Data-driven models perform poorly on part-of-speech tagging problems with the square Hmong language, a low-resource corpus. This paper designs a weight evaluation function to reduce the influence of unknown words. It proposes an improved harmony search algorithm utilizing the roulette and local evaluation strategies for handling the square Hmong part-of-speech tagging problem. The experiment shows that the average accuracy of the proposed model is 6%, 8% more than HMM and BiLSTM-CRF models, respectively. Meanwhile, the average F1 of the proposed model is also 6%, 3% more than HMM and BiLSTM-CRF models, respectively.

Offline handwritten signature is a type of behavioral biometric-based on an image. Its problem is the accuracy of the verification because once an individual signs, he/she seldom signs the same signature. This is referred to as intra-user variability. This research aims to improve the recognition accuracy of the offline signature. The proposed method is presented by using both signature length normalization and histogram orientation gradient (HOG) for the reason of accuracy improving. In terms of verification, a deep-learning technique using a convolution neural network (CNN) is exploited for building the reference model for a future prediction. Experiments are conducted by utilizing 4,000 genuine as well as 2,000 skilled forged signatu

Background: There is a recognized association between prolongation of the heart rate corrected QT interval (QTc) and antipsychotic drugs. QTc prolongation may increase the risk of ventricular tachyarrhythmias, especially torsade de pointes, and therefore, sudden cardiac death.

Objective: This study assess the effect of antipsychotics and prolongation of the heart rate corrected QT interval (QTC), and QTC prolongation may increase the risk of ventricular tachyarrhythmias, especially torsade de pointes and sudden cardiac death.

Results: Abnormal QTc was defined as an interval of more than 440 ms (0.44 second) and was present in 21.7% (43 patients of 198). Benzhexol (-0.333

This research includes the study of dual data models with mixed random parameters, which contain two types of parameters, the first is random and the other is fixed. For the random parameter, it is obtained as a result of differences in the marginal tendencies of the cross sections, and for the fixed parameter, it is obtained as a result of differences in fixed limits, and random errors for each section. Accidental bearing the characteristic of heterogeneity of variance in addition to the presence of serial correlation of the first degree, and the main objective in this research is the use of efficient methods commensurate with the paired data in the case of small samples, and to achieve this goal, the feasible general least squa

The investor needs to a clear strategy for the purpose of access to the financial market, that is, has a plan to increase The share of the profits thinking entrepreneur and new, and highlights the importance of this in that it sets for the investor when it goes to the market, and when it comes out of it, and at what price to buy or sell the stock, and what is the the amount of money it starts. Fortunately, he does not need to invent his own investment strategy, because over the years the development of effective methods of buying and selling, and once you understand how to work these methods investor can choose the most appropriate methods and adapted image that fit his style investment .

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In this paper, we introduce and study a new concept named couniform modules, which is a dual notion of uniform modules, where an R-module M is said to be couniform if every proper submodule N of M is either zero or there exists a proper submodule N1 of N such that is small submodule of Also many relationships are given between this class of modules and other related classes of modules. Finally, we consider the hereditary property between R-module M and R-module R in case M is couniform.