Handover based spectrum allocation in cognitive radio networks

Suganya, M.; Haldorai, Anandakumar

doi:10.1109/icgce.2013.6823431

Cited by 87 publications

(14 citation statements)

References 4 publications

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“…They proposed a new variation multiphase level set approach to clinical segmentation, calculated multiphase level set conditions and entropy for the LBM model for D2Q9 [60]. Demonstrated their method for MR breast images reveals it is more efficient and faster [81][82][83][84]. The LBM's merit is a pixel-based algorithm.…”

Section: Eai Endorsed Transactions On Pervasive Health and Technologymentioning

confidence: 99%

A Review of Medical Image Segmentation Algorithms

Ramesh¹,

Kumar²,

Swapna³

et al. 2021

EAI Endorsed Trans Perv Health Tech

140

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INTRODUCTION: Image segmentation in medical physics plays a vital role in image analysis to identify the affected tumour. The process of subdividing an image into its constituent parts that are homogeneous in feature is called Image segmentation, and this process concedes to extract some useful information. Numerous image segmentation techniques have been developed, and these techniques conquer different restrictions on conventional medical segmentation techniques. This paper presents a review of medical image segmentation techniques and statistical mechanics based on the novel method named as Lattice Boltzmann method (LBM). The beauty of LBM is to augment the computational speed in the process of medical image segmentation with an accuracy and specificity of more than 95% compared to traditional methods. As there is not much information on LBM in medical physics, it is intended to present a review of the research progress of LBM. OBJECTIVE: As there is no review paper on the research progress of the LB method, this paper presents a review with an objective to give some thought regarding the different segmentation for medical image and novel LB method to advance interest for future investigation and exploration in medical image segmentation. METHODS: This paper in attendance a short review of medical image segmentation techniques based on Thresholding, Region-based, Clustering, Edge detection, Model-based and the novel method Lattice Boltzmann method (LBM). CONCLUSION: In this paper, we outlined various segmentation techniques applied to medical images, emphasize that none of these problem areas has been acceptably settled, and all of the algorithms depicted are available for broad improvement. Since LBM has the benefits of speed and adaptability of modelling to guarantee excellent image processing quality with a reasonable amount of computer resources, we predict that this method will become a new research hotspot in image processing.

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Section: Eai Endorsed Transactions On Pervasive Health and Technologymentioning

confidence: 99%

A Review of Medical Image Segmentation Algorithms

Ramesh¹,

Kumar²,

Swapna³

et al. 2021

EAI Endorsed Trans Perv Health Tech

140

View full text Add to dashboard Cite

show abstract

“…They also create new derived attributes, for example, an average value. In the data preparation phase, data is tweaked multiple times [7] [8] in no prescribed order. Preparing the data for the modelling tool by selecting tables, records, and attributes, are typical tasks in this phase.…”

Section: Data Preparationmentioning

confidence: 99%

Spatiotemporal Traffic Analysis using Big Data

Haldorai¹,

Mr²,

Muthumeenal³

et al. 2020

International Journal of Advanced Information and Communication Technology

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In collaborated online technique traffic prediction methods is proposed with distributed context aware random forest learning algorithm .The random forest is ensemble classifier which learns different traffic and context model form distributed traffic patterns. One major challenge in predicting traffic is how much to rely on the prediction model constructed using historical data in the real-time traffic situation, which may differ from that of the historical data due to the fact that traffic situations are numerous and changing over time. The proposed algorithm is online predictor of real-time traffic, the global prediction is achieved with less convergence time .The distributed scenarios (traffic data and context data) are collected together to improve the learning accuracy of classifier. The conducted experimental results on prediction of traffic dataset prove that the proposed algorithm significantly outperforms the existing algorithm.

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“…This incompleteness of information might allow for many variants of a deal in cards distribution. The player should take into account all these variants and quickly approximation the predictable number of tricks to be taken in each case [18], [19].…”

Section: Double Dummy Bridge Problemmentioning

confidence: 99%

“…A card that belongs to the suit has been chosen to have the highest value in a particular game, since a trump can be any of the cards belonging to any one of the players in the pair. The rule of the game still necessitates that if a player can track suit, the player must do so, otherwise a player can no longer go behind suit, however, a trump can be played, and the trump is higher and more influential than any card in the suit led [18].…”

Section: No-trump and Trump-suitmentioning

confidence: 99%

Artificial Neural Network Architectures for Solving the Contract Bridge

Dharmalingam

2020

International Journal of Advanced Information and Communication Technology

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Contract Bridge is an intelligent game, which enhances the creativity with multiple skills and quest to acquire the intricacies of the game, because no player knows exactly what moves other players are capable of during their turn. The Bridge being a game of imperfect information is to be equally well defined, since the outcome at any intermediate stage is purely based on the decision made on the immediate preceding stage. One among the architectures of Artificial Neural Networks (ANN) is applied by training on sample deals and used to estimate the number of tricks to be taken by one pair of bridge players is the key idea behind Double Dummy Bridge Problem (DDBP) implemented with the neural network paradigm. This study mainly focuses on Cascade-Correlation Neural Network (CCNN) and Elman Neural Network (ENN) which is used to solve the Bridge problem by using Resilient Back-Propagation (R-prop) Algorithm and Work Point Count System.

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Handover based spectrum allocation in cognitive radio networks

Cited by 87 publications

References 4 publications

A Review of Medical Image Segmentation Algorithms

A Review of Medical Image Segmentation Algorithms

Spatiotemporal Traffic Analysis using Big Data

Artificial Neural Network Architectures for Solving the Contract Bridge

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