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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...

2020 International Conference on data analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

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

ISBN: (纸本)9781728196756

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. © 2020 IEEE.

关键词： Decision trees

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Applying Parallel Processing to Improve the Computation Speed of K-Nearest Neighbor Algorithm

Applying Parallel Processing to Improve the Computation Spee...

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2020 International Conference on data analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

作者： Alanezi, Maha A. Alqaddoumi, Abdulla University of Bahrain Collage of Science Big Data Science and Analytics Master Program Sakhir Bahrain University of Bahrain Collage of Information Technology Department of Information System Sakhir Bahrain

ISBN: (纸本)9781728196756

K-Nearest Neighbor (KNN) is a widely used algorithm to gain an accurate and efficient classification. One of the drawbacks of the algorithm is the time required to calculate the distance for each point. In this paper, the aim is to speed up the KNN algorithm with the implementation of multi threads and multiprocessors to reduce the time to execute the algorithm. There are two medical datasets used to apply the KNN algorithm for the comparison of the sequential and parallel performance. The datasets utilized are Heart Test and Breast Cancer Wisconsin (Diagnostic) data Sets. The multiprocessing is used to parallelize the computation of the distance of KNN. The parallel KNN outperforms the sequential version with the speedup increase, leading to reduction in time for both the processes and threads. © 2020 IEEE.

关键词： Learning algorithms

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Quantum Fourier Transform of Atrial Fibrillation

Quantum Fourier Transform of Atrial Fibrillation

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Quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Yu-Ching Chen Chih-Yu Chen Tsung-Wei Huang Chia-Ho Ou Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan Taiwan Quantum Information Center Chung Yuan Christian University Taoyuan Taiwan Department of Computer Science and Information Engineering National Pingtung University Pingtung Taiwan

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Electrocardiograms (ECGs) are crucial for detecting cardiac diseases like atrial fibrillation (AF). Traditional analysis methods like fast Fourier transform (FFT) face challenges with increasing data complexity. This study uses Taipei Veterans General Hospital data to explore quantum Fourier transform (QFT) for ECG analysis. Results show that QFT effectively analyzes ECG signals, matching FFT performance while benefiting from quantum computing's efficiency.

关键词： Heart Quantum computing Fast Fourier transforms Hospitals High performance computing Atrial fibrillation Machine learning Electrocardiography Real-time systems Signal analysis

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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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2020 International Conference on data analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

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

ISBN: (纸本)9781728196756

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. © 2020 IEEE.

关键词： COVID-19

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Theoretical insight and molecular recognition of fluconazole molecularly imprinted polymers: a combined computational and experimental analysis

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RSC Advances 2025年第24期15卷 19158-19175页

作者： Untung Gunawan Slamet Ibrahim Atthar Luqman Ivansyah Sophi Damayanti Department of Pharmacy School of Medicine and Health Sciences Atma Jaya Catholic University of Indonesia Indonesia Faculty of Pharmacy Universitas Jenderal Achmad Yani Cimahi Indonesia Instrumentation and Computational Physics Research Group Department of Physics Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Indonesia Master Program in Computational Science Faculty of Mathematics and Natural Science Institut Teknologi Bandung Indonesia Department of Pharmacochemistry School of Pharmacy Institut Teknologi Bandung Indonesia University Center of Excellence on Artificial Intelligence for Vision Natural Language Processing & Big Data Analytics (U-CoE Al-VLB) Institut Teknologi Bandung Indonesia

