A golden eagle optimized hybrid multilayer perceptron convolutional neural network architecture‐based three‐stage mechanism for multiuser cognitive radio network

2021 ◽  
Author(s):  
J. Jean Justus ◽  
M. Anuradha
Keyword(s):  
Neural Network ◽  
Cognitive Radio ◽  
Convolutional Neural Network ◽  
Multilayer Perceptron ◽  
Network Architecture ◽  
Cognitive Radio Network ◽  
Radio Network ◽  
Golden Eagle ◽  
Neural Network Architecture

Adaptive TCS creation and assignment mechanism for cognitive radio network using Golden Eagle optimized Hybrid Multilayer Perceptron-Convolutional Neural Network

2021 ◽  
Author(s):  
Anjana Devi Javar ◽  
V. Prasanna Sriniva
Keyword(s):  
Neural Network ◽  
Cognitive Radio ◽  
Multilayer Perceptron ◽  
Cognitive Radio Network ◽  
Radio Network ◽  
Wireless Devices ◽  
Secondary Users ◽  
Neural Network Approach ◽  
Dynamic Channel ◽  
Fair Assignment

Abstract The increasing number of wireless devices tends to produce a scarcity of spectrum resources. Cognitive Radio is introduced to tackle this scarcity issue since the cognitive radio users are unlicensed users and utilize the underutilized licensed spectrum without impacting any damage to the licensed users. However, this CR generates a target channel sequence (TCS) to assign the channel for the multi-user while occurring handoff. Besides it also faces some challenges like the fair assignment of TCS problems to multiple secondary users, channel access conflicts, channel obsolesces effects during the creation of TCS. To tackle the issues, we proposed a novel Three-stage mechanism to create and assign TCS for the multi-users along with the hybrid optimized Multilayer perceptron-based Convolution neural network approach. Here, the protection to safeguard VoIP communication can be performed with the aid of a proposed hybrid MLP-CNN-GEO approach. The experimental analysis depicts that the proposed work reduces handoff delays and call-drops, and assigns a fair and dynamic channel for secondary users.

scholarly journals BengaliNet: A Low-Cost Novel Convolutional Neural Network for Bengali Handwritten Characters Recognition

2021 ◽  
Vol 11 (15) ◽  
pp. 6845
Author(s):  
Abu Sayeed ◽  
Jungpil Shin ◽  
Md. Al Mehedi Hasan ◽  
Azmain Yakin Srizon ◽  
Md. Mehedi Hasan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Character Recognition ◽  
High Performance ◽  
Low Cost ◽  
Traditional Learning ◽  
Neural Network Architecture ◽  
Handwritten Character Recognition ◽  
Handwritten Character

As it is the seventh most-spoken language and fifth most-spoken native language in the world, the domain of Bengali handwritten character recognition has fascinated researchers for decades. Although other popular languages i.e., English, Chinese, Hindi, Spanish, etc. have received many contributions in the area of handwritten character recognition, Bengali has not received many noteworthy contributions in this domain because of the complex curvatures and similar writing fashions of Bengali characters. Previously, studies were conducted by using different approaches based on traditional learning, and deep learning. In this research, we proposed a low-cost novel convolutional neural network architecture for the recognition of Bengali characters with only 2.24 to 2.43 million parameters based on the number of output classes. We considered 8 different formations of CMATERdb datasets based on previous studies for the training phase. With experimental analysis, we showed that our proposed system outperformed previous works by a noteworthy margin for all 8 datasets. Moreover, we tested our trained models on other available Bengali characters datasets such as Ekush, BanglaLekha, and NumtaDB datasets. Our proposed architecture achieved 96–99% overall accuracies for these datasets as well. We believe our contributions will be beneficial for developing an automated high-performance recognition tool for Bengali handwritten characters.

Human activity recognition using temporal convolutional neural network architecture

2021 ◽  
pp. 116287
Author(s):  
Yair A. Andrade-Ambriz ◽  
Sergio Ledesma ◽  
Mario-Alberto Ibarra-Manzano ◽  
Marvella I. Oros-Flores ◽  
Dora-Luz Almanza-Ojeda
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Activity Recognition ◽  
Human Activity ◽  
Network Architecture ◽  
Human Activity Recognition ◽  
Neural Network Architecture
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