Computer vision seeks to mimic the human visual system and plays an essential role in artificial intelligence. It is based on different signal reprocessing techniques; therefore, developing efficient techniques becomes essential to achieving fast and reliable processing. Various signal preprocessing operations have been used for computer vision, including smoothing techniques, signal analyzing, resizing, sharpening, and enhancement, to reduce reluctant falsifications, segmentation, and image feature improvement. For example, to reduce the noise in a disturbed signal, smoothing kernels can be effectively used. This is achievedby convolving the distributed signal with smoothing kernels. In addition, orthogonal moments (OMs) are a cruc

Tchebichef polynomials (TPs) play a crucial role in various fields of mathematics and applied sciences, including numerical analysis, image and signal processing, and computer vision. This is due to the unique properties of the TPs and their remarkable performance. Nowadays, the demand for high-quality images (2D signals) is increasing and is expected to continue growing. The processing of these signals requires the generation of accurate and fast polynomials. The existing algorithms generate the TPs sequentially, and this is considered as computationally costly for high-order and larger-sized polynomials. To this end, we present a new efficient solution to overcome the limitation of sequential algorithms. The presented algorithm us

Numeral recognition is considered an essential preliminary step for optical character recognition, document understanding, and others. Although several handwritten numeral recognition algorithms have been proposed so far, achieving adequate recognition accuracy and execution time remain challenging to date. In particular, recognition accuracy depends on the features extraction mechanism. As such, a fast and robust numeral recognition method is essential, which meets the desired accuracy by extracting the features efficiently while maintaining fast implementation time. Furthermore, to date most of the existing studies are focused on evaluating their methods based on clean environments, thus limiting understanding of their potential a

Computer vision seeks to mimic the human visual system and plays an essential role in artificial intelligence. It is based on different signal reprocessing techniques; therefore, developing efficient techniques becomes essential to achieving fast and reliable processing. Various signal preprocessing operations have been used for computer vision, including smoothing techniques, signal analyzing, resizing, sharpening, and enhancement, to reduce reluctant falsifications, segmentation, and image feature improvement. For example, to reduce the noise in a disturbed signal, smoothing kernels can be effectively used. This is achievedby convolving the distributed signal with smoothing kernels. In addition, orthogonal moments (OMs) are a cruc

Some relations of inclusion and their properties are investigated for functions of type " -valent that involves the generalized operator of Srivastava-Attiya by using the principle of strong differential subordination.

The study was conducted to measure diatom species diversity in the lotic ecosystem across the Wasit Province for 12 months. The quantitative study of diatoms (phytoplankton) was investigated in the Tigris river. The density of algae was ranged from 60989 cell×103/l to 112780.82 cell×103/l in the five sites. These algae were belonging to 39 genera. The richness index values ranged from 1.53 at site 5 in January 2016 to 6.34 at site 1 and June2015. Shannon-Weiner diversity index (H´) was 2.33 in February 2016 and 3.72 in June 2015 both values at site 3, whereas Evenness index was 0.54 at site 5 in March2016 and 0.98 at site 1 in both August2015 and May2016. The lack of homogeneity of the appearance of species indicates the dominance of a