Dipak Dulal; Ramin Goudarzi Karim; Carmeliza Navasca
Abstract
In this paper, we use tensor models to analyze the Covid-19 pandemic data. First, we use tensor models, canonical polyadic, and higher-order Tucker decompositions to extract patterns over multiple modes. Second, we implement a tensor completion algorithm using canonical polyadic tensor decomposition ...
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In this paper, we use tensor models to analyze the Covid-19 pandemic data. First, we use tensor models, canonical polyadic, and higher-order Tucker decompositions to extract patterns over multiple modes. Second, we implement a tensor completion algorithm using canonical polyadic tensor decomposition to predict spatiotemporal data from multiple spatial sources and to identifyCovid-19 hotspots. We apply a regularized iterative tensor completion technique with a practical regularization parameter estimator to predict the spread of Covid-19 cases and to find and identify hotspots. Our method can predict weekly, and quarterly Covid-19 spreads with high accuracy. Third, we analyze Covid-19 data in the US using a novel sampling method for alternating leastsquares. Moreover, we compare the algorithms with standard tensor decompositions concerning their interpretability, visualization, and cost analysis. Finally, we demonstrate the efficacy of the methods by applying the techniques to the New Jersey Covid-19 case tensor data.
Aadil Rashid Sheergojri; Pervaiz Iqbal
Abstract
To better understand how things work in biology, you can use mathematics to figure things out. Cancer modelling is a complex biological process that is highly unpredictable, which fuzzy models in this study have achieved. Biomolecular components of malignant disorders and their analysis are frequently ...
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To better understand how things work in biology, you can use mathematics to figure things out. Cancer modelling is a complex biological process that is highly unpredictable, which fuzzy models in this study have achieved. Biomolecular components of malignant disorders and their analysis are frequently examined. Even though most documented investigations have not yet reached the competency needed for "curing cancer," research has contributed to understanding the statistical principles governing cancer cell development, invasion, and proliferation. This article begins with an overview of mathematical models for tumor development and treatment, which consider stochastic perturbations a therapeutic remedy. Also, it has been demonstrated that fuzzy modeling approaches may be used to investigate the impacts of various components on tumor development estimation, minimize tumor uncertainty, and attain a degree of realism.
