An Advanced Circular Adaptive Search Butterfly Optimization Algorithm for the CNN-based Sleep Apnea Detection Approach

Geetha, J. Kamala; Raja, J.

doi:10.1080/03772063.2022.2150692

Cited by 2 publications

(1 citation statement)

References 42 publications

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“…As a result, processing the data comes first. To improve prediction, this model will eliminate punctuation [18]. String data types will be used to provide the data for this model.…”

Section: Data Pre-processingmentioning

confidence: 99%

An Efficient Mechanism of Modified Sand Cat Swarm Optimization and DCNN For Detection of Spam In Online Social Media Network

Nicholas,

Nirmalrani

2024

Preprint

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In recent years, social media has become an indispensable part of people's daily lives. The use of social media networks, particularly WhatsApp, Facebook, and email, has significantly surged worldwide due to their widespread popularity. These platforms serve as potent tools for users to share information and opinions, post photos and videos, and disseminate news. The growing popularity and importance of social media platforms attract spammers to a greater extent. Detecting social network spam is particularly challenging due to its brief content and constantly streaming nature. Many spam detection methods for social media rely on machine learning, utilizing a variety of classification and clustering algorithms. Deep learning techniques have achieved significant success globally in solving diverse problems across different domains. This research aims to develop a robust spam detection model for social media networks using deep CNN methods and to evaluate the effectiveness of the models using Modified Sand Cat Swarm (MCSO) based optimization models. N-gram and Bag of Words features are utilized for ensemble feature extraction and selection to rank and analyze the significance of features in spam detection. The performance of the proposed models is compared with other deep learning algorithms. A comparative analysis of spam detection models reveals that the random forest classifier outperforms when trained with randomly chosen data of varying sizes. A DCNN-based MSCSO successfully validates 99.02% of spam on social media networks, as confirmed by the classification. These unconventional pairings reinforce the original premise behind these classification findings.

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“…As a result, processing the data comes first. To improve prediction, this model will eliminate punctuation [18]. String data types will be used to provide the data for this model.…”

Section: Data Pre-processingmentioning

confidence: 99%

An Efficient Mechanism of Modified Sand Cat Swarm Optimization and DCNN For Detection of Spam In Online Social Media Network

Nicholas,

Nirmalrani

2024

Preprint

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Butterfly Algorithm for Sustainable Lot Size Optimization

Benmamoun,

Fethallah,

Ahlaqqach

et al. 2023

Sustainability

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The challenges faced by classical supply chain management affect efficiency with regard to business. Classical supply chain management is associated with high risks due to a lack of accountability and transparency. The use of optimization algorithms is considered decision-making support to improve the operations and processes in green manufacturing. This paper suggests a solution to the green lot size optimization problem using bio-inspired algorithms, specifically, the butterfly algorithm. For this, our methodology consisted of first collecting the real data, then the data were expressed with a simple function with several constraints to optimize the total costs while reducing the CO2 emission, serving as input for the butterfly algorithm BA model. The BA model was then used to find the optimal lot size that balances cost-effectiveness and sustainability. Through extensive experiments, we compared the results of BA with those of other bio-inspired algorithms, showing that BA consistently outperformed the alternatives. The contribution of this work is to provide an efficient solution to the sustainable lot-size optimization problem, thereby reducing the environmental impact and optimizing the supply chain well. Conclusions: BA has shown that it can achieve the best results compared to other existing optimization methods. It is also a valuable chainsaw tool.

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An Advanced Circular Adaptive Search Butterfly Optimization Algorithm for the CNN-based Sleep Apnea Detection Approach

Cited by 2 publications

References 42 publications

An Efficient Mechanism of Modified Sand Cat Swarm Optimization and DCNN For Detection of Spam In Online Social Media Network

An Efficient Mechanism of Modified Sand Cat Swarm Optimization and DCNN For Detection of Spam In Online Social Media Network

Butterfly Algorithm for Sustainable Lot Size Optimization

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