Covid-19 Forecasting with Deep Learning-based Half-binomial Distribution Cat Swarm Optimization

About 170 nations have been affected by the COvid VIrus Disease-19(COVID-19)epidemic.On governing bodies across the globe,a lot of stress is created by COVID-19 as there is a continuous rise in patient count testing positive,and they feel challenging to tackle this situation.Most researchers concentrate on COVID-19 data analysis using the machine learning paradigm in these situations.In the previous works,Long Short-Term Memory(LSTM)was used to predict future COVID-19 cases.According to LSTM network data,the outbreak is expected tofinish by June 2020.However,there is a chance of an over-fitting problem in LSTM and true positive;it may not produce the required results.The COVID-19 dataset has lower accuracy and a higher error rate in the existing system.The proposed method has been introduced to overcome the above-mentioned issues.For COVID-19 prediction,a Linear Decreasing Inertia Weight-based Cat Swarm Optimization with Half Binomial Distribution based Convolutional Neural Network(LDIWCSO-HBDCNN)approach is presented.In this suggested research study,the COVID-19 predicting dataset is employed as an input,and the min-max normalization approach is employed to normalize it.Optimum features are selected using Linear Decreasing Inertia Weight-based Cat Swarm Optimization(LDIWCSO)algorithm,enhancing the accuracy of classification.The Cat Swarm Optimization(CSO)algorithm’s convergence is enhanced using inertia weight in the LDIWCSO algorithm.It is used to select the essential features using the bestfitness function values.For a specified time across India,death and confirmed cases are predicted using the Half Binomial Distribution based Convolutional Neural Network(HBDCNN)technique based on selected features.As demonstrated by empirical observations,the proposed system produces significant performance in terms of f-measure,recall,precision,and accuracy.

Log-binomial回归在社区干预效果评价中的应用

目的介绍利用log-binomial回归评价社区干预效果的原理与方法。方法通过在log-binomial模型中增加干预变量和时间变量的交互项,获得相对率比(RRR)的估计值,来进行社区干预效果评价,并以实例进行分析。结果通过"儿童关爱"干预措施对婴幼儿腹泻患病率影响的实例分析了在有无协变量的log-binomial回归在社区干预效果评价中的应用效果,并与Poisson回归模型做了相应的比较。调整混杂因子后的log-binomial回归模型是在控制了个体因素后对干预效应的有效估计,因此,评价的结果更为可靠。结论 log-binomial回归模型是社区干预研究的一种有效方法。

Conditional autoregressive negative binomial model for analysis of crash count using Bayesian methods

In order to improve crash occurrence models to account for the influence of various contributing factors, a conditional autoregressive negative binomial （CAR-NB） model is employed to allow for overdispersion （tackled by the NB component）, unobserved heterogeneity and spatial autocorrelation （captured by the CAR process）, using Markov chain Monte Carlo methods and the Gibbs sampler. Statistical tests suggest that the CAR-NB model is preferred over the CAR-Poisson, NB, zero-inflated Poisson, zero-inflated NB models, due to its lower prediction errors and more robust parameter inference. The study results show that crash frequency and fatalities are positively associated with the number of lanes, curve length, annual average daily traffic （AADT） per lane, as well as rainfall. Speed limit and the distances to the nearest hospitals have negative associations with segment-based crash counts but positive associations with fatality counts, presumably as a result of worsened collision impacts at higher speed and time loss during transporting crash victims.

非独立数据时稳健Poisson和Log-binomial随机效应模型研究

目的比较基于稳健Poisson回归及Log-binomial的随机效应模型对具有层次结构的非罕见结局事件资料的参数估计结果。方法通过计算机模拟生成具有类内相关的数据集,设置了32种参数情况,分别反映实际资料中的少数类别数和中等类别数,小强度的类内聚集和中等强度的类内聚集,基线患病率分别为0.15和0.3,关联强度PR值分别是1.5和2.0,暴露率分别为20%和50%的情况;比较各模型在模拟条件下的收敛率、参数估计结果以及参数的95%CI覆盖率。结果 Log-binomial随机效应模型的收敛效果劣于稳健Poisson随机效应模型;两随机效应模型对各参数的估计结果均和真值较为接近;在类内聚集性较小时,两随机效应模型95%CI覆盖率均较好,但随类内聚集性增大,Log-binomial的随机效应模型95%CI覆盖率变动较大,且类别数增加时覆盖率普遍较低。结论基于稳健Poisson的随机效应模型收敛率高,参数估计结果与真实值接近,对各固定效应参数估计的95%CI的覆盖率高且稳定,优于Log-binomial随机效应模型。

