Summary money-laundering has been one of the most serious crimes affecting the national economy, including the private business sector, and it affects the moral system of society, as the beneficiaries of this process become the owners of the money-launderers that contribute to the investment activity by accumulating their accumulated wealth from Illegal economic activity. The biggest burden of money laundering is on banks, as they are the main channel in which money launderers pour their money, especially under bank secrecy laws, and banks are the first balaka in the fight against money laundering, in order to protect themselves from the risks Financial and legal responsibility for their participation in such operations, and certainly money-laundering is evolving in terms of means, so it is necessary to develop legal procedures in this regard. Therefore, efforts must be victorious between government institutions
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In this paper, we propose a method using continuous wavelets to study the multivariate fractional Brownian motion through the deviations of the transformed random process to find an efficient estimate of Hurst exponent using eigenvalue regression of the covariance matrix. The results of simulations experiments shown that the performance of the proposed estimator was efficient in bias but the variance get increase as signal change from short to long memory the MASE increase relatively. The estimation process was made by calculating the eigenvalues for the variance-covariance matrix of Meyer’s continuous wavelet details coefficients.
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In many video and image processing applications, the frames are partitioned into blocks, which are extracted and processed sequentially. In this paper, we propose a fast algorithm for calculation of features of overlapping image blocks. We assume the features are projections of the block on separable 2D basis functions (usually orthogonal polynomials) where we benefit from the symmetry with respect to spatial variables. The main idea is based on a construction of auxiliary matrices that virtually extends the original image and makes it possible to avoid a time-consuming computation in loops. These matrices can be pre-calculated, stored and used repeatedly since they are independent of the image itself. We validated experimentally th
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HM Al-Dabbas, RA Azeez, AE Ali, Iraqi Journal of Science, 2023
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In this paper, we propose a method using continuous wavelets to study the multivariate fractional Brownian motion through the deviations of the transformed random process to find an efficient estimate of Hurst exponent using eigenvalue regression of the covariance matrix. The results of simulations experiments shown that the performance of the proposed estimator was efficient in bias but the variance get increase as signal change from short to long memory the MASE increase relatively. The estimation process was made by calculating the eigenvalues for the variance-covariance matrix of Meyer’s continuous wavelet details coefficients.
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