SummaryBackgroundPost-partum haemorrhage is the leading cause of maternal death worldwide. Early administration of tranexamic acid reduces deaths due to bleeding in trauma patients. We aimed to assess the effects of early administration of tranexamic acid on death, hysterectomy, and other relevant outcomes in women with post-partum haemorrhage.MethodsIn this randomised, double-blind, placebo-controlled trial, we recruited women aged 16 years and older with a clinical diagnosis of post-partum haemorrhage after a vaginal birth or caesarean section from 193 hospitals in 21 countries. We randomly assigned women to receive either 1 g intravenous tranexamic acid or matching placebo in addition to usual care. If bleeding continued after 30 min, or stopped and restarted within 24 h of the first dose, a second dose of 1 g of tranexamic acid or placebo could be given. Patients were assigned by selection of a numbered treatment pack from a box containing eight numbered packs that were identical apart from the pack number. Participants, care givers, and those assessing outcomes were masked to allocation. We originally planned to enrol 15 000 women with a composite primary endpoint of death from all-causes or hysterectomy within 42 days of giving birth. However, during the trial it became apparent that the decision to conduct a hysterectomy was often made at the same time as randomisation. Although tranexamic acid could influence the risk of death in these cases, it could not affect the risk of hysterectomy. We therefore increased the sample size from 15 000 to 20 000 women in order to estimate the effect of tranexamic acid on the risk of death from post-partum haemorrhage. All analyses were done on an intention-to-treat basis. This trial is registered with ISRCTN76912190 (Dec 8, 2008); ClinicalTrials.gov, number NCT00872469; and PACTR201007000192283.FindingsBetween March, 2010, and April, 2016, 20 060 women were enrolled and randomly assigned to receive tranexamic acid (n=10 051) or placebo (n=10 009), of whom 10 036 and 9985, respectively, were included in the analysis. Death due to bleeding was significantly reduced in women given tranexamic acid (155 [1·5%] of 10 036 patients vs 191 [1·9%] of 9985 in the placebo group, risk ratio [RR] 0·81, 95% CI 0·65–1·00; p=0·045), especially in women given treatment within 3 h of giving birth (89 [1·2%] in the tranexamic acid group vs 127 [1·7%] in the placebo group, RR 0·69, 95% CI 0·52–0·91; p=0·008). All other causes of death did not differ significantly by group. Hysterectomy was not reduced with tranexamic acid (358 [3·6%] patients in the tranexamic acid group vs 351 [3·5%] in the placebo group, RR 1·02, 95% CI 0·88–1·07; p=0·84). The composite primary endpoint of death from all causes or hysterectomy was not reduced with tranexamic acid (534 [5·3%] deaths or hysterectomies in the tranexamic acid group vs 546 [5·5%] in the placebo group, RR 0·97, 95% CI 0·87-1·09; p=0·65). Adverse events (including thromboembolic events) did not differ significantly in the tranexamic acid versus ...

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Sentiment analysis is also known as opinion mining which shows the people's opinions and emotions about certain products or services. The main problem in sentiment analysis is the sentiment polarity categorization that determines whether a review is positive, negative or neutral. Previous studies proposed different techniques, but still there are some research gaps, i) some studies include only 3 sentiment classes: positive, neutral and negative, but none of them considered more than 3 classes ii) sentiment polarity features were considered on individual basis but none of them considered on both individual and on combined basis iii) No previous technique considered five sentiment classes with 3 sentiment polarity features such as a verb, adverb, adjective and their combinations. In this study, we propose a sentiment polarity categorization technique for a large data set of online reviews of Instant Videos. A comprehensive data set of five hundred thousand online reviews is used in our research. There are five classes (Strongly Negative, Negative, Neutral, Positive and Strongly Positive). We also consider three polarity features Verb, Adverb, Adjective and their combinations with their different senses in review-level categorization. Our experiments for review-level categorization show promising outcomes as the accuracy of our results is 81 percent which is 3 percent better than many previous techniques whose average accuracy is 78 percent. INDEX TERMS Sentiment, opinion mining, social media, natural language processing.

Recent progress in technology has altered the learning behaviors of students; besides giving a new impulse which reshapes the education itself. It can easily be said that the improvements in technologies empower students to learn more efficiently, effectively and contentedly. Smart Learning (SL) despite not being a new concept describing learning methods in the digital age- has caught attention of researchers. Smart Learning Analytics (SLA) provides students of all ages with research-proven frameworks, helping students to benefit from all kinds of resources and intelligent tools. It aims to stimulate students to have a deep comprehension of the context and leads to higher levels of achievements. The transformation of education to smart learning will be realized by reengineering the fundamental structures and operations of conventional educational systems. Accordingly, students can learn the proper information yet to support to learn real-world context, more and more factors are needed to be taken into account. Learning has shifted from web-based dumb materials to context-aware smart ubiquitous learning. In the study, a SLA dataset was explored and advanced ensemble techniques were applied for the classification task. Bagging Tree and Stacking Classifiers have outperformed other classical techniques with an accuracy of 79% and 78% respectively.

Feature selection plays a crucial role in order to mitigate the high dimensional feature space in different classification problems. The computational cost is reduced, and the accuracy of the classification is improved by reducing the dimension of feature space. Hence, in the classification task, finding the optimal subset of features is of utmost importance. Metaheuristic techniques have proved their efficacy in solving many real-world optimization issues. One of the recently introduced physics-inspired optimization methods is Archimedes Optimization Algorithm (AOA). This paper proposes an Enhanced Archimedes Optimization Algorithm (EAOA) by adding a new parameter that depends on the step length of each individual while revising the individual location. The EAOA algorithm is proposed to improve the AOA exploration and exploitation balance and enhance the classification performance for the feature selection issue in real-world data sets. Experiments were performed on twenty-three standard benchmark functions and sixteen real-world data sets to investigate the performance of the proposed EAOA algorithm. The experimental results based on the standard benchmark functions show that the EAOA algorithm provides very competitive results compared to the basic AOA algorithm and five well-known optimization algorithms in terms of improved exploitation, exploration, local optima avoidance and convergence rate. In addition, the results based on sixteen real-world data sets ascertain that reduced feature subset yields higher classification performance when compared with the other feature selection methods.

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Educational data-mining is an evolving discipline that focuses on the improvement of self-learning and adaptive methods. It is used for finding hidden patterns or intrinsic structures of educational data. In the arena of education, the heterogeneous data is involved and continuously growing in the paradigm of big-data. To extract meaningful information adaptively from big educational data, some specific data mining techniques are needed. This paper presents a clustering approach to partition students into different groups or clusters based on their learning behavior. Furthermore, personalized e-learning system architecture is also presented which detects and responds teaching contents according to the students’ learning capabilities. The primary objective includes the discovery of optimal settings, in which learners can improve their learning capabilities. Moreover, the administration can find essential hidden patterns to bring the effective reforms in the existing system. The clustering methods K-Means, K-Medoids, Density-based Spatial Clustering of Applications with Noise, Agglomerative Hierarchical Cluster Tree and Clustering by Fast Search and Finding of Density Peaks via Heat Diffusion (CFSFDP-HD) are analyzed using educational data mining. It is observed that more robust results can be achieved by the replacement of existing methods with CFSFDP-HD. The data mining techniques are equally effective to analyze the big data to make education systems vigorous.