We propose a new method for combining multialgorithm score-based face recognition systems, which we call the two-step calibration method. Typically, algorithms for face recognition systems produce dependent scores. The two-step method is based on parametric copulas to handle this dependence. Its goal is to minimize discrimination loss. For synthetic and real databases (NIST-face and Face3D) we will show that our method is accurate and reliable using the cost of log likelihood ratio and the information-theoretical empirical cross-entropy (ECE).

Fixed FAR correction factor of score level fusion Susyanto, N.; Veldhuis, R.N.J.; Spreeuwers, L.J.; Klaassen, C.A.J. AbstractIn biometric score level fusion, the scores are often assumed to be independent to simplify the fusion algorithm. In some cases, the "average" performance under this independence assumption is surprisingly successful, even competing with a fusion that incorporates dependence. We present two main contributions in score level fusion: (i) proposing a new method of measuring the performance of a fusion strategy at fixed FAR via Jeffreys credible interval analysis and (ii) subsequently providing a method to improve the fusion strategy under the independence assumption by taking the dependence into account via parametric copulas, which we call fixed FAR fusion. Using synthetic data, we will show that one should take the dependence into account even for scores with a low dependence level. Finally, we test our method on some public databases (FVC2002, NIST-face, and Face3D), compare it to Gaussian mixture model and linear logistic methods, which are also designed to handle dependence, and notice its significance improvement with respect to our evaluation method.

A family of Sigmoidal non-linear models is commonly used to determine the critical period of weed control (CPWC) and acceptable yield loss (AYL) in annual crops. We tried to prove another non-linear model to determine CPWC and AYL in a soybean agroforestry system with kayu putih. The three-year experiment (from 2019–2021) was conducted using a randomised complete block design factorial with five blocks as replications. The treatments comprised weedy and weed-free periods. Non-linear models comprised 45 functions. The results show that the Sigmoidal and Dose-Response Curve (DRC) families were the most suitable for estimating CPWC and AYL. The best fitted non-linear model for weedy and weed-free periods in the dry season used the Sigmoidal family consisting of the Weibull and Richards models, while in the wet season the best fit was obtained using the DRC and Sigmoidal families consisting of the DR-Hill and Richards models, respectively. The CPWC of soybean in the dry season for AYL was 5, 10, and 15%, beginning at 20, 22, and 24 days after emergence (DAE) and ended at 56, 54, and 52 DAE. The AYL in the wet season started at 20, 23, and 26 DAE and ended at 59, 53, and 49 DAE.

This work presents a mathematical model that investigates the impact of smokers on the transmission dynamics of smoking behavior in the Indonesian population. The population is classified into three classes: potential smokers, smokers, and ex-smokers. This model is described by non-linear differential equations using fractional quantities instead of actual populations by scaling the population of each class by the total population. There is also the density-dependent and density-independent death rate in the model to accommodate the difference between the death rate of potential smokers, smokers, and ex-smokers. In this model, two equilibrium points are found. One of them is the smoking-free equilibrium and the other relates to the presence of smoking. Then, the local stability of both equilibrium points is examined. Lastly, numerical simulations are carried out to illustrate the sensitivity of the smoker class to the parameters: the rate of non-smokers become smokers, the rate of smokers become smokers, also the rate of ex-smokers re-adapt smoking habit. The result of this paper can be considered to make a policy to reduce the number of smokers in Indonesia.

In this paper, we discussed a stochastic optimal control of hepatitis C that minimizes the side effect and reduces the viral load. The control variables represent the drug therapy used for blocking a new infection and virus production. The solution of control problem is solved using the stochastic minimum principle and a four-step scheme. The numerical simulation is carried out to justify the theoretical analysis. The result shows that using both types of drugs for therapy is much more effective.