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检索条件"机构=MSc. in Big Data Science and Analytics"

共 31 条记录，以下是21-30 订阅

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Comparing the Performance of Individual and Hybrid Classifiers for Medical datasets

Comparing the Performance of Individual and Hybrid Classifie...

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data analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Aysha Alkhayyat Elham Musaaed Isa AlHeddi Nabil Hewahi MSc. in Big Data Science and Analytics College of Science University of Bahrain Department of Computer Science College of Information Technology University of Bahrain

ISBN: (纸本)9781665490597

An accurate prediction of a certain medical condition is a crucial matter that is of great benefit to patients and healthcare systems. Machine Learning can be of a great help to the medical professionals in assisting them to precisely predict the occurrence of medical conditions. This research paper aims to enhance the prediction capabilities of machine learning models, so it become more adopted in the medical field. To fill in this gap, the capabilities of the ensemble hybrid model were explored in comparison to individual models. The machine learning algorithms were tested using five medical datasets pertaining to five different diseases. The Random Forest model was the best performing individual model. In terms of accuracy, the ensemble hybrid model performance was close to the individual models in all datasets, except for the Liver Disease dataset where it exceled. This research could be extended to experiment the integrated hybrid models along with the optimization techniques.

关键词： Recurrent neural networks Machine learning algorithms Support vector machine classification Stochastic processes Predictive models Diabetes Particle swarm optimization

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Classification of Water Potability Using Machine Learning Algorithms

Classification of Water Potability Using Machine Learning Al...

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data analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Husain Yusuf Salman Alhaddad Salman Yusuf Nabil Hewahi MSc. in Big Data Science and Analytics College of Science University of Bahrain Department of Computer Science College of Information Technology University of Bahrain

ISBN: (纸本)9781665490597

Water is the most important resource for sustaining life. Everyone has the right to have access to pollution-free water. The achievement of safe drinking water leads to tangible benefits to health. According to the World Health organization (WHO), the number of global deaths due to water diseases is about two and a half million deaths per year. Therefore, it is important to study new applications for analyzing and classifying safe water quality. In this research, the classification algorithms of K Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), Logistic Regression (LR), and Support Vector Machine (SVM) are used to classify the potability of the drinking water. RF and DT achieved the highest performance with an accuracy of 83.78% and 74.98% respectively. The lowest classifier accuracy is LR with 48.74%. In addition, the most important features of the RF model are pH, followed by Hardness, followed by Sulfate. On other hand, the least important feature is Turbidity.

关键词： Support vector machines Industries Machine learning algorithms Artificial neural networks Water quality Organizations Water pollution

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Comparing K-Nearest Neighbors, Random Forest and Naïve Bayes Models to Classify Fetal Health Using Resampling Methods

Comparing K-Nearest Neighbors, Random Forest and Naïve Baye...

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data analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Yasmeen Aldossary Salman Alhaddad Majeed Ebrahim Ahmed M. Zeki MSc. in Big Data Science and Analytics College of Science University of Bahrain Department of Information System College of Information Technology University of Bahrain

ISBN: (纸本)9781665490597

To preserve the lives of pregnant women and their fetuses, Cardiotocography (CTG) data is used to check the fetus’s condition and for early intervention if necessary. As a result of the existence of imbalanced classes of fetus’s condition, the performance of some models is affected when applied to real data. Accordingly, this research aims to observe the effect of data resampling methods on models’ performance by adopting the CRISP methodology. The performance of k-Nearest Neighbors (KNN), Random Forest (RF), and Naive Bayes (NB) models are compared using resampled data with several methods with the help of the CorrelationAttributeEval feature selection method for high dimension reduction. The results stated that NB and RF models’ performance has not improved with the resampling methods, while KNN has significantly improved across all metrics. RF is considered a good classifier as it was the best, with an accuracy of 93.8%. NB was the only improved model using feature selection with imbalanced data. Also, NB has the worst performance in terms of accuracy compared to KNN and RF.

关键词： Pregnancy Measurement Industries Analytical models Pediatrics Inspection Feature extraction

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Predicting Performance of Primary School Students and Determining The Dominant Factors Using data Mining with Feature Selection Methods

Predicting Performance of Primary School Students and Determ...

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data analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Yasmeen Aldossary Majeed Ebrahim Salman Alhaddad Ahmed M. Zeki MSc. in Big Data Science and Analytics College of Science University of Bahrain Department of Information System College of Information Technology University of Bahrain

ISBN: (纸本)9781665490597

Educational organizations consistently attempt to investigate students' behaviors in learning and give early predictions for intervening and improving their learning performance, especially in primary education, which is the core stone of education. Educational data mining offers different practical prediction algorithms to predict student performance and various feature selection methods to determine the dominant factors which affect student performance. This study applied three data mining algorithms, K-Nearest Neighbor, Naive Bayes, and Decision Tree, to predict the performance of students based on their socio-demographic characteristics. The feature selection methods Information Gain Attribute Evaluation, Wrapper Subset Evaluation, and Classifier Subset Evaluation were used to identify the dominant factors. The research was done by adopting the CRISP methodology using a data set obtained from one of Bahrain's public primary boys' schools. The results showed that the model of the k-Nearest Neighbors algorithm with Wrapper Subset Evaluation as a feature selection method had achieved the highest accuracy rate of 67.6%. For the dominant factors, Student Age and Student Level have the most impact on student performance, followed by Parent education level.

