Radioactive elements were identified in samples of imported coffee consumed in the province of Basra using gamma spectrometry SAM940TM. It is a scintillation detector of NaI(Tl) crystal and the dimensions of 2×2 inch. We have identified specific concentration As(Bq/kg) and annual effective dose D(Sv/y) for radioactive elements (_^40)K, (_^131)I, (_^134)Cs and (_^137)Cs. The estimated average effective dose for adults from coffee samples were found to be 0.037mSv/y, 88.434nSv/y, 46.909nSv/y, 27.212nSv/y for ((_^40)K,(_^131)I,(_^134)Cs,(_^137)Cs) respectively. The present results of the study revealed that the radioactivity was relatively low in the coffee and within the permissiblelimit.

The current research creates an overall relative analysis concerning the estimation of Meixner process parameters via the wavelet packet transform. Of noteworthy presentation relevance, it compares the moment method and the wavelet packet estimator for the four parameters of the Meixner process. In this paper, the research focuses on finding the best threshold value using the square root log and modified square root log methods with the wavelet packets in the presence of noise to enhance the efficiency and effectiveness of the denoising process for the financial asset market signal. In this regard, a simulation study compares the performance of moment estimation and wavelet packets for different sample sizes. The results show that wavelet p

         The issue of penalized regression model has received considerable critical attention to variable selection. It plays an essential role in dealing with high dimensional data. Arctangent denoted by the Atan penalty has been used in both estimation and variable selection as an efficient method recently. However, the Atan penalty is very sensitive to outliers in response to variables or heavy-tailed error distribution. While the least absolute deviation is a good method to get robustness in regression estimation. The specific objective of this research is to propose a robust Atan estimator from combining these two ideas at once. Simulation experiments and real data applications show that the proposed LAD-Atan estimator

         The issue of penalized regression model has received considerable critical attention to variable selection. It plays an essential role in dealing with high dimensional data. Arctangent denoted by the Atan penalty has been used in both estimation and variable selection as an efficient method recently. However, the Atan penalty is very sensitive to outliers in response to variables or heavy-tailed error distribution. While the least absolute deviation is a good method to get robustness in regression estimation. The specific objective of this research is to propose a robust Atan estimator from combining these two ideas at once. Simulation experiments and real data applications show that the p

This study employs evolutionary optimization and Artificial Intelligence algorithms to determine an individual’s age using a single-faced image as the basis for the identification process. Additionally, we used the WIKI dataset, widely considered the most comprehensive collection of facial images to date, including descriptions of age and gender attributes. However, estimating age from facial images is a recent topic of study, even though much research has been undertaken on establishing chronological age from facial photographs. Retrained artificial neural networks are used for classification after applying reprocessing and optimization techniques to achieve this goal. It is possible that the difficulty of determining age could be reduce

This study was established to investigate the correlation between the expression of matrix metalloproteinases (MMP-1) and the pathogenesis of osteoarthritis (OA). Blood samples were collected from 55 female patients with inflammatory OA and controls for estimation of serum (MMP-1) levels. In the current study, there is significant increase (p<0.001) in the mean of serum MMP-1 levels in osteoarthritis females (4027.73 ± 1345.28 pg/ml) than that in control females (798.76 ± 136.79 pg/ml). It was concluded that MMP-1 may be associated with the pathogenesis of osteoarthritis.

Methods of estimating statistical distribution have attracted many researchers when it comes to fitting a specific distribution to data. However, when the data belong to more than one component, a popular distribution cannot be fitted to such data. To tackle this issue, mixture models are fitted by choosing the correct number of components that represent the data. This can be obvious in lifetime processes that are involved in a wide range of engineering applications as well as biological systems. In this paper, we introduce an application of estimating a finite mixture of Inverse Rayleigh distribution by the use of the Bayesian framework when considering the model as Markov chain Monte Carlo (MCMC). We employed the Gibbs sampler and

Kidney tumors are of different types having different characteristics and also remain challenging in the field of biomedicine. It becomes very important to detect the tumor and classify it at the early stage so that appropriate treatment can be planned. Accurate estimation of kidney tumor volume is essential for clinical diagnoses and therapeutic decisions related to renal diseases. The main objective of this research is to use the Computer-Aided Diagnosis (CAD) algorithms to help the early detection of kidney tumors that addresses the challenges of accurate kidney tumor volume estimation caused by extensive variations in kidney shape, size and orientation across subjects.
In this paper, have tried to implement an automated segmentati