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

作者： Sadiq Jaffer Saleh Sayed Qasim Ali Ahmed M. Zeki Big Data Science & Analytics Master Program University of Bahrain College of Information Technology University of Bahrain

ISBN: (数字)9781728196756

ISBN: (纸本)9781728196763

Nowadays IoT sensors become one of the major sources of data, starting from home automation going to daily devices. Focusing on handsets that have the most significant contribution factor on data blooming and booming. The problem at hand is data mining methods comparison, for selecting the best method that serves sensor data. Methodology used was CRISP-DM. This paper found Random Forest data mining method to work best under the described circumstances.

关键词： Support vector machines Legged locomotion data mining Business Activity recognition Accelerometers Bibliographies

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Exploring the Relation between Blood Tests and Covid-19 Using Machine Learning

Exploring the Relation between Blood Tests and Covid-19 Usin...

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

作者： Mohamed Almansoor Nabil M. Hewahi Big Data Science & Analytics Program University of Bahrain University of Bahrain

ISBN: (数字)9781728196756

ISBN: (纸本)9781728196763

COVID-19 is a global pandemic that hit the world in 2019-2020 and caused massive losses. Every day, hundreds of thousands of tests are being done on possible infected cases. It usually takes several hours to get the results of virus test in advanced countries, whereas in other countries might take days. The aim of this study is to investigate whether normal blood medical tests help in detecting covid-19 using various machine learning approaches. If true, this would give an indication to people who should undergo the virus test. In this paper we independently use machine learning algorithms including support vector machines, adaptive boosting, random forest and k-nearest neighbors. These algorithms are then merged to form ensemble learning which leads to the classification. The results show that the ensemble learning is having the highest true positive rate of 30%. The obtained results show that normal blood tests do not help much in giving right indications about detecting COVID-19.

关键词： COVID-19 Biological system modeling Machine learning Support vector machines Blood Random forests Predictive models

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Comparing Naïve Bayes, Decision Tree and Logistic Regression Methods in Fraudulent Credit Card Transactions

Comparing Naïve Bayes, Decision Tree and Logistic Regressio...

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

作者： Maha A. Alanezi Mawra T. Homeed Zahra S. Mohamed Ahmed M. Zeki Big Data Science & Analytics Master Program University of Bahrain Sakhir Kingdom of Bahrain Collage of Information Technology University of Bahrain Sakhir Kingdom of Bahrain

ISBN: (数字)9781728196756

ISBN: (纸本)9781728196763

data mining is utilized to explore banks' data to unravel any hidden scams and detect potential frauds. The aim of this paper is to compare between the Naïve Bayes, Decision Tree and Logistic Regression in fraudulent credit card transactions. Cross-Industry Standard Process for data Mining (CRISP-DM) is followed to achieve the aim of this research. In terms of accuracy, the best classification model was Logistic Regression with 94.6% accuracy, compared with the Decision Tree and Naïve Bayes that showed accuracy of 89.1% and 90.9% respectively. Other measures were also calculated like time needed to build the model among others.

关键词： Support vector machines Business Bayes methods Hidden Markov models Logistics data models Credit cards

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Tweets Sentiment Analysis During COVID-19 Pandemic

Tweets Sentiment Analysis During COVID-19 Pandemic

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

作者： Maha A. Alanezi Nabil M. Hewahi Big Data Science & Analytics Master Program Collage of Science University of Bahrain Sakhir Kingdom of Bahrain Collage of Information Technology University of Bahrain Sakhir Kingdom of Bahrain

ISBN: (数字)9781728196756

ISBN: (纸本)9781728196763

The aim of this paper is to investigate the impact of social distance on people during COVID-19 pandemic using twitter sentiment analysis through a comparison between the k-means clustering and Mini-Batch k-means clustering approaches. To find the most common frequent words, two datasets have been investigated (WHO and Bahrain ministry of health datasets) to be as data preparation and exploration. Another two datasets (English and Arabic datasets) are used in the clustering of k-means. In this paper, a comparison between k-means and Mini-Batch k-means is performed to find a pattern. The word frequency shows that there are several words related to the pandemic. The sentiment analysis result show that in USA, Australia, Nigeria, Canada, and England, most tweets are neutral. However, the majority of tweets are positive tweets from both Italy and India. In addition, the k-means cluster in the English dataset reveals several cluster trends where COVID-19 pandemic procedures are addressed in cluster 1, and health workers are encouraged in cluster 3.

