Journal of Advances in Computer Research
Volume:11 Issue: 3, Summer 2020

By pervasiveness of cloud computing, a colossal amount of applications from gigantic organizations increasingly tend to rely on cloud services. These demands caused a great number of applications in form of couple of virtual machines (VMs) requests to be executed on data centers’ servers. Some of applications are as big as not possible to be processed upon a single VM. Also, there exists several distributed applications such as MapReduce projects which exploit much number of VMs dispersed over physical machines (PMs) attached with high speed networks. These types of VMs involve mutual traffic transferring which is completely processed as an atomic application. High volume of traffic transfer among VMs may saturate network links and leads performance bottleneck for both data center and applications which seriously threat users’ service level agreement (SLA). Furthermore, communication energy consumption increases when network devices are heavily in use. This paper addresses the virtual machine placement (VMP) problem by considering inter-VM communications on VL2 topology. This is an optimization problem with the aim of network traffic transferring minimization. Dependent VMs are tried to be co-hosted or to be placed in close neighborhoods to minimize the amount of total traffic streaming over the network. A combined meta-heuristic approach based and ACO and GA algorithms is employed to solve the problem. The results of simulations imply the superiority of our proposed approach in comparison with other state-of-the-art approaches in terms of reducing total traffic flow, saving energy, and declining resource dissipation in servers.

Nowadays, firms apply the merger and acquisition strategy for gaining synergy, increasing the wealth of stockholders, economics of scales, enhancing efficiency, increasing the ability to research and develop, developing the firm and decreasing the risk. Developing an optimized model with the ability to identify the effective variables on the merger and acquisition process has a significant role on predicting the profit and the risk of companies involved in this process. Recently, regression-based approaches have been used for providing the optimized model. Regression method has some challenges such as sensitivity to considering the inner data structure, inability to addressing the non-linear relations between data, improper handling of data with continuous value. Neural network has the ability to more precise analysis of the relation between data due to lack of the challenges with regression method as well as examining both internal and external data structure. In one hand, since regression methods are the simplest type of neural method or neural method without hidden layer, developing regression method into neural network and concentrating on neural network and improving these networks can play more effective role on applying prediction in comparison with regression or other ordinary neural networks. Hence, this paper suggests using the optimized neural network for providing an optimized model in order to measuring the effective factors on the merger and acquisition process from multiple dimensions. The results from the experiments indicate the appropriateness of the optimal neural network-based model.

Recommender systems are the systems that try to make recommendations to each user based on performance, personal tastes, user behaviors, and the context that match their personal preferences and help them in the decision-making process. One of the most important subjects regarding these systems is to increase the system accuracy which means how much the recommendations are close to the user interests. In this paper, to achieve the mentioned aim we use a combination of K-means and differential evolution algorithms. The K-means algorithm determines the best recommendations for the current user based on the behavior of the other users. The differential evolution algorithm is used to optimize the user clustering in the recommender system. Given that the proposed model has been tested in a movie domain, the films suggested to the current user, have the highest rates from the users who are similar to the current user. The results gained from the simulation show the superior performance of the proposed model in comparison to the related works with an average increased accuracy of 0.01.

Crime detection is one of the major issues in the field of criminology. In fact, criminology includes knowing the details of a crime and its intangible relations with the offender. In spite of the enormous amount of data on offenses and offenders, and the complex and intangible semantic relationships between this information, criminology has become one of the most important areas in the field of clustering. With the development of computer systems and the development of clustering algorithms, it has been possible to interpret mass data and extract knowledge from them. There are different types of attribute in the mass data set, each of which can be suitable for crime detection. By clustering, different groups of crime can be identified and also the percentage of their occurrence. In this paper, a K-Means improved by Artificial Bee Colony (ABC) algorithm is proposed for crime clustering. In the proposed model, an ABC algorithm has been used to improve cluster centers and increase the accuracy of clustering and assignment of samples to appropriate clusters. The main motivation is to exploit the search ability of ABC algorithm and to avoid the original limitation of falling into locally optimal values of the K-Means. Evaluation has done on data set with 1994 samples and 128 features. The results show that the accuracy of the proposed model is higher than K-Means, and the Purity value of the proposed model with 500 iterations is 0.943.

