The degree of the woman’s satisfaction on clothing depends, to a large extent, on the body measurements. If clothing is very wide, it shows her enormous and if it is too tight it may draw attention to the defects of the body. It may also lead to the compatibility or incompatibility of clothing with fashion. Whatever the quality of the garment in terms of sewing and design, the costume which is not suitable for body size may affect the physical style negatively and may give the wearer an improper look. Clothing was carried out without measurements and did not use models (templates). The method of preparation affected the overall appearance because it often did not fit the shape of the body completely. Therefore, people thought in many w

The aim of this paper is to propose a reliable iterative method for resolving many types of Volterra - Fredholm Integro - Differential Equations of the second kind with initial conditions. The series solutions of the problems under consideration are obtained by means of the iterative method. Four various problems are resolved with high accuracy to make evident the enforcement of the iterative method on such type of integro differential equations. Results were compared with the exact solution which exhibits that this technique was compatible with the right solutions, simple, effective and easy for solving such problems. To evaluate the results in an iterative process the MATLAB is used as a math program for the calculations.

In this paper, a method based on modified adomian decomposition method for solving Seventh order integro-differential equations (MADM). The distinctive feature of the method is that it can be used to find the analytic solution without transformation of boundary value problems. To test the efficiency of the method presented two examples are solved by proposed method.

In the field of data security, the critical challenge of preserving sensitive information during its transmission through public channels takes centre stage. Steganography, a method employed to conceal data within various carrier objects such as text, can be proposed to address these security challenges. Text, owing to its extensive usage and constrained bandwidth, stands out as an optimal medium for this purpose. Despite the richness of the Arabic language in its linguistic features, only a small number of studies have explored Arabic text steganography. Arabic text, characterized by its distinctive script and linguistic features, has gained notable attention as a promising domain for steganographic ventures. Arabic text steganography harn

Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to

An oxidative polymerization approach was used to create polyaniline (PANI) and Fe₂O₃ /PANI nanoparticle combination. Various characterization approaches were used to investigate the structural, morphological, and Fe₂O₃ /PANI nanoparticle structures. The findings support the synthesis of polycrystalline nanoparticle PANI and Fe₂O₃ /PANI spherical nanoparticle composites. Gram-positive bacteria are tested for antibacterial activity. Various quantities of Nanoparticles of PANI and Fe₂O₃ /PANI nanoparticle composites were used to test Staph-aureus and gram-negative bacteria, E-coli, and candida species. PANI has antibacterial properties against all microo

In high-dimensional semiparametric regression, balancing accuracy and interpretability often requires combining dimension reduction with variable selection. This study intro- duces two novel methods for dimension reduction in additive partial linear models: (i) minimum average variance estimation (MAVE) combined with the adaptive least abso- lute shrinkage and selection operator (MAVE-ALASSO) and (ii) MAVE with smoothly clipped absolute deviation (MAVE-SCAD). These methods leverage the flexibility of MAVE for sufficient dimension reduction while incorporating adaptive penalties to en- sure sparse and interpretable models. The performance of both methods is evaluated through simulations using the mean squared error and variable selection cri