Classification model has received great attention in any domain of research and also a reliable tool for medical disease diagnosis. The domain of classification model is used in disease diagnosis, disease prediction, ...Classification model has received great attention in any domain of research and also a reliable tool for medical disease diagnosis. The domain of classification model is used in disease diagnosis, disease prediction, bio informatics, crime prediction and so on. However, an efficient disease diagnosis model was compromised the disease prediction. In this paper, a Rough Set Rule-based Multitude Classifier (RS-RMC) is developed to improve the disease prediction rate and enhance the class accuracy of disease being diagnosed. The RS-RMC involves two steps. Initially, a Rough Set model is used for Feature Selection aiming at minimizing the execution time for obtaining the disease feature set. A Multitude Classifier model is presented in second step for detection of heart disease and for efficient classification. The Na?ve Bayes Classifier algorithm is designed for efficient identification of classes to measure the relationship between disease features and improving disease prediction rate. Experimental analysis shows that RS-RMC is used to reduce the execution time for extracting the disease feature with minimum false positive rate compared to the state-of-the-art works.展开更多
AIM:To investigate the efficacy and safety of repeated dexamethasone implants with real-life data in eyes with naive retinal vein occlusion(RVO)with macular edema(ME)at a minimum of 60mo follow-up.METHODS:In this retr...AIM:To investigate the efficacy and safety of repeated dexamethasone implants with real-life data in eyes with naive retinal vein occlusion(RVO)with macular edema(ME)at a minimum of 60mo follow-up.METHODS:In this retrospective cohort study,the data about best corrected visual acuity(BCVA),central macular thickness(CMT),serous macular detachment(SMD),hard exudate,hyperreflective foci(HRF),cystoid degeneration,pearl necklace sign,epiretinal membrane(ERM),disorganization of retinal inner layers(DRIL),ellipsoid zone and external limiting membrane(EZ-ELM)integrity,intraocular pressure(IOP)and lens condition were recorded.RESULTS:Thirty-eight eyes of 38 patients were included in the study.Thirteen patients presented with central RVO(CRVO)and 25 with branch RVO(BRVO).The mean follow-up time was 69.9±15.8mo,and the mean number of injections was 7.9±4.0.The mean BCVA gain was 25.0±36 letters,and this difference was statistically significant(P=0.021).The BCVA gain was 19.4±20.4 letters in the CRVO group,and 26.5±38.6 letters in the BRVO group(P=0.763).Besides,21(55.2%)of the patients achieved≥15 letters improvement.At the end of the follow-up period,SMD was not observed in any of the patients(P=0.016).Hard exudate,HRF number were decreased;while DRIL,ERM and EZ-ELM defects were increased but not significantly.CONCLUSION:Intravitreal dexamethasone monotherapy is an effective and safe treatment option for the treatment-naive RVO-ME patients in the long-term follow-up.展开更多
The naïve Bayes classifier is one of the commonly used data mining methods for classification.Despite its simplicity,naïve Bayes is effective and computationally efficient.Although the strong attribute indep...The naïve Bayes classifier is one of the commonly used data mining methods for classification.Despite its simplicity,naïve Bayes is effective and computationally efficient.Although the strong attribute independence assumption in the naïve Bayes classifier makes it a tractable method for learning,this assumption may not hold in real-world applications.Many enhancements to the basic algorithm have been proposed in order to alleviate the violation of attribute independence assumption.While these methods improve the classification performance,they do not necessarily retain the mathematical structure of the naïve Bayes model and some at the expense of computational time.One approach to reduce the naïvetéof the classifier is to incorporate attribute weights in the conditional probability.In this paper,we proposed a method to incorporate attribute weights to naïve Bayes.To evaluate the performance of our method,we used the public benchmark datasets.We compared our method with the standard naïve Bayes and baseline attribute weighting methods.Experimental results show that our method to incorporate attribute weights improves the classification performance compared to both standard naïve Bayes and baseline attribute weighting methods in terms of classification accuracy and F1,especially when the independence assumption is strongly violated,which was validated using the Chi-square test of independence.展开更多
