Considering the great significant role that routing protocols play in transfer rate and choosing the optimum path for exchange of data packages, and further in the amount of consumed energy in the routing protocol, the present study has focused on developing an efficient compound energy algorithm based on cluster structure which is called active node with cluster structure. The purpose of this algorithm is to distribute the heavy traffic of data and equal load of highly-consumed energy throughout the networks by introducing unequal and unbalanced clusters into the network. In the second stage, the light sensor mechanism which is called active node sensor algorithm has been proposed. The major purpose of this mechanism is to prevent excessive interfering data of sensors through incorporating a set of active nodes in each cluster with a defensive shield near to the incident node. The third stage has aimed at proposing an active node algorithm for complexity of internal and external addressing due to clusters routing in high density distribution based on the values within node range. The obtained results indicate relative success of the design in terms of energy optimization on the basis of routing protocol. Manuscript profile

The Firefly optimization algorithm (FA) is one of the practical nature-inspired metaheuristic approaches in 2008, which simulated the behavior of fireflies in the movement toward the light sources. Recent studies on this beautiful creature have revealed new behaviors that strongly require us to review them. The proposed algorithm NMFA is the simulation results with the latest information from the behavior of fireflies. The NMFA is used for data clustering and optimization of continuous problems. The experimental results of the testing on optimization of 26 standard functions show that the proposed method works best in terms of success rate and convergence than the FA, HS, ABC, and IWO algorithms and makes an important and substantial difference in optimization. The non-parametric, statistical, and pairwise tests show the superiority of the modern firefly algorithm. The NMFA can cluster the datasets like the conventional K-means algorithm and obtain a significant result among the well-known methods. Manuscript profile

Signature identification plays an important role in many areas such as banking, administrative and judicial systems. For this purpose, in this paper, an automatic intelligent framework is developed by combining a deep pre-trained network with a recurrent neural network. The results of the proposed model were evaluated on several valid datasets and collected datasets. Since there was no suitable Persian signature dataset, we collected a Persian signature dataset based on US ASTM guidelines and standards, which can be very effective and profound for deep approaches. Due to the very promising results of the proposed model in comparison with recent studies and conventional methods, to evaluate the resistance of the proposed model to different noises, we added Gaussian Noise, Salt and Pepper Noise, Speckle Noise, and Local var Noise in different SNRs to the raw data. The results show that the proposed model can still be resistant to a wide range of SNRs; So at 15 dB, the accuracy of the proposed method is still above 90%. Manuscript profile