An Intelligent Internet of Things network based on an Artificial Intelligent System, can substantially control and reduce the congestion effects in the network. In this paper, an artificial intelligent system is proposed for eliminating the congestion effects in traffic load in an Intelligent Internet of Things network based on a deep learning Convolutional Recurrent Neural Network with a modified Element-wise Attention Gate. The invisible layer of the modified Element-wise Attention Gate structure has self-feedback to increase its long short-term memory. The artificial intelligent system is implemented for next step ahead traffic estimation and clustering the network. In the proposed architecture, each sensing node is adaptive and able to change its affiliation with other clusters based on a deep learning modified Element-wise Attention Gate. The modified Element-wise Attention Gate has the ability to handle the buffer capacity in all the network, thereby enriching the Quality of Service. A deep learning modified training algorithm is proposed to learn the artificial intelligent system allowing the neurons to have greater concentration ability. The simulation results demonstrate that the Root Mean Square error is minimized by 37.14% when using modified Element-wise Attention Gate when compared with a Deep Learning Recurrent Neural Network. Also, the Quality of Service of the network is improved, for example, the network lifetime is enhanced by 12.7% more than with Deep Learning Recurrent Neural Network.
The prostaglandins inside inflamed tissues are produced by cyclooxygenase-2 (COX-2), making it an important target for improving anti-inflammatory medications over a long period. Adverse effects have been related to the traditional usage of non-steroidal anti-inflammatory drugs (NSAIDs) for the treatment of inflammation, mainly centered around gastrointestinal (GI) complications. The current research involves the creation of a virtual library of innovative molecules showing similar drug properties via a structure-based drug design. A library that includes five novel derivatives of Diclofenac was designed. Subsequently, molecular docking through the Glide module and determining the binding free energy implementing the P
... Show MoreThe 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]).
This paper proposes a new structure of the hybrid neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Weight parameters of the hybrid neural structure with its serial-parallel configuration are adapted by using the Back propagation learning algorithm. The ability of the proposed hybrid neural structure for nonlinear system has achieved a fast learning with minimum number
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Purpose of this study is to investigate the relationship between Advertising Appeals, Attitudes toward Advertising, and Consumer Buying Behavior for Smart Phone The study was carried out on the students of Middle East University (MEU) In Amman- Jordan. A measurement scales with acceptable reliability and validity is developed to capture the dimensions of study variables. Four hypotheses were tested using Statistical package (SPSS-17). A two-step detailed statistical analysis of data was involved. First, descriptive statistics was performed to understand the underlying components of study variables; second, regression analysis and Path analysis using AMOS 7 were performed t
... Show MoreThe operation and management of water resources projects have direct and significant effects on the optimum use of water. Artificial intelligence techniques are a new tool used to help in making optimized decisions, based on knowledge bases in the planning, implementation, operation and management of projects as well as controlling flowing water quantities to prevent flooding and storage of excess water and use it during drought.
In this research, an Expert System was designed for operating and managing the system of AthTharthar Lake (ESSTAR). It was applied for all expected conditions of flow, including the cases of drought, normal flow, and during floods. Moreover, the cases of hypothetical op
... Show MoreIn this work, the rate of charge transfer (CT) reaction at the N3-ZnS interface was calculated using a quantitative computational model to evaluate the efficiency of N3-ZnS heterojunction dye-sensitized solar cell devices using different types of solvents. This work discussed the influence of the effective driving energy force on the charge transport rate and performance of N3-ZnS devices with various solvents based on a donor-acceptor model. A solar cell model was used to study the optical efficiency when changing some of its parameters, such as the type of material and the thickness of the film, as they are important factors influencing the quality of the solar cell. It was found that the transition energy varies with different so
... Show MoreThe mixed-spin ferrimagnetic Ising system consists of two-dimensional sublattices A and B with spin values and respectively .By used the mean-field approximation MFA of Ising model to find magnetism( ).In order to determined the best stabile magnetism , Gibbs free energy employ a variational method based on the Bogoliubov inequality .The ground-state (Phase diagram) structure of our system can easily be determined at , we find six phases with different spins values depend on the effect of a single-ion anisotropies .these lead to determined the second , first orders transition ,and the tricritical points as well as the compensation phenomenon .