Seismic events such as earthquakes are one of the most important issues in the field of geology. Meanwhile, less attention has been paid to micro-seismic events, despite the high number of earthquakes. Earthquakes, regardless of their size, affect human life; therefore, their detection and management is considered an important issue. For this purpose, experts developed seismic arrays as a system of linked seismometers. These systems equipped with sensors and seismographs are able to receive a range of waves from the earth, which are then sent to the central seismic station for analysis. So far, many tools and methods have been devised to analyze seismic data. However, the dominant method in most seismic mechanisms is trigger function, based on STA/LTA (short-time-average through long-time-average trigger). These mechanisms have considerable threshold in terms of earthquake range, so many micro-events are ignored as noise. Generally, in this field of geology, computer science techniques have been used to detect and classify these events. Statistical methods such as kurtosis, variance, and skewness can be applied to understand the changes in the signal curves of geophones in a seismic event, thereby helping in the initial detection of fuzzy features. According to the last 3 years' reports of global data mining agencies such as Rexer, KDnugget, and Gartner, Rapid Miner is one of the most popular tools for data mining in recent years. Furthermore, these institutions considered artificial neural networks, especially multilayer perceptron (MLP) and base radial function (RBF), to be among the most successful algorithms for detection and classification of stream data. In this research, the recorded data from several seismic experiments has been classified by a hybrid model. Hence, the present study was aimed to enhance the authenticity of data based on the application of effective variables. This was undertaken through use of a fuzzy method and an integrated neural network algorithm, involving MLP perceptron and radial network of RBF in the form of a collective learning system, in order to identify seismic events on a small scale. Based on the results, in comparison to basic methods, the proposed method significantly improved using the actual error and root-mean-square error (RMSE) criteria.
Cloud computing technology forms a computational ensemble of large computing services and systems. Recently, it has been the focus of research on resource management, task scheduling, and effective resource sharing among users. Given the computational and resource management challenges in cloud computing, an improved method is required to approach the optimal allocation of resources. The current research combines evolutionary algorithms, fuzzy logic and task scheduling techniques to improve computational cloud resource allocation with the aim of maintaining load balance in cloud providers. Simulation of the proposed model reveals that the response time, task execution time, and energy consumption of the proposed method are better than for those of other methods. KEYWORDS cloud computing, fuzzy logic, load balancing, resource allocation INTRODUCTIONUsers demand access to computer services according to their needs, no matter where these services are located or how they are delivered. In recent years, an increasing number of business-to-consumer and enterprise applications have been run in heterogeneous clouds. 1 The explosive growth in multimedia content is shifting the global infrastructure towards a cloud-based paradigm facilitated by enormous storage capacity and high end computational resources. 2 Cloud computing is one of the best computing frameworks that can offer such services to users.Cloud computing is a model for enabling ubiquitous, on-demand network-based access to a shared pool of configurable computing resources (eg, networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. Cloud computing, as a model for distributed computing, provides network-based access to a shared collection of reconfigurable computing resources, which are provided and released with minimal management and service provider interaction. Nowadays, IT companies are moving from the traditional CAPEX model (buy the dedicated hardware and depreciate it over a period of time) to the OPEX model (use a shared cloud infrastructure and pay as one uses it). 3 Cloud computing providers designate their resources as different examples of virtual machine (VM), which are provided to a consumer for a specified period. 4 Storage, memory, processing, and bandwidth are often more efficiently provided through cloud computing. The growth of clouds has been driven to some degree by the use of virtualization. Distributed computing suppliers provide applications through the Internet, which are accessed from a web program, while the application programs and data are secured on servers at a remote location. 5Clouds can be thought of as a service model consisting of three layers, ie, software service provider (SaaS), platform service provider (PaaS), and infrastructure service provider (IaaS). 6 The base layer of any cloud computing system is IaaS. The IaaS layer offers physical servers, virtual machines, storage, and connectivity resources to run enter...
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