关键词： COVID-19 Social networking (online) Pandemics Sentiment analysis Blogs Elbow Viruses (medical)

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Prediction of Stock Price and Direction Using Neural Networks: datasets Hybrid Modeling Approach

Prediction of Stock Price and Direction Using Neural Network...

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

作者： Mohamed Al Aradi Nabil Hewahi Big Data Science & Analytics Master Program Collage of Science Univestity of Bahain Sakhir Kingdom of Bahrain Collage of Information Technology Univestity of Bahain Sakhir Kingdom of Bahrain

ISBN: (数字)9781728196756

ISBN: (纸本)9781728196763

Stock prices prediction is one of the most daunting tasks to achieve for day traders, investors, and data scientists. They are complex functions of a wide array of contributing factors that affects the movement dynamics. Politics, social outlook, the company's sales performance, and socio-economic factors all have an influence of how the stock is perceived globally and thus affecting the price movement based on the supply/demand balance. In this work a Framework is proposed to predict the price and movement direction by using multiple datasets that includes news sentiment, social sentiment, company earnings announcement, and technical indicators. A case study was applied on the Apple Inc. stock using Long short-term Memory Neural Networks (LSTM) and Deep Neural Networks (DNN). Results showed superiority of the LSTM model in both predicting stock value [ 75.4 MSE, +-2.52 PE] and the direction with [70.1% classification accuracy] over the DNN model which suffered from prediction lag and dependency on trend indicators achieving [53.1%] in predicting the direction of the stock.

关键词： Predictive models Machine learning Companies Neural networks Market research data models Recurrent neural networks

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An Enhanced BERTopic Framework and Algorithm for Improving Topic Coherence and Diversity

An Enhanced BERTopic Framework and Algorithm for Improving T...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Sahil Sawant Jinhong Yu Kirtikumar Pandya Chun-Kit Ngan Rolf Bardeli Data Science Program Worcester Polytechnic Institute Worcester MA USA Big Data & Consumer Analytics Vodafone GmbH Duesseldorf NRW Germany

In this paper, we enhance and customize the existing BERTopic framework to develop and implement an automated pipeline that delivers a more coherent and diverse set of topics with an even moderate dataset. More specifically, the contributions of this work are threefold: (1) integrate a dynamic and advanced optimizer into the existing BERTopic framework to learn the optimal number of dimensions of different document embeddings, (2) develop a k-means-based algorithm in the optimizer to support the dimension-embedding learning, and (3) conduct an extensive experimental study on three distinct types of datasets, including DBPedia, AG News, and Reuters, to evaluate the performance of our approach in terms of the topic quality (TQ) score computed by the topic coherence and the topic diversity. From the results, we can conclude that our enhanced, automated BERTopic framework with its dimension-embedding learning algorithm on documents outperforms the TQ score of the existing framework by 4.49% (before removing the stop words) and 16.52% (after removing the stop words) among all the four representable document-embedding approaches, including the BERTopic's Default Sentence Transformer, Google's Universal Sentence Encoder, OpenAI GPT-2, and our investigators' developed Context-aware Embedding Model, on all the three datasets.

