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

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

ISBN: (纸本)9781665490580

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 © 2022 IEEE.

关键词： Efficiency

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Chronic Kidney Disease Detection: A Comparative study

Chronic Kidney Disease Detection: A Comparative study

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

作者： Zyad Allam Reem Jamal Fathan Shajrah Sawsan Hilal MSc. in Big Data Science and Analytics College of Science University of Bahrain Department of Mathematics College of Science University of Bahrain

ISBN: (数字)9798350369786

ISBN: (纸本)9798350369793

Chronic Kidney Disease (CKD) is a life-threatening disease that impairs the kidney making it unable to filter blood properly. Early detection and appropriate treatment of CKD are crucial to increase the survival rate of patients. This study provides a comparative analysis for the identification of CKD. Toward this end, the performance of Logistic Regression, Random Forest, K-Nearest Neighbor (KNN), and Support Vector Machines (SVM) classifiers have been compared. The CKD dataset of the UCI machine learning repository was used to train the models. The dataset consists of 400 cases of which 246 (62%) have CKD and 149 (38%) are healthy cases. The dataset has 25 features distributed into 11 numerical features and 14 categorical features, including the response. The data were loaded into R for exploratory data analysis and pre-processing. To solve the issue of data imbalance, the researchers performed oversampling on the dataset. This study revealed that Random Forest outperforms others with an accuracy of 0.992 and 0.993 F1Score after oversampling. Additionally, the study found that oversampling increased the performance of Logistic Regression, Random Forest and SVM models. However, KNN experienced a decrease in performance after resampling, from 0.988 to 0.967 accuracy after oversampling.

关键词： Support vector machines Logistic regression data analysis Accuracy Nearest neighbor methods Chronic kidney disease Vectors

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

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

ISBN: (纸本)9781665490580

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. © 2022 IEEE.

关键词： Feature Selection

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Parallel Implementation of Random Forest using MPI

Parallel Implementation of Random Forest using MPI

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

作者： Zyad Atef Allam Fatehan Tael Shajera Reem Jamal Majed Abdulla Alqaddoumi MSc. in Big Data Science and Analytics University of Bahrain Sakhir Bahrain Department of Computer Science University of Bahrain Sakhir Bahrain

ISBN: (数字)9798350369786

ISBN: (纸本)9798350369793

Computer vision is a quickly rising field in machine learning. It has many use cases and implementations across various industries. Powerful classifiers can be trained to recognize visual patterns and detect people and objects. However, since these models use images as inputs, they often end up being trained with very complex architectures, with a high number of epochs and very large data sizes. This often results in very long training times, especially for weaker machines. This study investigated the use of parallel processing to speed up machine learning model training. Message-passing Interface (MPI) is used to parallelize the training of Random Forest, achieving speed-ups of up to 5 and an efficiency of up to 1.1. The study concludes that when dealing with big and complex data and building complex models, parallel processing can significantly reduce the training time of the model with minimal loss in accuracy.

关键词： Training Industries Radio frequency Visualization Parallel processing data models Hardware

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Review previous studies of machine learning techniques to analyze road traffic accidents in different countries

Review previous studies of machine learning techniques to an...

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

作者： 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: (数字)9798350369786

ISBN: (纸本)9798350369793

RTAs are considered public health issues. For that, many countries and organizations, such as the Gulf Cooperation Council and United Nations, are trying to reduce the number of accidents caused by road traffic. This study reviews several previous studies that used machine learning algorithms to find the best models that can deal with the dataset related to RTAs, it is found the results differentiated by changing the dataset, some high results achieved with the NB algorithm, and the others achieved by using MLP and DT. This study includes more than 30 studies with more than 15 machine learning algorithms. There are several factors that affect the RTAs such as gender driver, age, type of vehicle, cause of accident, and lighting condition. The high number of algorithms used in the previous studies discussed during this study are DT, followed by RF, SVM, and NB respectively. This study provides a review to help researchers to understand the techniques used to analyze the dataset in the RTA sector.

关键词： Support vector machines Radio frequency Machine learning algorithms Sensitivity Reviews Prediction algorithms Road traffic

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A Comparative Study on Machine Learning Models to Classify Diseases Based on Patient Behaviour and Habits

arXiv

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arXiv 2024年

作者： Musaaed, Elham Hewah, Nabil Alasaadi, Abdulla MSc. in Big Data Science and Analytics College of Science University of Bahrain Al-Riffa Bahrain Department of Information Systems College of Information Technology University of Bahrain Al-Riffa Bahrain

In recent years, ML algorithms have been shown to be useful for predicting diseases based on health data and posed a potential application area for these algorithms such as modeling of diseases. The majority of these applications employ supervised rather than unsupervised ML algorithms. In addition, each year, the amount of data in medical science grows rapidly. Moreover, these data include clinical and Patient-Related Factors (PRF), such as height, weight, age, other physical characteristics, blood sugar, lipids, insulin, etc., all of which will change continually over time. Analysis of historical data can help identify disease risk factors and their interactions, which is useful for disease diagnosis and prediction. This wealth of valuable information in these data will help doctors diagnose accurately and people can become more aware of the risk factors and key indicators to act proactively. The purpose of this study is to use six supervised ML approaches to fill this gap by conducting a comprehensive experiment to investigate the correlation between PRF and Diabetes, Stroke, Heart Disease (HD), and Kidney Disease (KD). Moreover, it will investigate the link between Diabetes, Stroke, and KD and PRF with HD. Further, the research aims to compare and evaluate various ML algorithms for classifying diseases based on the PRF. Additionally, it aims to compare and evaluate ML algorithms for classifying HD based on PRF as well as Diabetes, Stroke, Asthma, Skin Cancer, and KD as attributes. Lastly, HD predictions will be provided through a Web-based application on the most accurate classifier, which allows the users to input their values and predict the output. Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), K-Nearest Neighbor (KNN), Extreme Gradient Boost (XGB), and Support Vector Machine (SVM) were the algorithms used. The dataset was obtained from the Kaggle repository. The attributes are divided into PRF and diseases. The selected algorithms were imple

关键词： Support vector machines

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

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

ISBN: (纸本)9781665490597

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.

关键词： Industries Machine learning algorithms Urban areas Companies Predictive models Prediction algorithms data models

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Factors that Impact Tension String Tennis Racket

Factors that Impact Tension String Tennis Racket

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

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

ISBN: (纸本)9781665490597

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.

关键词： Training Industries data analysis Sports equipment Shape Friction data mining

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Developing A Parallel Program For Image Similarity Search Using Hashing Methods

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

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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 Abdallah Alqaddoumi MSc. in Big Data Science and Analytics College of Science University of Bahrain College of Information Technology College of Science University of Bahrain

ISBN: (纸本)9781665490597

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.

关键词： Industries data analysis Parallel processing Business

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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...

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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 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

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.

关键词： Heart Industries Stacking Boosting Decision trees Medical diagnosis Task analysis

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