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Understanding the green consumption behaviors of college students is highly demanded to update the public and educational policies of universities. For this purpose, this research is devoted to advance an efficient model for identifying prominent features and predicting the green consumption behaviors of college students. The proposed prediction model is based on the K-Nearest Neighbor (KNN) with an effective swarm intelligence method, which is called OBLFA_GWO. The optimization core takes advantage of the firefly algorithm (FA) and opposition-based learning (OBL) to mitigate the immature convergence of the grey wolf algorithm (GWO). In the proposed prediction framework, OBLFA_GWO is utilized to identify influential features. Then, the enhanced KNN model is used to identify the importance and interrelationships of features in samples and construct an effective and stable predictive model for decision support. Five other wellknown algorithms are employed to validate the effectiveness of the proposed OBLFA_GWO strategy using 13 benchmark test problems. Also, the non-parametric statistical Wilcoxon sign rank and Friedman tests are conducted to validate the significance of the proposed OBLFA_GWO against other peers. Experimental results indicate that the FA and OBL can significantly boost the core exploratory and exploitative trends of GWO in dealing with the optimization tasks. Also, the OBLFA_GWO-based KNN (OBLFA_GWO-KNN) model is compared with four classical classifiers, such as kernel extreme learning machine (KELM), backpropagation neural network method (BPNN), and random forest (RF) and five advanced feature selection methods in terms of four standard evaluation indexes. The experimental results show that the classification accuracy of the proposed OBLFA_GWO-KNN can reach to 96.334 % on the real-life dataset collected from nine universities. Also, the proposed binary OBLFA_GWO algorithm has improved the classification performance of KNN compared to the other peers. Hopefully, the established adaptive OBLFA_GWO-KNN model can be considered as a useful tool for predicting students' behavior of green consumption. INDEX TERMS K-nearest neighbor, firefly algorithm, grey wolf algorithm, opposition-based learning, green consumption behavior, feature selection. The associate editor coordinating the review of this manuscript and approving it for publication was Josue Antonio Nescolarde Selva .

Under the background of "innovation and entrepreneurship," how to scientifically and rationally choose employment or independent entrepreneurship according to their own comprehensive situation is of great significance to the planning and development of their own career and the social adaptation of university personnel training. This study aims to develop an adaptive support vector machine framework, called RF-CSCA-SVM, for predicting college students' entrepreneurial intention in advance; that is, students choose to start a business or find a job after graduation. RF-CSCA-SVM combines random forest (RF), support vector machine (SVM), sine cosine algorithm (SCA), and chaotic local search. In this framework, RF is used to select the most important factors; SVM is employed to establish the relationship model between the factors and the students' decision to choose to start their own business or look for jobs. SCA is used to tune the optimal parameters for SVM. Additionally, chaotic local search is utilized to enhance the search capability of SCA. A total of 300 students were collected to develop the predictive model. To validate the developed method, other four meta-heuristic based SVM methods were used for comparison in terms of classification accuracy, Matthews Correlation Coefficients (MCC), sensitivity, and specificity. The experimental results demonstrate that the proposed method can be regarded as a promising success with the excellent predictive performance. Promisingly, the established adaptive SVM framework might serve as a new candidate of powerful tools for entrepreneurial intention prediction.

Objective To assess the relationship between chronic obstructive pulmonary disease (COPD) severity and bone mineral density (BMD) in the whole body and different body areas. Methods This retrospective, cross-sectional study included patients with COPD. Demographic and lung function data, COPD severity scales, BMD, and T scores were collected. Patients were grouped by high (≥–1) and low (<–1) T scores, and stratified by body mass index, airway obstruction, dyspnoea, and exercise capacity (BODE) index. The relationship between whole-body BMD and BODE was evaluated by Kendall’s tau-b correlation coefficient. Risk factors associated with COPD severity were identified by univariate analyses. BMD as an independent predictor of severe COPD (BODE ≥5) was verified by multivariate logistic regression. BMD values in different body areas for predicting severe COPD were assessed by receiver operating characteristic curves. Results Of 88 patients with COPD, lung-function indicators and COPD severity were significantly different between those with high and low T scores. Whole-body BMD was inversely related to COPD severity scales, including BODE. Multivariate logistic regression revealed that BMD was independently associated with COPD severity. The area under the curve for pelvic BMD in predicting severe COPD was 0.728. Conclusion BMD may be a novel marker in predicting COPD severity, and pelvic BMD may have the strongest relative predictive power.