In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as computer vision, this study proposes a deep learning approach that utilizes a deep learning algorithm.

The interests toward developing accurate automatic face emotion recognition methodologies are growing vastly, and it is still one of an ever growing research field in the region of computer vision, artificial intelligent and automation. However, there is a challenge to build an automated system which equals human ability to recognize facial emotion because of the lack of an effective facial feature descriptor and the difficulty of choosing proper classification method. In this paper, a geometric based feature vector has been proposed. For the classification purpose, three different types of classification methods are tested: statistical, artificial neural network (NN) and Support Vector Machine (SVM). A modified K-Means clustering algorithm

This article studies a comprehensive methods of edge detection and algorithms in digital images which is reflected a basic process in the field of image processing and analysis. The purpose of edge detection technique is discovering the borders that distinct diverse areas of an image, which donates to refining the understanding of the image contents and extracting structural information. The article starts by clarifying the idea of an edge and its importance in image analysis and studying the most noticeable edge detection methods utilized in this field, (e.g. Sobel, Prewitt, and Canny filters), besides other schemes based on distinguishing unexpected modifications in light intensity and color gradation. The research as well discuss

Face Recognition Systems (FRS) are increasingly targeted by morphing attacks, where facial features of multiple individuals are blended into a synthetic image to deceive biometric verification. This paper proposes an enhanced Siamese Neural Network (SNN)-based system for robust morph detection. The methodology involves four stages. First, a dataset of real and morphed images is generated using StyleGAN, producing high-quality facial images. Second, facial regions are extracted using Faster Region-based Convolutional Neural Networks (R-CNN) to isolate relevant features and eliminate background noise. Third, a Local Binary Pattern-Convolutional Neural Network (LBP-CNN) is used to build a baseline FRS and assess its susceptibility to d

Oxazine and quinazoline has a very important in organic chemistry especially in hetero cyclic fields. this research consist the preparation of 4H,4'H-2,2'-bibenzo[d][1,3]oxazine-4,4'-dione compound (1) from di acid chloride with 2-aminobenzoic acid in pyridine as solvent to give compound (2) 3,3'-diamino-2,2'- biquinazoline-4,4'(3H,3'H)-dione .compound 2 include free amino group .this compound was reacted with maleic and phthalic anhydride for synthesized of cyclic imide compounds (3,4).another reaction for compound 2 with some substituted aromatic aldehyde for prepared of some novel Schiff bases (5-9) contains quinazoline ring. compound 1 was treated with sulfathiazole and sulfadiazine for synthesized of sulfa compounds contains sulf

The Mannich base ligand was synthesized in an ethanol medium through a condensation reaction of 2-mercaptobenzimidazole and ciprofloxacin at room temperature. Subsequently, several metal complexes of this ligand were prepared. To characterize both the base ligand and the metal complexes, various techniques were employed, including elemental analysis, FT-IR spectroscopy, UV-Vis spectroscopy, molar conductivity measurements, magnetic moment determination, and melting point analysis. The results were shown that the metal complexes formed have the formula [Cr(L)2Cl2] Cl.H2O and [Rh(L)2(H2O)2] Cl3.H2O, where L= mannich base ligand. Based on spectroscopic analytical, coordination with metal ions involves the 'N' donor atom of mannich base