An improved optimization model for predicting Pb recovery efficiency from residual of liberator cells: a hybrid of support vector regression and modified tunicate swarm algorithm

Abdolinejhad, Fatemeh; Khayati, Gholam Reza; Raiszadeh, Ramin; Yaghoobi, Nahid Sadat; Khorasani, Seyed Mohammad Javad

doi:10.1007/s10163-021-01256-x

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…Negative measures were taken as Type II measures such as false discovery rate (FDR), false negative rate (FNR) and false positive rate (FPR).” This validation analysis was undergone with the population count as 10, and maximum iterative count as 25 for the suggested leaf disease recognition method. The proposed FS‐SSO was compared with other meta‐heuristic algorithms like “particle swarm optimization (PSO) (Xiong et al, 2020), grey wolf optimizer (GWO) (Sathiyabhama et al, 2021), tunicate swarm algorithm (TSA) (Abdolinejhad et al, 2021), SSO (Abedinia et al, 2014) and machine learning algorithms like CNN (Lee et al, 2021; Muppala & Guruviah, 2020), ResNet (Prabu & Chelliah 2022), YOLO (Aly et al, 2021), and Res‐Yolo (Vallabhajosyula et al, 2021). ”…”

Section: Resultsmentioning

confidence: 99%

Hybridization of ResNet with YOLO classifier for automated paddy leaf disease recognition: An optimized model

Ganesan

Jayakumar

2022

Journal of Field Robotics

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Paddy is the most significant crop utilized by more than 2.6 billion people. The paddy crops are affected by various diseases that are unidentified and reduced the production of crop yield. Nowadays, the plants diseases and pests spread increasingly due to the climate change, trade, and globalization. The plant pathogens can be viral, fungal, nematodes or bacterial that affects all parts of the plants. The challenging tasks are to determine the symptoms and identify the controlling measures of the plant diseases. The plant leaves can be affected by numerous diseases, which results in destruction in terms of crop field to various social and economic aspects. The deep structured architectures and machine learning are implemented in the conventional models for detecting the leaf diseases.Hence, the main intention of this study is to develop the novel model for paddy leaf disease recognition using the hybrid deep learning. Initially, the input paddy leaf images are collected from standard sources that undergo filtering and contrast enhancement approaches. Further, the segmentation of the abnormal region of the paddy leaf is done by "adaptive K-means clustering." This is also accomplished by the Fitness Sorted-Shark Smell Optimization (FS-SSO). With the segmented images, the recognition of the disease is performed by the hybrid deep learning using the Resnet and YOLO classifier. As the modification, the fully connected layer of the ResNet model is replaced by the YOLO classifier for disease recognition. The significant parameters of the hybrid deep learning are optimized by the FS-SSO for attaining the high recognition rate. Experimental analysis is performed for computing the performance metrics and the accuracy of the classification for evaluating the efficiency of the suggested method.

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Section: Resultsmentioning

confidence: 99%

Hybridization of ResNet with YOLO classifier for automated paddy leaf disease recognition: An optimized model

Ganesan

Jayakumar

2022

Journal of Field Robotics

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“…They applied it successfully to expand the base optimization [61]. Besides, Le ´vy distribution [62], Cauchy distribution [62], Gaussian distribution [62], adaptive competitive window [63], adaptive distribution [64] and adaptive parameters [44] are also used for the dynamic adaptive update of the tunicate position.…”

Section: Plos One (1) Population Diversity Enhancement Mechanismmentioning

confidence: 99%

MSHHOTSA: A variant of tunicate swarm algorithm combining multi-strategy mechanism and hybrid Harris optimization

Liu,

Guo,

Liu

et al. 2023

PLoS ONE

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This paper proposes a novel hybrid algorithm, named Multi-Strategy Hybrid Harris Hawks Tunicate Swarm Optimization Algorithm (MSHHOTSA). The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. Firstly, inspired by the idea of the neighborhood and thermal distribution map, the hyperbolic tangent domain is introduced to modify the position of new tunicate individuals, which can not only effectively enhance the convergence performance of the algorithm but also ensure that the data generated between the unknown parameters and the old parameters have a similar distribution. Secondly, the nonlinear convergence factor is constructed to replace the original random factor c1 to coordinate the algorithm’s local exploitation and global exploration performance, which effectively improves the ability of the algorithm to escape extreme values and fast convergence. Finally, the swarm update mechanism of the HHO algorithm is introduced into the position update of the TSA algorithm, which further balances the local exploitation and global exploration performance of the MSHHOTSA. The proposed algorithm was evaluated on eight standard benchmark functions, CEC2019 benchmark functions, four engineering design problems, and a PID parameter optimization problem. It was compared with seven recently proposed metaheuristic algorithms, including HHO and TSA. The results were analyzed and discussed using statistical indicators such as mean, standard deviation, Wilcoxon’s rank sum test, and average running time. Experimental results demonstrate that the improved algorithm (MSHHOTSA) exhibits higher local convergence, global exploration, robustness, and universality than BOA, GWO, MVO, HHO, TSA, ASO, and WOA algorithms under the same experimental conditions.

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An improved optimization model for predicting Pb recovery efficiency from residual of liberator cells: a hybrid of support vector regression and modified tunicate swarm algorithm

Cited by 2 publications

References 25 publications

Hybridization of ResNet with YOLO classifier for automated paddy leaf disease recognition: An optimized model

Hybridization of ResNet with YOLO classifier for automated paddy leaf disease recognition: An optimized model

MSHHOTSA: A variant of tunicate swarm algorithm combining multi-strategy mechanism and hybrid Harris optimization

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