A fast genetic algorithm for a critical protection problem in biomedical supply chain networks

Khanduzi, Raheleh; Sangaiah, Arun Kumar

doi:10.1016/j.asoc.2018.11.010

Cited by 19 publications

(8 citation statements)

References 35 publications

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“…Segmenting blood vessels integrally and accurately is necessary for accurate analysis of main blood vessels and branches [1]. Currently, physicians mark blood vessels manually according to experiences, which is characterized by low efficiency and easy interference by subjective factors [2,3]. Therefore, the automatic segmentation of retinal vessels is of important significance [4,5].…”

Section: Introductionmentioning

confidence: 99%

Blood Vessel Segmentation of Fundus Retinal Images Based on Improved Frangi and Mathematical Morphology

Feng

Wang

et al. 2021

Computational and Mathematical Methods in Medicine

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An improved blood vessel segmentation algorithm on the basis of traditional Frangi filtering and the mathematical morphological method was proposed to solve the low accuracy of automatic blood vessel segmentation of fundus retinal images and high complexity of algorithms. First, a global enhanced image was generated by using the contrast-limited adaptive histogram equalization algorithm of the retinal image. An improved Frangi Hessian model was constructed by introducing the scale equivalence factor and eigenvector direction angle of the Hessian matrix into the traditional Frangi filtering algorithm to enhance blood vessels of the global enhanced image. Next, noise interferences surrounding small blood vessels were eliminated through the improved mathematical morphological method. Then, blood vessels were segmented using the Otsu threshold method. The improved algorithm was tested by the public DRIVE and STARE data sets. According to the test results, the average segmentation accuracy, sensitivity, and specificity of retinal images in DRIVE and STARE are 95.54%, 69.42%, and 98.02% and 94.92%, 70.19%, and 97.71%, respectively. The improved algorithm achieved high average segmentation accuracy and low complexity while promising segmentation sensitivity. This improved algorithm can segment retinal vessels more accurately than other algorithms.

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Section: Introductionmentioning

confidence: 99%

Blood Vessel Segmentation of Fundus Retinal Images Based on Improved Frangi and Mathematical Morphology

Feng

Wang

et al. 2021

Computational and Mathematical Methods in Medicine

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“…GA-based solutions have also increasingly been used at the molecular level in tasks such as handling and predicting transposon-derived piRNAs [8]. Yet the importance of GAbased solutions in the medical field is not limited to solving problems on the microscopic scope as applications have been developed to handle larger scale infrastructure and logistics that can be vital for entire health care systems [9,10]. Among the most frequent uses of GAs however, is their role in feature selection where they help to narrow down the possible features so that a complementary algorithm can achieve far greater performance [7,11,12,13,14].…”

Section: Popularity Of Genetic Algorithms In Biomedical Applicationsmentioning

confidence: 99%

“…Additionally, GAs have been used for imaging and visualizing applications both due to their importance in feature selection and their ability to combine representations of learned information such as known shapes, and relative position into a single framework that can be used in three-dimensional segmentation [17]. Finally GAs have been employed to handle logistics both in handling complex hospital supply chains [9] and in optimizing ambulance dispatches to non-emergency situations [10]. Therefore, it can be easily seen that bioinformatics research entails many problems that can be solved using machine learning tasks, and that GA is well-suited for such tasks.…”

Section: Applications Of Ga In Bioinformaticsmentioning

confidence: 99%

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A summary of the prevalence of Genetic Algorithms in Bioinformatics from 2015 onwards

Swerhun,

Foley,

Massop

et al. 2020

Preprint

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Background: In recent years, machine learning has seen an increasing presence in a large variety of fields, especially in health care and bioinformatics. More specifically, the field where machine learning algorithms have found most application is Genetic Algorithms.Objective: The objective of this paper is to conduct a survey of articles published from 2015 onwards that deal with Genetic Algorithms and how they are used in bioinformatics.Methods: To achieve the objective, a scoping review was conducted that utilized Google Scholar alongside Publish or Perish and the Scimago Journal& Country Rank to search for respectable sources.Results: Upon analyzing 31 articles from the field of bioinformatics, it became apparent that genetic algorithms rarely form a full application, instead they rely on other vital algorithms such as support vector machines. Indeed, support vector machines were the most prevalent algorithms used alongside genetic algorithms (GA); however, while the usage of such algorithms contributes to the heavy focus on accuracy by GA programs, it often sidelines computation times in the process. In fact, most applications employing GAs for classification and feature selection are nearing or at 100% success rate, and the focus of future GA development should be directed elsewhere. Conclusion:Population-based searches, like GA, are often combined with other machine learning algorithms. In this scoping review, genetic algorithms combined with Support Vector Machines were found to perform best. The performance metric that was evaluated most often was the accuracy. Measuring the accuracy avoids measuring the main weakness of GAs, which is computational time. The future of genetic algorithms could be "open-ended" evolutionary algorithms, which attempt to increase complexity and find diverse solutions, rather than optimize a fitness function and converge to a single "best" solution from the initial population of solutions.

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“…Rahmani Hosseinabadi et al [ 19 ] described an extended genetic algorithm which reached better solutions in terms of computational times and objective values. The fast genetic algorithm suggested by Khanduzia et al [ 20 ] has higher accuracy and faster running speed, and is the combination of a genetic algorithm and a fast branch and cut method. Additionally, adaptive genetic algorithms (AGAs) were developed to improve the ability to find the optimal solution.…”

Section: Introductionmentioning

confidence: 99%

Predicting human body composition using a modified adaptive genetic algorithm with a novel selection operator

et al. 2020

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Background Changes to human body composition reflect changes in health status to some extent. It has been recognized that these changes occur earlier than diseases. This means that a reasonable prediction of body composition helps to improve model users’ health. To overcome the low accuracy and poor adaptability of existing human body composition prediction models and obtain higher efficiency, we proposed a novel method for predicting human body composition which uses a modified adaptive genetic algorithm (MAGA). Methods Firstly, since there are many parameters for a human body composition model, and these parameters are associated, we designed a new parameter selection approach by combining the improved RReliefF method with the mRMR method. Following this, selected parameters were used to establish a model that fits body composition. Secondly, in order to accurately calculate the weight of parameters in this model, we proposed a solution which used a modified adaptive genetic algorithm, taking advantage of both roulette and optimum reservation strategies. This solution also has an improved selection operator. Thirdly, taking the percentage of body fat (PBF) as an example of body composition, we conducted experiments to compare performance between our algorithm and other algorithms. Results Through our simulations, we demonstrated that the adaptability of the proposed model is 0.9921, the mean relative error is 0.05%, the mean square error is 1.3 and the correlation coefficient is 0.982. When compared with the indexes of other models, our model has the highest adaptability and the smallest error. Additionally, the suggested model, which has a training time of 28.58s and a running time of 2.84s, is faster than some models. Conclusion The PBF prediction model established by MAGA has high accuracy, stronger generalization ability and higher efficiency, which could provide a new method for human composition prediction.

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A fast genetic algorithm for a critical protection problem in biomedical supply chain networks

Cited by 19 publications

References 35 publications

Blood Vessel Segmentation of Fundus Retinal Images Based on Improved Frangi and Mathematical Morphology

Blood Vessel Segmentation of Fundus Retinal Images Based on Improved Frangi and Mathematical Morphology

A summary of the prevalence of Genetic Algorithms in Bioinformatics from 2015 onwards

Predicting human body composition using a modified adaptive genetic algorithm with a novel selection operator

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