The main purpose of this study is to find the awareness level as well as the determinants of awareness on HIV/AIDS among the garments workers in Dhaka City, Bangladesh. To do so, 200 garments workers were interviewed ...The main purpose of this study is to find the awareness level as well as the determinants of awareness on HIV/AIDS among the garments workers in Dhaka City, Bangladesh. To do so, 200 garments workers were interviewed through a structured questionnaire using purposive sampling technique. As the statistical tools, univariate analysis was completed to figure out frequency distribution and the binary logistic regression model was used to predict the probability occurrence of the events by fitting data. The results revealed that the majority of the garments workers (63.5%) are very young (18 - 27 years), almost all (97.5%) are literate and most of them (57.0%) used contraceptives. Importantly, most of the respondents (64.0%) had not participated in any type of seminar or workshop related to HIV/AIDS, though almost all the respondents (84.5%) know HIV is a dangerous and life threatening disease. The logistic regression model identified that respondents' education, contraceptive usage, mass media and HIV workshops have statistically significant positive effects on HIV/AIDS awareness. Various media campaigns are strongly suggested to be increased knowledge and awareness to control the spread of HIV as well as STDs among garments workers in Bangladesh.展开更多
Feature selection(FS)is a data preprocessing step in machine learning(ML)that selects a subset of relevant and informative features from a large feature pool.FS helps ML models improve their predictive accuracy at low...Feature selection(FS)is a data preprocessing step in machine learning(ML)that selects a subset of relevant and informative features from a large feature pool.FS helps ML models improve their predictive accuracy at lower computational costs.Moreover,FS can handle the model overfitting problem on a high-dimensional dataset.A major problem with the filter and wrapper FS methods is that they consume a significant amount of time during FS on high-dimensional datasets.The proposed“HDFS(PSO-MI):hybrid distribute feature selection using particle swarm optimization-mutual information(PSO-MI)”,is a PSO-based hybrid method that can overcome the problem mentioned above.This method hybridizes the filter and wrapper techniques in a distributed manner.A new combiner is also introduced to merge the effective features selected from multiple data distributions.The effectiveness of the proposed HDFS(PSO-MI)method is evaluated using five ML classifiers,i.e.,logistic regression(LR),k-NN,support vector machine(SVM),decision tree(DT),and random forest(RF),on various datasets in terms of accuracy and Matthew’s correlation coefficient(MCC).From the experimental analysis,we observed that HDFS(PSO-MI)method yielded more than 98%,95%,92%,90%,and 85%accuracy for the unbalanced,kidney disease,emotions,wafer manufacturing,and breast cancer datasets,respectively.Our method shows promising results comapred to other methods,such as mutual information,gain ratio,Spearman correlation,analysis of variance(ANOVA),Pearson correlation,and an ensemble feature selection with ranking method(EFSRank).展开更多
文摘The main purpose of this study is to find the awareness level as well as the determinants of awareness on HIV/AIDS among the garments workers in Dhaka City, Bangladesh. To do so, 200 garments workers were interviewed through a structured questionnaire using purposive sampling technique. As the statistical tools, univariate analysis was completed to figure out frequency distribution and the binary logistic regression model was used to predict the probability occurrence of the events by fitting data. The results revealed that the majority of the garments workers (63.5%) are very young (18 - 27 years), almost all (97.5%) are literate and most of them (57.0%) used contraceptives. Importantly, most of the respondents (64.0%) had not participated in any type of seminar or workshop related to HIV/AIDS, though almost all the respondents (84.5%) know HIV is a dangerous and life threatening disease. The logistic regression model identified that respondents' education, contraceptive usage, mass media and HIV workshops have statistically significant positive effects on HIV/AIDS awareness. Various media campaigns are strongly suggested to be increased knowledge and awareness to control the spread of HIV as well as STDs among garments workers in Bangladesh.
基金The work is funded by the University Grant Commission(UGC)under(Start-up-Grant No.:F 30-592/2021(BSR)).
文摘Feature selection(FS)is a data preprocessing step in machine learning(ML)that selects a subset of relevant and informative features from a large feature pool.FS helps ML models improve their predictive accuracy at lower computational costs.Moreover,FS can handle the model overfitting problem on a high-dimensional dataset.A major problem with the filter and wrapper FS methods is that they consume a significant amount of time during FS on high-dimensional datasets.The proposed“HDFS(PSO-MI):hybrid distribute feature selection using particle swarm optimization-mutual information(PSO-MI)”,is a PSO-based hybrid method that can overcome the problem mentioned above.This method hybridizes the filter and wrapper techniques in a distributed manner.A new combiner is also introduced to merge the effective features selected from multiple data distributions.The effectiveness of the proposed HDFS(PSO-MI)method is evaluated using five ML classifiers,i.e.,logistic regression(LR),k-NN,support vector machine(SVM),decision tree(DT),and random forest(RF),on various datasets in terms of accuracy and Matthew’s correlation coefficient(MCC).From the experimental analysis,we observed that HDFS(PSO-MI)method yielded more than 98%,95%,92%,90%,and 85%accuracy for the unbalanced,kidney disease,emotions,wafer manufacturing,and breast cancer datasets,respectively.Our method shows promising results comapred to other methods,such as mutual information,gain ratio,Spearman correlation,analysis of variance(ANOVA),Pearson correlation,and an ensemble feature selection with ranking method(EFSRank).
