In this research an Artificial Neural Network (ANN) technique was applied for the prediction of Ryznar Index (RI) of the flowing water from WTPs in Al-Karakh side (left side) in Baghdad city for year 2013. Three models (ANN1, ANN2 and ANN3) have been developed and tested using data from Baghdad Mayoralty (Amanat Baghdad) including drinking water quality for the period 2004 to 2013. The results indicate that it is quite possible to use an artificial neural networks in predicting the stability index (RI) with a good degree of accuracy. Where ANN 2 model could be used to predict RI for the effluents from Al-Karakh, Al-Qadisiya and Al-Karama WTPs as the highest correlation coefficient were obtained 92.4, 82.9 and 79.1% respectively. For

The integration of Artificial Intelligence with Big Data Analytics is one of the most groundbreaking developments that could change the face of educational sustainability in higher education.. Using AI and Big Data technologies not only makes the educational process more efficient but also changes the way people learn and thus opens the door for educators and institutions to make decisions based on the data. The document imparts the manner that the use of AI and the digital revolution can remove student requirements, execute the efficiency of the curriculum, and acquire the balance of educational resources through a majority of instances and the latest developments in that field. Furthermore, the paper, along with the issues of morality wit

Face Recognition Systems (FRS) are increasingly targeted by morphing attacks, where facial features of multiple individuals are blended into a synthetic image to deceive biometric verification. This paper proposes an enhanced Siamese Neural Network (SNN)-based system for robust morph detection. The methodology involves four stages. First, a dataset of real and morphed images is generated using StyleGAN, producing high-quality facial images. Second, facial regions are extracted using Faster Region-based Convolutional Neural Networks (R-CNN) to isolate relevant features and eliminate background noise. Third, a Local Binary Pattern-Convolutional Neural Network (LBP-CNN) is used to build a baseline FRS and assess its susceptibility to d

The dynamic development of computer and software technology in recent years was accompanied by the expansion and widespread implementation of artificial intelligence (AI) based methods in many aspects of human life. A prominent field where rapid progress was observed are high‐throughput methods in biology that generate big amounts of data that need to be processed and analyzed. Therefore, AI methods are more and more applied in the biomedical field, among others for RNA‐protein binding sites prediction, DNA sequence function prediction, protein‐protein interaction prediction, or biomedical image classification. Stem cells are widely used in biomedical research, e.g., leukemia or other disease studies. Our proposed approach of

The question about the existence of correlation between the parameters A and m of the Paris function is re-examined theoretically for brittle material such as alumina ceramic (Al2O3) with different grain size. Investigation about existence of the exponential function which fit a good approximation to the majority of experimental data of crack velocity versus stress intensity factor diagram. The rate theory of crack growth was applied for data of alumina ceramics samples in region I and making use of the values of the exponential function parameters the crack growth rate theory parameters were estimated.

Activated carbon was Produced from coconut shell and was used for removing sulfate from industrial waste water in batch Processes. The influence of various parameter were studied such as pH (4.5 – 9.) , agitation time (0 – 120)min and adsorbent dose (2 – 10) gm.

The Langmuir and frandlich adsorption capacity models were been investigated where showed there are fitting with langmmuir model with squre regression value ( 0.76). The percent of removal of  sulfate (22% - 38%) at (PH=7) in the isotherm experiment increased  with adsorbent mass increasing. The maximum removal value of sulfate at  different pH experiments is (43%) at pH=7.

This study explores the role of nanomaterials in the performance of asphalt binders and mixtures. Two commonly available nanomaterials, i.e., nanosilica (NS) and nanoalumina (NA), were used at contents of 0%, 2%, 4%, 6%, and 8% by weight of asphalt binder. A set of experiments was carried out at the binder level to investigate properties such as penetration, softening point, aging-related mass loss, nanomaterial dispersion (storage stability), and workability (rotational viscosity). In addition, the suitability of NS and NS was also assessed through the testing of nanomodified asphalt mixtures, which focused on Marshall properties, the resilient modulus, moisture susceptibility, permanent deformation, and fatigue resistance. The findings in

The present work describes the adsorption of Ba2+ and Mg2+ions from aqueous solutions by activated alumina in single and binary system using batch adsorption. The effect of different parameters such as amount of alumina, concentration of metal ions, pH of solution, contact time and agitation speed on the adsorption process was studied. The optimum adsorbent dosage was found to be 0.5 g and 1.5 g for removal of Ba2+ and Mg2+, respectively. The optimum pH, contact time and agitation speed, were found to be pH 6, 2h and 300 rpm, respectively, for removal of both metal ions. The equilibrium data were analyzed by Langmuir and Freundlich isotherm models and the data fitted well to both isotherm modes as indicated by higher correlation of deter