Background Bacterial vaginosis is a polymicrobial syndrome in which the homeostasis exerted by the Latobacillus species that protect the vaginal mucosa has been lost.This study explored the data balancing process with...Background Bacterial vaginosis is a polymicrobial syndrome in which the homeostasis exerted by the Latobacillus species that protect the vaginal mucosa has been lost.This study explored the data balancing process with the intention of improving the quality of association rules.The article aimed to balance the unbalanced multiclass dataset to improve association rule creation.Methods A dataset with 201 observations and 58 variables was analyzed.A preconstructed dataset was used.The authors collected the data between August 2016 and October 2018 in Tabasco,Mexico.The study population comprised sexually active women ages 18 to 50 who underwent gynecological inspection at the infectious and metabolic diseases research laboratory at the Universidad Juarez Autonoma de Tabasco.To determine the best κ-value,the random-forest algorithm was used and the balancing was performed with the synthetic minority over-sampling technique(SMOTE),random over-sampling examples(ROSE),and adaptive syntetic sampling approach for imbalanced learning(ADASYN)algorithms.The Apriori algorithm created the rules and to select rules with statistical significance,the is.redundant(),is.significant(),and is.maximal()functions and quality metric Fisher’s exact tes were used.The biological validation was carried out by the expert(bacteriologist).Results The ADASYN algorithm at K=9 the out of the bag(OOB)error was zero,this was the best𝐾-values.In the balancing process the ADASYN algorithm show best the performance.From the dataset balanced with ADASYN,the apriori algorithm created the association rules and the selection with the quality metric Fisher’s exact test,and the biological validation reported 13 rules.Gram-bacteria Atopobium vaginae,Gardnerella vaginalis,Megasphaera filotipo 1,Mycoplasma hominis and Ureaplasma parvum were detected by the apriori algorithm from the balanced dataset.Conclusion Balancing may improve the creation of association rules to efficiently model the bacteria that cause bacterial vaginosis.展开更多
Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited t...Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited training data,imbalance data distribution,and inadequate feature extraction persist,hindering both the segmentation performance and optimal model generalization.Addressing these critical issues,the DEFFA-Unet is proposed featuring an additional encoder to process domain-invariant pre-processed inputs,thereby improving both richer feature encoding and enhanced model generalization.A feature filtering fusion module is developed to ensure the precise feature filtering and robust hybrid feature fusion.In response to the task-specific need for higher precision where false positives are very costly,traditional skip connections are replaced with the attention-guided feature reconstructing fusion module.Additionally,innovative data augmentation and balancing methods are proposed to counter data scarcity and distribution imbalance,further boosting the robustness and generalization of the model.With a comprehensive suite of evaluation metrics,extensive validations on four benchmark datasets(DRIVE,CHASEDB1,STARE,and HRF)and an SLO dataset(IOSTAR),demonstrate the proposed method’s superiority over both baseline and state-of-the-art models.Particularly the proposed method significantly outperforms the compared methods in cross-validation model generalization.展开更多
Software defect prediction is a critical component in maintaining software quality,enabling early identification and resolution of issues that could lead to system failures and significant financial losses.With the in...Software defect prediction is a critical component in maintaining software quality,enabling early identification and resolution of issues that could lead to system failures and significant financial losses.With the increasing reliance on user-generated content,social media reviews have emerged as a valuable source of real-time feedback,offering insights into potential software defects that traditional testing methods may overlook.However,existing models face challenges like handling imbalanced data,high computational complexity,and insufficient inte-gration of contextual information from these reviews.To overcome these limitations,this paper introduces the SESDP(Sentiment Analysis-Based Early Software Defect Prediction)model.SESDP employs a Transformer-Based Multi-Task Learning approach using Robustly Optimized Bidirectional Encoder Representations from Transformers Approach(RoBERTa)to simultaneously perform sentiment analysis and defect prediction.By integrating text embedding extraction,sentiment score computation,and feature fusion,the model effectively captures both the contextual nuances and sentiment expressed in user reviews.Experimental results show that SESDP achieves superior performance with an accuracy of 96.37%,precision of 94.7%,and recall of 95.4%,particularly excelling in handling imbalanced datasets compared to baseline models.This approach offers a scalable and efficient solution for early software defect detection,enhancing proactive software quality assurance.展开更多
