Whale optimization algorithm: An implementation to design low-pass FIR filter

Mukherjee, Adrika; Chakraborty, Niloy; Das, Badhan Kumar

doi:10.1109/ipact.2017.8244929

Cited by 14 publications

(6 citation statements)

References 15 publications

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“…WOA, first proposed in Mukherjee et al (42), has sparked renewed interest in recent years. This stochastic search technique is computed by the following simulation of humpback whale behavior and movements in their search for food and supplies.…”

Section: Whale Optimization Algorithmsmentioning

confidence: 99%

Early Prediction of Lung Cancers Using Deep Saliency Capsule and Pre-Trained Deep Learning Frameworks

et al. 2022

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Lung cancer is the cellular fission of abnormal cells inside the lungs that leads to 72% of total deaths worldwide. Lung cancer are also recognized to be one of the leading causes of mortality, with a chance of survival of only 19%. Tumors can be diagnosed using a variety of procedures, including X-rays, CT scans, biopsies, and PET-CT scans. From the above techniques, Computer Tomography (CT) scan technique is considered to be one of the most powerful tools for an early diagnosis of lung cancers. Recently, machine and deep learning algorithms have picked up peak energy, and this aids in building a strong diagnosis and prediction system using CT scan images. But achieving the best performances in diagnosis still remains on the darker side of the research. To solve this problem, this paper proposes novel saliency-based capsule networks for better segmentation and employs the optimized pre-trained transfer learning for the better prediction of lung cancers from the input CT images. The integration of capsule-based saliency segmentation leads to the reduction and eventually reduces the risk of computational complexity and overfitting problem. Additionally, hyperparameters of pretrained networks are tuned by the whale optimization algorithm to improve the prediction accuracy by sacrificing the complexity. The extensive experimentation carried out using the LUNA-16 and LIDC Lung Image datasets and various performance metrics such as accuracy, precision, recall, specificity, and F1-score are evaluated and analyzed. Experimental results demonstrate that the proposed framework has achieved the peak performance of 98.5% accuracy, 99.0% precision, 98.8% recall, and 99.1% F1-score and outperformed the DenseNet, AlexNet, Resnets-50, Resnets-100, VGG-16, and Inception models.

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Section: Whale Optimization Algorithmsmentioning

confidence: 99%

Early Prediction of Lung Cancers Using Deep Saliency Capsule and Pre-Trained Deep Learning Frameworks

et al. 2022

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“…To save this purpose, a hybrid whale-firefly algorithm is used for selecting the optimal path. The working mechanism utilizes the firefly [17] and whale algorithm [18]…”

Section: Routing Phasementioning

confidence: 99%

“…The proposed protocol uses the hybrid combination of the firefly [17] and whale algorithm [18] for finding the optimized energy-efficient path. Since the WSN-assisted IoT networks cover a larger area, usage of metaheuristic algorithms may lead to the trapping problem which may lead to low efficiency.…”

Section: Proposed Optimizer For Routingmentioning

confidence: 99%

HELP-WSN-A Novel Adaptive Multi-Tier Hybrid Intelligent Framework for QoS Aware WSN-IoT Networks

Sampathkumar¹,

Malmurugan²

2022

Computers, Materials &Amp; Continua

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Wireless Sensor Network is considered as the intermediate layer in the paradigm of Internet of things (IoT) and its effectiveness depends on the mode of deployment without sacrificing the performance and energy efficiency. WSN provides ubiquitous access to location, the status of different entities of the environment and data acquisition for long term IoT monitoring. Achieving the high performance of the WSN-IoT network remains to be a real challenge since the deployment of these networks in the large area consumes more power which in turn degrades the performance of the networks. So, developing the robust and QoS (quality of services) aware energy-efficient routing protocol for WSN assisted IoT devices needs its brighter light of research to enhance the network lifetime. This paper proposed a Hybrid Energy Efficient Learning Protocol (HELP). The proposed protocol leverages the multi-tier adaptive framework to minimize energy consumption. HELP works in a two-tier mechanism in which it integrates the powerful Extreme Learning Machines for clustering framework and employs the zonal based optimization technique which works on hybrid Whale-dragonfly algorithms to achieve high QoS parameters. The proposed framework uses the sub-area division algorithm to divide the network area into different zones. Extreme learning machines (ELM) which are employed in this framework categories the Zone's Cluster Head (ZCH) based on distance and energy. After categorizing the zone's cluster head, the optimal routing path for an energy-efficient data transfer will be selected based on the new hybrid whale-swarm algorithms. The extensive simulations were carried out using OMNET++-Python userdefined plugins by injecting the dynamic mobility models in networks to make it a more realistic environment. Furthermore, the effectiveness of the proposed HELP is examined against the existing protocols such as LEACH, M-LEACH, SEP, EACRP and SEEP and results show the proposed framework has outperformed other techniques in terms of QoS parameters such as network lifetime, energy, latency.

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“…Threshold values, MSE, PSNR for three level and four level image segmentation are shown in Table5. [10].In this paper, performance of WOA and PSO are also discussed by showing their convergence curve behavior based on various objective functions for different number of iterations using Whale Optimization Algorithm WOA is tested on various benchmark functions and has faster convergence rate than gravitational search algorithm and particle swarm optimization PSO (Seyedali & Andrew,2016) [3].…”

Section: Table1mentioning

confidence: 99%

“…Best solution means best position which is near to prey and get optimal cost to search prey. WOA has been widely used for multilevel image segmentation (M.A.El Aziz et al, 2018) [4], clustering applications [5], design of low pass filter [10] etc.…”

Section: Introductionmentioning

confidence: 99%

Multilevel Thresholding based Image Segmentation using Whale Optimization Algorithm

Shivahare¹,

Gupta²

2019

IJITEE

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 Abstract: Whale Optimization Algorithm (WOA) was proposed by Seyedali Mirjalili and Andrew Lewis in 2016. WOA is nature-inspired, meta-heuristic (randomization and deterministic) algorithm, which is being used to solve various single objective, multi objective and multi-dimensional optimization problems. To determine threshold value for image segmentation Otsu, kapur, thresholding etc. methods are used. In this paper multilevel threshold values are computed using WOA and these multilevel threshold values are used for image segmentation. Fitness is computed using Otsu thresholding. Minimum fitness score is considered as best optimal value. WOA has capability to explore, exploit the search s pace and avoid local optima. In multilevel thresholding, complex images are segmented into L+1 levels for multiple threshold values L =2, 3 etc. This paper addresses about performance of Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) for various benchmark objective functions such as unimodel, multimodel, fix dimension multimodel based on their convergence curves for different number of iterations400,500 600 and compute multilevel threshold values for various level image segmentation using Whale Optimization Algorithm.

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Whale optimization algorithm: An implementation to design low-pass FIR filter

Cited by 14 publications

References 15 publications

Early Prediction of Lung Cancers Using Deep Saliency Capsule and Pre-Trained Deep Learning Frameworks

Early Prediction of Lung Cancers Using Deep Saliency Capsule and Pre-Trained Deep Learning Frameworks

HELP-WSN-A Novel Adaptive Multi-Tier Hybrid Intelligent Framework for QoS Aware WSN-IoT Networks

Multilevel Thresholding based Image Segmentation using Whale Optimization Algorithm

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