Machine-to-machine (M2M) communication devices communicate and exchange information with each other in an independent manner to perform necessary tasks. The machine communicates another over a wireless network. Wireless opens up the environment huge vulnerabilities, making it very easy for hackers gain access sensitive carry out malicious actions. This paper proposes M2M system through internet...

Error Minimized Extreme Learning Machine (EM-ELM) proposed by Feng et al. [1] can automatically determine the number of hidden nodes in generalized Single-hidden Layer Feedforward Networks (SLFNs). We recently found that some of the hidden nodes that are added into the network may play a very minor role in the network output, which increases the network complexity. Hence, this paper proposes an...

We consider the scheduling problems arising when several agents, each owning a set of nonpreemptive jobs, compete to perform their respective jobs on one shared processing resource. Each agent wants to minimize a certain cost function, which depends on the completion times of its jobs only. The cost functions we consider in this paper are maximum of regular functions (associated with each job),...

in this paper, a neural network model reference adaptive system speed observer is designed, which can be used in speed control of linear induction motors (lims). dynamical equations of lim have been considered accurate. in other words, the end effect and the electrical losses of the motor have been included in the motor equivalent circuit. then equations of the reference model and adaptive mode...

In this paper, we investigate the effectiveness of the Extreme Learning Machine (ELM) network in facial image classification. In order to enhance performance, we exploit knowledge related to the human face structure. We train a multi-view ELM network by employing automatically created facial regions of interest to this end. By jointly learning the network parameters and optimized network output...

In this paper we deal with the dual criteria optimization, explore the complexity status of hierarchical scheduling on a single machine. Hierarchical optimization is a special case of dual criteria optimization, where the most important criterion is at its optimal value. We'll assume the complexity of hierarchical scheduling on a single machine. While doing this, we'll use the case of two crite...

In this paper we present a recurrent neural network model to recognize efficient Decision Making Units(DMUs) in Data Envelopment Analysis(DEA). The proposed neural network model is derived from an unconstrained minimization problem. In theoretical aspect, it is shown that the proposed neural network is stable in the sense of lyapunov and globally convergent. The proposed model has a single-laye...