<span style="font-family:Verdana;">The presence of bearing faults reduces the efficiency of rotating machines and thus increases energy consumption or even the total stoppage of the machine. </span&...<span style="font-family:Verdana;">The presence of bearing faults reduces the efficiency of rotating machines and thus increases energy consumption or even the total stoppage of the machine. </span><span style="font-family:Verdana;">It becomes essential to correctly diagnose the fault caused by the bearing.</span><span style="font-family:Verdana;"> Hence the importance of determining an effective features extraction method that best describes the fault. The vision of this paper is to merge the features selection methods in order to define the most relevant featuresin the texture </span><span style="font-family:Verdana;">of the vibration signal images. In this study, the Gray Level Co-occurrence </span><span style="font-family:Verdana;">Matrix (GLCM) in texture analysis is applied on the vibration signal represented in images. Features</span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;">selection based on the merge of PCA (Principal component Analysis) method and SFE (Sequential Features Extraction) method is </span><span style="font-family:Verdana;">done to obtain the most relevant features. The multiclass-Na<span style="white-space:nowrap;">?</span>ve Bayesclassifi</span><span style="font-family:Verdana;">er is used to test the proposed approach. The success rate of this classification is 98.27%. The relevant features obtained give promising results and are more efficient than the methods observed in the literature.</span></span></span></span>展开更多
As the importance of email increases,the amount of malicious email is also increasing,so the need for malicious email filtering is growing.Since it is more economical to combine commodity hardware consisting of a medi...As the importance of email increases,the amount of malicious email is also increasing,so the need for malicious email filtering is growing.Since it is more economical to combine commodity hardware consisting of a medium server or PC with a virtual environment to use as a single server resource and filter malicious email using machine learning techniques,we used a Hadoop MapReduce framework and Naïve Bayes among machine learning methods for malicious email filtering.Naïve Bayes was selected because it is one of the top machine learning methods(Support Vector Machine(SVM),Naïve Bayes,K-Nearest Neighbor(KNN),and Decision Tree)in terms of execution time and accuracy.Malicious email was filtered with MapReduce programming using the Naïve Bayes technique,which is a supervised machine learning method,in a Hadoop framework with optimized performance and also with the Python program technique with the Naïve Bayes technique applied in a bare metal server environment with the Hadoop environment not applied.According to the results of a comparison of the accuracy and predictive error rates of the two methods,the Hadoop MapReduce Naïve Bayes method improved the accuracy of spam and ham email identification 1.11 times and the prediction error rate 14.13 times compared to the non-Hadoop Python Naïve Bayes method.展开更多
AIM: To characterize the prevalence of subpopulations of CD4+ cells along with that of major inhibitor or stimulator cell types in therapy-nave childhood Crohn's disease (CD) and to test whether abnormalities of...AIM: To characterize the prevalence of subpopulations of CD4+ cells along with that of major inhibitor or stimulator cell types in therapy-nave childhood Crohn's disease (CD) and to test whether abnormalities of immune phenotype are normalized with the improvement of clinical signs and symptoms of disease. METHODS: We enrolled 26 pediatric patients with CD. 14 therapy-nave CD children; of those, 10 children remitted on conventional therapy and formed the remission group. We also tested another group of 12 chil-dren who relapsed with conventional therapy and were given