Beta-Binomial回归模型及其应用

在成败型试验中或满意度支持率调查中,Beta-Binomial分布常被用来刻画具有偏大离差的计数型比例数据,由此提出Beta-Binomial回归模型,研究参数的最大似然估计方法并基于Newton-Raphson算法给出参数估计的迭代方法;重点讨论模型中回归参数和相关性参数存在的检验问题,提出Score检验方法并通过数值模拟研究Score检验统计量的检验功效问题;实例分析证明Beta-Binomial回归模型的有用性。

Kumaraswamy Binomial分布(英文)

在Binomial模型中考虑over-dispersion时,每一个独立事件成功的概率通常被视为一个连续随机变量.在本文中,我们提出了成功概率服从Kumaxaswamy分布的混合Binomial模型.讨论了这个模型的随机序和相依性;并用数据来拟合这些模型,数值计算结果表明在拟合某些数据时KB模型比BB模型拟合效果更好.

Beta-Binomial模型基于EM算法的应用研究

在很多问题中,Beta-Binomial模型有着较为广泛的应用,本文基于EM算法研究了Beta-Binomial模型的参数估计问题,并把它应用于实际的案例中.结果表明我们提出的方法计算方便,对具体问题的解释更具合理性、科学性.

结合降维思想的BinomialBoosting方法

文章利用充分降维的思想,对分类问题的BinomialBoosting(BBoosting)算法进行了改进,提出了一种新的方法——Dimension Reduction BinomialBoosting(DRBBoosting)。这种算法在每次迭代中,结合充分降维方法,充分提取X与Y之间的信息,得到X的线性组合βTX,用βTX进行boosting迭代,避免了BBoosting对所有变量逐个分析。与BBoosting相比,收敛速度快,预测精度高;模拟比较也表明了DRBBoosting的优点。

An Introduction to Botanic Binomial System

每种植物都有它自己的名称。现代植物的种名，即世界通用的科学名称的命名，都是采用双名法。双名法一直被各国植物学工作者采用，并在〈国际植物命名法规》中加以肯定，为国际间法定的命名原则。双名法不但简明、规范，而且能反映相近种组成的属。

EXPECTED PRESENT VALUE OF TOTAL DIVIDENDS IN THE COMPOUND BINOMIAL MODEL WITH DELAYED CLAIMS AND RANDOM INCOME

In this paper, a compound binomial model with a constant dividend barrier and random income is considered. Two types of individual claims, main claims and by-claims, are defined, where every by-claim is induced by the main claim and may be delayed for one time period with a certain probability. The premium income is assumed to another binomial process to capture the uncertainty of the customer＇s arrivals and payments. A system of difference equations with certain boundary conditions for the expected present value of total dividend payments prior to ruin is derived and solved. Explicit results are obtained when the claim sizes are Kn distributed or the claim size distributions have finite support. Numerical results are also provided to illustrate the impact of the delay of by-claims on the expected present value of dividends.

Statistical analysis of generalized exponential distribution under progressive censoring with binomial removals

The estimation of generalized exponential distribution based on progressive censoring with binomial removals is presented, where the number of units removed at each failure time follows a binomial distribution. Maximum likelihood estimators of the parameters and their confidence intervals are derived. The expected time required to complete the life test under this censoring scheme is investigated. Finally, the numerical examples are given to illustrate some theoretical results by means of Monte-Carlo simulation.

RUIN PROBABILITY IN THE CONTINUOUS-TIME COMPOUND BINOMIAL MODEL WITH INVESTMENT

This article deals with the problem of minimizing ruin probability under optimal control for the continuous-time compound binomial model with investment. The jump mechanism in our article is different from that of Liu et al [4]. Comparing with [4], the introduction of the investment, and hence, the additional Brownian motion term, makes the problem technically challenging. To overcome this technical difficulty, the theory of change of measure is used and an exponential martingale is obtained by virtue of the extended generator. The ruin probability is minimized through maximizing adjustment coefficient in the sense of Lundberg bounds. At the same time, the optimal investment strategy is obtained.

