A Srinivas Reddy,P Krishna Reddy,Anirban Mondal et al.
A Srinivas Reddy et al.
Pattern mining from graph transactional data (GTD) is an active area of research with applications in the domains of bioinformatics, chemical informatics and social networks. Existing works address the problem of mining frequent subgraphs f...
Big social data provenance framework for Zero-Information Loss Key-Value Pair (KVP) Database [0.03%]
用于零信息损失键值对(KVP)数据库的大社会数据谱系框架
Asma Rani,Navneet Goyal,Shashi K Gadia
Asma Rani
Social media has been playing a vital importance in information sharing at massive scale due to its easy access, low cost, and faster dissemination of information. Its competence to disseminate the information across a wide audience has rai...
An analysis of the impact of policies and political affiliation on racial disparities in COVID-19 infections and deaths in the USA [0.03%]
对政策和政治派别在美国COVID-19感染和死亡率的种族差异中影响的分析
Michael A Hamilton,Danielle Hamilton,Oluwatamilore Soneye et al.
Michael A Hamilton et al.
This research aimed to quantify the racial disparities of COVID-19 for primarily positive tests and deaths across the US and territories individually and collectively. The first research hypothesis investigated whether positive cases and de...
A novel ensemble deep learning model for stock prediction based on stock prices and news [0.03%]
基于股票价格和新闻的新型集成深度学习模型在股市预测中的应用研究
Yang Li,Yi Pan
Yang Li
In recent years, machine learning and deep learning have become popular methods for financial data analysis, including financial textual data, numerical data, and graphical data. One of the most popular and complex deep learning in finance ...
Ralph Foorthuis
Ralph Foorthuis
Anomalies are occurrences in a dataset that are in some way unusual and do not fit the general patterns. The concept of the anomaly is typically ill defined and perceived as vague and domain-dependent. Moreover, despite some 250 years of pu...
Modelling the epidemic dynamics of COVID-19 with consideration of human mobility [0.03%]
考虑人类流动性的COVID-19流行病学动态模型研究
Bowen Du,Zirong Zhao,Jiejie Zhao et al.
Bowen Du et al.
So far COVID-19 has resulted in mass deaths and huge economic losses across the world. Various measures such as quarantine and social distancing have been taken to prevent the spread of this disease. These prevention measures have changed t...
A review of machine learning experiments in equity investment decision-making: why most published research findings do not live up to their promise in real life [0.03%]
机器学习在股票投资决策中的实验研究为何大多数发表的研究结果无法达到实际应用的期望——基于文献和实证的系统性分析
Wojtek Buczynski,Fabio Cuzzolin,Barbara Sahakian
Wojtek Buczynski
The numerical nature of financial markets makes market forecasting and portfolio construction a good use case for machine learning (ML), a branch of artificial intelligence (AI). Over the past two decades, a number of academics worldwide (m...
Least squares and maximum likelihood estimation of sufficient reductions in regressions with matrix-valued predictors [0.03%]
矩阵预测变量的回归中的最小二乘和最大似然充分约简估计法
Ruth M Pfeiffer,Daniel B Kapla,Efstathia Bura
Ruth M Pfeiffer
We propose methods to estimate sufficient reductions in matrix-valued predictors for regression or classification. We assume that the first moment of the predictor matrix given the response can be decomposed into a row and column component ...
Modeling of laser-induced breakdown spectroscopic data analysis by an automatic classifier [0.03%]
基于自动分类器的激光诱导击穿光谱数据模型分析方法研究
David D Pokrajac,Poopalasingam Sivakumar,Yuriy Markushin et al.
David D Pokrajac et al.
Laser-induced breakdown spectroscopy (LIBS) is a multi-elemental and real-time analytical technique with simultaneous detection of all the elements in any type of sample matrix including solid, liquid, gas, and aerosol. LIBS produces vast a...
Fast Causal Inference with Non-Random Missingness by Test-Wise Deletion [0.03%]
基于逐个检验剔除法进行非随机缺失的快速因果推断
Eric V Strobl,Shyam Visweswaran,Peter L Spirtes
Eric V Strobl
Many real datasets contain values missing not at random (MNAR). In this scenario, investigators often perform list-wise deletion, or delete samples with any missing values, before applying causal discovery algorithms. List-wise deletion is ...