infliximab; and 15 healthy children who served as controls. The prevalence of Th1 and Th2, nave and memory, activated and regulatory T cells, along with the members of innate immunity such as natural killer (NK), NK-T, myeloid and plasmocytoid dendritic cells (DCs), monocytes and Toll-like receptor (TLR)-2 and TLR-4 expression were determined in peripheral blood samples. RESULTS: Children with therapy-nave CD and those in relapse showed a decrease in Th1 cell prevalence. Simultaneously, an increased prevalence of memory and activated lymphocytes along with that of DCs and monocytes was observed. In addition, the ratio of myeloid /plasmocytoid DCs and the prevalence of TLR-2 or TLR-4 positive DCs and monocytes were also higher in therapy-nave CD than in controls. The majority of alterations diminished in remitted CD irrespective of whether remission was obtained by conventional or biological therapy. CONCLUSION: The finding that immune phenotype is normalized in remission suggests a link between immune phenotype and disease activity in childhood CD. Our observations support the involvement of members of the adaptive and innate immune systems in childhood CD.展开更多
Intrusion detection is the investigation process of information about the system activities or its data to detect any malicious behavior or unauthorized activity.Most of the IDS implement K-means clustering technique ...Intrusion detection is the investigation process of information about the system activities or its data to detect any malicious behavior or unauthorized activity.Most of the IDS implement K-means clustering technique due to its linear complexity and fast computing ability.Nonetheless,it is Naïve use of the mean data value for the cluster core that presents a major drawback.The chances of two circular clusters having different radius and centering at the same mean will occur.This condition cannot be addressed by the K-means algorithm because the mean value of the various clusters is very similar together.However,if the clusters are not spherical,it fails.To overcome this issue,a new integrated hybrid model by integrating expectation maximizing(EM)clustering using a Gaussian mixture model(GMM)and naïve Bays classifier have been proposed.In this model,GMM give more flexibility than K-Means in terms of cluster covariance.Also,they use probabilities function and soft clustering,that’s why they can have multiple cluster for a single data.In GMM,we can define the cluster form in GMM by two parameters:the mean and the standard deviation.This means that by using these two parameters,the cluster can take any kind of elliptical shape.EM-GMM will be used to cluster data based on data activity into the corresponding category.展开更多
This paper proposed an improved Naïve Bayes Classifier for sentimental analysis from a large-scale dataset such as in YouTube.YouTube contains large unstructured and unorganized comments and reactions,which carry...This paper proposed an improved Naïve Bayes Classifier for sentimental analysis from a large-scale dataset such as in YouTube.YouTube contains large unstructured and unorganized comments and reactions,which carry important information.Organizing large amounts of data and extracting useful information is a challenging task.The extracted information can be considered as new knowledge and can be used for deci sion-making.We extract comments from YouTube on videos and categorized them in domain-specific,and then apply the Naïve Bayes classifier with improved techniques.Our method provided a decent 80%accuracy in classifying those comments.This experiment shows that the proposed method provides excellent adaptability for large-scale text classification.展开更多
目的 探讨初治2型糖尿病(T2DM)患者内脏脂肪面积(VFA)与代谢相关脂肪性肝病(MAFLD)的相关性。方法 回顾性选取2021年8月—2023年4月住院的426例首次治疗2型糖尿病患者作为研究对象,以体重指数(BMI)分组,BMI<24 kg/m2为A(正常)组,24≤...目的 探讨初治2型糖尿病(T2DM)患者内脏脂肪面积(VFA)与代谢相关脂肪性肝病(MAFLD)的相关性。方法 回顾性选取2021年8月—2023年4月住院的426例首次治疗2型糖尿病患者作为研究对象,以体重指数(BMI)分组,BMI<24 kg/m2为A(正常)组,24≤BMI≤28 kg/m2为B(超重)组,BMI≥28 kg/m2为C(肥胖)组,比较各组的一般资料、血清学指标、VFA、MAFLD等指标的差异。使用Spearman相关探讨VFA、MAFLD与各指标的相关性。采用多变量线性回归分析T2DM患者VFA的影响因素,采用二分类Logistic回归分析T2DM患者MAFLD发生的影响因素。结果 初治T2DM患者C组在年龄、高血压、腰围、收缩压、舒张压、VFA、MAFLD、谷丙转氨酶、谷草转氨酶、谷氨酰转肽酶、尿酸、甘油三酯、高密度脂蛋白胆固醇、空腹C肽、餐后2 h C肽与A组比较存在差异;C组在年龄、腰围、收缩压、舒张压、VFA、谷丙转氨酶、谷草转氨酶、空腹C肽、餐后2 h C肽与B组比较存在差异;B组在年龄、腰围、收缩压、VFA、MAFLD、尿酸、甘油三酯、高密度脂蛋白胆固醇、空腹C肽、餐后2 h C肽与A组比较存在差异;相关性分析显示VFA与高血压、腰围、体重指数、MAFLD、收缩压、舒张压、甘油三酯、总胆固醇、谷丙转氨酶、谷草转氨酶、血肌酐、血尿酸、空腹C肽、餐后2 h C肽存在正相关性,与性别(女)、年龄、高密度脂蛋白胆固醇存在负相关。线性回归分析显示性别(男)、高血压、体重指数、MAFLD是初治T2DM患者VFA的独立危险因素,二分类Logistic回归分析显示年龄、偶有饮酒、VFA是初治T2DM患者发生MAFLD的独立危险因素(P<0.05)。结论 初治T2DM糖尿病患者VFA与MAFLD具有相关性,在初治T2DM患者中进行VFA、MAFLD的筛查及尽早干预十分有必要。展开更多