文摘Background Bacterial vaginosis is a polymicrobial syndrome in which the homeostasis exerted by the Latobacillus species that protect the vaginal mucosa has been lost.This study explored the data balancing process with the intention of improving the quality of association rules.The article aimed to balance the unbalanced multiclass dataset to improve association rule creation.Methods A dataset with 201 observations and 58 variables was analyzed.A preconstructed dataset was used.The authors collected the data between August 2016 and October 2018 in Tabasco,Mexico.The study population comprised sexually active women ages 18 to 50 who underwent gynecological inspection at the infectious and metabolic diseases research laboratory at the Universidad Juarez Autonoma de Tabasco.To determine the best κ-value,the random-forest algorithm was used and the balancing was performed with the synthetic minority over-sampling technique(SMOTE),random over-sampling examples(ROSE),and adaptive syntetic sampling approach for imbalanced learning(ADASYN)algorithms.The Apriori algorithm created the rules and to select rules with statistical significance,the is.redundant(),is.significant(),and is.maximal()functions and quality metric Fisher’s exact tes were used.The biological validation was carried out by the expert(bacteriologist).Results The ADASYN algorithm at K=9 the out of the bag(OOB)error was zero,this was the best𝐾-values.In the balancing process the ADASYN algorithm show best the performance.From the dataset balanced with ADASYN,the apriori algorithm created the association rules and the selection with the quality metric Fisher’s exact test,and the biological validation reported 13 rules.Gram-bacteria Atopobium vaginae,Gardnerella vaginalis,Megasphaera filotipo 1,Mycoplasma hominis and Ureaplasma parvum were detected by the apriori algorithm from the balanced dataset.Conclusion Balancing may improve the creation of association rules to efficiently model the bacteria that cause bacterial vaginosis.
文摘Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited training data,imbalance data distribution,and inadequate feature extraction persist,hindering both the segmentation performance and optimal model generalization.Addressing these critical issues,the DEFFA-Unet is proposed featuring an additional encoder to process domain-invariant pre-processed inputs,thereby improving both richer feature encoding and enhanced model generalization.A feature filtering fusion module is developed to ensure the precise feature filtering and robust hybrid feature fusion.In response to the task-specific need for higher precision where false positives are very costly,traditional skip connections are replaced with the attention-guided feature reconstructing fusion module.Additionally,innovative data augmentation and balancing methods are proposed to counter data scarcity and distribution imbalance,further boosting the robustness and generalization of the model.With a comprehensive suite of evaluation metrics,extensive validations on four benchmark datasets(DRIVE,CHASEDB1,STARE,and HRF)and an SLO dataset(IOSTAR),demonstrate the proposed method’s superiority over both baseline and state-of-the-art models.Particularly the proposed method significantly outperforms the compared methods in cross-validation model generalization.
基金funded by a grant from the Center of Excellence in Information Assurance(CoEIA),King Saud University(KSU).
文摘Software defect prediction is a critical component in maintaining software quality,enabling early identification and resolution of issues that could lead to system failures and significant financial losses.With the increasing reliance on user-generated content,social media reviews have emerged as a valuable source of real-time feedback,offering insights into potential software defects that traditional testing methods may overlook.However,existing models face challenges like handling imbalanced data,high computational complexity,and insufficient inte-gration of contextual information from these reviews.To overcome these limitations,this paper introduces the SESDP(Sentiment Analysis-Based Early Software Defect Prediction)model.SESDP employs a Transformer-Based Multi-Task Learning approach using Robustly Optimized Bidirectional Encoder Representations from Transformers Approach(RoBERTa)to simultaneously perform sentiment analysis and defect prediction.By integrating text embedding extraction,sentiment score computation,and feature fusion,the model effectively captures both the contextual nuances and sentiment expressed in user reviews.Experimental results show that SESDP achieves superior performance with an accuracy of 96.37%,precision of 94.7%,and recall of 95.4%,particularly excelling in handling imbalanced datasets compared to baseline models.This approach offers a scalable and efficient solution for early software defect detection,enhancing proactive software quality assurance.