关键词： Industries data analysis Education Organizations Feature extraction Prediction algorithms Classification algorithms

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Investigation on Machine Learning Algorithms to Predict High School Students Performance at Risk of Failure

Investigation on Machine Learning Algorithms to Predict High...

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data analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Fatema Almehaiza Layla Jaafar Iyad Ali Mohammad Alatoom Nabil Mahmood Hewahi MSc. in Big Data Science and Analytics College of Science University of Bahrain Department of Computer Science College of Information Technology University of Bahrain

ISBN: (纸本)9781665490597

Successful learning is dependent on the success of the learners; ensuring students’ achievement is the responsibility of the educational system. Over the years, educational institutions have utilized Machine Learning (ML) technology to facilitate and support decision-making in a variety of learning areas. Predicting students’ performance in the early stages of their learning journey is one of ML’s most significant contributions to educational institutions. Using ML technology, the institutes will be able to improve teaching and learning practices for students who are at risk of failing. The objective of this study is to build a model that can predict students’ performance and identify students at risk of failure. The study examines the application of three ML algorithms in predicting: Logistic Regression (LR), Decision Tree (DT) and Random Forest (RF). The study used a sample size of 647 with one dependent variable and 33 distinct variables. The study found that the two tree-based methods performed better than LR, although the precession measure out of four measures shows that LR is slightly better. Overall, in this study RF has showed to be the best among them with an accuracy of80.37%, recall of 87.98%, and F1 rate of 91.78%.

关键词： Radio frequency Machine learning algorithms Education Sociology Predictive models Prediction algorithms Reproducibility of results

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Parallel Implementation of KNN Algorithm for Dry Beans dataset

Parallel Implementation of KNN Algorithm for Dry Beans Datas...

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data analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Salman Hammad Salman Alhaddad Husain Yusuf Abdulla Alqaddoumi MSc. in Big Data Science and Analytics College of Science University of Bahrain Department of Information System College of Information TechnologyUniversity of Bahrain

ISBN: (纸本)9781665490597

In recent years, the interest in using machine learning to solve complex problems in different sectors using parallel algorithms has increased. The KNN algorithm is the most popular method to classify the dataset. The main problem in implementing this algorithm is how to quickly find K nearest neighbors. Accordingly, this research aims to study the effect of the parallel execution of the KNN algorithm by using different metrics such as speedup and efficiency. The performance of serial and parallel KNN algorithms was compared using different values of K and a different number of processers. The results show that The Message Passing Interface (MPI4py) improves performance by increasing speedup and decreasing execution time. Also, the impact of serial execution on the size of the dataset is discussed. It was found that as the size of the dataset increases, the serial time increases. Meanwhile, the use of seven processers in parallel execution with the KNN algorithm needs less time. In addition, as the number of processers increases, the speedup increases, and the efficiency decreases

关键词： Measurement Industries Machine learning algorithms data analysis Message passing Machine learning Classification algorithms

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Predicting the Impact of University Majors on the Length of Post-Graduation Unemployment Periods in Bahrain

Predicting the Impact of University Majors on the Length of ...

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data analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Aysha Alkhayyat Elham Musaaed Isa AlHeddi Ahmed Zeki MSc. in Big Data Science and Analytics College of Science University of Bahrain Department of Information Systems College of Information Technology University of Bahrain

ISBN: (纸本)9781665490597

Unemployment rate is among the most important measures that reflects the country’s development. International investors and creditors will closely monitor the unemployment rate prior to making any decision to invest in any country. In addition, this measure is being monitored by the governments to set plans to keep it within the norm, should it exceed the acceptable limits. By understanding the importance of this measure and in pursuance of guiding high school graduates to enroll in majors of high demand and hence reduce the waiting time, this research studied the ability of three classifiers, namely Support Vector Machine (SVM), KNearest Neighbors (KNN), and Naive Bayes (NB) to accurately predict the length of post-graduation unemployment periods based on the university major of University of Bahrain (UOB) graduates. SVM classifier was found to provide the best predictions, though not significantly outperforming the KNN and NB classifiers. Moreover, other factors such as the Grade Point Average (GPA) and gender can be tested for their impact on unemployment period individually or grouped with the major. Additionally, this paper can be used to set the basis for deploying real life applications that will predict the unemployment period based on multiple factors.

