Real Time Experimental Study and Analysis of Elliott Wave Theory in Signal Strength Prediction

Vishvaksenan, K. S.; Kalaiarasan, R.; Kalidoss, R.; Karthipan, R.

doi:10.1007/s40010-016-0323-8

Cited by 13 publications

(8 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

Concurrency and Computation

View full text Add to dashboard Cite

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.

show abstract

Section: Adaboost Classifiermentioning

confidence: 99%

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

Sankar

Maheswari

2019

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…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

Concurrency and Computation

View full text Add to dashboard Cite

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.

show abstract

“…Regarding importance of this topic, this article deals with approaches conducted in service level agreement and its comparison regarding improved parameters, simulation or implementation environment, workload and application. In [8][9][10][11][12][13][14][15][16][17], various performance enhancement techniques are discussed.…”

Section: Related Workmentioning

confidence: 99%

Analytic Hierarchy Process for ResourceAllocation in Cloud Environment

Revathy¹,

Sekar

2018

JCSM

View full text Add to dashboard Cite

Resource management is the primary issue as the demand grows for provisioning resources and computation in cloud systems. When the resource management fails to meet the QoS parameter (response time) requirements, there occurs the due-date assignment problem leading to "resource allocation problem". This can be optimized by various scheduling algorithms. In this paper analytic hierarchy process is discussed for resource sharing. The results demonstrates that this approach reduces the execution time of algorithm and effectively handles vertical elasticity by adding resources in cloud server. Moreover, the experimental results shows that proposed method can effectively allocates the resources than any other algorithms. Finally, this algorithm is also focused on how efficiency is effectively handled in cloud computing environment while allocating resources since efficiency based resource allocation scheme has been taken into account.

show abstract

Real Time Experimental Study and Analysis of Elliott Wave Theory in Signal Strength Prediction

Cited by 13 publications

References 15 publications

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

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

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

Analytic Hierarchy Process for ResourceAllocation in Cloud Environment

Contact Info

Product

Resources

About