The present article delves into the examination of groundwater quality, based on WQI, for drinking purposes in Baghdad City. Further, for carrying out the investigation, the data was collected from the Ministry of Water Resources of Baghdad, which represents water samples drawn from 114 wells in Al-Karkh and Al-Rusafa sides of Baghdad city. With the aim of further determining WQI, four water parameters such as (i) pH, (ii) Chloride (Cl), (iii) Sulfate (SO4), and (iv) Total dissolved solids (TDS), were taken into consideration. According to the computed WQI, the distribution of the groundwater samples, with respect to their quality classes such as excellent, good, poor, very poor and unfit for human drinking purpose, was found to be 14.9 %, 39.5 %, 22.8 %, 6.1 %, and 16.7 %, respectively. Additionally, to anticipate changes in groundwater WQI, IBM® SPSS® Statistics 19 software (SPSS) was used to develop an artificial neural network model (ANNM). With the application of this ANNM model, the results obtained illustrated high prediction efficiency, as the sum of squares error functions (for training and testing samples) and coefficient of determination (R2), were found to be (0.038 and 0.005) and 0.973, respectively. However, the parameters pH and Cl influenced model prediction significantly, thereby becoming crucial factors in the anticipation carried out by using ANNM model.
Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr
... Show MoreThe Umm Al-Naaj Marsh was chosen in Maysan province, and it is one of the sections of Mar Al-Hawza, which is one of the most prominent Iraqi marshes in the south. The marshes are located between latitudes 30 35 and 32 45 latitudes and longitudes 13 46 and 48 00. The area of the study area is 76479.432142 hectares to evaluate soil quality and health index and their spatial distribution based on measuring physical, chemical, biological and fertility traits and calculating the total quality index for those characteristics. Using an auger drilling machine, we collected 50 randomly selected surface samples, evenly distributed across the study region, from Al-Aq 0.0–0.30 m, noting their precise locations along the way. Soil health and quality w
... Show Moremodel is derived, and the methodology is given in detail. The model is constructed depending on some measurement criteria, Akaike and Bayesian information criterion. For the new time series model, a new algorithm has been generated. The forecasting process, one and two steps ahead, is discussed in detail. Some exploratory data analysis is given in the beginning. The best model is selected based on some criteria; it is compared with some naïve models. The modified model is applied to a monthly chemical sales dataset (January 1992 to Dec 2019), where the dataset in this work has been downloaded from the United States of America census (www.census.gov). Ultimately, the forecasted sales
This study proposes a new version of the Autoregressive Integrated Moving Average (ARIMA) model using Artificial Neural Networks (ANNs) denoted by ARIMA-NN. The new model incorporates a multi-layer perceptron with matrix multiplication within a feed-forward network. The logistic, hyperbolic tangent (tanh), and sigmoid activation functions are used for weight updates in ARIMA-NN. A new forecasting algorithm is proposed, and one-step and multiple-steps forecasting procedures are rigorously analyzed. The proposed model was evaluated against existing forecasting model using performance metrics such as the Akaike Information Criterion (AIC) and Bayesian Information Criterion (
... Show MoreThis research aims to identify the role of external environment factors on the quality of educational services, from the academic point of view, where the distribution of a questionnaire to a random sample of (100) university professors, and then analyzing a model, and test the validity of this model using structural modeling (SEM) (Structural Equation Modeling).
And then test the relationships between variables using the software of Statistical Package for Social Sciences (SPSS V.18), the research found a number of conclusions, the most important conclusion is: the external environment factors has significant impact on the quality of educational services.
Iraqi provinces suffer many of the environmental problems, appear explained these problems in the city of Basra, Basra, the largest province and is among Iraq's provinces in the annexation of oil fields, which is experiencing an oil production operations of large, it suffers from the burning of gas associated with oil extraction resulting in the emission severe gas process including toxic hydrogen sulfide gas (h2S) and oxides of carbon, sulfur and nitrogen in addition to the release of toxic metal elements such as mercury, arsenic, vanadium, which caused the man many serious diseases. , And perhaps one of the most important of these problems are worsening air quality and increasing cases of air pollution and the deterioration of the qual
... Show MoreGeneral Directorate of Surveying is considered one of the most important sources of maps in Iraq. It produced digital maps for whole Iraq in the last six years. These maps are produced from different data sources with unknown accuracy; therefore, the quality of these maps needs to be assessed. The main aim of this study is to evaluate the positional accuracy of digital maps that produced from General Directorate of Surveying. Two different study areas were selected: AL-Rusafa and AL-Karkh in Baghdad / Iraq with an area of 172.826 and 135.106 square kilometers, respectively. Different statistical analyses were conducted to calculate the elements of positional accuracy assessment (mean µ, root mean square error RMSE, mini
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