Advanced algorithms for retrieving pileup peaks of digital alpha spectroscopy using antlions and particle swarm optimizations

El_Tokhy, Mohamed S.

doi:10.1007/s41365-020-0745-5

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…Ref. [ 61 ] used ant colony and PSO techniques to retrieve the packing peaks of the digital alpha spectrum to solve the second-order packing problem caused by the high count rate in digital alpha spectroscopy.…”

Section: Swarm Intelligence and Its Application In Image Processingmentioning

confidence: 99%

Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization

Cao

et al. 2023

Biomimetics

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Image processing technology has always been a hot and difficult topic in the field of artificial intelligence. With the rise and development of machine learning and deep learning methods, swarm intelligence algorithms have become a hot research direction, and combining image processing technology with swarm intelligence algorithms has become a new and effective improvement method. Swarm intelligence algorithm refers to an intelligent computing method formed by simulating the evolutionary laws, behavior characteristics, and thinking patterns of insects, birds, natural phenomena, and other biological populations. It has efficient and parallel global optimization capabilities and strong optimization performance. In this paper, the ant colony algorithm, particle swarm optimization algorithm, sparrow search algorithm, bat algorithm, thimble colony algorithm, and other swarm intelligent optimization algorithms are deeply studied. The model, features, improvement strategies, and application fields of the algorithm in image processing, such as image segmentation, image matching, image classification, image feature extraction, and image edge detection, are comprehensively reviewed. The theoretical research, improvement strategies, and application research of image processing are comprehensively analyzed and compared. Combined with the current literature, the improvement methods of the above algorithms and the comprehensive improvement and application of image processing technology are analyzed and summarized. The representative algorithms of the swarm intelligence algorithm combined with image segmentation technology are extracted for list analysis and summary. Then, the unified framework, common characteristics, different differences of the swarm intelligence algorithm are summarized, existing problems are raised, and finally, the future trend is projected.

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Section: Swarm Intelligence and Its Application In Image Processingmentioning

confidence: 99%

Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization

Cao

et al. 2023

Biomimetics

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Quantitative algorithm for airborne gamma spectrum of large sample based on improved shuffled frog leaping–particle swarm optimization convolutional neural network

Huang

Chen

et al. 2023

NUCL SCI TECH

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Rapid and robust radioisotopes identification algorithms of X-Ray and gamma spectra

El_Tokhy

2021

Measurement

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Advanced algorithms for retrieving pileup peaks of digital alpha spectroscopy using antlions and particle swarm optimizations

Cited by 3 publications

References 38 publications

Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization

Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization

Quantitative algorithm for airborne gamma spectrum of large sample based on improved shuffled frog leaping–particle swarm optimization convolutional neural network

Rapid and robust radioisotopes identification algorithms of X-Ray and gamma spectra

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