Bacteria strain H8, which produces high amount of exopolysaccharide (EPS), was isolated from soil, and identified as strain of Azotobacter chrococcum by its biochemical /physiological characteristics, EPS was extracted, partially purified and used as bioflocculant. The biochemical analysis of the partially purified EPS revealed that it was an alginate. analysis of EPS by Fourier transform infrared spectrometry (FTIR) show that the -OH groups present in bioflocculant are clearly seen at 3433.06 cm-1, the peaks attributed to the -CH3 groups present at 2916.17 cm-1 , and some distinct peaks such as carboxyl group showed strong absorption bands at 1604.66 cm-1, 1411.80 cm-1 and 1303.79 cm-1 indicate the chemical structure of alginate. The effects of various salts, temperature, bioflocculant dosage and pH on the flocculation activity (turbidity removal) were evaluated. When various salts, were used as cation, results showed that CaCl2 and ZnSO4.7H2O enhanced flocculation activity 57.3% and 55.3% respectively compared with CuSO4 and MnCl2 which was 37.5% and 10 % respectively. The bioflocculant had strong thermal stability within the range of 20-100 °C, and the flocculating activity was over 50 %, the maximum flocculation activity was observed at an optimum bioflocculant dosage of 0.25 mL /10 mL (60.8%)., the bioflocculant had highest flocculating activity at alkaline condition pH 10 (77.7 %). After optimization of flocculation conditions, bioflocculant produced was applied to deal with river water and the flocculating activity (turbidity removal) increased to 81%.
The evolution in the field of Artificial Intelligent (AI) with its training algorithms make AI very important in different aspect of the life. The prediction problem of behavior of dynamical control system is one of the most important issue that the AI can be employed to solve it. In this paper, a Convolutional Multi-Spike Neural Network (CMSNN) is proposed as smart system to predict the response of nonlinear dynamical systems. The proposed structure mixed the advantages of Convolutional Neural Network (CNN) with Multi -Spike Neural Network (MSNN) to generate the smart structure. The CMSNN has the capability of training weights based on a proposed training algorithm. The simulation results demonstrated that the proposed
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XML is being incorporated into the foundation of E-business data applications. This paper addresses the problem of the freeform information that stored in any organization and how XML with using this new approach will make the operation of the search very efficient and time consuming. This paper introduces new solution and methodology that has been developed to capture and manage such unstructured freeform information (multi information) depending on the use of XML schema technologies, neural network idea and object oriented relational database, in order to provide a practical solution for efficiently management multi freeform information system.
The aim of this paper is to approximate multidimensional functions by using the type of Feedforward neural networks (FFNNs) which is called Greedy radial basis function neural networks (GRBFNNs). Also, we introduce a modification to the greedy algorithm which is used to train the greedy radial basis function neural networks. An error bound are introduced in Sobolev space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result is published in [16]).
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The study addressed the change in the nature of the land cover of the Al-Jadriya Twist area for the period from 1976-2024 with an area of (140 km2)and for a period of (48 years) based on satellite images and their analysis using geographic information systems. The main classifications of the area were reached (water cover, residential areas, vegetation cover, in addition to empty, unused areas). The extracted data indicate a decrease in the water cover and the change rate reached (-14.29) and the residential areas increased with a change rate of (28.26), while the vegetation cover rate was recorded from (45 km2) to (66 km2) and the empty areas had a change rate of (-78.57).
Earth cover of the city of Baghdad was studied exclusively within its administrative border during the period 1986-2019 using satellite scenes every five years, as Landsat TM5 and OLI8 satellite images were used. The land has been classified into ten subclasses according to the characteristics of the land cover and was classified using the Maximum Likelihood classifier. A study of the changing urban reality of the city of Baghdad during that period and the change of vegetation due to environmental factors, human influences and some human phenomena that affected the accuracy of the classification for some areas east of the city of Baghdad is presented. The year 2019 has been highlighted because of its privacy in changing the land cover of th
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The inhibitive action of Phenyl Thiourea (PTU) on the corrosion of mild steel in strong Hydrochloric acid, HCl, has been investigated by weight loss and potentiostatic polarization. The effect of PTU concentration, HCl concentration, and temperature on corrosion rate of mild steel were verified using 2 levels factorial design and surface response analysis through weight loss approach, while the electrochemical measurements were used to study the behavior of mild steel in 5-7N HCl at temperatures 30, 40 and 50 °C, in absence and presence of PTU. It was verified that all variables and their interaction were statistically significant. The adsorption of (PTU) is found to obey the Langmuir adsorption isotherm. The effect of temperature on th
... Show MoreThe 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
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Classification of imbalanced data is an important issue. Many algorithms have been developed for classification, such as Back Propagation (BP) neural networks, decision tree, Bayesian networks etc., and have been used repeatedly in many fields. These algorithms speak of the problem of imbalanced data, where there are situations that belong to more classes than others. Imbalanced data result in poor performance and bias to a class without other classes. In this paper, we proposed three techniques based on the Over-Sampling (O.S.) technique for processing imbalanced dataset and redistributing it and converting it into balanced dataset. These techniques are (Improved Synthetic Minority Over-Sampling Technique (Improved SMOTE), Border
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