Vehicular ad hoc networks (VANETs) are high priority safety applications that need medium access control (MAC) protocol for efficient broadcast service. The existing work presented VeMAC, a novel multi channel TDMA MAC protocol for VANET supports efficient one-hop and multi-hop broadcast services on control channel using implicit acknowledgments and eliminating hidden terminal. The drawbacks of...

A two-layer neural network can be used to approximate any nonlinear function. T h e behavior of the hidden nodes tha t allows the network to do this is described. Networks w i th one input are analyzed first, and the analysis is then extended to networks w i t h mult iple inputs. T h e result of th is analysis is used to formulate a method for ini t ial izat ion o f the weights o f neural netwo...

Cancer of unknown primary site (CUP) is a fatal cancer ranking among the five most common cancer deaths. CUP is diagnosed through metastases, which are limited to lymph nodes in some patients. Cause-specific survival data could guide the search for hidden primary tumors and help with therapeutic choices. The CUP patients were identified from the Swedish Cancer Registry between 1987 and 2008; 1,...

Abstract   Introduction: The experiences of learners in the educational system are not just limited to the overt curriculum. Other factors also play a significant role in shaping student learning in the form of hidden curriculum. Students’ views on this issue can help design a suitable curriculum. Objectives: The purpose of this study was to determine the viewpoint of nursing stud...

IEEE 802.11 is a standard for mobile ad hoc networks (MANET), implemented with various different protocols. Destination-Sequenced Distance-Vector Routing (DSDV) is one of the several protocols of IEEE 802.11 intended to provide various Quality of Service (QOS) parameters under acceptable range. To avoid the collision and interference the MAC protocol has only two solutions, one, to sense the ph...

Among many approaches to choosing the proper size of neural networks, one popular approach is to start with an oversized network and then prune it to a smaller size so as to attain better performance with less computational complexity. In this paper, a new hidden node pruning method is proposed based on the redundancy reduction among hidden nodes. The redundancy information is given by correlat...

Neural networks have been used in many problems such as character recognition, time series forecasting and image coding. The generalisation of the network depends on its intemal structure. Network parameters should be set correctly so that data outside the class will not be overfitted. One mechanism to achieve an optimal neural network structure is to identify the essential components (hidden n...

We introduce and study random bipartite networks with hidden variables. Nodes in these networks are characterized by hidden variables that control the appearance of links between node pairs. We derive analytic expressions for the degree distribution, degree correlations, the distribution of the number of common neighbors, and the bipartite clustering coefficient in these networks. We also estab...

We extend the Bayesian Information Criterion (BIC), an asymptotic approximation for the marginal likelihood, to Bayesian networks with hidden variables. This approximation can be used to select models given large samples of data. The standard BIC as well as our extension punishes the complexity of a model according to the dimension of its parameters. We argue that the dimension of a Bayesian ne...

In this paper, a novel learning algorithm termed Hybrid Online Sequential Extreme Learning Machine (HOSELM) is proposed. The proposed HOS-ELM algorithm is a fusion of the Online Sequential Extreme Learning Machine (OS-ELM) and the Minimal Resource Allocation Network (MRAN). It is capable of reducing the number of hidden nodes in Single-hidden Layer Feed-forward Neural Networks (SLFNs) with Radi...