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Najah A.,National University of Malaysia | El-Shafie A.,National University of Malaysia | Karim O.A.,National University of Malaysia | Jaafar O.,National University of Malaysia | El-Shafie A.H.,University of Garyounis
International Journal of Physical Sciences | Year: 2011

Artificial Intelligence (AI) is a new technique with a flexible mathematical structure that is capable of identifying complex non-linear relationships between input and output data when compared with other classical modelling techniques. In this study, different techniques of AI have been investigated in prediction of water quality parameters including: multi-layer perceptron neural networks (MLP-ANN), ensemble neural networks (E-ANN) and support vector machine (SVM). The parameters were investigated in terms of the following: the dissolved oxygen (DO), biochemical oxygen demand (BOD) and chemical oxygen demand (COD). To assess the effect of input parameters on the model, the sensitivity analysis was adopted. To evaluate the performance of the proposed model, three statistical indexes were used, namely; correlation coefficient (CC), mean square error (MSE) and correlation of efficiency (CE). The principle aim of this study is to develop a computationally efficient and robust approach for predicting water quality parameters which could reduce the cost and labour for measuring these parameters. This research concentrates on the Johor river in Johor State, Malaysia where the dynamics of river water quality are significantly altered. © 2011 Academic Journals. Source


Edbey K.,University of Garyounis | Moran G.,University of New South Wales | Willett G.,University of New South Wales
International Journal of ChemTech Research | Year: 2010

The analytical potential of the complexation of flavone with Zn(II), Cd(II), and Co(II) was investigated by positive-ion electrospray ionization Fourier transform ion cyclotron resonance (ESI-FTICR) mass spectrometry. The binding selectivity of the flavone with three different divalent metal ions Cd(II), Co(II), and Zn(II), reveals that the flavone favors binding to Zn(II) over Co(II) and Cd(II) and that the order of relative binding affinity is Zn(II) > Co(II) > Cd(II). This is can be attributed to the polarizability, or relative hardness of the metal ions. The counter-ion binding selectivities measured by FTICR mass spectrometry of flavone with ZnC12, Zn(NO 3) 2, and Zn(C1O4)2 show that the order of selectivity for the complexation of flavone metal complex with the counter-ions is C1O4- > C1- > NO 3-. The exact reason for such selectivity order is not known, so that further experiments and calculation are required to understand these effects of counter-ion selectivities. Source


Najah A.,University of Malaysia, Terengganu | El-Shafie A.,National University of Malaysia | Karim O.A.,National University of Malaysia | El-Shafie A.H.,University of Garyounis
Environmental Science and Pollution Research | Year: 2014

We discuss the accuracy and performance of the adaptive neuro-fuzzy inference system (ANFIS) in training and prediction of dissolved oxygen (DO) concentrations. The model was used to analyze historical data generated through continuous monitoring of water quality parameters at several stations on the Johor River to predict DO concentrations. Four water quality parameters were selected for ANFIS modeling, including temperature, pH, nitrate (NO3) concentration, and ammoniacal nitrogen concentration (NH3-NL). Sensitivity analysis was performed to evaluate the effects of the input parameters. The inputs with the greatest effect were those related to oxygen content (NO3) or oxygen demand (NH3-NL). Temperature was the parameter with the least effect, whereas pH provided the lowest contribution to the proposed model. To evaluate the performance of the model, three statistical indices were used: the coefficient of determination (R 2), the mean absolute prediction error, and the correlation coefficient. The performance of the ANFIS model was compared with an artificial neural network model. The ANFIS model was capable of providing greater accuracy, particularly in the case of extreme events. © 2013 Springer-Verlag Berlin Heidelberg. Source


Abdelaziz T.M.,University of Garyounis
Communications in Computer and Information Science | Year: 2011

Many of agent systems concepts are proposed in last decade. Most of them are inspired from the approach or discipline upon which they are based. Some of these concepts are developed based on the approach of extending existing Object Oriented concepts to include the relevant aspects of agents. Others are developed based on agent-based concepts or based on knowledge engineering concepts. The difference between disciplines of knowledge is causing the misunderstanding of some concepts that relate to agent systems, as well as some inconsistencies. In this paper, a conceptual formal framework is constructed to determine the essential MAS concepts and to reduce the possible turmoil and define the relationships among those concepts. The proposed framework is well-structured formal system that was constructed to ensure that the proposed MAS conceptual system is logically coherent and free of contradiction. Z language is used to represent this formal system. © 2011 Springer-Verlag. Source


El-Shafie A.H.,University of Garyounis | El-Shafie A.,National University of Malaysia | El Mazoghi H.G.,National University of Malaysia | Shehata A.,Northwest Research Extension Center | Taha M.R.,University of Garyounis
International Journal of Physical Sciences | Year: 2011

Two rainfall prediction models were developed and implemented in Alexandria, Egypt. These models are Artificial Neural Network ANN model and Multi Regression MLR model. A Feed Forward Neural Network FFNN model was developed and implemented to predict the rainfall on yearly and monthly basis. In order to evaluate the incomes of both models, statistical parameters were used to make the comparison between the two models. These parameters include the Root Mean Square Error RMSE, Mean Absolute Error MAE, Coefficient Of Correlation CC and BIAS. The data set that has been used in this study includes daily measurements for the rainfall and temperature and cover the period from 1957 to 2009. The FFNN model has shown better performance than the MLR model. The MLR model revealed a humble prediction performance. The linear nature of MLR model estimators makes it inadequate to provide good prognostics for a variable characterized by a highly nonlinear physics. On the other hand, the ANN model is a nonlinear mapping tool, which potentially is more suitable for rain (nonlinear physics) forecasts. More detailed studies are necessary due to uncertainties inherent in weather forecasting and efforts should be addressed to the problem of quantifying them in the ANN models. © 2011 Academic Journals. Source

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