Ad hoc networks are characterized by ease of setup, low costs, and frequent use in the corporate world. They ensure safety to the user and maintain the confidentiality of the information circulated. They also allow the user to address the cases of communication failure in areas subject to destruction of communication infrastructure. The proposed protocols in the ad hoc networks often build only one path to achieve communication between the nodes, due to the restrictions of battery run out and the movement of the nodes. This connection is often subject to a failure within a certain range. Thus, multiple alternate paths in ad hoc networks use a solution to failing node communications. In addition, when looking at the situation where interf

Densely deployment of sensors is generally employed in wireless sensor networks (WSNs) to ensure energy-efficient covering of a target area. Many sensors scheduling techniques have been recently proposed for designing such energy-efficient WSNs. Sensors scheduling has been modeled, in the literature, as a generalization of minimum set covering problem (MSCP) problem. MSCP is a well-known NP-hard optimization problem used to model a large range of problems arising from scheduling, manufacturing, service planning, information retrieval, etc. In this paper, the MSCP is modeled to design an energy-efficient wireless sensor networks (WSNs) that can reliably cover a target area. Unlike other attempts in the literature, which consider only a si

Discriminant between groups is one of the common procedures because of its ability to analyze many practical phenomena, and there are several methods can be used for this purpose, such as linear and quadratic discriminant functions. recently, neural networks is used as a tool to distinguish between groups.

In this paper the simulation is used to compare neural networks and classical method for classify observations to group that is belong to, in case of some variables that don’t follow the normal distribution. we use the proportion of number of misclassification observations to the all observations as a criterion of comparison.  

    This research includes the use of an artificial intelligence algorithm, which is one of the algorithms of biological systems which is the algorithm of genetic regulatory networks (GRNs), which is a dynamic system for a group of variables representing space within time. To construct this biological system, we use (ODEs) and to analyze the stationarity of the model we use Euler's method. And through the factors that affect the process of gene expression in terms of inhibition and activation of the transcription process on DNA, we will use TF transcription factors. The current research aims to use the latest methods of the artificial intelligence algorithm. To apply Gene Regulation Networks (GRNs), we used a progr

In this paper, we prove that our proposed localization algorithm named Improved
Accuracy Distribution localization for wireless sensor networks (IADLoc) [1] is the
best when it is compared with the other localization algorithms by introducing many
cases of studies. The IADLoc is used to minimize the error rate of localization
without any additional cost and minimum energy consumption and also
decentralized implementation. The IADLoc is a range free and also range based
localization algorithm that uses both type of antenna (directional and omnidirectional)
it allows sensors to determine their location based on the region of
intersection (ROI) when the beacon nodes send the information to the sink node and
the la

In this paper, we prove that our proposed localization algorithm named Improved
Accuracy Distribution localization for wireless sensor networks (IADLoc) [1] is the
best when it is compared with the other localization algorithms by introducing many
cases of studies. The IADLoc is used to minimize the error rate of localization
without any additional cost and minimum energy consumption and also
decentralized implementation. The IADLoc is a range free and also range based
localization algorithm that uses both type of antenna (directional and omnidirectional)
it allows sensors to determine their location based on the region of
intersection (ROI) when the beacon nodes send the information to the sink node and
the la

 This paper adapted the neural network for the estimating of the direction of arrival (DOA). It uses an unsupervised adaptive neural network with GHA algorithm to extract the principal components that in turn, are used by Capon method to estimate the DOA, where by the PCA neural network we take signal subspace only and use it in Capon (i.e. we will ignore the noise subspace, and take the signal subspace only).

This paper presents a proposed neural network algorithm to solve the shortest path problem (SPP) for communication routing. The solution extends the traditional recurrent Hopfield architecture introducing the optimal routing for any request by choosing single and multi link path node-to-node traffic to minimize the loss. This suggested neural network algorithm implemented by using 20-nodes network example. The result shows that a clear convergence can be achieved by 95% valid convergence (about 361 optimal routes from 380-pairs). Additionally computation performance is also mentioned at the expense of slightly worse results.

Future generations of wireless networks are expected to heavily rely on unmanned aerial vehicles (UAVs). UAV networks have extraordinary features like high mobility, frequent topology change, tolerance to link failure, and extending the coverage area by adding external UAVs. UAV network provides several advantages for civilian, commercial, search and rescue applications. A realistic mobility model must be used to assess the dependability and effectiveness of UAV protocols and algorithms.  In this research paper, the performance of the Gauss Markov (GM) and Random Waypoint (RWP) mobility models in multi-UAV networks for a search and rescue scenario is analyzed and evaluated. Additionally, the two mobility models GM and RWP are descr

The developed financial system is essential for increasing economic growth and poverty reduction in the world. The financial development helps in poverty reduction indirectly via intermediate channel which is the economic growth. The financial development enhancing economic development through mobilization of savings and channel them to the most efficient uses with higher economic and social returns. In addition, the economic growth reduces the poverty through two channels. The first is direct by increasing the introduction factors held by poor and improve the situations into the sectors and areas where the poor live. The second is indirect through redistribution the realized incomes from the economic growth as well as the realiz