Application of Chicken Swarm Optimization in Detection of Cancer and Virtual Reality

Tripathi, Ayush Kumar; Garg, Priyam; Tripathy, Aruna; Vats, Navender; Gupta, Deepak; Khanna, Ashish

doi:10.1007/978-3-030-35252-3_9

Cited by 5 publications

(4 citation statements)

References 24 publications

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“…The optimization results achieved using the CSO method enhanced the accuracy by 55% compared to the PSO and BPSO optimization algorithms, according to experiments and simulations reported in the literature (Tripathi et al 2020). In terms of speed, the CSO algorithm decreased the computing time by 50%.…”

Section: Positioningmentioning

confidence: 82%

“…A small number of GPS sensors (anchor nodes) are used in real-world situations to get over GPS's limitations, while other sensors rely on localization algorithms that take use of the precise location supplied by GPS nodes (Han et al 2022). According to the literature (Tripathi et al 2020), the major focus for developing this technology should be on natural-inspired algorithms, with the decrease of computing time and localization mistakes as the primary goals. Equation ( 63), which accounts for the influence of the environment on wireless sensor networks, determines the separation between other nodes and anchor nodes:…”

Section: Positioningmentioning

confidence: 99%

“…The CSO optimization procedure, whose objective function is represented by Eq. ( 58), is first described in the literature (Tripathi et al 2020) to determine the ideal distance.…”

Section: Positioningmentioning

confidence: 99%

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A comprehensive survey on the chicken swarm optimization algorithm and its applications: state-of-the-art and research challenges

Chen,

Cao,

Chen

et al. 2024

Artif Intell Rev

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The application of optimization theory and the algorithms that are generated from it has increased along with science and technology's continued advancement. Numerous issues in daily life can be categorized as combinatorial optimization issues. Swarm intelligence optimization algorithms have been successful in machine learning, process control, and engineering prediction throughout the years and have been shown to be efficient in handling combinatorial optimization issues. An intelligent optimization system called the chicken swarm optimization algorithm (CSO) mimics the organic behavior of flocks of chickens. In the benchmark problem's optimization process as the objective function, it outperforms several popular intelligent optimization methods like PSO. The concept and advancement of the flock optimization algorithm, the comparison with other meta-heuristic algorithms, and the development trend are reviewed in order to further enhance the search performance of the algorithm and quicken the research and application process of the algorithm. The fundamental algorithm model is first described, and the enhanced chicken swarm optimization algorithm based on algorithm parameters, chaos and quantum optimization, learning strategy, and population diversity is then categorized and summarized using both domestic and international literature. The use of group optimization algorithms in the areas of feature extraction, image processing, robotic engineering, wireless sensor networks, and power. Second, it is evaluated in terms of benefits, drawbacks, and application in comparison to other meta-heuristic algorithms. Finally, the direction of flock optimization algorithm research and development is anticipated.

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Section: Positioningmentioning

confidence: 82%

Section: Positioningmentioning

confidence: 99%

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A comprehensive survey on the chicken swarm optimization algorithm and its applications: state-of-the-art and research challenges

Chen,

Cao,

Chen

et al. 2024

Artif Intell Rev

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show abstract

“…of instances and measure matches between dimensional values. The performance of heatmap measured by bin for distribution of instances and find all selected features are highly correlated to each other [21] (Fig. 9).…”

Section: Correlation Heatmapmentioning

confidence: 97%

An Experimental Study of Diversity of Diabetes Disease Features by Bagging and Boosting Ensemble Method with Rule Based Machine Learning Classifier Algorithms

Yadav

Pal

2021

SN COMPUT. SCI.

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Energy produces by blood sugar in human body. Diabetes mellitus is a condition in which human body cannot manage energy. Blood glucose, Insulin etc., and their functions become unmanaged in the glucose energy system in blood. This unbalanced system generates many dangerous diseases as like blood pressure, diabetes etc., in body. Many different recourses of energy are available in the nature. In this paper we present dataset with their pattern by box whisker plot, histograms and extract nine best features by Chi-Square and plot correlation matrix for correlation of each feature with their heat_map. We have applied rule-based classification algorithms as Decision table, OneR and JRIP on prepared dataset. In proposed work we have managed these three selected algorithms by Bagging and Boosting ensemble methods. These ensemble methods calculate Classification Accuracy, Precision, Recall and F1-Score for diabetes UCI dataset. The summary with the results, finally we concluded that the highest accuracy (98%) of Bagging Ensemble Method and the accuracy, precision, recall and f1-score of the Decision Table, OneR, Jrip and Boosting algorithms were carrying less.

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Optimal Design of Fuzzy Logic Systems Through a Chicken Search Optimization Algorithm Applied to a Benchmark Problem

Amador-Angulo

Castillo

2020

Recent Advances of Hybrid Intelligent Systems Based on Soft Computing

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Application of Chicken Swarm Optimization in Detection of Cancer and Virtual Reality

Cited by 5 publications

References 24 publications

A comprehensive survey on the chicken swarm optimization algorithm and its applications: state-of-the-art and research challenges

A comprehensive survey on the chicken swarm optimization algorithm and its applications: state-of-the-art and research challenges

An Experimental Study of Diversity of Diabetes Disease Features by Bagging and Boosting Ensemble Method with Rule Based Machine Learning Classifier Algorithms

Optimal Design of Fuzzy Logic Systems Through a Chicken Search Optimization Algorithm Applied to a Benchmark Problem

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