Today, people are increasingly connected and extensively interact with each other using technology-enabled media. Hence, customers are more frequently exposed to social influence of other customers when making purchase decisions. However, established approaches for customer valuation most widely neglect network effects based on social influence leading to a misallocation of resources. Therefore...

This paper proposes a novel deep reinforcement learning (RL) architecture, called Value Prediction Network (VPN), which integrates model-free and model-based RL methods into a single neural network. In contrast to typical model-based RL methods, VPN learns a dynamics model whose abstract states are trained to make option-conditional predictions of future values (discounted sum of rewards) rathe...

The purpose of the current research is to provide a performance appraisal system capable of considering the value chain network structure of research and development (R&D) projects for Complex products and systems (CoPS) under uncertainty of data. Therefore, in order to achieve this goal, a network data envelopment analysis (NDEA) approach and the possibilistic programming to provide a new fuzz...

in this paper a network comprising alternative branching nodes with probabilistic outcomes is considered. in other words, network nodes are probabilistic with exclusive-or receiver and exclusive-or emitter. first, an analytical approach is proposed to simplify the structure of network. then, it is assumed that the duration of activities is positive trapezoidal fuzzy number (tfn). this paper com...

It is essential for most organizations and financial institutes to be able to evaluate their decision-making units (DMUs), when there is only a ratio of inputs to outputs (or vice versa) available. In this paper, we will propose our two-stage DEA-R models, which are a combination of data envelopment analysis and ratio data, based on value efficiency. Integrating value efficiency into data envel...

Neural networks were used to estimate the cost of jet engine components, specifically shafts and cases. The neural network process was compared with results produced by the current conventional cost estimation software and linear regression methods. Due to the complex nature of the parts and the limited amount of information available, data expansion techniques such as doubling-data and data-cr...

the method of artificial neural network is used as a suitable tool for intelligent interpretation of gravity data in this paper. we have designed a hopfield neural network to estimate the gravity source depth. the designed network was tested by both synthetic and real data. as real data, this artificial neural network was used to estimate the depth of a qanat (an underground channel) located at...

recently, particular attention has been paid to the stepped spillways due to the increasing effect of energy dissipation and the reduction of cavitations risks with the development of roller compacted concrete (rcc) technique. flow regimes on the spillways divide into three groups, namely skimming, jet and transition flow. compared to the numerical methods, the majority of performed studies in ...