Efficacy Of Tranexamic Acid in Reducing Blood Loss in Primary Total Knee Replacement

Objective: To determine the efficacy of tranexamic acid in reducing blood loss in primary total knee replacement. Material and Methods: A total of 96 patients having a diagnosis of primary knee osteoarthritis made up the population sample. The Total Knee Replacement patients were separated into two groups. Patients in Group B used Intra venous tranexamic acid, but those in Group A did not use tranexamic acid during the course of the operation or afterwards. Results: Mean age of the patients recorded in group A 63.79±6.60 (years) and in group B 62.96±7.89 (years). The majority of the patients in both groups were females. After surgery, Group B patients who received tranexamic acid reported less blood loss and less haemoglobin reduction as compared to the control group. Conclusion: From our study, we conclude that Tranexamic acid used intravenously during total knee arthroplasty considerably lowers postoperative blood loss

Predicting Divorce Prospect Using Ensemble Learning:Support Vector Machine, Linear Model, and Neural Network

A divorce is a legal step taken by married people to end their marriage. It occurs after a couple decides to no longer live together as husband and wife. Globally, the divorce rate has more than doubled from 1970 until 2008, with divorces per 1,000 married people rising from 2.6 to 5.5. Divorce occurs at a rate of 16.9 per 1,000 married women. According to the experts, over half of all marriages ends in divorce or separation in the United States. A novel ensemble learning technique based on advanced machine learning algorithms is proposed in this study. The support vector machine (SVM), passive aggressive classifier, and neural network (MLP) are applied in the context of divorce prediction. A question-based dataset is created by the field specialist. The responses to the questions provide important information about whether a marriage is likely to turn into divorce in the future. The cross-validation is applied in 5 folds, and the performance results of the evaluation metrics are examined. The accuracy score is 100%, and Receiver Operating Characteristic (ROC) curve accuracy score, recall score, the precision score, and the F1 accuracy score are close to 97% confidently. Our findings examined the key indicators for divorce and the factors that are most significant when predicting the divorce

Predicting Employee Attrition Using Machine Learning Approaches

Employee attrition refers to the natural reduction in the employees in an organization due to many unavoidable factors. Employee attrition results in a massive loss for an organization. The Society for Human Resource Management (SHRM) determines that USD 4129 is the average cost-per-hire for a new employee. According to recent stats, 57.3% is the attrition rate in the year 2021. A research study needs to be implemented to find the causes of employee attrition and a learning framework to predict employee attrition. This research study aimed to analyze the organizational factors that caused employee attrition and the prediction of employee attrition using machine learning techniques. The four machine learning techniques were applied in comparison. The proposed optimized Extra Trees Classifier (ETC) approach achieved an accuracy score of 93% for employee attrition prediction. The proposed approach outperformed recent state-of-the-art studies. The Employee Exploratory Data Analysis (EEDA) was applied to determine the factors that caused employee attrition. Our study revealed that the monthly income, hourly rate, job level, and age are the key factors that cause employee attrition. Our proposed approach and research findings help organizations overcome employee attrition by improving the factors that cause attrition

Predicting Employee Attrition Using Machine Learning Approaches

Employee attrition refers to the natural reduction in the employees in an organization due to many unavoidable factors. Employee attrition results in a massive loss for an organization. The Society for Human Resource Management (SHRM) determines that USD 4129 is the average cost-per-hire for a new employee. According to recent stats, 57.3% is the attrition rate in the year 2021. A research study needs to be implemented to find the causes of employee attrition and a learning framework to predict employee attrition. This research study aimed to analyze the organizational factors that caused employee attrition and the prediction of employee attrition using machine learning techniques. The four machine learning techniques were applied in comparison. The proposed optimized Extra Trees Classifier (ETC) approach achieved an accuracy score of 93% for employee attrition prediction. The proposed approach outperformed recent state-of-the-art studies. The Employee Exploratory Data Analysis (EEDA) was applied to determine the factors that caused employee attrition. Our study revealed that the monthly income, hourly rate, job level, and age are the key factors that cause employee attrition. Our proposed approach and research findings help organizations overcome employee attrition by improving the factors that cause attrition

A Novel Approach to Classify Telescopic Sensors Data Using Bidirectional-Gated Recurrent Neural Networks

Asteroseismology studies the physical structure of stars by analyzing their solar-type oscillations as seismic waves and frequency spectra. The physical processes in stars and oscillations are similar to the Sun, which is more evolved to the red-giant branch (RGB), representing the Sun’s future. In stellar astrophysics, the RGB is a crucial problem to determine. An RGB is formed when a star expands and fuses all the hydrogen in its core into helium which starts burning, resulting in helium burning (HeB). According to a recent state by NASA Kepler mission, 7000 HeB and RGB were observed. A study based on an advanced system needs to be implemented to classify RGB and HeB, which helps astronomers. The main aim of this research study is to classify the RGB and HeB in asteroseismology using a deep learning approach. Novel bidirectional-gated recurrent units and a recurrent neural network (BiGR)-based deep learning approach are proposed. The proposed model achieved a 93% accuracy score for asteroseismology classification. The proposed technique outperforms other state-of-the-art studies. The analyzed fundamental properties of RGB and HeB are based on the frequency separation of modes in consecutive order with the same degree, maximum oscillation power frequency, and mode location. Asteroseismology Exploratory Data Analysis (AEDA) is applied to find critical fundamental parameters and patterns that accurately infer from the asteroseismology dataset. Our key findings from the research are based on a novel classification model and analysis of root causes for the formation of HeB and RGB. The study analysis identified that the cause of HeB increases when the value of feature Numax is high and feature Epsilon is low. Our research study helps astronomers and space star oscillations analyzers meet their astronomy findings

A Novel Approach to Classify Telescopic Sensors Data Using Bidirectional-Gated Recurrent Neural Networks

Asteroseismology studies the physical structure of stars by analyzing their solar-type oscillations as seismic waves and frequency spectra. The physical processes in stars and oscillations are similar to the Sun, which is more evolved to the red-giant branch (RGB), representing the Sun’s future. In stellar astrophysics, the RGB is a crucial problem to determine. An RGB is formed when a star expands and fuses all the hydrogen in its core into helium which starts burning, resulting in helium burning (HeB). According to a recent state by NASA Kepler mission, 7000 HeB and RGB were observed. A study based on an advanced system needs to be implemented to classify RGB and HeB, which helps astronomers. The main aim of this research study is to classify the RGB and HeB in asteroseismology using a deep learning approach. Novel bidirectional-gated recurrent units and a recurrent neural network (BiGR)-based deep learning approach are proposed. The proposed model achieved a 93% accuracy score for asteroseismology classification. The proposed technique outperforms other state-of-the-art studies. The analyzed fundamental properties of RGB and HeB are based on the frequency separation of modes in consecutive order with the same degree, maximum oscillation power frequency, and mode location. Asteroseismology Exploratory Data Analysis (AEDA) is applied to find critical fundamental parameters and patterns that accurately infer from the asteroseismology dataset. Our key findings from the research are based on a novel classification model and analysis of root causes for the formation of HeB and RGB. The study analysis identified that the cause of HeB increases when the value of feature Numax is high and feature Epsilon is low. Our research study helps astronomers and space star oscillations analyzers meet their astronomy findings