Journal of Advances in Computer Research
Volume:11 Issue: 2, Spring 2020

In this paper, a new method is proposed for solving the data clustering problem using Cat Swarm Optimization (CSO) algorithm based on chaotic behavior. The problem of data clustering is an important section in the field of the data mining, which has always been noted by researchers and experts in data mining for its numerous applications in solving real-world problems. The CSO algorithm is one of the latest meta-heuristic algorithms, which has a simple structure and it is easy to implement. The purpose of Chaos embedded Cat Swarm Optimization (CCSO) algorithm is to replace random values by chaotic ones to offer a stable algorithm that can allow for reaching the global optima to a large extent and improve the algorithm’s convergence speed. The proposed algorithm has been compared to other heuristic algorithms on standard data sets from UCI repository, and the experimental results demonstrate that the proposed algorithm yields high performance for solving the data clustering problem.Keywords: Data clustering, K-means, Cat Swarm Optimization, Chaos theory.

Morphology has a special place in any language, including written and spoken applications. Markov method is used to labeingl and determine the role of words.emergence in software sciences has eliminated 0 and 1 computations, putting them within an infinite space of between 0,1. This characteristic of fuzzy logic has resolved ambiguity in numerous previous problems. The sentence roles in Persian language were specified based on the fuzzy logic’s capability to resolve ambiguity. In two defuzzification methods Mean Of Max, Central Average, the role of words in the sentence is identified and the success rate of each method is obtained. Finally, Mean Of Max with a success rate of 64% proved to be a defuzzifier delivering the best output among two different defuzzification methods. 

Among the supply chain risk types, disruptions that result from natural disasters, sanctions, transportation problems and equipment failure can seriously disrupt or delay the flow of material, information and cash. The aim of this research was to propose a hybrid model for disruption management, which is the process of achieving plans or strategies to reduce the expenses incurred by the disruption. For this purpose; first, we identified disruptions and mitigation strategies by using the nominal group technique. Then, the interaction between disruptions was formulated by the fuzzy DEMATEL technique. Consequently, with regard to the uncertainty of data, fuzzy logic was used for modeling the uncertainty of disruptions. Finally, mitigating strategies were selected and ranked with PROMETHEEΙΙ. Considering the existence of 4 types of responses of chain against risks, which include: 1- risk control and endurance 2-risk flexibility 3- risk avoidance 4- risk transfer and assignment; results show that according to the type of disorder, the risk management strategy changes and in general (taking into account the causal relationship between disorders), the risk transfer strategy it was more suitable

Data Grid provides sharing services for very large data around the world. Data replication is one of the most effective approaches to reduce access latency and response time. In addition to the benefits, replication has costs such as storage and bandwidth consumption, especially when storage space is low and limited. Therefore,  the data replacement should be done wisely. In this paper, we proposed a replacement method called FRA. The algorithm defines a weight for each replica that represents its value. This algorithm uses this weight to prevent the removal of valuable replicas. The results demonstrated that FRA algorithm has better performance than other replication methods in terms of the number of replications, the percentage of storage used, and the job execution time.

In this paper, we focus on the availability of software systems. Software systems with long-running execution may lead to software aging. This phenomenon affects the performance of software system and may eventually cause them to crash or failures. To counteract the phenomenon aging, software rejuvenation is implemented to prevent severe software failures. Generally, when software is initiated, amounts of memory are allocated. Then the body of software is executed for providing a service and when the software is terminated, the allocated memory is released. We propose a software rejuvenation model based on degraded physical memory. This model is implemented with a Markov chain and the system performance due to memory usage, by services, divided into four equal level. Hence, we offer four types of policies for software rejuvenation. The goal of the proposed method is to optimize availability of system. The results we achieve show the superiority of proposed method.

Flexible AC Transmission System (FACTs) devices are used in power transmission networks to increase maximum power transmission and stability. On the one hand, they help to damp low-frequency oscillations for both local and internal areas. But on the other, the design of these devices with uncoordinated Power System Stability (PSS) may degrade the performance of the power system. In addition, the power systems are vast, complex, and nonlinear. Linear control strategies do not have satisfactory performance for these systems, especially when some disturbances occur. In this study, a nonlinear Coordinated Control Strategy based on Recurrent Neural Network (CCSRNN) is designed to control PSS and Static Synchronous Compensators (STATCOM) coordinates for a standard multi-machine power system and also using the Multi-Bound Power System Stability (MB-PSS) and Multi-Bound Recurrent Neural Network MB-RNN PSS and compare the result of each one for having better stability of the system. The results simulation proves that the controller leads to transient stability and low-frequency oscillation.

Power consumption in today's modern high-performance computing systems is one of the most important design issues. Reversible computations have attracted a lot of attention compared to classical calculations due to their ability to reduce energy loss and power consumption of circuits. The reversible logic has applications in various technologies such as quantum circuits, low power circuit design, nanotechnology, optical information processing, DNA and bioinformatics calculations. Adder and multiplier are the main parts of any computing systems and therefore play an important role in the performance of reversible computations. Features of a reversible ripple-carry adder are high quantum depth, low quantum cost, low garbage outputs, and low constant input bits. In this paper, a new reversible kogge-Stone parallel-prefix adder and multiplier is proposed for modulo 2n±1. The analysis shows that the reversible-logic parallel-prefix adder and multiplier are faster and have the lowest depth compared to the reversible-logic-based ripple-carry adder and multiplier.

Web Services provides a solution to web application integration. Due to the significance of trust in choosing the proper web service, a novel optimal configuration of neural networks by multi-objective genetic algorithm and ensemble-classifier approach is used to evaluate the trust of single web services. For evaluating trust in the single web services, first, a set of neural networks were trained by the settings their parameters through the multi-objective genetic algorithm. Next, the best combination of neural networks was selected to make an ensemble classifier. This method was evaluated with single WS dataset considered eight criteria. Three measurements, accuracy, time and ROC curve were considered to assess the efficiency. Ultimately, the results show that the proposed approach can achieve a trade-off between time and accuracy by the multi-objective genetic algorithm. Also using ensemble-classifiers approach increases the reliability of the model. Consequently, the proposed method promote the detection accuracy.