Hybrid black widow optimization with iterated greedy algorithm for gene selection problems

Alweshah, Mohammed; Aldabbas, Yasmeen; Abu-Salih, Bilal; Oqeil, Saleh; Hasan, Hazem S.; Alkhalaileh, Saleh; Kassaymeh, Sofian

doi:10.1016/j.heliyon.2023.e20133

Cited by 11 publications

(2 citation statements)

References 76 publications

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“… Alweshah et al (2023) presented a new study. This study aims to simplify and computationally effectively solve gene selection problems in microarray gene expression data using the Black Widow Optimization algorithm (BWO) and the Iterated Greedy algorithm (BWO-IG).…”

Section: Survey Methodologymentioning

confidence: 99%

Optimized models and deep learning methods for drug response prediction in cancer treatments: a review

Hajim,

Zainudin,

Mohd Daud

et al. 2024

PeerJ Computer Science

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Recent advancements in deep learning (DL) have played a crucial role in aiding experts to develop personalized healthcare services, particularly in drug response prediction (DRP) for cancer patients. The DL’s techniques contribution to this field is significant, and they have proven indispensable in the medical field. This review aims to analyze the diverse effectiveness of various DL models in making these predictions, drawing on research published from 2017 to 2023. We utilized the VOS-Viewer 1.6.18 software to create a word cloud from the titles and abstracts of the selected studies. This study offers insights into the focus areas within DL models used for drug response. The word cloud revealed a strong link between certain keywords and grouped themes, highlighting terms such as deep learning, machine learning, precision medicine, precision oncology, drug response prediction, and personalized medicine. In order to achieve an advance in DRP using DL, the researchers need to work on enhancing the models’ generalizability and interoperability. It is also crucial to develop models that not only accurately represent various architectures but also simplify these architectures, balancing the complexity with the predictive capabilities. In the future, researchers should try to combine methods that make DL models easier to understand; this will make DRP reviews more open and help doctors trust the decisions made by DL models in cancer DRP.

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Section: Survey Methodologymentioning

confidence: 99%

Optimized models and deep learning methods for drug response prediction in cancer treatments: a review

Hajim,

Zainudin,

Mohd Daud

et al. 2024

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…A novel metaheuristic optimisation method based on black widow spider mating behaviour was initially developed by V. Hayyolalam and A. Pourhaji Kazem in 2020, and because of its adaptability and simplicity of usage, it has been utilised for a variety of engineering and scientific issues solutions [20]. The peculiar mating habits of black widow spiders served as the model for the Black Widow Optimization Algorithm (BWO).…”

Section: Feature Extraction and Selection Using Black Widow Optimizat...mentioning

confidence: 99%

Enhancing Cryptojacking Detection Through Hybrid Black Widow Optimization and Generative Adversarial Networks

Kale,

et al. 2024

IJACSA

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Cybercriminals now find cryptocurrency mining to be a lucrative endeavour. This is frequently seen in the form of cryptojacking, which is the illegal use of computer resources for cryptocurrency mining. Protecting user resources and preserving the integrity of digital ecosystems depend heavily on the detection and mitigation of such threats. This research presents a unique method that combines Black Widow Optimisation (HBWO) with Generative Adversarial Networks (GANs) to improve the detection of cryptojacking. Due to its covert nature and tendency to elude conventional detection methods, cryptojacking is still a widespread concern. In order to overcome this difficulty, our work makes use of the complementary abilities of deep learning and metaheuristic optimisation. To maximise feature selection for efficient identification of cryptojacking activity, BWO-which draws inspiration from the foraging behaviour of spiders-is utilised. Simultaneously, GANs are employed to produce artificial intelligence (AI) augmentations, which strengthen the detection model's resilience and enrich the training dataset. Utilising HBWO to identify the most discriminative features is the first step in our technique, which also includes preprocessing the dataset to extract pertinent features. The training dataset is then supplemented with artificial data samples created using GANs, which enhances the detection model's capacity for generalisation. Experiments conducted on real-world datasets show the effectiveness of our solution, outperforming baseline techniques. The hybrid technique that has been suggested offers a viable way to improve the detection of cryptojacking. Through the combination of HBWO for feature optimisation and GANs for data augmentation, our approach demonstrates improved 98.02% accuracy and resilience in detecting cryptojacking activity. With its novel framework for fending against new dangers in the digital sphere, this research adds to the continuing efforts in cybersecurity.

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Optimizing beyond boundaries: empowering the salp swarm algorithm for global optimization and defective software module classification

Kassaymeh,

Al-Betar,

Rjoubd

et al. 2024

Neural Comput & Applic

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Hybrid black widow optimization with iterated greedy algorithm for gene selection problems

Cited by 11 publications

References 76 publications

Optimized models and deep learning methods for drug response prediction in cancer treatments: a review

Optimized models and deep learning methods for drug response prediction in cancer treatments: a review

Enhancing Cryptojacking Detection Through Hybrid Black Widow Optimization and Generative Adversarial Networks

Optimizing beyond boundaries: empowering the salp swarm algorithm for global optimization and defective software module classification

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