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 fr...
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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,...
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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 ...
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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.
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 r...
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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....
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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.
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 sco...
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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 r...
详细信息
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.
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 fr...
详细信息
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.
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 clus...
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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.
The study investigates a quantum-inspired approach to image reconstruction using Ising machines and demonstrates its significant improvements over the contrastive divergence method in Restricted Boltzmann Machines tra...
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ISBN:
(数字)9798331541378
ISBN:
(纸本)9798331541385
The study investigates a quantum-inspired approach to image reconstruction using Ising machines and demonstrates its significant improvements over the contrastive divergence method in Restricted Boltzmann Machines training and the quality of image reconstruction on the MINST digits and fashion datasets.
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