The present study developed an artificial neural network (ANN) model to overcome the difficulties in training the ANN models with continuous data consisting of rainy and non-rainy days. Among the six models analyzed the ANN model which used generalized feedforward type network and a hyperbolic tangent function and a combination of 5 meteorological parameters (relative humidity, air pressure, we...

The Boolean network can be used as a mathematical model for gene regulatory networks. An attractor, which is a state of a Boolean network repeating itself periodically, can represent a stable stage of a gene regulatory network. It is known that the problem of finding an attractor of the shortest period is NP-hard. In this article, we give a fixed-parameter algorithm for detecting a singleton at...

We discuss network neutrality regulation of the Internet in the context of a two-sided market model. Platforms sell broadband Internet access services to residential consumers and may set fees to content and application providers on the Internet. When access is monopolized, cross-group externalities (network effects) can give a rationale for network neutrality regulation (requiring zero fees to...

Most work in the deep learning systems community has focused on faster inference, but arriving at a trained model requires lengthy experiments. Accelerating training lets developers iterate faster and come up with better models. DNN training is often seen as a compute-bound problem, best done in a single large compute node with many GPUs. As DNNs get bigger, training requires going distributed....

Neural networks are a powerful tool used to model properties and behaviour of materials in many areas of civil engineering applications. In the present paper, the models in artificial neural networks for predicting the temperatures in timber under fire loading have been developed. For building these models, training and testing using the available numerical results obtained using design methods...

Network security is related to the proper protection of the network system hardware, software, and the data in the system. They are not subjected to accidental or malicious destruction, alteration and disclosure to make the system run continuously and reliably. Then, the network service is not interrupted. As we all know, BP neural network is used more fully in the network security. It has a st...

Node capability parameter configuration is the validation and settings of node capability parameter values according to node capability parameter configuration specification (CapSpc). A node capability is a property of a node required as basis for service implementation. This paper presents a Node Capability Parameter Configuration System (CapCon). Node Capability Ontology (CapOnt) is the basis...

Extreme Learning Machine, ELM, is a newly available learning algorithm for single layer feedforward neural networks (SLFNs), and it has proved to show the best compromise between learning speed and accuracy of the estimations. In this paper, a methodology based on Optimal-Pruned ELM (OP-ELM) for function approximation enhanced with variable selection using the Delta Test is introduced. The leas...

The paper presents the potential of fuzzy logic (FL-I) and neural network techniques (ANN-I) for predicting the compressive strength, for SCC mixtures. Six input parameters that is contents of cement, sand, coarse aggregate, fly ash, superplasticizer percentage and water-to-binder ratio and an output parameter i.e. 28day compressive strength for ANN-I and FL-I are used for modeling. The fuzzy l...

The reconstruction of a gene regulatory network expressed in terms of a Ssystem model may be accomplished by a simple task of parameter estimation. Empirical data indicate that biological gene networks are sparsely connected and the average number of upstream-regulators per gene is less than two, implying that most of parameter variables in the S-system model are zero. It is thus desired to sea...