A hybrid firefly and particle swarm optimization algorithm applied to multilevel image thresholding

Farshi, Taymaz Rahkar; Ardabili, Ahad K.

doi:10.1007/s00530-020-00716-y

Cited by 35 publications

(8 citation statements)

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“…PSO 15 is a popular swarm-intelligence based meta-heuristic algorithm that has been leveraged on a wide range of problems pertaining to the optimization paradigm, including continuous optimization 51 , task scheduling 52 , data clustering 53 , image thresholding 54 and segmentation 55 . Binary variants of PSO 56 , 57 have also been introduced and used for FS tasks 58 .…”

Section: Related Workmentioning

confidence: 99%

SnapEnsemFS: a snapshot ensembling-based deep feature selection model for colorectal cancer histological analysis

Chattopadhyay

Singh

Ijaz

et al. 2023

Sci Rep

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Colorectal cancer is the third most common type of cancer diagnosed annually, and the second leading cause of death due to cancer. Early diagnosis of this ailment is vital for preventing the tumours to spread and plan treatment to possibly eradicate the disease. However, population-wide screening is stunted by the requirement of medical professionals to analyse histological slides manually. Thus, an automated computer-aided detection (CAD) framework based on deep learning is proposed in this research that uses histological slide images for predictions. Ensemble learning is a popular strategy for fusing the salient properties of several models to make the final predictions. However, such frameworks are computationally costly since it requires the training of multiple base learners. Instead, in this study, we adopt a snapshot ensemble method, wherein, instead of the traditional method of fusing decision scores from the snapshots of a Convolutional Neural Network (CNN) model, we extract deep features from the penultimate layer of the CNN model. Since the deep features are extracted from the same CNN model but for different learning environments, there may be redundancy in the feature set. To alleviate this, the features are fed into Particle Swarm Optimization, a popular meta-heuristic, for dimensionality reduction of the feature space and better classification. Upon evaluation on a publicly available colorectal cancer histology dataset using a five-fold cross-validation scheme, the proposed method obtains a highest accuracy of 97.60% and F1-Score of 97.61%, outperforming existing state-of-the-art methods on the same dataset. Further, qualitative investigation of class activation maps provide visual explainability to medical practitioners, as well as justifies the use of the CAD framework in screening of colorectal histology. Our source codes are publicly accessible at: https://github.com/soumitri2001/SnapEnsemFS.

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Section: Related Workmentioning

confidence: 99%

SnapEnsemFS: a snapshot ensembling-based deep feature selection model for colorectal cancer histological analysis

Chattopadhyay

Singh

Ijaz

et al. 2023

Sci Rep

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“…( 2011 ) used Particle Swarm Optimization Algorithm for image clustering using two distinct fitness functions and showed that PSO works better than conventional K-means method because of its ability to generate more compact clusters. A hybrid Firefly and Particle Swarm Optimization Algorithm was used in the similiar purpose in the works of Rahkar Farshi and Ardabili ( 2021 ) which resulted in comparatively superior performance than 4 other metaheuristic algorithms that they compared with. In Rahkar Farshi et al.…”

Section: Related Workmentioning

confidence: 99%

Chimp optimization algorithm in multilevel image thresholding and image clustering

et al. 2022

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Multilevel image thresholding and image clustering, two extensively used image processing techniques, have sparked renewed interest in recent years due to their wide range of applications. The approach of yielding multiple threshold values for each color channel to generate clustered and segmented images appears to be quite efficient and it provides significant performance, although this method is computationally heavy. To ease this complicated process, nature inspired optimization algorithms are quite handy tools. In this paper, the performance of Chimp Optimization Algorithm (ChOA) in image clustering and segmentation has been analyzed, based on multilevel thresholding for each color channel. To evaluate the performance of ChOA in this regard, several performance metrics have been used, namely, Segment evolution function, peak signal-to-noise ratio, Variation of information, Probability Rand Index, global consistency error, Feature Similarity Index and Structural Similarity Index, Blind/Referenceless Image Spatial Quality Evaluatoe, Perception based Image Quality Evaluator, Naturalness Image Quality Evaluator. This performance has been compared with eight other well known metaheuristic algorithms: Particle Swarm Optimization Algorithm, Whale Optimization Algorithm, Salp Swarm Algorithm, Harris Hawks Optimization Algorithm, Moth Flame Optimization Algorithm, Grey Wolf Optimization Algorithm, Archimedes Optimization Algorithm, African Vulture Optimization Algorithm using two popular thresholding techniques-Kapur’s entropy method and Otsu’s class variance method. The results demonstrate the effectiveness and competitive performance of Chimp Optimization Algorithm.

