Face Detection by skin color in the field of computer vision is a difficult challenge. Detection of human skin focuses on the identification of pixels and skin-colored areas of a given picture. Since skin colors are invariant in orientation and size and rapid to process, they are used in the identification of human skin. In addition features like ethnicity, sensor, optics and lighting conditions that are different are sensitive factors for the relationship between surface colors and lighting (an issue that is strongly related to color stability). This paper presents a new technique for face detection based on human skin. Three methods of Probability Density Function (PDF) were applied to detect the face by skin color; these are the Extreme Value Distribution Function and the Exponential Distribution Function methods, in addition to a new proposed model, over the HSV (Hue, Saturation, and Value) color space. The suggested technique aims to enhance skin pixel detection and improve the detection accuracy of a colored region in the human skin in a specific photo. The new model has proved to be 96.05% more accurate than the Extreme value distribution function and Exponential distribution function according to the selected region of the face during experiments. The images used in this paper were 380 color images from CalTech (California Technology Institute) dataset.
Fungi produce a series of toxic compounds on corn, especially Fumonisin B1 (FB1) toxin produced by Fusarium spp. and promoting cancer activity in humans and animals. This study aimed to the isolation and identification of fungi associated with local corn seeds and the detection for the presence of FB1 by using ELISA technique. Thirty samples of corn ears were collected from silos and markets in Baghdad city during the period from November 2018 to March 2019. The present study found that Fusarium was the dominant isolate among fungi in terms of the relative density 57.07%, followed by Aspergillus 31.17%, Rhizopus 3.36%, Alternaria 2.88%, Mucor 2.16%, Penicillium 1.92%, Trichothecium 0.96%, and Helminthosporium 0.48%. FB1 was detected in a
... Show MoreIn this paper, thin films of undoped and nickel oxide (NiO) doped titanium dioxide (TiO2) were prepared using the chemical spray pyrolysis deposition (CSP) technique, with different concentrations of nickel oxide (NiO) in the range (3-9) wt%. The morphological, structural, electrical, and sensing properties of a gas of the prepared thin films were examined. XRD measurements showed that TiO2 films have a polycrystalline structure. AFM analysis showed that these films have a regular structure both before and after doping . The roughness of these films decreased after adding impurities but then the opposite of that took place. The electrical and gas sens
... Show MoreThe digital world has been witnessing a fast progress in technology, which led to an enormous increase in using digital devices, such as cell phones, laptops, and digital cameras. Thus, photographs and videos function as the primary sources of legal proof in courtrooms concerning any incident or crime. It has become important to prove the trustworthiness of digital multimedia. Inter-frame video forgery one of common types of video manipulation performed in temporal domain. It deals with inter-frame video forgery detection that involves frame deletion, insertion, duplication, and shuffling. Deep Learning (DL) techniques have been proven effective in analysis and processing of visual media. Dealing with video data needs to handle th
... Show MoreThe main objective of" this paper is to study a subclass of holomrphic and univalent functions with negative coefficients in the open unit disk U= defined by Hadamard Product. We obtain coefficients estimates, distortion theorem , fractional derivatives, fractional integrals, and some results.
Agriculture improvement is a national economic issue that extremely depends on productivity. The explanation of disease detection in plants plays a significant role in the agriculture field. Accurate prediction of the plant disease can help treat the leaf as early as possible, which controls the economic loss. This paper aims to use the Image processing techniques with Convolutional Neural Network (CNN). It is one of the deep learning techniques to classify and detect plant leaf diseases. A publicly available Plant village dataset was used, which consists of 15 classes, including 12 diseases classes and 3 healthy classes. The data augmentation techniques have been used. In addition to dropout and weight reg
... Show MoreThe theories of metric spaces and fuzzy metric spaces are crucial topics in mathematics.
Compactness is one of the most important and fundamental properties that have been widely used in Functional Analysis. In this paper, the definition of compact fuzzy soft metric space is introduced and some of its important theorems are investigated. Also, sequentially compact fuzzy soft metric space and locally compact fuzzy soft metric space are defined and the relationships between them are studied. Moreover, the relationships between each of the previous two concepts and several other known concepts are investigated separately. Besides, the compact fuzzy soft continuous functions are studie
... Show MoreVancomycin Staphylococcus aureus (VRSA) is a strain belonging to S. aureus that is considered the main cause of bacterial skin and soft tissue infections. It has acquired resistance to vancomycin and represents a therapeutic challenge. The current study aimed to compare the possible therapeutic effects of VRSA bacteriocin (VRSAcin) on the treatment of skin infection in mice compared with an antibiotic (linezolid). The results showed that from fifty swabs obtained from human skin wounds, only 30 samples were identified as Staphylococcus spp., and 20 samples of them were identified as VRSA strains. One isolate was selected for VRSAcin extraction depending on its antibiotic resistance using an
Canonical correlation analysis is one of the common methods for analyzing data and know the relationship between two sets of variables under study, as it depends on the process of analyzing the variance matrix or the correlation matrix. Researchers resort to the use of many methods to estimate canonical correlation (CC); some are biased for outliers, and others are resistant to those values; in addition, there are standards that check the efficiency of estimation methods.
In our research, we dealt with robust estimation methods that depend on the correlation matrix in the analysis process to obtain a robust canonical correlation coefficient, which is the method of Biwe
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