In addition to the improved calculation of the parameter values, a high yield estimation is necessary for designing analog integrated circuits. Although Monte-Carlo (MC) simulation is popular and precise for yield estimation; however, its efficiency is not high enough and it requires too many costly transistor-level simulations. Therefore, some accelerated methods are needed for MC simulations. This paper presents a novel approach for improving automated analog yield optimization using a three-stage strategy. Firstly, critical solutions are recognized using Critical Analysis (CA) and Multi-objective Optimal Computing Budget Allocation (MOCBA). Then they are separated from non-critical answers. It's so helpful to avoid repeating the Monte Carlo (MC) simulations of non-critical solutions. Due to the existence of several objective functions (typically more than one) in the yield optimization problem, by using the Multi-Objective Optimization (MOO) in the second stage, more precise answers can be found. Finally, MC simulations are performed to explore the proposed algorithm performance. Simulation results show that our approach locates higher quality in terms of yield rate within less run time and without affecting the accuracy.

A five-level Power Factor Correction incremental rectifier (PFC) is proposed in this paper. In this topology, the output voltage and current of the rectifier are controlled using the multilevel modulation and smart controller technologies. A multi-carrier pulse width modulation is used to create the switching pulse. In this topology, the number of semiconductor switches is reduced to 3. The smart controller is implemented using a Multilayer Perceptron (MLP) neural network and it is trained using the backpropagation algorithm. This controller is used instead of the well-known PID controller to control the input voltage and current. It should be noted that in this work, the goal is to design an intelligent controller using a neural network instead of a PID controller. The results obtained using this controller as compared to the PID controller show a decrease in the peak voltage, an increase in the rise time, and a ripple reduction in the output voltage. This study is conducted using the Simulink environment in MATLAB and the results suggest that a smart controller can be an alternative to the PID controller.

Recommender Systems are part of our life nowadays. In almost every big business, recommender systems are suggesting items to users automatically. The only factor that traditional recommender systems take into account is accuracy. They try to estimate user-item ratings as accurate as possible to recommend the more preferable items to users. But from the supplier point of view, profit is the most desirable achievement. In this paper we have proposed a profit-aware recommender system based on the multi-objective genetic algorithm. Our proposed method consider two objectives at the same time: Profit and Accuracy. Profit is amount of profit that company will gain of selling items and accuracy measures how much recommendations are close to user preferences. the NSGA-II as the most successful MO-GA has been selected here and its Crossover and Mutation operations have been designed. Our method and traditional collaborative-filtering method have been implemented and tested on three different datasets: Movielens, Netflix and Yahoo. Results confirm that in both accuracy and net-profit our method prevail over CF method.

Considering the significant advancements in autonomous vehicle technology, research in this field is of interest to researchers. To automatic parking, controlling steer angle, gas hatch, and brakes need to be learned. Due to the increase in the number of cars and road traffic, car parking space has decreased. Its main reason is information error. Because the driver does not receive the necessary information or receives it too late, he cannot take appropriate action against it. This paper uses two phases: the first phase, for goal coordination, was used genetic algorithms and the Cuckoo search algorithm was used to increase driver information from the surroundings. Using the Cuckoo search algorithm and considering the limitations, it increases the driver’s level of information from the environment. Also, by exchanging information through the application, it enables the information to reach the driver much more quickly and the driver reacts appropriately at the right time. The suggested protocol is called the multi-objective decision-based solution (MODM)-based solution. Here, the technique is assessed through extensive simulations performed in the NS-3 environment. Based on the simulation outcomes, it is indicated that the parking system performance metrics are enhanced based on the detection rate, false-negative rate, and false-positive rate.