scholarly journals A Novel Approach of ECG Signal Enhancement Using Adaptive Filter Based on Whale Optimization Algorithm

2021 ◽  
Vol 14 (4) ◽  
pp. 1895-1903
Author(s):  
Nidhi Lakhera ◽  
A R Verma ◽  
Bhumika Gupta ◽  
Surjeet Singh Patel
Keyword(s):  
Adaptive Filter ◽  
Optimization Algorithm ◽  
Biomedical Signal Processing ◽  
Whale Optimization Algorithm ◽  
Ecg Signal ◽  
Biomedical Signal ◽  
Novel Approach ◽  
Whale Optimization ◽  
Optimum Weight

An Electro cardiogram is commonly used in biomedical signal processing. It is used to monitor minor electrical changes in the human body. The electrical changes originate due to the function of heart. The anomalies of heart are found by ECG. In this work the Whale optimization algorithm is used to de-noising the ECG signal. The Whale optimization algorithm is used with Adaptive filter which filter the corrupted ECG signal. The performance of the ANC will be improved by calculating the optimum weight value. The WOA technique gives the best result on the different fidelity parameter compare to PSO, MPSO and ABC. The WOA technique gives the significant improvement in accuracy. It gives a good SNR, MSE, ME result compare to PSO, MPSO and ABC. The WOA gives 80% improvement in SNR 88% in MSE and 89% in ME as compared to PSO. So, by using WOA we get a desired ECG component. The WOA reduces the noise in ECG signal and improves the quality of signal.

scholarly journals Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2628
Author(s):  
Mengxing Huang ◽  
Qianhao Zhai ◽  
Yinjie Chen ◽  
Siling Feng ◽  
Feng Shu
Keyword(s):  
Optimization Algorithm ◽  
Edge Computing ◽  
Solution Set ◽  
Whale Optimization Algorithm ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Multi Objective ◽  
Whale Optimization ◽  
Artificial Immune System Algorithm

Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions.

scholarly journals A Novel Approach to Oil Layer Recognition Model Using Whale Optimization Algorithm and Semi-Supervised SVM

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 757
Author(s):  
Yongke Pan ◽  
Kewen Xia ◽  
Li Wang ◽  
Ziping He
Keyword(s):  
Optimization Algorithm ◽  
Oil And Gas ◽  
The Other ◽  
Whale Optimization Algorithm ◽  
Classification Model ◽  
Support Vector ◽  
Recognition Model ◽  
Novel Approach ◽  
Whale Optimization ◽  
Oil And Gas Reservoir

The dataset distribution of actual logging is asymmetric, as most logging data are unlabeled. With the traditional classification model, it is hard to predict the oil and gas reservoir accurately. Therefore, a novel approach to the oil layer recognition model using the improved whale swarm algorithm (WOA) and semi-supervised support vector machine (S3VM) is proposed in this paper. At first, in order to overcome the shortcomings of the Whale Optimization Algorithm applied in the parameter-optimization of the S3VM model, such as falling into a local optimization and low convergence precision, an improved WOA was proposed according to the adaptive cloud strategy and the catfish effect. Then, the improved WOA was used to optimize the kernel parameters of S3VM for oil layer recognition. In this paper, the improved WOA is used to test 15 benchmark functions of CEC2005 compared with five other algorithms. The IWOA–S3VM model is used to classify the five kinds of UCI datasets compared with the other two algorithms. Finally, the IWOA–S3VM model is used for oil layer recognition. The result shows that (1) the improved WOA has better convergence speed and optimization ability than the other five algorithms, and (2) the IWOA–S3VM model has better recognition precision when the dataset contains a labeled and unlabeled dataset in oil layer recognition.

scholarly journals Image Fusion Technique Based on Hybrid Whale Optimization Algorithm Simulated Annealing (hWOA-SA)

2019 ◽  
Vol 8 (11) ◽  
pp. 19-24
Keyword(s):  
Simulated Annealing ◽  
Image Fusion ◽  
Optimization Algorithm ◽  
Curvelet Transform ◽  
Whale Optimization Algorithm ◽  
Fusion Technique ◽  
Whale Optimization ◽  
Image Fusion Technique ◽  
Combined Algorithm

