One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services through our ca

The precise classification of DNA sequences is pivotal in genomics, holding significant implications for personalized medicine. The stakes are particularly high when classifying key genetic markers such as BRAC, related to breast cancer susceptibility; BRAF, associated with various malignancies; and KRAS, a recognized oncogene. Conventional machine learning techniques often necessitate intricate feature engineering and may not capture the full spectrum of sequence dependencies. To ameliorate these limitations, this study employs an adapted UNet architecture, originally designed for biomedical image segmentation, to classify DNA sequences.The attention mechanism was also tested LONG WITH u-Net architecture to precisely classify DNA sequences

One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services th

The purpose of the current investigation is to distinguish between working memory ( ) in five patients with vascular dementia ( ), fifteen post-stroke patients with mild cognitive impairment ( ), and fifteen healthy control individuals ( ) based on background electroencephalography (EEG) activity. The elimination of EEG artifacts using wavelet (WT) pre-processing denoising is demonstrated in this study. In the current study, spectral entropy ( ), permutation entropy ( ), and approximation entropy ( ) were all explored. To improve the  classification using the k-nearest neighbors ( NN) classifier scheme, a comparative study of using fuzzy neighbourhood preserving analysis with -decomposition ( ) as a dimensionality reduction technique an

In this paper, we build a fuzzy classification system for classifying the nutritional status of children under 5 years old in Iraq using the Mamdani method based on input variables such as weight and height to determine the nutritional status of the child. Also, Classifying the nutritional status faces a difficult challenge in the medical field due to uncertainty and ambiguity in the variables and attributes that determine the categories of nutritional status for children, which are relied upon in medical diagnosis to determine the types of malnutrition problems and identify the categories or groups suffering from malnutrition to determine the risks faced by each group or category of children. Malnutrition in children is one of the most

The research work was conducted to investigate the effect of oral administration of water extract of black pepper at doses of (1, 5) mg/kg body weight for two weeks daily by determining the genotoxic effect (mitotic index), evaluation of immunological effect (IgG, IgM, IgA, C3, C4) and measuring fertility hormones (follicles stimulation hormone/FSH, lutenising hormone/LH) levels with histopathological examinations of female albino swiss mice ovaries in comparison with control (normal saline). A clear effect in increasing mitotic activity was reveled for both doses in comparison with control. Results also showed a significant increase in the value of the all immunological parameters at both doses in comparison with control. Also obvious rais

The study aimed to detect the VrPIP2;7 gene using PCR approach, as well as to know the effect of the treatment with four increased melatonin concentrations of 50, 100, 150 and 200 ppm in addition to control treatment were 0 ppm on the gene expression of plasma membrane intrinsic proteins (PIP) genes in Vigna radiata L. plant exhibition for five periods of drought which is irrigation every 24 hours, 48 hours, 5 days, 10 days and every 15 days. The electrophoresis of agarose gel at a concentration of 2% showed one band when detecting the VrPIP2;7 gene with a sizeable 732 bp and using the 100 bp volume index. This gene was selected for sequencing study based on its importance as well as on the results of its gene expression. The sequencing of

The central nervous system is the most important system and is very sensitive to any accidental infection during ontogenesis; it includes brain and spinal cord. The cerebellum is the second largest part of the brain after cerebrum and it’s very sensitive to the abnormal changes during the embryological development. This study was designed to investigate the effect of the maternal exposure of selected concentrations of suspension of nanoparticles on the ontogenesis of the rat cerebellum after embryos implanted in uterus. A total of 60 female pregnant rats were divided in to three groups, each contains 20 females. Group1 (G1) was treated orally with 2mg/kg /body weight (b. wt) of suspension of silver nanoparticles (Ag NPs). While group 2 (G