The proper operation, and control of wastewater treatment plants, is receiving an increasing attention, because of the rising concern about environmental issues. In this research a mathematical model was developed to predict biochemical oxygen demand in the waste water discharged from Abu-Ghraib diary factory in Baghdad using Artificial Neural Network (ANN).In this study the best selection of the input data were selected from the recorded parameters of the wastewater from the factory. The ANN model developed was built up with the following parameters: Chemical oxygen demand, Dissolved oxygen, pH, Total dissolved solids, Total suspended solids, Sulphate, Phosphate, Chloride and Influent flow rate. The results indicated that the constructed ANN model to predict BOD of the effluent gave a high degree of correlation reaching 93.5% with seven input parameters. This model can be queried to determine the best combination of operating conditions needed in the wastewater treatment plant to achieve the desired effluent disposal limits.
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Neoechinorhynchus iraqensis sp. n.(Acanthocephala: Neoechinorhynchidae) from the Freshwater Mullet, Liza abu.
This study aims to inspect the marginalization strategies implemented by Iraqi newspapers, with a focus on the daily newspaper (Al-Sabah) and its coverage of the October 2019 protests in Iraq, by employing the “Paradigm” Module. The research is descriptive the researcher implemented a descriptive-analytical survey method by using a non-probability sampling represented by (74) issues of Al-Sabah newspaper and used (content analysis form) as a tool to analyze (93) news content related to the October protests.
The Researcher reached several conclusions:
1- Al-Sabah newspaper mainly depends on “Economic Reforms” as a marginalization strategy in its news coverage of the October 2019 protests.
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In this paper, an algorithm is suggested to train a single layer feedforward neural network to function as a heteroassociative memory. This algorithm enhances the ability of the memory to recall the stored patterns when partially described noisy inputs patterns are presented. The algorithm relies on adapting the standard delta rule by introducing new terms, first order term and second order term to it. Results show that the heteroassociative neural network trained with this algorithm perfectly recalls the desired stored pattern when 1.6% and 3.2% special partially described noisy inputs patterns are presented.
Abstract
This research aim to overcome the problem of dimensionality by using the methods of non-linear regression, which reduces the root of the average square error (RMSE), and is called the method of projection pursuit regression (PPR), which is one of the methods for reducing dimensions that work to overcome the problem of dimensionality (curse of dimensionality), The (PPR) method is a statistical technique that deals with finding the most important projections in multi-dimensional data , and With each finding projection , the data is reduced by linear compounds overall the projection. The process repeated to produce good projections until the best projections are obtained. The main idea of the PPR is to model
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Whenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model bas
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A total of 54 abu mullet Planiliza abu (Heckel, 1843) were collected from two stations (north and south stations) along the Euphrates River near Samawa City, Al-Muthanna province and were examined during the period from October 2016 till September 2017 for parasites. Six out of 35 examined fishes from the north station (17.1%) and one out of 19 examined fishes (5.3%) from south station were infected with the microcotylid Solostamenides paucitesticulatus Kritsky & Öktener, 2015. The parasite was illustrated and described, and it is considered as a new record for the parasitic fauna of fishes of Iraq.
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Finger vein recognition and user identification is a relatively recent biometric recognition technology with a broad variety of applications, and biometric authentication is extensively employed in the information age. As one of the most essential authentication technologies available today, finger vein recognition captures our attention owing to its high level of security, dependability, and track record of performance. Embedded convolutional neural networks are based on the early or intermediate fusing of input. In early fusion, pictures are categorized according to their location in the input space. In this study, we employ a highly optimized network and late fusion rather than early fusion to create a Fusion convolutional neural network
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