A cost matrix agent for shortest path routing in ad hoc networks

Wu, Mary; Kim, Chonggun

doi:10.1016/j.jnca.2010.03.013

Cited by 19 publications

(7 citation statements)

References 20 publications

(20 reference statements)

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“…Therefore, it may cause difficulties in controlling false positives and false negatives, which means the model is hard to find the CMB samples. Therefore, in order to overcome this challenge, we introduced cost matrix (Wu and Kim, 2010). The cost ratio ct was set as 961 via Eq.…”

Section: Cmb Detection Based On the Transfer Learning And Densenetmentioning

confidence: 99%

Cerebral Micro-Bleeding Detection Based on Densely Connected Neural Network

et al. 2019

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Cerebral micro-bleedings (CMBs) are small chronic brain hemorrhages that have many side effects. For example, CMBs can result in long-term disability, neurologic dysfunction, cognitive impairment and side effects from other medications and treatment. Therefore, it is important and essential to detect CMBs timely and in an early stage for prompt treatment. In this research, because of the limited labeled samples, it is hard to train a classifier to achieve high accuracy. Therefore, we proposed employing Densely connected neural network (DenseNet) as the basic algorithm for transfer learning to detect CMBs. To generate the subsamples for training and test, we used a sliding window to cover the whole original images from left to right and from top to bottom. Based on the central pixel of the subsamples, we could decide the target value. Considering the data imbalance, the cost matrix was also employed. Then, based on the new model, we tested the classification accuracy, and it achieved 97.71%, which provided better performance than the state of art methods.

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Section: Cmb Detection Based On the Transfer Learning And Densenetmentioning

confidence: 99%

Cerebral Micro-Bleeding Detection Based on Densely Connected Neural Network

et al. 2019

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“…Thus the problem of minimum energy broadcast and multicast is solved in wire-line cases by various minimum weight-spanning tree algorithms but the solutions are generally suboptimal [23]. However, alternative approach using the network coding method was employed [24][25][26][27].…”

Section: Energy-efficient Multicastmentioning

confidence: 99%

“…In this section, the performance of a network coded algorithm is investigated and the results serve as imputes for the frontier analysis. We consider a flow-based approach that addresses networks with costs such as energy using a linear programming technique [26]. The cost is a function of coding subgraph z.…”

Section: Network Coding Performancementioning

confidence: 99%

Efficient Frontier and Benchmarking Models for Energy Multicast in Wireless Network Coding

Ajibesin¹

2018

Network Coding

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This chapter introduces efficiency frontier and benchmarking concepts to evaluate the efficiency performance of wireless networks for multicast energy. These concepts are efficiency models based on the data envelopment analysis (DEA) technique. The DEA framework allows network administrators to evaluate the technical efficiency and determine how the inefficient wireless networks will attain a targeted efficiency frontier. In order to achieve efficiency frontier and benchmark by a wireless network, this chapter presents several models including the envelopment and the slack. The envelopment model evaluates the technical efficiency scores of each wireless network, while the slack model shows how the inefficient wireless network achieves efficiency frontier. The benchmark model evaluates the efficiency reference set and the lambda values of each network. The efficiency frontier algorithm has shown that many of the wireless networks sampled are inefficient. However, the algorithm has capability to help the inefficient wireless networks to achieve efficiency frontier and benchmark with their peers that are fully efficient.

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“…(3) shows the initial EC matrix of the cluster topology of figure 3(a). In the initial EC, when two clusters are adjacent, the elements are represented by "n" and are not adjacent the elements are represented by "0" [20]. (3) (4) (4) shows the result the result of execution of the exclusive channel allocation algorithm.…”

Section: Related Studiesmentioning

confidence: 99%