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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1-10 订阅

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Factors that Impact Tension String Tennis Racket

Factors that Impact Tension String Tennis Racket

引用

2022 International Conference on data analytics for Business and Industry, ICDABI 2022

作者： Yusuf, Husain Yusuf, Salman Hilal, Sawsan University of Bahrain MSc. in Big Data Science and Analytics College of Science Bahrain University of Bahrain College of Science Department of Mathematics Bahrain

ISBN: (纸本)9781665490580

Tennis is growing in popularity year after year. It is an expensive sport that requires special equipment, such as rackets, balls, and tennis strings. One of the problems with tennis is tennis strings that have to be changed in short periods due to the loss of their tension. Most tennis strings are not recyclable as their excessive consumption negatively affects the environment. This study aims at identifying the factors that impact tennis strings. The data were extracted from the Tennis Warehouse University website which specializes in tennis and its equipment. After deleting missing values, the 80:20 ratio was used to divide the data into two distinct sets: training and testing. To this end, three regression methods were implemented: least squares, ridge, and lasso. Across all variables, only the Friction between the strings themselves and the Spin Potential was found to have an insignificant impact on the tennis strings. The results showed that polyester strings could withstand the most tension and those made of the gut were the worst performers. Furthermore, it's better to string the racket at medium tension instead of low tension. Despite the interesting results obtained concerning the factors that impact the tennis strings, the study can be improved by including other variables such as the tennis racket type, the racket shape, the methods of stringing the rackets, the types of balls, and the string thicknesses. © 2022 IEEE.

关键词： Tennis

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Comparing the Performance of Machine Learning Algorithms for Predicting Employees' Turnover

Comparing the Performance of Machine Learning Algorithms for...

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2022 International Conference on data analytics for Business and Industry, ICDABI 2022

作者： Aldossary, Yasmeen Ebrahim, Majeed Alhaddad, Salman Aldossary, Manar Hilal, Sawsan University of Bahrain MSc. in Big Data Science and Analytics College of Science Bahrain University of Bahrain Department of Mathematics College of Science Bahrain

ISBN: (纸本)9781665490580

Employee turnover directly impacts companies ' performance due to losing qualified staff and invested costs, so predicting the ability to turnover or staying will help companies reduce these effects. Therefore, this study aimed to predict turnover by building a model using selected machine learning algorithms based on secondary data of a big data company. The algorithms are Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree, and Random Forest, which have been improved with different methods for better performance, like balancing data and important feature selection. The Random Forest was characterized by the best performance with 80.2% accuracy and 72.4% AUC, with city development index, experience, and university enrolment being the most important features. However, the performance of the Random Forest classifier can be enhanced by including other important features besides those highlighted by the model. © 2022 IEEE.

关键词： Forecasting

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Developing A Parallel Program For Image Similarity Search Using Hashing Methods

Developing A Parallel Program For Image Similarity Search Us...

引用

2022 International Conference on data analytics for Business and Industry, ICDABI 2022

作者： Aldossary, Yasmeen Ebrahim, Majeed Alqaddoumi, Abdallah University of Bahrain MSc. in Big Data Science and Analytics College of Science Bahrain University of Bahrain College of Information Technology College of Science Bahrain

ISBN: (纸本)9781665490580

Nowadays, images play an essential role in people's lives as they document their memories and affairs. Therefore, a large number of images have been generated in different storage. As a result, there was a need to search for similar images in this colossal quantity. While enormous data processing requires speed in execution, it needs the parallel processing method. This study aims to develop a program to search for similar images using the image hashing methods, the Average Hashing, and the Difference Hashing, then to compare their performance. In addition, it aims to explore the effect of using parallelization on the speedup and efficiency of the program. The first part's results stated that the performance of the Average Hashing and the Difference Hashing methods was affected by the type of data used, despite their effectiveness in detecting identical images. As for the second part, the parallel program reduced the execution time by almost half using two workers, while the best speedup value was when using eight workers. In terms of efficiency, it achieved the highest results with a value greater than 0.9 when using two workers. © 2022 IEEE.

关键词： Efficiency

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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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2022 International Conference on data analytics for Business and Industry, ICDABI 2022

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

ISBN: (纸本)9781665490580

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%. © 2022 IEEE.

关键词： Logistic regression

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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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2022 International Conference on data analytics for Business and Industry, ICDABI 2022

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

ISBN: (纸本)9781665490580

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. © 2022 IEEE.

关键词： Support vector machines

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Classification of Water Potability Using Machine Learning Algorithms

Classification of Water Potability Using Machine Learning Al...

引用

2022 International Conference on data analytics for Business and Industry, ICDABI 2022

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

ISBN: (纸本)9781665490580

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. © 2022 IEEE.

关键词： Neural networks

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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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2022 International Conference on data analytics for Business and Industry, ICDABI 2022

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

ISBN: (纸本)9781665490580

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. © 2022 IEEE.

关键词： Logistic regression

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A Comparative Study Of Heart Disease Prediction Using Tree-Based Ensemble Classification Techniques

A Comparative Study Of Heart Disease Prediction Using Tree-B...

引用

2022 International Conference on data analytics for Business and Industry, ICDABI 2022

作者： Aldossary, Yasmeen Ebrahim, Majeed Hewahi, Nabil University of Bahrain MSc. in Big Data Science and Analytics College of Science Bahrain University of Bahrain College of Information Technology Department of Computer Science Bahrain

ISBN: (纸本)9781665490580

In recent years, the prediction of heart disease has been one of the most complicated tasks in the medical field. Approximately one person dies per minute due to heart disease in the modern era. To help the healthcare industry experts give an early detection in preventing the progression of the disease, Machine Learning offers various algorithms and techniques to achieve this goal. This study compares the performance of tree-based models Random Forest, Decision Tree, Extra Trees, and Gradient Boosting using the Cleveland heart dataset. It investigates whether applying two different ensemble techniques Voting and Stacking, to tree-based models improves heart disease diagnosis performance. The obtained results revealed that the Extra Trees model outweighs the other three models with an accuracy of 92%, whereas the Decision Tree model has the lowest accuracy with 84%. Applying feature selection has enhanced the performance of Random Forest and Gradient Boosting models. As for the ensemble techniques, the Staking ensemble model had the same performance as the Extra trees, with an accuracy of 92%. In contrast, the Voting ensemble model has a lower performance with a 90% accuracy. © 2022 IEEE.

关键词： Heart

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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...

引用

2022 International Conference on data analytics for Business and Industry, ICDABI 2022

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

ISBN: (纸本)9781665490580

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. © 2022 IEEE.

关键词： Decision trees

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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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2022 International Conference on data analytics for Business and Industry, ICDABI 2022

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

ISBN: (纸本)9781665490580

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. © 2022 IEEE.

关键词： Feature Selection

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