关键词： Heuristic algorithms Pipelines Coherence Transformers Internet Context modeling

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A Novel Machine Learning Classifier Based on Genetic Algorithms and data Importance Reformatting

arXiv

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arXiv 2024年

作者： Alkhayyat, A.K. Hewahi, N.M. Big Data Science and Analytics Program College of Science University of Bahrain Bahrain Department of Computer Science College of Information Technology University of Bahrain Bahrain

In this paper, a novel classification algorithm that is based on data Importance (DI) reformatting and Genetic Algorithms (GA) named GADIC is proposed to overcome the issues related to the nature of data which may hinder the performance of the Machine Learning (ML) classifiers. GADIC comprises three phases which are data reformatting phase which depends on DI concept, training phase where GA is applied on the reformatted training dataset, and testing phase where the instances of the reformatted testing dataset are being averaged based on similar instances in the training dataset. GADIC is an approach that utilizes the exiting ML classifiers with involvement of data reformatting, using GA to tune the inputs, and averaging the similar instances to the unknown instance. The averaging of the instances becomes the unknown instance to be classified in the stage of testing. GADIC has been tested on five existing ML classifiers which are Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Logistic Regression (LR), Decision Tree (DT), and Naïve Bayes (NB). All were evaluated using seven open-source UCI ML repository and Kaggle datasets which are Cleveland heart disease, Indian liver patient, Pima Indian diabetes, employee future prediction, telecom churn prediction, bank customer churn, and tech students. In terms of accuracy, the results showed that, with the exception of approximately 1% decrease in the accuracy of NB classifier in Cleveland heart disease dataset, GADIC significantly enhanced the performance of most ML classifiers using various datasets. In addition, KNN with GADIC showed the greatest performance gain when compared with other ML classifiers with GADIC followed by SVM while LR had the lowest improvement. The lowest average improvement that GADIC could achieve is 5.96%, whereas the maximum average improvement reached 16.79%. © 2024, CC BY.

关键词： Adversarial machine learning

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Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains

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Computational and Structural Biotechnology Journal 2024年 23卷 1864-1876页

作者： Do, Duyen Thi Yang, Ming-Ren Vo, Tran Nam Son Le, Nguyen Quoc Khanh Wu, Yu-Wei Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Business Administration College of Management Lunghwa University of Science and Technology Taoyuan City Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan TMU Research Center for Digestive Medicine Taipei Medical University Taipei Taiwan

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for Pseudomonas aeruginosa, we determined that using machine learning models on unitig-centered pan-genomes showed significant promise for accurately predicting the antibiotic resistance or susceptibility of microbial strains. Applying a feature-selection-based machine learning algorithm led to satisfactory predictive performance for the training dataset (with an area under the receiver operating characteristic curve (AUC) of > 0.929) and an independent validation dataset (AUC, approximately 0.77). Furthermore, the selected unitigs revealed previously unidentified resistance genes, allowing for the expansion of the resistance gene repertoire to those that have not previously been described in the literature on antibiotic resistance. These results demonstrate that our proposed unitig-based pan-genome feature set was effective in constructing machine learning predictors that could accurately identify AMR pathogens. Gene sets extracted using this approach may offer valuable insights into expanding known AMR genes and forming new hypotheses to uncover the underlying mechanisms of bacterial AMR. © 2024 The Authors

关键词： Genes

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Research on Traffic Sign Detection and Recognition System Using Deep Ensemble Learning

Research on Traffic Sign Detection and Recognition System Us...

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IEEE International Conference on Computer Communication and the Internet (ICCCI)

作者： Lung-Jen Wang Taweelap Suwattanapunkul Jarinya Thalauy Parinya Jansengrat Department of Computer Science and Information Engineering National Pingtung University Pingtung Taiwan International Master Program in Information Technology and Applications International College National Pingtung University Pingtung Taiwan Department of Big Data Management and Analytics Rajamangala University of Technology Thanyaburi Pathum Thani Thailand