文摘Classification model has received great attention in any domain of research and also a reliable tool for medical disease diagnosis. The domain of classification model is used in disease diagnosis, disease prediction, bio informatics, crime prediction and so on. However, an efficient disease diagnosis model was compromised the disease prediction. In this paper, a Rough Set Rule-based Multitude Classifier (RS-RMC) is developed to improve the disease prediction rate and enhance the class accuracy of disease being diagnosed. The RS-RMC involves two steps. Initially, a Rough Set model is used for Feature Selection aiming at minimizing the execution time for obtaining the disease feature set. A Multitude Classifier model is presented in second step for detection of heart disease and for efficient classification. The Na?ve Bayes Classifier algorithm is designed for efficient identification of classes to measure the relationship between disease features and improving disease prediction rate. Experimental analysis shows that RS-RMC is used to reduce the execution time for extracting the disease feature with minimum false positive rate compared to the state-of-the-art works.
文摘AIM:To investigate the efficacy and safety of repeated dexamethasone implants with real-life data in eyes with naive retinal vein occlusion(RVO)with macular edema(ME)at a minimum of 60mo follow-up.METHODS:In this retrospective cohort study,the data about best corrected visual acuity(BCVA),central macular thickness(CMT),serous macular detachment(SMD),hard exudate,hyperreflective foci(HRF),cystoid degeneration,pearl necklace sign,epiretinal membrane(ERM),disorganization of retinal inner layers(DRIL),ellipsoid zone and external limiting membrane(EZ-ELM)integrity,intraocular pressure(IOP)and lens condition were recorded.RESULTS:Thirty-eight eyes of 38 patients were included in the study.Thirteen patients presented with central RVO(CRVO)and 25 with branch RVO(BRVO).The mean follow-up time was 69.9±15.8mo,and the mean number of injections was 7.9±4.0.The mean BCVA gain was 25.0±36 letters,and this difference was statistically significant(P=0.021).The BCVA gain was 19.4±20.4 letters in the CRVO group,and 26.5±38.6 letters in the BRVO group(P=0.763).Besides,21(55.2%)of the patients achieved≥15 letters improvement.At the end of the follow-up period,SMD was not observed in any of the patients(P=0.016).Hard exudate,HRF number were decreased;while DRIL,ERM and EZ-ELM defects were increased but not significantly.CONCLUSION:Intravitreal dexamethasone monotherapy is an effective and safe treatment option for the treatment-naive RVO-ME patients in the long-term follow-up.
文摘The naïve Bayes classifier is one of the commonly used data mining methods for classification.Despite its simplicity,naïve Bayes is effective and computationally efficient.Although the strong attribute independence assumption in the naïve Bayes classifier makes it a tractable method for learning,this assumption may not hold in real-world applications.Many enhancements to the basic algorithm have been proposed in order to alleviate the violation of attribute independence assumption.While these methods improve the classification performance,they do not necessarily retain the mathematical structure of the naïve Bayes model and some at the expense of computational time.One approach to reduce the naïvetéof the classifier is to incorporate attribute weights in the conditional probability.In this paper,we proposed a method to incorporate attribute weights to naïve Bayes.To evaluate the performance of our method,we used the public benchmark datasets.We compared our method with the standard naïve Bayes and baseline attribute weighting methods.Experimental results show that our method to incorporate attribute weights improves the classification performance compared to both standard naïve Bayes and baseline attribute weighting methods in terms of classification accuracy and F1,especially when the independence assumption is strongly violated,which was validated using the Chi-square test of independence.
