Sentiment classification is a useful tool to classify reviews about sentiments and attitudes towards a product or service.Existing studies heavily rely on sentiment classification methods that require fully annotated ...Sentiment classification is a useful tool to classify reviews about sentiments and attitudes towards a product or service.Existing studies heavily rely on sentiment classification methods that require fully annotated inputs.However,there is limited labelled text available,making the acquirement process of the fully annotated input costly and labour-intensive.Lately,semi-supervised methods emerge as they require only partially labelled input but perform comparably to supervised methods.Nevertheless,some works reported that the performance of the semi-supervised model degraded after adding unlabelled instances into training.Literature also shows that not all unlabelled instances are equally useful;thus identifying the informative unlabelled instances is beneficial in training a semi-supervised model.To achieve this,an informative score is proposed and incorporated into semisupervised sentiment classification.The evaluation is performed on a semisupervised method without an informative score and with an informative score.By using the informative score in the instance selection strategy to identify informative unlabelled instances,semi-supervised models perform better compared to models that do not incorporate informative scores into their training.Although the performance of semi-supervised models incorporated with an informative score is not able to surpass the supervised models,the results are still found promising as the differences in performance are subtle with a small difference of 2%to 5%,but the number of labelled instances used is greatly reduced from100%to 40%.The best finding of the proposed instance selection strategy is achieved when incorporating an informative score with a baseline confidence score at a 0.5:0.5 ratio using only 40%labelled data.展开更多
BACKGROUND Many classification systems of thoracolumbar spinal fractures have been proposed to enhance treatment protocols,but none have achieved universal adoption.AIM To develop a new patient scoring system for case...BACKGROUND Many classification systems of thoracolumbar spinal fractures have been proposed to enhance treatment protocols,but none have achieved universal adoption.AIM To develop a new patient scoring system for cases with thoracolumbar injury classification and severity score(TLICS)=4,namely the load-sharing thoracolumbar injury score(LSTLIS).METHODS Based on thoracolumbar injury classification and severity score,this study proposes the use of the established load-sharing classification(LSC)to develop an improved classification system(LSTLIS).To prove the reliability and reproducibility of LSTLIS,a retrospective analysis for patients with thoracolumbar vertebral fractures has been conducted.RESULTS A total of 102 cases were enrolled in the study.The scoring trend of LSTLIS is roughly similar as the LSC scoring,however,the average deviation based on the former method is relatively smaller than that of the latter.Thus,the robustness of the LSTLIS scoring method is better than that of LSC.LSTLIS can further classify patients with TLICS=4,so as to assess more accurately this particular circumstance,and the majority of LSTLIS recommendations are consistent with actual clinical decisions.LSTLIS is a scoring system that combines LSC and TLICS to compensate for the lack of appropriate inclusion of anterior and middle column compression fractures with TLICS.Following preliminary clinical verification,LSTLIS has greater feasibility and reliability value,is more practical in comprehensively assessing certain clinical circumstances,and has better accuracy with clinically significant guidelines.展开更多
A brainwave classification,which does not involve any limb movement and stimulus for character-writing applications,benefits impaired people,in terms of practical communication,because it allows users to command a dev...A brainwave classification,which does not involve any limb movement and stimulus for character-writing applications,benefits impaired people,in terms of practical communication,because it allows users to command a device/computer directly via electroencephalogram signals.In this paper,we propose a new framework based on Empirical Mode Decomposition(EMD)features along with theGaussianMixtureModel(GMM)andKernel Extreme Learning Machine(KELM)-based classifiers.For this purpose,firstly,we introduce EMD to decompose EEG signals into Intrinsic Mode Functions(IMFs),which actually are used as the input features of the brainwave classification for the character-writing application.We hypothesize that EMD along with the appropriate IMF is quite powerful for the brainwave classification,in terms of character applications,because of the wavelet-like decomposition without any down sampling process.Secondly,by getting motivated with shallow learning classifiers,we can provide promising performance for the classification of binary classes,GMM and KELM,which are applied for the learning of features along with the brainwave classification.Lastly,we propose a new method by combining GMMand KELM to fuse the merits of different classifiers.Moreover,the proposed methods are validated by using the volunteer-independent 5-fold cross-validation and accuracy as a standard measurement.The experimental results showed that EMD with the proper IMF achieved better results than the conventional discrete wavelet transform(DWT)feature.Moreover,we found that the EMD feature along with the GMM/KELM-based classifier provides the average accuracy of 77.40%and 80.10%,respectively,which could perform better than the conventional methods where we use DWT along with the artificial neural network classifier in order to get the average accuracy of 80.60%.Furthermore,we obtained the improved performance by combining GMM and KELM,i.e.,average accuracy of 80.60%.These outcomes exhibit the usefulness of the EMD feature combining with GMMand KELM based classifiers for the brainwaveclassification in terms of the Character-Writing application,which do notrequire any limb movement and stimulus.展开更多
