Algae have been considered a sources task of biofuels, which is a future alternative to fossil fuels, and this lead the environmental studies concerned with the lifting of curves or growth rates and time of replication of different kinds of algae, as well as algae cells in response to different environmental conditions, whether chemical or physical, to assess their impact on the composition of these cells and the extent of affected components that make up the living, especially fatty acid ,total fats, proteins and carbohydrates, Gbrha. Green Chlorococcum humicola showed a different response when treated with an average of agriculture Chu-10 and Chu-13 which used as control media,Compared with the degree of its response when exposed to e

In this work, the effects of solvent properties on the characteristics of absorption and fluorescence for two laser dyes was studied. Dyes used in this work include Coumarin 5400 and DCM, while the solvents include ethanol, methanol, acetone, propanol and chloroform. Coumarin 5400 dye shows sharp fluorescence peaks in the green band of visible region while the DCM dye shows relatively wide band within 590-630 nm. Therefore, the selection of any dye for random gain medium applications should be performed after determining the most appropriate solvent as the optimum fluorescence characteristics are obtained.

The intelligent buildings provided various incentives to get highly inefficient energy-saving caused by the non-stationary building environments. In the presence of such dynamic excitation with higher levels of nonlinearity and coupling effect of temperature and humidity, the HVAC system transitions from underdamped to overdamped indoor conditions. This led to the promotion of highly inefficient energy use and fluctuating indoor thermal comfort. To address these concerns, this study develops a novel framework based on deep clustering of lagrangian trajectories for multi-task learning (DCLTML) and adding a pre-cooling coil in the air handling unit (AHU) to alleviate a coupling issue. The proposed DCLTML exhibits great overall control and is

Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep lea

Analyzing sentiment and emotions in Arabic texts on social networking sites has gained wide interest from researchers. It has been an active research topic in recent years due to its importance in analyzing reviewers' opinions. The Iraqi dialect is one of the Arabic dialects used in social networking sites, characterized by its complexity and, therefore, the difficulty of analyzing sentiment. This work presents a hybrid deep learning model consisting of a Convolution Neural Network (CNN) and the Gated Recurrent Units (GRU) to analyze sentiment and emotions in Iraqi texts. Three Iraqi datasets (Iraqi Arab Emotions Data Set (IAEDS), Annotated Corpus of Mesopotamian-Iraqi Dialect (ACMID), and Iraqi Arabic Dataset (IAD)) col

The proliferation of many editing programs based on artificial intelligence techniques has contributed to the emergence of deepfake technology. Deepfakes are committed to fabricating and falsifying facts by making a person do actions or say words that he never did or said. So that developing an algorithm for deepfakes detection is very important to discriminate real from fake media. Convolutional neural networks (CNNs) are among the most complex classifiers, but choosing the nature of the data fed to these networks is extremely important. For this reason, we capture fine texture details of input data frames using 16 Gabor filters indifferent directions and then feed them to a binary CNN classifier instead of using the red-green-blue

Background/Objectives: The purpose of this study was to classify Alzheimer’s disease (AD) patients from Normal Control (NC) patients using Magnetic Resonance Imaging (MRI). Methods/Statistical analysis: The performance evolution is carried out for 346 MR images from Alzheimer's Neuroimaging Initiative (ADNI) dataset. The classifier Deep Belief Network (DBN) is used for the function of classification. The network is trained using a sample training set, and the weights produced are then used to check the system's recognition capability. Findings: As a result, this paper presented a novel method of automated classification system for AD determination. The suggested method offers good performance of the experiments carried out show that the

In hemodialysis patients, pain associated with needle insertion into an arteriovenous fistula is a physical and psychological problem. The aim of this study was to assess the effectiveness of pre‐puncture application of an ice pack, EMLA cream, or lidocaine spray to reduce pain associated with access puncture.