文摘<span style="font-family:Verdana;">The presence of bearing faults reduces the efficiency of rotating machines and thus increases energy consumption or even the total stoppage of the machine. </span><span style="font-family:Verdana;">It becomes essential to correctly diagnose the fault caused by the bearing.</span><span style="font-family:Verdana;"> Hence the importance of determining an effective features extraction method that best describes the fault. The vision of this paper is to merge the features selection methods in order to define the most relevant featuresin the texture </span><span style="font-family:Verdana;">of the vibration signal images. In this study, the Gray Level Co-occurrence </span><span style="font-family:Verdana;">Matrix (GLCM) in texture analysis is applied on the vibration signal represented in images. Features</span><span><span><span style="font-family:;" "=""> </span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;">selection based on the merge of PCA (Principal component Analysis) method and SFE (Sequential Features Extraction) method is </span><span style="font-family:Verdana;">done to obtain the most relevant features. The multiclass-Na<span style="white-space:nowrap;">?</span>ve Bayesclassifi</span><span style="font-family:Verdana;">er is used to test the proposed approach. The success rate of this classification is 98.27%. The relevant features obtained give promising results and are more efficient than the methods observed in the literature.</span></span></span></span>
文摘As the importance of email increases,the amount of malicious email is also increasing,so the need for malicious email filtering is growing.Since it is more economical to combine commodity hardware consisting of a medium server or PC with a virtual environment to use as a single server resource and filter malicious email using machine learning techniques,we used a Hadoop MapReduce framework and Naïve Bayes among machine learning methods for malicious email filtering.Naïve Bayes was selected because it is one of the top machine learning methods(Support Vector Machine(SVM),Naïve Bayes,K-Nearest Neighbor(KNN),and Decision Tree)in terms of execution time and accuracy.Malicious email was filtered with MapReduce programming using the Naïve Bayes technique,which is a supervised machine learning method,in a Hadoop framework with optimized performance and also with the Python program technique with the Naïve Bayes technique applied in a bare metal server environment with the Hadoop environment not applied.According to the results of a comparison of the accuracy and predictive error rates of the two methods,the Hadoop MapReduce Naïve Bayes method improved the accuracy of spam and ham email identification 1.11 times and the prediction error rate 14.13 times compared to the non-Hadoop Python Naïve Bayes method.
文摘AIM: To characterize the prevalence of subpopulations of CD4+ cells along with that of major inhibitor or stimulator cell types in therapy-nave childhood Crohn's disease (CD) and to test whether abnormalities of immune phenotype are normalized with the improvement of clinical signs and symptoms of disease. METHODS: We enrolled 26 pediatric patients with CD. 14 therapy-nave CD children; of those, 10 children remitted on conventional therapy and formed the remission group. We also tested another group of 12 chil-dren who relapsed with conventional therapy and were given infliximab; and 15 healthy children who served as controls. The prevalence of Th1 and Th2, nave and memory, activated and regulatory T cells, along with the members of innate immunity such as natural killer (NK), NK-T, myeloid and plasmocytoid dendritic cells (DCs), monocytes and Toll-like receptor (TLR)-2 and TLR-4 expression were determined in peripheral blood samples. RESULTS: Children with therapy-nave CD and those in relapse showed a decrease in Th1 cell prevalence. Simultaneously, an increased prevalence of memory and activated lymphocytes along with that of DCs and monocytes was observed. In addition, the ratio of myeloid /plasmocytoid DCs and the prevalence of TLR-2 or TLR-4 positive DCs and monocytes were also higher in therapy-nave CD than in controls. The majority of alterations diminished in remitted CD irrespective of whether remission was obtained by conventional or biological therapy. CONCLUSION: The finding that immune phenotype is normalized in remission suggests a link between immune phenotype and disease activity in childhood CD. Our observations support the involvement of members of the adaptive and innate immune systems in childhood CD.
