Cyber-security garnered significant attention due to the increased dependency of individuals and organizations on Internet their concern about security privacy online activities. Several previous machine learning (ML)-based network intrusion detection systems (NIDSs) have been developed protect against malicious behavior. This paper proposes a novel multi-stage optimized ML-based NIDS framework...

Recently, deep convolutional neural networks (CNNs) have been widely explored in single image super-resolution (SISR) and contribute remarkable progress. However, most of the existing CNNs-based SISR methods do not adequately explore contextual information feature extraction stage pay little attention to final high-resolution (HR) reconstruction step, hence hindering desired SR performance. To ...

In wide area measurement systems, PMUs are the main component measuring real time synchronized data from different buses. Installation of PMUs at all buses is a primary way to provide full observability of the power network. However, it is not practical in real networks due to the relatively expensive cost of PMUs and other technical limitations. Optimal PMU Placement (OPP) is an optimization p...

in the present study, through the application of self-determination theory the impact of work climate and social network size on the volunteers motivation were investigated. research type of this study is applied and research method type is descriptive. in this study active volunteers of tehran red crescent society were selected as statistical population .concerning the population of studied ce...

electricity price predictions have become a major discussion on competitive market under deregulated power system. but, the exclusive characteristics of electricity price such as non-linearity, non-stationary and time-varying volatility structure present several challenges for this task. in this paper, a new forecast strategy based on the iterative neural network is proposed for day-ahead price...

We describe a two-stage robust optimization approach for solving network flow and design problems with uncertain demand. In two-stage network optimization, one defers a subset of the flow decisions until after the realization of the uncertain demand. Availability of such a recourse action allows one to come up with less conservative solutions compared to singlestage optimization. However, this ...

A teaching-learning-based optimization (TLBO) algorithm is a new population-based algorithm applied in some applications in the literature successfully. Moreover, a genetic algorithm (GA) is a popular tool employed widely in many disciplines of engineering. In this paper, a hybrid GA-TLBO algorithm is proposed for the capacitated three-stage supply chain network design (SCND) problem. The SCND ...

Development of every society is incumbent upon energy sector’s technological and economic effectiveness. The electricity industry is a growing and needs to have a better performance to effectively cover the demand. The industry requires a balance between cost and efficiency through careful design and planning. In this paper, a two-stage stochastic programming model is presented for the design o...