Supporting the construction of mystery novel knowledge graphs using BERT
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In the Knowledge Graph Reasoning Challenge, the plot of a mystery novel is converted into a knowledge graph. Building the graph requires the work of choosing the parts to put into the knowledge graph. This research aims to automate this task. Specifically, BERT, a Natural Language Processing model, is used to classify with either summary or non-summary, with the results proposed as the parts to put into the knowledge graph. We established a binary classification model and verified its accuracy. Its was found to have an F-value of 0.59. Given the likelihood of improving the accuracy, it is thought that organizing the dataset could have a significant impact. This is to be pursued as a next step.Keywords:
Knowledge graph
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Abstract Utilizing a dataset sourced from a higher education institution, this study aims to assess the efficacy of diverse machine learning algorithms in predicting student dropout and academic success. Our focus was on algorithms capable of effectively handling imbalanced data. To tackle class imbalance, we employed the SMOTE resampling technique. We applied a range of algorithms, including Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), as well as boosting algorithms such as Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), CatBoost (CB), and Light Gradient Boosting Machine (LB). To enhance the models' performance, we conducted hyperparameter tuning using Optuna. Additionally, we employed the Isolation Forest (IF) method to identify outliers or anomalies within the dataset. Notably, our findings indicate that boosting algorithms, particularly LightGBM and CatBoost with Optuna, outperformed traditional classification methods. Our study's generalizability to other contexts is constrained due to its reliance on a single dataset, with inherent limitations. Nevertheless, this research provides valuable insights into the effectiveness of various machine learning algorithms for predicting student dropout and academic success. By benchmarking these algorithms, our project offers guidance to both researchers and practitioners in their choice of suitable approaches for similar predictive tasks.
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Machine Learning (ML) is a technology that can revolutionize the world. It is a technology based on AI (Artificial Intelligence) and can predict the outcomes using the previous algorithms without programming it. A subset of artificial intelligence is called machine learning (AI). A machine may automatically learn from data and get better at what it does thanks to machine learning. “If additional data can be gathered to help a machine perform better, it can learn. A developing technology called machine learning allows computers to learn from historical data. Machines can predict the outcomes by machine learning. For Nowadays machine learning is very important for us because it makes our work easy. to many companies are using machine learning in their products, like google is using google its google assistant, which takes our voice command and gives what do we want from it, and google is also using its goggle lens form which we can find anything just by clicking a picture, and Netflix is using machine learning for recommendation of any movies or series, Machine learning has a very deep effect on our life, like nowadays we are using selfdriving car’s.
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This paper deals with the deployment and evaluation of machine learning classifiers for prediction of tuberculosis. This research paper deploys five key machine learning classifiers Naive Bayes, Support Vector Machine, Decision Tree, K Nearest Neighbors and Random Forest. It is clearly understood that Support Vector Machine provides the best accuracy 99.3 % for the prediction of Pulmonary Tuberculosis (PTB) and Extrapulmonary Tuberculosis (EPTB) when compared with all other machine learning classifiers on Tuberculosis data set. An important challenge in machine learning is to build accurate and competent machine learning classifiers. Hence Support Vector Machine is a best suited Machine Learning Classifier for prediction of the PTB and EPTB.
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