ISBN: (数字)9798350351767

ISBN: (纸本)9798350351774

There is a rise in car accidents due to human errors on the road. A critical task of self-driving cars that can reduce accidents on the road is traffic sign detection and recognition (TSDR), which is vital in alerting drivers to the presence of traffic signs in advance. This research will separate the proposed deep ensemble learning algorithm into two methods. First, after the traffic scene process, the algorithm will detect the traffic sign as two categories with the YOLOv5s network. Then, process the traffic sign to recognize the traffic sign into seven classes with the MobileNet network. The detection model was trained with the Taiwan Traffic Sign Detection (TTSD) dataset collected from Taiwan roads. The recognition model was trained with the Taiwan Traffic Sign Recognition (TTSR) dataset. The result of the proposed algorithm showed high performance when experimenting with 95.83% accuracy, 87.34% true prediction, and 191.3 milliseconds (ms) of inference time.

关键词： YOLO Roads Prediction algorithms Inference algorithms Internet Autonomous automobiles Ensemble learning

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Risks of liver cirrhosis,hepatocellular carcinoma,hepatic-related complications,and mortality in patients with type 2 diabetes in Taiwan

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World Journal of Diabetes 2025年第5期16卷 202-215页

作者： Hua-Fen Chen Yung-Yueh Chang Peter Chen Xiao-Han Shen Chin-Huan Chang Wan-Lun Hsu Department of Endocrinology Far Eastern Memorial HospitalNew Taipei City 220TaiwanChina School of Medicine College of MedicineFu Jen Catholic UniversityNew Taipei City 242TaiwanChina Department of Public Health College of MedicineFu Jen Catholic UniversityNew Taipei City 242TaiwanChina Institute of Epidemiology and Preventive Medicine College of Public HealthNational Taiwan UniversityTaipei City 100TaiwanChina Department of Gastroenterology Choninn HospitalChoninn Medical GroupNew Taipei City 220TaiwanChina Master Program of Big Data in Medical Healthcare Industry College of MedicineFu Jen Catholic UniversityNew Taipei City 242TaiwanChina Data Science Center Fu Jen Catholic UniversityNew Taipei City 242TaiwanChina

BACKGROUND Hepatitis B and C and alcoholic liver disease are the principal causes of hepaticrelated morbidity and ***,evidence of the associations between diabetes without the above risk factors and hepatic-related study endpoints is not well *** addition,the effects of associated metabolic dysfunction and exercise on hepatic outcomes are still not *** To investigate the incidence and relative hazards of cirrhosis of the liver,hepato cellular carcinoma(HCC),hepatic-related complications and mortality in patients with type 2 diabetes(T2D)who were nonalcoholic and serologically negative for hepatitis B and C in *** A total of 33184 T2D patients and 648746 nondiabetic subjects selected from Taiwan’s adult preventive health care service were linked to various National Health Insurance databases,cancer registry,and death registry to identify cirrhosis of the liver,HCC,hepatic-related complications,and *** Poisson assumption and Cox proportional hazard regression model were used to estimate the incidences and relative hazards of all hepatic-related study endpoints,*** also compared the risk of hepatic outcomes stratified by age,sex,associated metabolic dysfunctions,and regular exercise between T2D patients and nondiabetic *** Compared with nondiabetic subjects,T2D patients had a significantly greater incidence(6.32 vs 17.20 per 10000 person-years)and greater risk of cirrhosis of the liver[adjusted hazard ratio(aHR)1.45;95%CI:1.30-1.62].The aHRs for HCC,hepatic complications,and mortality were 1.81,1.87,and 2.08,*** older age,male sex,obesity,hypertension,and dyslipidemia further increased the risks of all hepatic-related study endpoints,and regular exercise decreased the risk,irrespective of diabetes *** Patients with T2D are at increased risk of cirrhosis of the liver,HCC,hepatic-related complications,and mortality,and associated metabolic dysfunctions provide additional

关键词： Type 2 diabetes mellitus Liver cirrhosis Hepatocellular carcinoma Hepatic-related complications Mortality Metabolic dysfunction-associated steatotic liver disease Metabolic dysfunction-associated steatohepatitis

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