关键词： Support vector machines Industries data analysis Government Forestry Length measurement Time measurement

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Multilingual Sentiment Analysis using Deep-Learning Architectures 5

Multilingual Sentiment Analysis using Deep-Learning Architec...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Dominic, Praveen Purushothaman, Niranjan Kumar, Anish Skanda Anil Prabagaran, A. Blessy, J. Angelin John, A. School of Graduate Studies MSc in Big Data Analytics Trent University Ontario Canada Visa Consolidated Support Services India Pvt. Ltd Bengaluru India School of Computing Science and Engineering Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9781665474672

Machine learning techniques such as NLP (Natural language processing) play a key role in a context where mining social media data could add great value to governments of the world countries. The posts and tweets shared by the people on social media can be mined to infer the valuable 'mindset' of the people which is much required for any ruling government in the world. The objective of this study is to conduct sentiment analysis to mine the sentiment of the people regarding the ongoing war between Russia and Ukraine, using machine learning techniques. The idea is to analyze and infer if the countries have reacted in some way, considering the sentiment of their citizens, in the context of economic effects. The pipeline of the implementation associated starts with the data collection from social media such as Twitter and Reddit using Snscraper and the PRAW (Python Reddit API Wrapper). The larger posts from Reddit are handled by implementing suitable text summarization techniques. Sentiment analysis is performed for the social media data using the BERT transformer model. The non-English posts are translated to English using neural machine translation. Also, sentiment analysis is performed at various granularities such as the location and the people that are tagged using Named Entity Recognition techniques. Finally, a comparative analysis of the world countries' sentiment and their corresponding reliance on Russian oil is performed. © 2023 IEEE.

关键词： Sentiment analysis

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Hybrid Machine Learning Algorithms for Polycystic Ovary Syndrome Detection

Hybrid Machine Learning Algorithms for Polycystic Ovary Synd...

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data analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Sara Alshakrani Sawsan Hilal Ahmed M. Zeki MSc. in Big Data Science and Analytics College of Science University of Bahrain Department of Mathematics College of Science University of Bahrain Department of Information Systems College of Information Technology University of Bahrain

ISBN: (纸本)9781665490597

This study focuses on identifying Polycystic Ovary Syndrome (PCOS), which is a serious medical hormonal disorder condition that affects a woman’s ability in childbearing age and causes infertility, diabetes, heart problems, endometrial cancer, and other related health issues as it raises the possibility of long-term problems. Therefore, early detection is of great importance. Taking into account Machine Learning (ML) and ensemble learning algorithms with their superior detection capabilities, especially in the medical field. The Hybrid Random Forest Logistic Regression (HRFLR), combined Extreme Boosting with Random Forest, Linear Support Vector Machine, Light Gradient Boosting Model, and CatBoost model are among the hybrid ML models used in this study. To support the unique techniques, the classification models have been investigated using the PCOS dataset containing physical and clinical parameters of women collected from 10 different hospitals across Kerala, India, and downloaded from the Kaggle repository. The top 14 physical and clinical features were selected to detect PCOS by using the univariate feature selection approach. Follicle No. (R) and Follicle No. (L) were found to be the most significant among all features. The evaluation metrics used to test all the models are Accuracy, Precision, Recall, F1-Score, ROC curve plot, Area Under the Curve Score, and K-fold Cross Validation (CV). Finally, results were discussed and compared to indicate that CatBoost outperforms other models, with an accuracy score of 92% also HRFLR obtained the best CV accuracy with 92% when K = 30, both applied on the top 14 features only using 80:20 data split ratio. Consequently, CatBoost is effective at detecting PCOS patients.

关键词： Support vector machines Measurement Industries Heart Pediatrics Machine learning algorithms Hospitals

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Tree-Based Methods for Loan Approval

Tree-Based Methods for Loan Approval

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data analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Mohamed Alaradi Sawsan Hilal MSc. in Big Data Science and Analytics University of Bahrain College of Science University of Bahrain

ISBN: (数字)9781728196756

ISBN: (纸本)9781728196763

Loan approval is one of the most important processes that any banking organization owns. The acceptance or rejection of any loan application has a direct impact on the bank revenue and the profitability in quarterly issued financial statements. Though loan approval is a critical process, the actual decision made is not a straightforward procedure and comes with a lot of uncertainties. Recently, statisticians and data scientists have tried to automate this process to minimize risk and increase profitability by applying different statistical learning methods. In this work we explore a framework with an application by applying tree-based methods on publicly available dataset. This work aimed at developing a high performance predictive model for loan approval prediction using decision trees. Experiments were made in different varieties of tree methods ranging from the most simplified and comprehensible decision tree reaching up to the most complex random forests. Results yielded inadequate performance with respect to simplified decision trees due to the highlight correlated and complex feature space, majority of critical parameters affecting loan approval was not reflected upon and yielded an impractically over-simplified tree. However, boosting came in superior in terms of performance, relevance and interpretation via the importance chart scoring accuracy on testing dataset [98.75%] specificity [100%], Minority class prediction accuracy [92.85%], and classification efficiency of [97.0%]. Therefore, boosting-based decision-tree predictive model was recommended to facilitate decision making regarding the eligibility of loan applicants based on their characteristics.

关键词： Vegetation Decision trees Predictive models Bagging Random forests data analysis Decision making

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