Introduction: Acromioclavicular (AC) joint dislocation is a common shoulder injury, comprising 9% - 12% of shoulder girdle injuries. Optimal management remains challenging, with treatment decisions guided by the Rockw...Introduction: Acromioclavicular (AC) joint dislocation is a common shoulder injury, comprising 9% - 12% of shoulder girdle injuries. Optimal management remains challenging, with treatment decisions guided by the Rockwood classification system. Controversies surround grade III injuries, necessitating further classification. Non-operative treatment has shown favorable outcomes, while surgical interventions vary. Anatomical coracoclavicular reconstruction (ACCR) has demonstrated biomechanical advantages over traditional methods. Arthroscopic techniques offer advantages, minimizing deltoid detachment and allowing concurrent pathology identification. This study evaluates the outcomes of arthroscopic-assisted ACCR in chronic AC joint dislocation. Surgical Technique: Arthroscopic-assisted ACCR involves meticulous portal placement, tendon graft harvesting, diagnostic arthroscopy, and coracoid exposure. The clavicle tunnels were made to mimic the conoid and trapezoid ligament positions, using FibreTape#2 loop and Dog Bone Button for correct placement against the coracoid base, and passing the semitendinosus graft through to reconstruct the conoid ligament, reduction done and graft follow through for anatomical reconstruction. Methods: A retrospective cohort study at Hospital Kuala Lumpur analyzed 35 patients undergoing arthroscopic-assisted ACCR for Rockwood grade III - V AC joint dislocations. Inclusion criteria encompassed trauma ≥ 3 weeks prior, no prior shoulder injuries, and ≥12-month follow-up. Functional and radiological assessments utilized ASES scores and coracoclavicular distances, respectively. Statistical analysis employed descriptive statistics and logistic regression. Results: The mean age was 38.9 years (SD 11.26), and 34 of 35 patients were male. Grade IV injuries were predominant (37.1%). Waiting time for surgery averaged 234.9 days. Functional improvement was substantial postoperatively (ASES: 55.5 to 88.9). Radiological outcomes demonstrated reduced coracoclavicular distances and maintained reduction. No significant correlation was observed between injury grade and outcomes. Conclusion: Arthroscopic-assisted ACCR for chronic AC joint dislocation yields significant functional and radiological improvement, irrespective of injury grade. Waiting time for surgery exhibits minor impact on outcomes, emphasizing the procedure’s efficacy. Concomitant injuries do not impede success, highlighting the versatility of this approach in managing shoulder instability. The study contributes valuable insights into the nuanced management of chronic AC joint dislocations and supports the adoption of arthroscopic-assisted ACCR as a viable treatment option.展开更多
基金This research is supported by Fundamental Research Grant Scheme(FRGS),Ministry of Education Malaysia(MOE)under the project code,FRGS/1/2018/ICT02/USM/02/9 titled,Automated Big Data Annotation for Training Semi-Supervised Deep Learning Model in Sentiment Classification.
文摘Sentiment classification is a useful tool to classify reviews about sentiments and attitudes towards a product or service.Existing studies heavily rely on sentiment classification methods that require fully annotated inputs.However,there is limited labelled text available,making the acquirement process of the fully annotated input costly and labour-intensive.Lately,semi-supervised methods emerge as they require only partially labelled input but perform comparably to supervised methods.Nevertheless,some works reported that the performance of the semi-supervised model degraded after adding unlabelled instances into training.Literature also shows that not all unlabelled instances are equally useful;thus identifying the informative unlabelled instances is beneficial in training a semi-supervised model.To achieve this,an informative score is proposed and incorporated into semisupervised sentiment classification.The evaluation is performed on a semisupervised method without an informative score and with an informative score.By using the informative score in the instance selection strategy to identify informative unlabelled instances,semi-supervised models perform better compared to models that do not incorporate informative scores into their training.Although the performance of semi-supervised models incorporated with an informative score is not able to surpass the supervised models,the results are still found promising as the differences in performance are subtle with a small difference of 2%to 5%,but the number of labelled instances used is greatly reduced from100%to 40%.The best finding of the proposed instance selection strategy is achieved when incorporating an informative score with a baseline confidence score at a 0.5:0.5 ratio using only 40%labelled data.
基金Supported by Multicenter Clinical Trial of hUC-MSCs in the Treatment of Late Chronic Spinal Cord Injury,No.2017YFA0105404the Project of Shanghai Science and Technology Commission,No.19441901702.
文摘BACKGROUND Many classification systems of thoracolumbar spinal fractures have been proposed to enhance treatment protocols,but none have achieved universal adoption.AIM To develop a new patient scoring system for cases with thoracolumbar injury classification and severity score(TLICS)=4,namely the load-sharing thoracolumbar injury score(LSTLIS).METHODS Based on thoracolumbar injury classification and severity score,this study proposes the use of the established load-sharing classification(LSC)to develop an improved classification system(LSTLIS).To prove the reliability and reproducibility of LSTLIS,a retrospective analysis for patients with thoracolumbar vertebral fractures has been conducted.RESULTS A total of 102 cases were enrolled in the study.The scoring trend of LSTLIS is roughly similar as the LSC scoring,however,the average deviation based on the former method is relatively smaller than that of the latter.Thus,the robustness of the LSTLIS scoring method is better than that of LSC.LSTLIS can further classify patients with TLICS=4,so as to assess more accurately this particular circumstance,and the majority of LSTLIS recommendations are consistent with actual clinical decisions.LSTLIS is a scoring system that combines LSC and TLICS to compensate for the lack of appropriate inclusion of anterior and middle column compression fractures with TLICS.Following preliminary clinical verification,LSTLIS has greater feasibility and reliability value,is more practical in comprehensively assessing certain clinical circumstances,and has better accuracy with clinically significant guidelines.
