become increasingly important to effectively ensure the quality of e-learning. Currently, one of the methods to ensure the quality of e-learning is to use mutually independent e-learning behaviour data to build a learning performance predictor to achieve real-time supervision and feedback during the learning process. However, this method ignores the inherent correlation between e-learning behaviours. Therefore, we propose the behaviour classification-based e-learning performance (BCEP) prediction framework, which selects the features of e-learning behaviours, uses feature fusion with behaviour data according to the behaviour classification model to obtain the category feature values of each type of behaviour, and finally builds a learning performance predictor based on machine learning. In addition, because existing e-learning behaviour classification methods do not fully consider the process of learning, we also propose an online behaviour classification model based on the e-learning process called the process-behaviour classification (PBC) model. Experimental results with the Open University Learning Analytics Dataset (OULAD) show that the learning performance predictor based on the BCEP prediction framework has a good prediction effect, and the performance of the PBC model in learning performance prediction is better than traditional classification methods. We construct an e-learning performance predictor from a new perspective and provide a new solution for the quantitative evaluation of e-learning classification methods.

Influence of creating an oligopoly through government intervention to improve partner collaboration intentions in the context of green supply chains.

Pollution from supply chains can be controlled through the high level of efficiency realized by green supply chains. However, realizing a green supply chain depends on partners' collaboration intentions. The purposes of this study are to explore the influence of oligopolies created through government intervention and how they can promote supply chain partners' collaboration intentions in the context of green supply chains. Spearman's correlation analysis, partial least squares, and the Sobel test are the main approaches adopted to evaluate the theoretical framework and hypotheses of this study. Our findings indicate that, in addition to a few leaders of the supply chain obtaining market order distribution authority, oligopolies created through government intervention guide consumers to more fully scrutinize these supply chain leaders and their partners. When their partners exhibit a lower collaboration intention in green supply chain improvement and increase the pollution emission risk, it is easy to induce and increase social pressure from consumers and thus pressure these leaders to reduce or even cancel the order distribution of these partners. To avoid order profit loss, these partners will continue to actively maintain their collaboration intentions in the green supply chain. In addition, environmental risk audits by supply chain leaders' interventions are a common approach to improve green supply chains. However, partners are usually unwilling to cooperate with leaders' audit interventions and show lower collaboration intentions. However, social pressure can threaten partners' order profits and thus drive partners to show positive collaboration intentions and further accept leaders' reasonable interventions in environmental risk audits and will have a positive effect on green supply chain improvement. Based on the above, to expedite the improvement of the green supply chain, oligopolies created through government intervention are not only an important external force but also an important national strategy in green environmental improvement.