There is various human biometrics used nowadays, one of the most important of these biometrics is the face. Many techniques have been suggested for face recognition, but they still face a variety of challenges for recognizing faces in images captured in the uncontrolled environment, and for real-life applications. Some of these challenges are pose variation, occlusion, facial expression, illumination, bad lighting, and image quality. New techniques are updating continuously. In this paper, the singular value decomposition is used to extract the features matrix for face recognition and classification. The input color image is converted into a grayscale image and then transformed into a local ternary pattern before splitting the image into the main sixteen blocks. Each block of these sixteen blocks is divided into more to thirty sub-blocks. For each sub-block, the SVD transformation is applied, and the norm of the diagonal matrix is calculated, which is used to create the 16x30 feature matrix. The sub-blocks of two images, (thirty elements in the main block) are compared with others using the Euclidean distance. The minimum value for each main block is selected to be one feature input to the neural network. Classification is implemented by a backpropagation neural network, where a 16-feature matrix is used as input to the neural network. The performance of the current proposal was up to 97% when using the FEI (Brazilian) database. Moreover, the performance of this study is promised when compared with recent state-of-the-art approaches and it solves some of the challenges such as illumination and facial expression.
A novel median filter based on crow optimization algorithms (OMF) is suggested to reduce the random salt and pepper noise and improve the quality of the RGB-colored and gray images. The fundamental idea of the approach is that first, the crow optimization algorithm detects noise pixels, and that replacing them with an optimum median value depending on a criterion of maximization fitness function. Finally, the standard measure peak signal-to-noise ratio (PSNR), Structural Similarity, absolute square error and mean square error have been used to test the performance of suggested filters (original and improved median filter) used to removed noise from images. It achieves the simulation based on MATLAB R2019b and the resul
... Show MoreProteus mirabilis is considered as a third common cause of catheter-associated urinary tract infection, with urease production, the potency of catheter blockage due to the formation of biofilm formation is significantly enhanced. Biofilms are major virulence factors expressed by pathogenic bacteria to resist antibiotics; in this concern the need for providing new alternatives for antibiotics is getting urgent need, This study aimed to explore whether green synthesized zinc oxide nanoparticles (ZnO NPs) can function as an anti-biofilm agent produced by P.mirabilis. Bacterial cells were capable of catalyzing the biosynthesis process by producing reductive enzymes. The nanoparticles were synthesized from cell free
... Show MoreThe investment budget represents a stage of the investment decision in service units, and the preparation and implementation needs to be a complement of the same planning part, because the planning does not end with the development of the plan, but includes a follow-up implementation, so it has to be effective and efficient oversight of the estimates and procedures for disbursement of funds approved for investment projects, The problem with research in that local governments suffer from the presence of Allkaat and problems facing the implementation of the investment budget projects due to the adoption budget items which can not be measured the efficiency of the performance of these units of government by, and shortcomings in the control
... Show MoreThe support qualitative information regards as an additional step in the process of decision-making where the method following by companies to provide information help in the creation of value because it is very important to deliver information to investors about their stratigies and what happen truly inside the companies i.e. every case relating with the expectations of stockhotslder and the prices of markets depending on those expectation ,and if the matter isn’t that there will be lack of confidence thate couldn’t be backed again. The decisions of the investors effected by security ,economic ,political, psychological, emotional ,and financial factors .
... Show MoreAbstract
One of the major components in an automobile engine is the throttle valve part. It is used to keep up with emissions and fuel efficiency low. Design a control system to the throttle valve is newly common requirement trend in automotive technology. The non-smoothness nonlinearity in throttle valve model are due to the friction model and the nonlinear spring, the uncertainty in system parameters and non-satisfying the matching condition are the main obstacles when designing a throttle plate controller.
In this work, the theory of the Integral Sliding Mode Control (ISMC) is utilized to design a robust controller for the Electronic Throttle Valve (ETV) system. From the first in
... Show MoreThe main goal of this paper is to introduce and study a new concept named d*-supplemented which can be considered as a generalization of W- supplemented modules and d-hollow module. Also, we introduce a d*-supplement submodule. Many relationships of d*-supplemented modules are studied. Especially, we give characterizations of d*-supplemented modules and relationship between this kind of modules and other kind modules for example every d-hollow (d-local) module is d*-supplemented and by an example we show that the converse is not true.
Gender classification is a critical task in computer vision. This task holds substantial importance in various domains, including surveillance, marketing, and human-computer interaction. In this work, the face gender classification model proposed consists of three main phases: the first phase involves applying the Viola-Jones algorithm to detect facial images, which includes four steps: 1) Haar-like features, 2) Integral Image, 3) Adaboost Learning, and 4) Cascade Classifier. In the second phase, four pre-processing operations are employed, namely cropping, resizing, converting the image from(RGB) Color Space to (LAB) color space, and enhancing the images using (HE, CLAHE). The final phase involves utilizing Transfer lea
... Show More