基金the SUT research and development fund,and in part by the National Natural Science Foundation of China under Grant 61771333All subjects gave their informed consent for inclusion before they participated in the study.The study was conducted in accordance with the Declaration of Helsinki and the protocol was approved by the Ethics Committee of Suranaree University of Technology(License EC-61-14 COA No.16/2561).
文摘A brainwave classification,which does not involve any limb movement and stimulus for character-writing applications,benefits impaired people,in terms of practical communication,because it allows users to command a device/computer directly via electroencephalogram signals.In this paper,we propose a new framework based on Empirical Mode Decomposition(EMD)features along with theGaussianMixtureModel(GMM)andKernel Extreme Learning Machine(KELM)-based classifiers.For this purpose,firstly,we introduce EMD to decompose EEG signals into Intrinsic Mode Functions(IMFs),which actually are used as the input features of the brainwave classification for the character-writing application.We hypothesize that EMD along with the appropriate IMF is quite powerful for the brainwave classification,in terms of character applications,because of the wavelet-like decomposition without any down sampling process.Secondly,by getting motivated with shallow learning classifiers,we can provide promising performance for the classification of binary classes,GMM and KELM,which are applied for the learning of features along with the brainwave classification.Lastly,we propose a new method by combining GMMand KELM to fuse the merits of different classifiers.Moreover,the proposed methods are validated by using the volunteer-independent 5-fold cross-validation and accuracy as a standard measurement.The experimental results showed that EMD with the proper IMF achieved better results than the conventional discrete wavelet transform(DWT)feature.Moreover,we found that the EMD feature along with the GMM/KELM-based classifier provides the average accuracy of 77.40%and 80.10%,respectively,which could perform better than the conventional methods where we use DWT along with the artificial neural network classifier in order to get the average accuracy of 80.60%.Furthermore,we obtained the improved performance by combining GMM and KELM,i.e.,average accuracy of 80.60%.These outcomes exhibit the usefulness of the EMD feature combining with GMMand KELM based classifiers for the brainwaveclassification in terms of the Character-Writing application,which do notrequire any limb movement and stimulus.
基金National Natural Science Foundation of China(Nos.61702094 and 62301142)“Chenguang Program”Supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission,China(No.18CG38)。
文摘Introduction: Acromioclavicular (AC) joint dislocation is a common shoulder injury, comprising 9% - 12% of shoulder girdle injuries. Optimal management remains challenging, with treatment decisions guided by the Rockwood classification system. Controversies surround grade III injuries, necessitating further classification. Non-operative treatment has shown favorable outcomes, while surgical interventions vary. Anatomical coracoclavicular reconstruction (ACCR) has demonstrated biomechanical advantages over traditional methods. Arthroscopic techniques offer advantages, minimizing deltoid detachment and allowing concurrent pathology identification. This study evaluates the outcomes of arthroscopic-assisted ACCR in chronic AC joint dislocation. Surgical Technique: Arthroscopic-assisted ACCR involves meticulous portal placement, tendon graft harvesting, diagnostic arthroscopy, and coracoid exposure. The clavicle tunnels were made to mimic the conoid and trapezoid ligament positions, using FibreTape#2 loop and Dog Bone Button for correct placement against the coracoid base, and passing the semitendinosus graft through to reconstruct the conoid ligament, reduction done and graft follow through for anatomical reconstruction. Methods: A retrospective cohort study at Hospital Kuala Lumpur analyzed 35 patients undergoing arthroscopic-assisted ACCR for Rockwood grade III - V AC joint dislocations. Inclusion criteria encompassed trauma ≥ 3 weeks prior, no prior shoulder injuries, and ≥12-month follow-up. Functional and radiological assessments utilized ASES scores and coracoclavicular distances, respectively. Statistical analysis employed descriptive statistics and logistic regression. Results: The mean age was 38.9 years (SD 11.26), and 34 of 35 patients were male. Grade IV injuries were predominant (37.1%). Waiting time for surgery averaged 234.9 days. Functional improvement was substantial postoperatively (ASES: 55.5 to 88.9). Radiological outcomes demonstrated reduced coracoclavicular distances and maintained reduction. No significant correlation was observed between injury grade and outcomes. Conclusion: Arthroscopic-assisted ACCR for chronic AC joint dislocation yields significant functional and radiological improvement, irrespective of injury grade. Waiting time for surgery exhibits minor impact on outcomes, emphasizing the procedure’s efficacy. Concomitant injuries do not impede success, highlighting the versatility of this approach in managing shoulder instability. The study contributes valuable insights into the nuanced management of chronic AC joint dislocations and supports the adoption of arthroscopic-assisted ACCR as a viable treatment option.