Evolution law of a negative binomial state in an amplitude dissipative channel

For the first time we derive the evolution law of the negative binomial state In） （nI in an ampli-tude dissipative channel with a damping constant to. We find that after passing through the channel, the final state is still a negative binomial state, however the parameter γ evolves into The decay law of theaverage photon number is also obtained.

TESTING FOR VARYING DISPERSION OF LONGITUDINAL BINOMIAL DATA IN NONLINEAR LOGISTIC MODELS WITH RANDOM EFFECTS

In this paper, it is discussed that two tests for varying dispersion of binomial data in the framework of nonlinear logistic models with random effects, which are widely used in analyzing longitudinal binomial data. One is the individual test and power calculation for varying dispersion through testing the randomness of cluster effects, which is extensions of Dean(1992) and Commenges et al (1994). The second test is the composite test for varying dispersion through simultaneously testing the randomness of cluster effects and the equality of random-effect means. The score test statistics are constructed and expressed in simple, easy to use, matrix formulas. The authors illustrate their test methods using the insecticide data (Giltinan, Capizzi & Malani (1988)).

The Series of Reciprocals of Non-central Binomial Coefficients

Utilizing Gamma-Beta function, we can build one series involving reciprocal of non-central binomial coefficients, then We can structure several new series of reciprocals of non-central binomial coefficients by item splitting, these new created denominator of series contain 1 to 4 odd factors of binomial coefficients. As the result of splitting items, some identities of series of numbers values of reciprocals of binomial coefficients are given. The method of splitting terms offered in this paper is a new combinatorial analysis way and elementary method to create new series.

Time evolution of angular momentum coherent state derived by virtue of entangled state representation and a new binomial theorem

We study how can an angular momentum coherent state |τ> keeps its form-invariant during time evolution governed by the Hamiltonian H = f(t)J++ f^*(t)J-+ g(t)Jz. We discuss this topic in the context of boson realization of |τ>. By employing the entangled state representation |ζ> and deriving a new binomial theorem involving two-subscript Hermite polynomials, we derive the wave function <ζ|τ>, which turns out to be a single-subscript Hermite polynomial. Based on this result the maintenance of angular momentum coherent state during time evolution is examined, and the value of τ(t) is totally determined by the parameters involved in the Hamiltonian.

A New Binomial Tree Method for European Options under the Jump Diffusion Model

In this paper, the binomial tree method is introduced to price the European option under a class of jump-diffusion model. The purpose of the addressed problem is to find the parameters of the binomial tree and design the pricing formula for European option. Compared with the continuous situation, the proposed value equation of option under the new binomial tree model converges to Merton’s accurate analytical solution, and the established binomial tree method can be proved to work better than the traditional binomial tree. Finally, a numerical example is presented to illustrate the effectiveness of the proposed pricing methods.

Modeling random telegraph signal noise in CMOS image sensor under low light based on binomial distribution

The random telegraph signal noise in the pixel source follower MOSFET is the principle component of the noise in the CMOS image sensor under low light. In this paper, the physical and statistical model of the random telegraph signal noise in the pixel source follower based on the binomial distribution is set up. The number of electrons captured or released by the oxide traps in the unit time is described as the random variables which obey the binomial distribution. As a result,the output states and the corresponding probabilities of the first and the second samples of the correlated double sampling circuit are acquired. The standard deviation of the output states after the correlated double sampling circuit can be obtained accordingly. In the simulation section, one hundred thousand samples of the source follower MOSFET have been simulated,and the simulation results show that the proposed model has the similar statistical characteristics with the existing models under the effect of the channel length and the density of the oxide trap. Moreover, the noise histogram of the proposed model has been evaluated at different environmental temperatures.

Extensions of An Approach to Generalized Fibonacci and Lucas Numbers with Binomial Coefficients

The purpose of this paper is to give the extensions of some identities involving generalized Fibonacci and Lucas numbers with binomial coefficients.These results generalize the identities by Gulec,Taskara and Uslu in Appl.Math.Lett.23(2010)68-72 and Appl.Math.Comput.220(2013)482-486.

Quantum Statistical Properties of Binomial Field Interacting with Two Entangled Atoms

Quantum statistical properties of the binomial field interacting with the two entangled atoms are investi-gated for the different initial conditions.It is found that the sub-Poissonian distribution and the antibunching effect canbe presented for the certain ranges of the involved parameters.