A finite switchboard state machine is a specialized finite state machine. It is built by binding the concepts of switching state machines and commutative state machines. The main purpose of this paper is to give a specific algorithm for fuzzy finite switchboard state machine and also, investigates the concepts of switching relation, covering, restricted cascade products and wreath products of f...

the structure of a self-routing interconnection network with a recurrent neural network," in Pro-Abstract A modiied Recurrent Neural Network (RNN) is used to learn a Self-Routing Interconnection Network (SRIN) from a set of routing examples. The RNN is modiied so that it has several distinct initial states. This is equivalent to a single RNN learning multiple diierent synchronous sequential mac...

The problem of determining the sequence of a set of jobs with the objective function of minimizing the maximum earliness and tardiness in one machine is studied. Production systems like JIT are one of the many applications of the problem. This problem is studied in special cases and their optimal solutions are introduced with simple orders. In general, some effective conditions for neig...

This paper considers the single machine scheduling problem with precedence constraints and deteriorating jobs. A mathematical model based on binary integer programming (BIP) is developed. By these precedence constraints, jobs can not start before completion of its all predecessors. The represented model is in two steps, in step one the earliest starting time of each job is computed, then the re...

Fast and accurate network traffic identification is becoming essential for network management, high quality of service control and early detection of network traffic abnormalities. Techniques based on statistical features of packet flows have recently become popular for network classification due to the limitations of traditional port and payload based methods. In this paper, we propose a metho...

Adversarial learning is a recently introduced term which refers to the machine learning process in the presence of an adversary whose main goal is to cause dysfunction to the learning machine. The key problem in adversarial learning is to determine when and how an adversary will launch its attacks. It is important to equip the deployed machine learning system with an appropriate defence strateg...

The field of optimization and machine learning are increasingly interplayed and optimization in different problems leads to the use of machine learning approaches‎. ‎Machine learning algorithms work in reasonable computational time for specific classes of problems and have important role in extracting knowledge from large amount of data‎. ‎In this paper‎, ‎a methodology has been employed to opt...

The problem of determining the sequence of a set of jobs with the objective function of minimizing the maximum earliness and tardiness in one machine is studied. &#10 Production systems like JIT are one of the many applications of the problem. This problem is studied in special cases and their optimal solutions are introduced with simple orders. In general, some effective conditions for ne...

This article investigates a JIT single machine scheduling problem with a periodic preventive maintenance. Also to maintain the quality of the products, there is a limitation on the maximum number of allowable jobs in each period. The proposed bi-objective mixed integer model minimizes total earliness-tardiness and makespan simultaneously. Due to the computational complexity of the problem, mult...