Insufficient surveillance and diagnosis result in a minimum of 150 million cases of serious fungal infections reported annually. The WHO has compiled a list of priority pathogens to encourage research and investment in fungal infections and antifungal resistance in late 2022. Among these, Candida albicans is classified as a critical pathogen. Fluconazole is widely recognized as an effective medication for the treatment and prevention of both mucosal and invasive candidiasis. Molecularly imprinted polymer (MIP) could enhance separation selectivity in fluconazole bioanalysis. The objective of this research is to develop an MIP for fluconazole by analyzing interactions identified in prior research and incorporating established improvements for MIP synthesis that cannot be observed through laboratory experimentation. Based on binding affinity, intermolecular hydrogen bonds, complexation energy, and thermodynamic characteristics, 2-acrylamido-1-ethanesulfonic acid was chosen as the optimal monomer. The HOMO–LUMO investigation revealed the localization of the orbitals from the guest to the host. The FMO study indicated that chloroform was the most suitable solvent for complex formation. The QTAIM, NBO, and NCI-RDG analyses identified the hydrogen bond formed between the H51 atom of the monomer and the N33 atom of fluconazole, determined to be the most significant hydrogen bond in the host–guest interaction. The interaction energy from multi-monomer interaction showed that a 1 : 6 ratio is the best ratio in forming a pre-polymerization complex between the template and monomer. Based on the findings of this study, it is anticipated that the computational analysis may be utilized for rational design for the enhancement of prior studies and future laboratory investigations.

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Random Forest vs. SVM vs. KNN in classifying Smartphone and Smartwatch sensor data using CRISP-DM

Random Forest vs. SVM vs. KNN in classifying Smartphone and ...

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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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The largest genome-wide association study for breast cancer in Taiwanese Han population

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Breast Cancer Research and Treatment 2024年第2期203卷 291-306页

作者： Hsu, Yu-Ching Chen, Hung-Lin Cheng, Chi-Fung Chattopadhyay, Amrita Chen, Pei-Shan Lin, Che-Chen Chiang, Hsiu-Yin Liu, Ting-Yuan Huang, Chi-Hao Kuo, Chin-Chi Chuang, Eric Y. Lu, Tzu-Pin Tsai, Fuu-Jen Bioinformatics Program Taiwan International Graduate Program National Taiwan University Taipei Taiwan Bioinformatics Program Institute of Statistical Science Taiwan International Graduate Program Academia Sinica Taipei Taiwan Institute of Health Data Analytics and Statistics Department of Public Health College of Public Health National Taiwan University Taipei Taiwan Big Data Center China Medical University Hospital Taichung Taiwan Center for Translational Genomic Research Department of Medical Research China Medical University Hospital Taichung Taiwan Million-Person Precision Medicine Initiative Department of Medical Research China Medical University Hospital Taichung Taiwan Division of Breast Surgery Department of Surgery China Medical University Hospital Taichung Taiwan Division of Nephrology Department of Internal Medicine China Medical University Hospital Taichung Taiwan Department of Medical Research China Medical University Hospital Taichung Taiwan Bioinformatics and Biostatistics Core Center of Genomic and Precision Medicine National Taiwan University Taipei Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Biomedical Technology and Device Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan Genetic Center Department of Medical Research China Medical University Hospital Taichung Taiwan

Purpose: Breast cancer is a molecularly heterogeneous disease, and multiple genetic variants contribute to its development and prognosis. Most of previous genome-wide association studies (GWASs) and polygenic risk scores (PRSs) analyses focused on studying breast cancers of Caucasian populations, which may not be applicable to other population. Therefore, we conducted the largest breast cancer cohort of Taiwanese population to fill in the knowledge gap. Methods: A total of 152,534 Participants recruited by China Medical University Hospital between 2003 and 2019 were filtered by several patient selection criteria and GWAS quality control steps, resulting in the inclusion of 2496 cases and 9984 controls for this study. We then conducted GWAS for all breast cancers and PRS analyses for all breast cancers and the four breast cancer subtypes, including luminal A, luminal B, basal-like, and HER2-enriched. Results: The GWAS analyses identified 113 SNPs, 50 of which were novel. The PRS models for all breast cancers and the luminal A subtype showed positively correlated trends between the PRS and the risk of developing breast cancer. The odds ratios (95% confidence intervals) for the groups with the highest PRS in all breast cancers and the luminal A subtype were 5.33 (3.79–7.66) and 3.55 (2.13–6.14), respectively. Conclusion: In summary, we explored the association of genetic variants with breast cancer in the largest Taiwanese cohort and developed two PRS models that can predict the risk of developing any breast cancer and the luminal A subtype in Taiwanese women. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Breast cancer Genome-wide association study Polygenic risk score Taiwanese population

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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...

引用

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

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...

引用

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

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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