The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

The life on earth is driven by energy, supplied by the tiny organelles of the cell called mitochondria and they are usually stated as the powerhouses of the cell.  In population genetics, Mitochondrial DNA (mtDNA) is used extensively to categorize individuals or populations.  The mutation sites observed in human mtDNA by comparing with the reference sequence (rCRS) are termed into definite human mtDNA haplogroups. Previous studies showed that mtDNA specific haplogroups and polymorphisms were established to be linked with various human diseases, including cancer in numerous populations. Furthermore, it is also known that several mitochondrial DNA polymorphisms are implicated in enhanced production of Reactive Oxygen Species (ROS

This work deals with thermal cracking of three samples of extract lubricating oil produced as a by-product from furfural extraction process of lubricating oil base stock in AL-Dura refinery. The thermal cracking processes were carried out at a temperature range of 325-400 ºC and atmospheric pressure by batch laboratory reactor. The distillation of cracking liquid products was achieved by general ASTM distillation (ASTM D -86) for separation of gasoline fraction up to 220 ºC from light cycle oil fraction above 220 ºC. The comparison between the conversions at different operating conditions of thermal cracking processes indicates that a high conversion was obtained at 375°C, according to gasoline production. According to gasoline produ

In gene regulation, transcription factors (TFs) play a key function. It transmits genetic information from DNA to messenger RNA during the process of DNA transcription. During this step, the transcription factor binds to a segment of the DNA sequence known as Transcription Factor Binding Sites (TFBS). The goal of this study is to build a model that predicts whether or not a DNA binding site attaches to a certain transcription factor (TF). TFs are regulatory molecules that bind to particular sequence motifs in the gene to induce or restrict targeted gene transcription. Two classification methods will be used, which are support vector machine (SVM) and kernel logistic regression (KLR). Moreover, the KLR algorithm depends on another regress

In this research, the focus was on estimating the parameters on (min- Gumbel distribution), using the maximum likelihood method and the Bayes method. The genetic algorithmmethod was employed in estimating the parameters of the maximum likelihood method as well as  the Bayes method. The comparison was made using the mean error squares (MSE), where the best  estimator  is the one who has the least mean squared error. It was noted that the best estimator was (BLG_GE).

The current study included bioremoval of chromium metal ions from aqueous solution by using seventeen Pseudomonas aeruginosa species isolated from different environments. The experimental results showed that isolates Pseudomonas aeruginosa have high efficiency in removal of chromium where the P. aeruginosa p.8 was the most efficient (P≥0.001) in bioremoval of chromium with a removal capacity reached 92.5 mg/L and removal index reached (96.5%). While P. aeruginosa p.4 was the least efficient (P≥0.001) in bioremoval of chromium from aqueous solutions reached 74.6 mg/L and removal index reached (79.8%). The REP-PCR detection using BOX-primer, showed genetic relatedness among the isolates of P.aeru

The catalytic cracking of three feeds of extract lubricating oil, that produced as a by-product from the process of furfural extraction of lubricating oil base stock in AL-Dura refinery at different operating condition, were carried out at a fixed bed laboratory reactor. The initial boiling point for these feeds was 140 ºC for sample (1), 86 ºC for sample (2) and 80 ºC for sample (3). The catalytic cracking processes were carried out at temperature range 325-400 ºC and initially at atmospheric pressure after 30 minutes over 9.88 % HY-zeolite catalyst load. The comparison between the conversion at different operating conditions of catalytic cracking processes indicates that a high yield was obtained at 375°C, according to gasoline pr

In this study, we investigated the ability of nanoliposomes preparation, as a nanoadjuvant, to entrap soluble Leismania donovani antigens (SLAs) and release in vitro. The parasite reactivation was carried out when inoculated into Rosewell park memorial institute media (RPMI) and incubated at 23 °C for 4 days. L. donovani promastigote inoculum (104 cell / ml) of 4 days was used to inoculate modified medium of Saline - Neopeptone and Blood agar 9 (SNB 9) to produce promastigote mass. SLAs were extracted from the promastigotes ghost membrane after fourth passages of subculturing in SNB. The membrane pellet obtained was suspended in 5 mM Tris buffer (pH 7.6) and sonicated three times at 4 °C and entrapped in freshly prepared nanoliposomes.

Deep drawing process to produce square cup is very complex process due to a lot of process parameters which control on this process, therefore associated with it many of defects such as earing, wrinkling and fracture. Study of the effect of some process parameters to determine the values of these parameters which give the best result, the distributions for the thickness and depths of the cup were used to estimate the effect of the parameters on the cup numerically, in addition to experimental verification just to the conditions which give the best numerical predictions in order to reduce the time, efforts and costs for producing square cup with less defects experimentally is the aim of this study. The numerical analysis is used to study

This paper presents a comparative study of two learning algorithms for the nonlinear PID neural trajectory tracking controller for mobile robot in order to follow a pre-defined path. As simple and fast tuning technique, genetic and particle swarm optimization algorithms are used to tune the nonlinear PID neural controller's parameters to find the best velocities control actions of the right wheel and left wheel for the real mobile robot. Polywog wavelet activation function is used in the structure of the nonlinear PID neural controller. Simulation results (Matlab) and experimental work (LabVIEW) show that the proposed nonlinear PID controller with PSO
learning algorithm is more effective and robust than genetic learning algorithm; thi