It is now widely accepted that the locations of intermediate facilities, such as logistics platforms or inland ports, are key elements multimodal freight distribution networks and heavily influence their effectiveness. This crucial role localization decisions even more significant if we consider impact on external costs entire logistic corridor, with reference to cost components associated envi...

With the popularity of deep learning, hardware implementation platform learning has received increasing interest. Unlike general purpose devices, e.g., CPU or GPU, where algorithms are executed at software level, neural network accelerators directly execute to achieve higher energy efficiency and performance improvements. However, as evolve frequently, engineering effort cost designing greatly ...

Confidence sets are of key importance in high-dimensional statistical inference. Under case–control study, a popular response-selective sampling design medical study or econometrics, we consider the confidence intervals and tests for single low-dimensional parameters logistic regression model. The asymptotic properties resulting estimators established under mild conditions. We also testing more...

Nowadays one of the most important challenges for integrated systems such as social networks is the evaluation of trust for agents which are interacting with each other in the environment. It plays an important role that the trust has been evaluated from the agent’s experiences. In this paper, we propose a new mathematical approach based on the entropy of Dirichlet distribution, to model the ag...

We propose a new method for learning the parameters of a Bayesian network with qualitative influences. The proposed method aims to remove unwanted (context-specific) independencies that are created by the order-constrained maximum likelihood (OCML) estimator. This is achieved by averaging the OCML estimator with the fitted probabilities of a first-order logistic regression model. We show experi...