A Novel Multi-Objective Rat Swarm Optimizer-Based Convolutional Neural Networks for the Diagnosis of COVID-19 Disease

Sayed, Gehad Ismail

doi:10.3103/s0146411622030075

Cited by 5 publications

(1 citation statement)

References 16 publications

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“…Automated waste classification models can offer a possible solution to optimize waste collection and recycling, resulting in better resource use and lower pollution levels (Kennedy 2018 ). Additionally, they can have positive effects on the environment as well as the economy by lowering operational expenses (Ruiz et al 2019 ). Utilizing these models aligns with the objectives outlined in Target 11 of the Sustainable Development Goals (SDGs), which aim for the development of inclusive, secure, resilient, and sustainable cities and human settlements.…”

Section: Introductionmentioning

confidence: 99%

Intelligent and sustainable waste classification model based on multi-objective beluga whale optimization and deep learning

Sayed,

Abd Elfattah,

Darwish

et al. 2024

Environ Sci Pollut Res

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Resource recycling is considered necessary for sustainable development, especially in smart cities where increased urbanization and the variety of waste generated require the development of automated waste management models. The development of smart technology offers a possible alternative to traditional waste management techniques that are proving insufficient to reduce the harmful effects of trash on the environment. This paper proposes an intelligent waste classification model to enhance the classification of waste materials, focusing on the critical aspect of waste classification. The proposed model leverages the InceptionV3 deep learning architecture, augmented by multi-objective beluga whale optimization (MBWO) for hyperparameter optimization. In MBWO, sensitivity and specificity evaluation criteria are integrated linearly as the objective function to find the optimal values of the dropout period, learning rate, and batch size. A benchmark dataset, namely TrashNet is adopted to verify the proposed model’s performance. By strategically integrating MBWO, the model achieves a considerable increase in accuracy and efficiency in identifying waste materials, contributing to more effective waste management strategies while encouraging sustainable waste management practices. The proposed intelligent waste classification model outperformed the state-of-the-art models with an accuracy of 97.75%, specificity of 99.55%, F1-score of 97.58%, and sensitivity of 98.88%.

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Section: Introductionmentioning

confidence: 99%

Intelligent and sustainable waste classification model based on multi-objective beluga whale optimization and deep learning

Sayed,

Abd Elfattah,

Darwish

et al. 2024

Environ Sci Pollut Res

View full text Add to dashboard Cite

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Rat Swarm Optimization Algorithm

Ehteram

Seifi

Banadkooki

2023

Application of Machine Learning Models in Agricultural and Meteorological Sciences

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A new multi-objective hyperparameter optimization algorithm for COVID-19 detection from x-ray images

Gülmez

2024

Soft Comput

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The coronavirus occurred in Wuhan (China) first and it was declared a global pandemic. To detect coronavirus X-ray images can be used. Convolutional neural networks (CNNs) are used commonly to detect illness from images. There can be lots of different alternative deep CNN models or architectures. To find the best architecture, hyper-parameter optimization can be used. In this study, the problem is modeled as a multi-objective optimization (MOO) problem. Objective functions are multi-class cross entropy, error ratio, and complexity of the CNN network. For the best solutions to the objective functions, multi-objective hyper-parameter optimization is made by NSGA-III, NSGA-II, R-NSGA-II, SMS-EMOA, MOEA/D, and proposed Swarm Genetic Algorithms (SGA). SGA is a swarm-based algorithm with a cross-over process. All six algorithms are run and give Pareto optimal solution sets. When the figures obtained from the algorithms are analyzed and algorithm hypervolume values are compared, SGA outperforms the NSGA-III, NSGA-II, R-NSGA-II, SMS-EMOA, and MOEA/D algorithms. It can be concluded that SGA is better than others for multi-objective hyper-parameter optimization algorithms for COVID-19 detection from X-ray images. Also, a sensitivity analysis has been made to understand the effect of the number of the parameters of CNN on model success.

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A Novel Multi-Objective Rat Swarm Optimizer-Based Convolutional Neural Networks for the Diagnosis of COVID-19 Disease

Cited by 5 publications

References 16 publications

Intelligent and sustainable waste classification model based on multi-objective beluga whale optimization and deep learning

Intelligent and sustainable waste classification model based on multi-objective beluga whale optimization and deep learning

Rat Swarm Optimization Algorithm

A new multi-objective hyperparameter optimization algorithm for COVID-19 detection from x-ray images

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