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Human leukocyte antigen distributions do not share a copula across sub-populations

Experimental Results 2022年 3卷 e24-e24页

作者： Schellhas, Dan Green, Robert C. Data Science Program Bowling Green State University Bowling Green 43402 OH United States Department of Computer Science Bowling Green State University Bowling Green 43402 OH United States

The distribution of human leukocyte antigens in the population assists in matching solid organ donors and recipients when the typing methods used do not provide sufficiently precise information. This is made possible by linkage disequilibrium (LD), where alleles co-occur more often than random chance would suggest. There is a trade-off between the high bias and low variance of a broad sample from the population and the low bias but high variance of a focused sample. Some of this trade-off could be alleviated if sub-populations shared LD despite having different allele frequencies. These experiments show that Bayesian estimation can balance bias and variance by tuning the effective sample size of the reference panel, but the LD as represented by an additive or multiplicative copula is not shared. © 2022. The Author(s).

关键词： copula estimation human leukocyte antigens imputation organ transplantation

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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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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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Randomized Spline Trees for Functional data Classification: Theory and Application to Environmental Time Series

arXiv

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arXiv 2024年

作者： Riccio, Donato Maturo, Fabrizio Romano, Elvira Machine Learning Engineer and Student in the Data Science Master’s Degree Program The University of Campania Luigi Vanvitelli Caserta Italy Department of Economics Statistics and Business Faculty of Technological and Innovation Sciences Universitas Mercatorum Rome Italy Department of Mathematics and Physics University of Campania Luigi Vanvitelli Caserta Italy

Functional data analysis (FDA) and ensemble learning can be powerful tools for analyzing complex environmental time series. Recent literature has highlighted the key role of diversity in enhancing accuracy and reducing variance in ensemble *** paper introduces Randomized Spline Trees (RST), a novel algorithm that bridges these two approaches by incorporating randomized functional representations into the Random Forest framework. RST generates diverse functional representations of input data using randomized B-spline parameters, creating an ensemble of decision trees trained on these varied representations. We provide a theoretical analysis of how this functional diversity contributes to reducing generalization error and present empirical evaluations on six environmental time series classification tasks from the UCR Time Series Archive. Results show that RST variants outperform standard Random Forests and Gradient Boosting on most datasets, improving classification accuracy by up to 14%. The success of RST demonstrates the potential of adaptive functional representations in capturing complex temporal patterns in environmental data. This work contributes to the growing field of machine learning techniques focused on functional data and opens new avenues for research in environmental time series *** Codes 62M10, 68T05, 65D07, 68T10 © 2024, CC BY.

关键词： Spatio-temporal data

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Multi-class Weather Forecasting from Twitter Using Machine Learning Aprroaches

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Procedia Computer science 2021年 179卷 47-54页

作者： Kartika Purwandari Join W.C. Sigalingging Tjeng Wawan Cenggoro Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta Indonesia 10720 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Now in the era of big data, many are applying information methods accurately especially by social media. The aims of this study to classify the weather based on Twitter automatically using text mining by using Support Vector Machine (SVM), MultinomialNaive Bayes (MNB), and Logistic Regression (LR) method. The experimental results show that SVM substantially outperforms various other machine learning algorithms for the task of text classification with an accuracy value of 93%. This result proves that SVM is very suitable for text categorization. We use clustering technique to read the pattern in customers’ opinion about the restaurant based on some measurement variables.

关键词： weather classification twitter machine learning support vector machine multinomial naive bayes logistic regression

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Ecology, Spatial Structure, and Selection Pressure Induce Strong Signatures in Phylogenetic Structure

arXiv

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arXiv 2024年

作者： Moreno, Matthew Andres Rodriguez-Papa, Santiago Dolson, Emily Department of Ecology and Evolutionary Biology University of Michigan Ann Arbor United States Center for the Study of Complex Systems University of Michigan Ann Arbor United States Michigan Institute for Data Science University of Michigan Ann Arbor United States Department of Computer Science and Engineering Michigan State University East Lansing United States Program in Ecology Evolution and Behavior Michigan State University East Lansing United States

Evolutionary dynamics are shaped by a variety of fundamental, generic drivers, including spatial structure, ecology, and selection pressure. These drivers impact the trajectory of evolution, and have been hypothesized to influence phylogenetic structure. For instance, they can help explain natural history, steer behavior of contemporary evolving populations, and influence efficacy of application-oriented evolutionary optimization. Likewise, in inquiry-oriented artificial life systems, these drivers constitute key building blocks for open-ended evolution. Here, we set out to assess (1) if spatial structure, ecology, and selection pressure leave detectable signatures in phylogenetic structure, (2) the extent, in particular, to which ecology can be detected and discerned in the presence of spatial structure, and (3) the extent to which these phylogenetic signatures generalize across evolutionary systems. To this end, we analyze phylogenies generated by manipulating spatial structure, ecology, and selection pressure within three computational models of varied scope and sophistication. We find that selection pressure, spatial structure, and ecology have characteristic effects on phylogenetic metrics, although these effects are complex and not always intuitive. Signatures have some consistency across systems when using equivalent taxonomic unit definitions (e.g., individual, genotype, species). Further, we find that sufficiently strong ecology can be detected in the presence of spatial structure. We also find that, while low-resolution phylogenetic reconstructions can bias some phylogenetic metrics, high-resolution reconstructions recapitulate them faithfully. Although our results suggest potential for evolutionary inference of spatial structure, ecology, and selection pressure through phylogenetic analysis, further methods development is needed to distinguish these drivers' phylometric signatures from each other and to appropriately normalize phylogenetic metrics. With s

关键词： Abiotic

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Descriptive Analytics Analysis as Support for Ancestral Domain Preservation: Empirical Evidence Generation as support for Decision Making

Descriptive Analytics Analysis as Support for Ancestral Doma...