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“…At the combination level of artificial neural network and intrusion detection technology, relevant European researchers tend to use artificial intelligence technology combined with encryption technology to monitor and analyze intrusion objects. The main technology includes random key management algorithm, which mainly adopts the random allocation of secret materials [ 16 , 17 ]; corresponding researchers proposed hybrid encryption algorithm based on the above random key allocation algorithm, which is mainly based on elliptic curve cryptography algorithm and advanced encryption standard algorithm for hybrid processing, so as to improve the performance of intrusion detection system [ 18 , 19 ]; relevant researchers combined with artificial intelligence algorithm proposed the intrusion detection technology of trust-based adaptive confirmation, which uses the trust algorithm of Kalman filter [ 20 ]; relevant American researchers propose refining the data model by constantly searching the corresponding relationship between data and reduce the corresponding redundant knowledge, so as to further reduce the information overload and improve the decision-making ability of the corresponding information model [ 21 , 22 ]. In the corresponding privacy protection strategy of wireless sensor networks, its main algorithms include data privacy protection scheme based on data disturbance, data privacy protection scheme based on secure multi-party technology, privacy data protection scheme based on polynomial regression, and encryption technology based on the same data state [ 23 , 24 ].…”

Section: Related Research: Research Status Of Privacy Protection and ...mentioning

confidence: 99%

Privacy Protection and Intrusion Detection System of Wireless Sensor Network Based on Artificial Neural Network

Shi¹,

Li²

2022

Computational Intelligence and Neuroscience

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With the increasing openness and development of network technology, the network based on the wireless sensor network system has increasingly become an important tool for human social life and production, but it also brings some network security problems. Among them, focus is on network privacy disclosure and foreign intrusion and the research of intrusion detection and privacy protection has increasingly become an important topic of network security. This paper deeply studies the wireless sensor network system based on neural network on the basis of traditional privacy protection and intrusion detection system. Firstly, it applies particle swarm optimization algorithm and constructs a wireless sensor network intrusion detection system based on particle swarm optimization algorithm. The system includes important modules such as data extraction, data analysis, data feedback, and auxiliary decision-making. Compared with other algorithms, particle swarm optimization algorithm does not rely on problem information. It mainly uses real numbers to solve, so the algorithm has strong universality. At the same time, its corresponding principle is simple and easy to implement and less parameters need to be adjusted. Compared with other algorithms, particle swarm optimization algorithm has fast convergence speed and little memory requirement for the computer. At the same time, this paper uses the leap of particle swarm optimization algorithm to make it easier to find the global optimal solution. At the corresponding level of wireless sensor network privacy protection, based on the original data aggregation privacy protection scheme, this paper proposes a privacy protection scheme based on polynomial regression and a user privacy protection scheme based on the same state encryption, which further improves the security of privacy protection and facilitates the management of information. To realize the integrity of user privacy information protection, this paper realizes the decryption of data based on the correlation between binary metadata and compares the corresponding decrypted data with aggregated data, so as to complete the integrity of privacy data protection. The experimental results show that the binary metadata correlation decryption method proposed in this paper and the introduction of the corresponding particle swarm optimization algorithm improve the stability of the system by about 10%, the corresponding system security by a positive proportion, and the integrity of private data by about 10%; therefore, the algorithm proposed in this paper has obvious advantages.

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A hybrid firefly and particle swarm optimization algorithm applied to multilevel image thresholding

Cited by 35 publications

References 35 publications

SnapEnsemFS: a snapshot ensembling-based deep feature selection model for colorectal cancer histological analysis

SnapEnsemFS: a snapshot ensembling-based deep feature selection model for colorectal cancer histological analysis

Chimp optimization algorithm in multilevel image thresholding and image clustering

Privacy Protection and Intrusion Detection System of Wireless Sensor Network Based on Artificial Neural Network

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