Curvelet transform is a multiscale directional transformer, which allows optimal non-adaptive sparse representation of object with edge. In this paper, a new image fusion technique has been developed by combination of whale optimization algorithm (WOA) and simulated annealing (SA) along with curvelet transform. The resulting combined algorithm is abbreviated as hybrid whale optimization algorithm with simulated annealing. Initially, hWOA-SA has been applied to enhancing the quality of image using de-noising scheme. Afterwards, the curvelet transform has been employed to carry out the fusion of images. In terms of PSNR, the curvelet transform exhibits the better performance. The effectiveness and validation of the proposed scheme has been carried-out using quality matrices. The performance analysis is carried out after checking the effectiveness of proposed approach by evaluating the various parameters such as: RSME, PFE, MAE, CORR, SNR, PSNR, MI, UQI and SSIM and compared with numerous techniques. Simulation results obtained from proposed hWOA-SA based image fusion are very competitive and better than other image fusion technique available in the literature.

scholarly journals Optimization of an appointment scheduling problem for healthcare systems based on the quality of fairness service using whale optimization algorithm and NSGA-II

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ali Ala ◽  
Fawaz E. Alsaadi ◽  
Mohsen Ahmadi ◽  
Seyedali Mirjalili
Keyword(s):  
Optimization Algorithm ◽  
Appointment Scheduling ◽  
Whale Optimization Algorithm ◽  
Nsga Ii ◽  
Appointment Date ◽  
Scheduling Model ◽  
Whale Optimization ◽  
First Come First Serve ◽  
Priority Factor

AbstractEffective appointment scheduling (EAS) is essential for the quality and patient satisfaction in hospital management. Healthcare schedulers typically refer patients to a suitable period of service before the admission call closes. The appointment date can no longer be adjusted. This research presents the whale optimization algorithm (WOA) based on the Pareto archive and NSGA-II algorithm to solve the appointment scheduling model by considering the simulation approach. Based on these two algorithms, this paper has addressed the multi-criteria method in appointment scheduling. This paper computes WOA and NSGA with various hypotheses to meet the analysis and different factors related to patients in the hospital. In the last part of the model, this paper has analyzed NSGA and WOA with three cases. Fairness policy first come first serve (FCFS) considers the most priority factor to obtain from figure to strategies optimized solution for best satisfaction results. In the proposed NSGA, the FCFS approach and the WOA approach are contrasted. Numerical results indicate that both the FCFS and WOA approaches outperform the strategy optimized by the proposed algorithm.

Performance Evaluation of Fiber Wireless (FiWi) Access Network using Position Optimization of ONUs

2020 ◽  
Vol 10 ◽  
Author(s):  
Nitin Chouhan ◽  
Uma Rathore Bhatt ◽  
Raksha Upadhyay
Keyword(s):  
Power Consumption ◽  
Optimization Algorithm ◽  
Optical Network ◽  
Access Network ◽  
Whale Optimization Algorithm ◽  
Wireless Access ◽  
Delivery Ratio ◽  
Packet Delivery ◽  
Whale Optimization ◽  
Wireless Access Network

: Fiber Wireless Access Network is the blend of passive optical network and wireless access network. This network provides higher capacity, better flexibility, more stability and improved reliability to the users at lower cost. Network component (such as Optical Network Unit (ONU)) placement is one of the major research issues which affects the network design, performance and cost. Considering all these concerns, we implement customized Whale Optimization Algorithm (WOA) for ONU placement. Initially whale optimization algorithm is applied to get optimized position of ONUs, which is followed by reduction of number of ONUs in the network. Reduction of ONUs is done such that with fewer number of ONUs all routers present in the network can communicate. In order to ensure the performance of the network we compute the network parameters such as Packet Delivery Ratio (PDR), Total Time for Delivering the Packets in the Network (TTDPN) and percentage reduction in power consumption for the proposed algorithm. The performance of the proposed work is compared with existing algorithms (deterministic and centrally placed ONUs with predefined hops) and has been analyzed through extensive simulation. The result shows that the proposed algorithm is superior to the other algorithms in terms of minimum required ONUs and reduced power consumption in the network with almost same packet delivery ratio and total time for delivering the packets in the network. Therefore, present work is suitable for developing cost-effective FiWi network with maintained network performance.

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