A
Arulmurugan Ramu
Researcher at Presidency University, Kolkata
Publications - 39
Citations - 815
Arulmurugan Ramu is an academic researcher from Presidency University, Kolkata. The author has contributed to research in topics: Computer science & Cognitive radio. The author has an hindex of 8, co-authored 33 publications receiving 598 citations. Previous affiliations of Arulmurugan Ramu include Bannari Amman Institute of Technology, Sathy.
Papers
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Journal ArticleDOI
Canonical Correlation Analysis Based Hyper Basis Feedforward Neural Network Classification for Urban Sustainability
TL;DR: An investigational result shows that the proposed CCA-HBFNNC model can increases the sustainability level and minimizes the time complexity of urban development when contrasted with contemporary works.
Journal ArticleDOI
Security and channel noise management in cognitive radio networks
TL;DR: The PKC-based McEliece secondary key provides an error correction capacity, which can remove the noise during secondary user allocation and enhance the effectiveness of the spectrum management, which collaborate effectively over the noise channel management.
Journal ArticleDOI
Efficient Diagnosis of Liver Disease using Support Vector Machine Optimized with Crows Search Algorithm
TL;DR: The performance of CSA-SVM is found to be outstanding among all other approaches in terms of all metrics taken for comparison, and has yielded the classification accuracy of 99.49%.
Book ChapterDOI
Social Aware Cognitive Radio Networks: Effectiveness of Social Networks as a Strategic Tool for Organizational Business Management
TL;DR: This chapter delves into the cognitive radio (CR) and its social relations and makes sufficient exploits in establishing a scheme that will be based on social-based cooperative sensing scheme (SBC).
Book ChapterDOI
An Intelligent-Based Wavelet Classifier for Accurate Prediction of Breast Cancer
TL;DR: The authors build up a framework for analysis, visualization, and prediction of cancer in breast tissue by utilizing Intelligent based wavelet classifier, a new approach constructed using texture value and wavelet neural network.