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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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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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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 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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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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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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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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Principles and Components of Federated Learning Architectures

arXiv

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arXiv 2025年

作者： Al Nasim, Abdullah Soshi, Fatema Tuj Johura Biswas, Parag Ferdous, A.S.M. Anas Rashid, Abdur Biswas, Angona Gupta, Kishor Datta Research and Development Department Pioneer Alpha Dhaka Bangladesh Msc in Data Science and Analytics University of Hertfordshire Hatfield United Kingdom Department of Biomedical Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh MSEM Department Westcliff university CA United States Department of Computer and Information Science Clark Atlanta University Georgia United States

Federated Learning (FL) is a machine learning framework where multiple clients, from mobiles to enterprises, collaboratively construct a model under the orchestration of a central server but still retain the decentralized nature of the training data. This decentralized training of models offers numerous advantages, including cost savings, enhanced privacy, improved security, and compliance with legal requirements. However, for all its apparent advantages, FL is not immune to the limitations of conventional machine learning methodologies. This article provides an elaborate explanation of the inherent concepts and features found within federated learning architecture, addressing five key domains: system heterogeneity, data partitioning, machine learning models, communication protocols, and privacy techniques. This article also highlights the limitations in this domain and proposes avenues for future work. Besides, we provide a set of architectural patterns for federated learning systems, which are derived from the systematic survey of the literature. The main elements of FL, the fundamentals of Federated Learning, and a few architectural specifics will all be better understood with the aid of this research. © 2025, CC0.

关键词： Federated learning

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