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IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)

作者： Eltimar T. Castro Nephi P. Romano Rev. Fr. Randy Jasper C. Odchigue Rev. Fr. John Christian U. Young Jannina de Jesus Alfante Rochelle A. Valencia Data Science Analytics Center Father Saturnino Urios University Butuan City Philippines Office of VP Academic Affairs and Research Father Saturnino Urios University Butuan City Philippines Office of the President dept. Father Saturnino Urios University Butuan City Philippines Arts and Science Program Father Saturnino Urios University Butuan City Philippines

Ancestral domain refers to the lands, territories, and resources possessed and administered collectively by indigenous peoples, such as the indigenous communities of the Philippines. Indigenous peoples who have inhabited and cared for these regions for generations attach great cultural, spiritual, and economic significance to them. Indigenous Peoples Rights Act (IPRA) of 1997 acknowledged for the first time the idea of ancestral domain in the Philippines. However, the IPRA’s implementation and the preservation of ancestral properties in the Philippines have encountered obstacles. There have been instances of unlawful logging, mining, and land appropriation in ancestral domains, typically with the participation of local government officials and influential persons. This has resulted in the uprooting of indigenous populations and the devastation of their land and resources. This research aimed to analyze the data in relation to tree planting activities initiated by several advocates, especially the Father Saturnino Urios University Foundation. Which aims, helping to preserve and saving Ancestral Land. This research employs data mining activity and develops insights according to generated patterns. Descriptive analytics is the branch of data analysis that involves summarizing and describing data. It typically involves using statistics, data visualization, and other techniques to identify patterns and trends in data. Descriptive analytics can be used to understand the characteristics of data and to communicate those characteristics to others. Further, the results show that tree planting activities conducted by some advocates resulted in good results, since planted trees near the watershed have had a survival rate of 92.28 (%) percent and around 43,270 seedlings planted in 88.05 hectares. On the other hand, abaca have been planted in the area for the livelihood of IP situated in the area, have a survival rate of 96 (%) percent. This result is the output of an ongoing reh

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data annotation system for intelligent energy conservator in smart building 3

Data annotation system for intelligent energy conservator in...

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3rd International Conference on Eco Engineering Development, ICEED 2019

作者： Pardamean, Bens Cenggoro, Tjeng Wawan Chandra, Bloomest Jansen Rahutomo, Reza Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department College of Letters and Science University of Wisconsin MadisonWI53706 United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

The concept of smart building includes the optimization of energy usage in a building. One of the possible solutions for this is to adaptively adjust appliances utilization according to activity level in the building. Thus, an intelligent activity estimation system needs to be developed. However, massive annotated dataset is necessary to train the system. Therefore, we propose a system that enables rapid data annotation to collect the massive dataset. With the proposed system, an image can be annotated within 4.8 seconds in average. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Historic preservation

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Not all spacetime coordinates for general-relativistic ray tracing are created equal

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Physical Review D 2023年第8期108卷 084004-084004页

作者： Gabriele Bozzola Chi-kwan Chan Vasileios Paschalidis Department of Astronomy University of Arizona Tucson Arizona USA Data Science Institute University of Arizona Tucson Arizona USA and Program in Applied Mathematics University of Arizona Tucson Arizona USA Department of Astronomy University of Arizona Tucson Arizona USA and Department of Physics University of Arizona Tucson Arizona USA

Models for the observational appearance of astrophysical black holes rely critically on accurate general-relativistic ray tracing and radiation transport to compute the intensity measured by a distant observer. In this paper, we illustrate how the choice of coordinates and initial conditions affect this process. In particular, we show that propagating rays from the camera to the source leads to different solutions if the spatial part of the momentum of the photon points towards the horizon or away from it. In doing this, we also show that coordinates that are well suited for numerical general-relativistic magnetohydrodynamic (GRMHD) simulations are typically not optimal for generic ray tracing. We discuss the implications for black hole images and show that radiation transport in optimal and nonoptimal spacetime coordinates lead to the same images up to numerical errors and algorithmic choices.

关键词： Classical black holes General relativity Accretion disk & black-hole plasma Astronomical black holes Numerical techniques Relativistic magnetohydrodynamics

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When data disappear: public health pays as US policy strays

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The Lancet Digital Health 2025年 100874页

作者： McAndrew, Thomas Lover, Andrew A Hoyt, Garrik Majumder, Maimuna S Department of Biostatistics and Health Data Science College of Health Lehigh University BethlehemPA United States Department of Biostatistics and Epidemiology School of Public Health and Health Sciences University of Massachusetts Amherst AmherstMA United States Department of Computer Science and Engineering PC Rossin College of Engineering and Applied Sciences Lehigh University BethlehemPA United States Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children's Hospital BostonMA United States

Presidential actions on Jan 20, 2025, by President Donald Trump, including executive orders, have delayed access to or led to the removal of crucial public health data sources in the USA. The continuous collection and maintenance of health data support public health, safety, and security associated with diseases such as seasonal influenza. To show how public health data surveillance enhances public health practice, we analysed data from seven US Government-maintained sources associated with seasonal influenza. We fit two models that forecast the number of national incident influenza hospitalisations in the USA: (1) a data-rich model incorporating data from all seven Government data sources;and (2) a data-poor model built using a single Government hospitalisation data source, representing the minimal required information to produce a forecast of influenza hospitalisations. The data-rich model generated reliable forecasts useful for public health decision making, whereas the predictions using the data-poor model were highly uncertain, rendering them impractical. Thus, health data can serve as a transparent and standardised foundation to improve domestic and global health. Therefore, a plan should be developed to safeguard public health data as a public good. © 2025 The Author(s)

关键词： Health insurance

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