Abstract

         Uncertainty, the deeply-rooted fact that surrounding the investment environment, especially the stock market which just prices have taken a specific trend until they moved to another one for its up or down. This means that the volatility characteristic of financial market requires the rational investor an argument led towards the adoption of planned acts to gain greater benefit in the goal of wealth maximizing. There is no possibility to achieve this goal without the burden of uncertainty and the risk of systematic fluctuations of investment returns in the financial market after the facts of  efficient diversification have pro

Face recognition is a crucial biometric technology used in various security and identification applications. Ensuring accuracy and reliability in facial recognition systems requires robust feature extraction and secure processing methods. This study presents an accurate facial recognition model using a feature extraction approach within a cloud environment. First, the facial images undergo preprocessing, including grayscale conversion, histogram equalization, Viola-Jones face detection, and resizing. Then, features are extracted using a hybrid approach that combines Linear Discriminant Analysis (LDA) and Gray-Level Co-occurrence Matrix (GLCM). The extracted features are encrypted using the Data Encryption Standard (DES) for security

The Cu(II) was found using a quick and uncomplicated procedure that involved reacting it with a freshly synthesized ligand to create an orange complex that had an absorbance peak of 481.5 nm in an acidic solution. The best conditions for the formation of the complex were studied from the concentration of the ligand, medium, the eff ect of the addition sequence, the eff ect of temperature, and the time of complex formation. The results obtained are scatter plot extending from 0.1–9 ppm and a linear range from 0.1–7 ppm. Relative standard deviation (RSD%) for n = 8 is less than 0.5, recovery % (R%) within acceptable values, correlation coeffi cient (r) equal 0.9986, coeffi cient of determination (r2) equal to 0.9973, and percentage capita

In this paper, the goal of proposed method is to protect data against different types of attacks by unauthorized parties. The basic idea of proposed method is generating a private key from a specific features of digital color image such as color (Red, Green and Blue); the generating process of private key from colors of digital color image performed via the computing process of color frequencies for blue color of an image then computing the maximum frequency of blue color, multiplying it by its number and adding process will performed to produce a generated key. After that the private key is generated, must be converting it into the binary representation form. The generated key is extracted from blue color of keyed image then we selects a c

We have studied Bayesian method in this paper by using the modified exponential growth model, where this model is more using to represent the growth phenomena. We focus on three of prior functions (Informative, Natural Conjugate, and the function that depends on previous experiments) to use it in the Bayesian method. Where almost of observations for the growth phenomena are depended on one another, which in turn leads to a correlation between those observations, which calls to treat such this problem, called Autocorrelation, and to verified this has been used Bayesian method.

The goal of this study is to knowledge the effect of Autocorrelation on the estimation by using Bayesian method. F

Olfactory impairment and abnormal frontal EEG oscillations are recognized as early markers of Alzheimer’s disease (AD). Using a publicly available olfactory EEG dataset of 35 subjects spanning normal cognition, amnestic mild cognitive impairment (aMCI), and AD, each with MMSE scores and demographics, stimulus-locked epochs from four electrodes (Fp1, Fz, Cz, Pz) were processed with wavelet-based time–frequency analysis. Band-limited power ratios (delta, theta, alpha, beta) were computed as log-transformed post-odor/baseline values and aggregated to subject-level features. Statistical analyses revealed graded attenuation of odor-evoked frontal (Fp1) band-power ratios across groups, with significant differences in several band–od

Improving" Jackknife Instrumental Variable Estimation method" using A class of immun algorithm with practical application

Multiplicative inverse in GF (2 m ) is a complex step in some important application such as Elliptic Curve Cryptography (ECC) and other applications. It operates by multiplying and squaring operation depending on the number of bits (m) in the field GF (2 m ). In this paper, a fast method is suggested to find inversion in GF (2 m ) using FPGA by reducing the number of multiplication operations in the Fermat's Theorem and transferring the squaring into a fast method to find exponentiation to (2 k ). In the proposed algorithm, the multiplicative inverse in GF(2 m ) is achieved by number of multiplications depending on log 2 (m) and each exponentiation is operates in a single clock cycle by generating a reduction matrix for high power of two ex