文摘Intrusion detection is the investigation process of information about the system activities or its data to detect any malicious behavior or unauthorized activity.Most of the IDS implement K-means clustering technique due to its linear complexity and fast computing ability.Nonetheless,it is Naïve use of the mean data value for the cluster core that presents a major drawback.The chances of two circular clusters having different radius and centering at the same mean will occur.This condition cannot be addressed by the K-means algorithm because the mean value of the various clusters is very similar together.However,if the clusters are not spherical,it fails.To overcome this issue,a new integrated hybrid model by integrating expectation maximizing(EM)clustering using a Gaussian mixture model(GMM)and naïve Bays classifier have been proposed.In this model,GMM give more flexibility than K-Means in terms of cluster covariance.Also,they use probabilities function and soft clustering,that’s why they can have multiple cluster for a single data.In GMM,we can define the cluster form in GMM by two parameters:the mean and the standard deviation.This means that by using these two parameters,the cluster can take any kind of elliptical shape.EM-GMM will be used to cluster data based on data activity into the corresponding category.
文摘This paper proposed an improved Naïve Bayes Classifier for sentimental analysis from a large-scale dataset such as in YouTube.YouTube contains large unstructured and unorganized comments and reactions,which carry important information.Organizing large amounts of data and extracting useful information is a challenging task.The extracted information can be considered as new knowledge and can be used for deci sion-making.We extract comments from YouTube on videos and categorized them in domain-specific,and then apply the Naïve Bayes classifier with improved techniques.Our method provided a decent 80%accuracy in classifying those comments.This experiment shows that the proposed method provides excellent adaptability for large-scale text classification.
文摘目的 探讨初治2型糖尿病(T2DM)患者内脏脂肪面积(VFA)与代谢相关脂肪性肝病(MAFLD)的相关性。方法 回顾性选取2021年8月—2023年4月住院的426例首次治疗2型糖尿病患者作为研究对象,以体重指数(BMI)分组,BMI<24 kg/m2为A(正常)组,24≤BMI≤28 kg/m2为B(超重)组,BMI≥28 kg/m2为C(肥胖)组,比较各组的一般资料、血清学指标、VFA、MAFLD等指标的差异。使用Spearman相关探讨VFA、MAFLD与各指标的相关性。采用多变量线性回归分析T2DM患者VFA的影响因素,采用二分类Logistic回归分析T2DM患者MAFLD发生的影响因素。结果 初治T2DM患者C组在年龄、高血压、腰围、收缩压、舒张压、VFA、MAFLD、谷丙转氨酶、谷草转氨酶、谷氨酰转肽酶、尿酸、甘油三酯、高密度脂蛋白胆固醇、空腹C肽、餐后2 h C肽与A组比较存在差异;C组在年龄、腰围、收缩压、舒张压、VFA、谷丙转氨酶、谷草转氨酶、空腹C肽、餐后2 h C肽与B组比较存在差异;B组在年龄、腰围、收缩压、VFA、MAFLD、尿酸、甘油三酯、高密度脂蛋白胆固醇、空腹C肽、餐后2 h C肽与A组比较存在差异;相关性分析显示VFA与高血压、腰围、体重指数、MAFLD、收缩压、舒张压、甘油三酯、总胆固醇、谷丙转氨酶、谷草转氨酶、血肌酐、血尿酸、空腹C肽、餐后2 h C肽存在正相关性,与性别(女)、年龄、高密度脂蛋白胆固醇存在负相关。线性回归分析显示性别(男)、高血压、体重指数、MAFLD是初治T2DM患者VFA的独立危险因素,二分类Logistic回归分析显示年龄、偶有饮酒、VFA是初治T2DM患者发生MAFLD的独立危险因素(P<0.05)。结论 初治T2DM糖尿病患者VFA与MAFLD具有相关性,在初治T2DM患者中进行VFA、MAFLD的筛查及尽早干预十分有必要。
