Multi-objective flower pollination algorithm: a new technique for EEG signal denoising

2022 ◽  
Author(s):  
Zaid Abdi Alkareem Alyasseri ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar ◽  
Xin-She Yang ◽  
Mazin Abed Mohammed ◽  
...  
Keyword(s):  
New Technique ◽  
Flower Pollination Algorithm ◽  
Signal Denoising ◽  
Eeg Signal ◽  
Multi Objective ◽  
Flower Pollination ◽  
A New Technique

scholarly journals Strategic Position and Tuning of UPFC using Multiple Indices and Flower pollination Algorithm for Contingency Management

2019 ◽  
Vol 9 (2S3) ◽  
pp. 154-159
Keyword(s):  
Transmission Line ◽  
Objective Function ◽  
Contingency Management ◽  
Flower Pollination Algorithm ◽  
Multi Objective ◽  
Flower Pollination ◽  
Strategic Position ◽  
Active Generation ◽  
Bus System

In this paper, a technique was proposed in the presence of UPFC to optimize the sizing of generators with Flower Pollination algorithm. The UPFC is based on an index incorporating both the L-index and the LUF index. For tuning the generators, a multi objective function has been selected. The multi-objective feature consists of deviation of voltage, cost of active generation of power and loss of transmission line. This approach was tested and implemented for regular loading and extreme network conditions due to line failure (contingency situation) on an IEEE 30 test bus system

scholarly journals Multi-objective flower pollination algorithm applied to 5G vehicular networks communication

2022 ◽  
Vol 11 (1) ◽  
pp. e33911125020
Author(s):  
Francisco Jonatas Siqueira Coelho ◽  
Eulogio Gutierrez Huampo ◽  
Henrique Figueirôa Lacerda ◽  
Arthur Doria Meneses de Freitas ◽  
Abel Guilhermino da Silva Filho
Keyword(s):  
Millimeter Waves ◽  
Vehicular Networks ◽  
Flower Pollination Algorithm ◽  
Media Access Control ◽  
Vehicular Communication ◽  
Genetic Classification ◽  
Nsga Ii ◽  
Multi Objective ◽  
Flower Pollination ◽  
Differential Evolutionary Algorithm

The Cellular Vehicle-to-Everything (C-V2X) technology, as a widest version of Vehicular Ad-hoc Network (VANET), aims to interconnect vehicles and any other latest technological infrastructures. In this context, the fifth generation of mobile networks (5G) based on millimeter waves (mmWave) is an excellent alternative for the implementation of vehicular networks, mainly because it is capable of providing high data rates (Gbps) and ultra-low latency, requirements of C-V2X. On the other hand, mmWave signals are highly susceptible to blocking, causing low quality of service (QoS) in VANETs, compromising network functionality and the safety of drivers and pedestrians. Thus, in this work evolutionary computing techniques are applied in the simulation of a 5G vehicular network based on millimeter waves, exploring Media Access Control (MAC) sublayer parameters to optimize packet loss, latency and throughput, in order to optimize inter-vehicular communication. The Multi-objective Flower Pollination Algorithm (MOFPA) was used for this purpose. The results obtained show that the adopted approach can reach results close to the optimal pareto of non-dominated solutions, with a 75% reduction in exploration time in relation to the exhaustive search process. Finally, the performance of the metaheuristics adopted is compared with the non-dominated genetic classification algorithm (NSGA-II) and the multi-objective differential evolutionary algorithm (MODE).

EEG-based Person Authentication Using Multi-objective Flower Pollination Algorithm

2018 ◽  
Author(s):  
Zaid Abdi Alkareem Alyasseri ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar ◽  
Joao P. Papa ◽  
Osama ahmad Alomari
Keyword(s):  
Flower Pollination Algorithm ◽  
Multi Objective ◽  
Flower Pollination

scholarly journals EEG Feature Extraction for Person Identification Using Wavelet Decomposition and Multi-Objective Flower Pollination Algorithm

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 76007-76024 ◽  
Author(s):  
Zaid Abdi Alkareem Alyasseri ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar ◽  
Joao P. Papa ◽  
Osama Ahmad Alomari
Keyword(s):  
Feature Extraction ◽  
Wavelet Decomposition ◽  
Flower Pollination Algorithm ◽  
Person Identification ◽  
Multi Objective ◽  
Flower Pollination

EEG signal artefact removal using flower pollination fractional calculus optimisation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jayalaxmi Anem ◽  
G. Sateeshkumar ◽  
R. Madhu
Keyword(s):  
Feature Extraction ◽  
Fractional Calculus ◽  
Random Noise ◽  
Vital Role ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Content Type ◽  
Flower Pollination ◽  
Optimisation Algorithm ◽  
A New Technique

PurposeThe main aim of this paper is to design a technique for improving the quality of EEG signal by removing artefacts which is obtained during acquisition. Initially, pre-processing is done on EEG signal for quality improvement. Then, by using wavelet transform (WT) feature extraction is done. The artefacts present in the EEG are removed using deep convLSTM. This deep convLSTM is trained by proposed fractional calculus based flower pollination optimisation algorithm.Design/methodology/approachNowadays' EEG signals play vital role in the field of neurophysiologic research. Brain activities of human can be analysed by using EEG signals. These signals are frequently affected by noise during acquisition and other external disturbances, which lead to degrade the signal quality. Denoising of EEG signals is necessary for the effective usage of signals in any application. This paper proposes a new technique named as flower pollination fractional calculus optimisation (FPFCO) algorithm for the removal of artefacts from EEG signal through deep learning scheme. FPFCO algorithm is the integration of flower pollination optimisation and fractional calculus which takes the advantages of both the flower pollination optimisation and fractional calculus which is used to train the deep convLSTM. The existed FPO algorithm is used for solution update through global and local pollinations. In this case, the fractional calculus (FC) method attempts to include the past solution by including the second order derivative. As a result, the suggested FPFCO algorithm approaches the best solution faster than the existing flower pollination optimization (FPO) method. Initially, 5 EEG signals are contaminated by artefacts such as EMG, EOG, EEG and random noise. These contaminated EEG signals are pre-processed to remove baseline and power line noises. Further, feature extraction is done by using WT and extracted features are applied to deep convLSTM, which is trained by proposed fractional calculus based flower pollination optimisation algorithm. FPFCO is used for the effective removal of artefacts from EEG signal. The proposed technique is compared with existing techniques in terms of SNR and MSE.FindingsThe proposed technique is compared with existing techniques in terms of SNR, RMSE and MSE.Originality/value100%.

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