Mitigation of mutual exclusion problem in 5G new radio standards by token and non token based algorithms

A Mobile Ad-hoc Network (MANET) is a form of mobile node which is known for its random movement and communication to other nodes without the need for any infrastructure. The Bayesian statistical model helps a routing protocol in MANET in getting a high mobility trusted node that relies on the outcome of the prediction. Fuzzy logic has been of immense use for trust in the calculation of RSSI value that helps finding a reliable and trusted route from source to destination. This innovative proposal that uses fuzzy logic in AOMDV is referred to as the Fuzzy trust with RSSI in Ad hoc On-demand Multipath Distance Vector (FT-AOMDV). It is known for its help in finding a reliable path in mobile Ad Hoc Networks. Simulation was conducted using a NS-2 network simulator under various scenarios that include varying node speeds and varying malicious nodes for the evaluation of the efficiency of the protocols. Metrics that include delivery ratio, end-to-end delay, routing packet overhead, and throughput have been used in the comparison of FT-AOMDV with AOMDV, TB-AOMDV1, and TB-AOMDV2.

Section: Methodsmentioning

confidence: 99%

Enhanced data security in MANET using trust‐based Bayesian statistical model with RSSI by AOMDV

Sadayan

Ramaiah

2019

“…Next generation networks performances are analyzed in terms of data rate, signal-to-noise ratio, and bit error rate. [20][21][22][23][24][25] The most popular ensemble technique is AdaBoost and can improve the prediction precision in order to generate a number of classifiers, thus building the best classifier. The algorithm's advantage is that less input features are required and much prior knowledge is not required about weak learner.…”

Section: Adaboost Classifiermentioning

confidence: 99%

A dynamic wrapper‐based feature selection for improved precision in content‐based image retrieval

Sankar

Maheswari

2019

The content-based image retrieval (CBIR) has been applied in the image processing as well as pattern recognition. A challenging task in the CBIR research has been through the feature extraction for decreasing any semantic gap that is an active research topic. Here, in this work, there is a texture feature that is extracted from an image, making use of a technique of curvelet transform. This curvelet is selected for a sparse representation that is quite critical for the estimation of images, which have been de-noised with some inversed problems. The wrapping-based curvelet transform will be even more robust as well as faster in the time of computation than the Ridge Transform. A technique of feature selection will be brought for selecting the optimal features. The correlation-based feature selection (CFS) method has been adapted for improving the accuracy of the CBIR systems. The non-deterministic polynomial (NP)-hard problems may be solved using the chemical reaction optimization (CRO) that has been motivated using the technique of chemical reaction. The benefits of proposed method is a necessity for marginal human intrusion for the purpose of retrieving the images needed from its database. The proposed mechanism has been again assessed depending on the Coral Database, and a performance study is ended using precision, f measure, and recall.

“…A centroid [9][10][11][12][13][14][15] is the point whose coordinates are obtained by means of calculating the average of each of the coordinates of the points of samples assigned to the clusters.…”

Section: K-means Algorithmmentioning

confidence: 99%

Performance validation of clustering algorithms using selection of attributes and application of filters in terms of data reduction

Chinnadurai

2019

Clustering in unsupervised learning is a method to find inherent group of set of unlabeled data. Such set of groups are termed as Clusters. Grouping of datasets in to clusters involves minimization of the interclass similarity and maximization of the intraclass similarity. Therefore, clustering the large datasets introduces the concept called Data Reduction. Data reduction is a simple process of identifying a relevant feature subset, which is enough to represent the selected large datasets. Here, the data reduction is done in terms of applying two clustering algorithms like Expectation and Maximization-EM and K-Means with filters in unsupervised category (i) Normalize filter in Instance level and (ii) Randomize filter in attribute level and selection of attributes. The results shows that the Livestock dataset applied with selection of attributes and preprocessing filter named Normalize in attribute level and Randomize in instance level on which the EM and K-Means algorithm are executed, and the results are compared and analyzed in terms of data reduction. Therefore, the K-Means algorithm applied with Randomize filter at instance level and selection of attributes and finally K-Means algorithm show good performance in case of large datasets when compared with other clustering algorithms.