A Distributed <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>K</mml:mi></mml:mrow></mml:math>-Means Segmentation Algorithm Applied to<i> Lobesia botrana</i> Recognition

García, José; Pope, Charles E.; Altimiras, Francisco

doi:10.1155/2017/5137317

Cited by 55 publications

(22 citation statements)

References 46 publications

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“…This hybrid algorithm was used to solve the problem of the counterfort retaining walls. The k-means technique has been widely used and in recent studies, it has been applied in [59] to bioinformatics for detecting gene expression profile, image segmentation for pest detection [60] in agriculture, and brain tumor identification [61], among others. Particularly the k-means technique has been previously applied in obtaining binary versions of continuous metaheuristics and used to solve the multidimensional knapsack problem [33] and the set covering problem [5] which are NP-hard problems.…”

Section: Hybridizing Metaheuristics With Machine Learningmentioning

confidence: 99%

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

2020

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The counterfort retaining wall is one of the most frequent structures used in civil engineering. In this structure, optimization of cost and CO2 emissions are important. The first is relevant in the competitiveness and efficiency of the company, the second in environmental impact. From the point of view of computational complexity, the problem is challenging due to the large number of possible combinations in the solution space. In this article, a k-means cuckoo search hybrid algorithm is proposed where the cuckoo search metaheuristic is used as an optimization mechanism in continuous spaces and the unsupervised k-means learning technique to discretize the solutions. A random operator is designed to determine the contribution of the k-means operator in the optimization process. The best values, the averages, and the interquartile ranges of the obtained distributions are compared. The hybrid algorithm was later compared to a version of harmony search that also solved the problem. The results show that the k-mean operator contributes significantly to the quality of the solutions and that our algorithm is highly competitive, surpassing the results obtained by harmony search.

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Section: Hybridizing Metaheuristics With Machine Learningmentioning

confidence: 99%

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

2020

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“…If we classify these algorithms according to the method of learning, there are three main categories: supervised learning, unsupervised learning, and learning by reinforcement. Machine learning algorithms are usually used to solve time series problems, anomaly detection, computational vision, data transformation, dimensionality reduction, regression, and data classification, among others [59].…”

Section: Hybridizing Metaheuristics With Machine Learningmentioning

confidence: 99%

A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem

et al. 2020

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This article proposes a hybrid algorithm that makes use of the db-scan unsupervised learning technique to obtain binary versions of continuous swarm intelligence algorithms. These binary versions are then applied to large instances of the well-known multidimensional knapsack problem. The contribution of the db-scan operator to the binarization process is systematically studied. For this, two random operators are built that serve as a baseline for comparison. Once the contribution is established, the db-scan operator is compared with two other binarization methods that have satisfactorily solved the multidimensional knapsack problem. The first method uses the unsupervised learning technique k-means as a binarization method. The second makes use of transfer functions as a mechanism to generate binary versions. The results show that the hybrid algorithm using db-scan produces more consistent results compared to transfer function (TF) and random operators.

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“…The authors in [30] successfully implemented such a system to classify Lobesia botrana moths using a K-means classifier. These moths are considered to be the major pest for the grapevine industry.…”

Section: Hand-crafted Feature Based Pest Detectionmentioning

confidence: 99%

A survey on wireless sensor network technologies in pest management applications

2019

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Animal pests notoriously cause billions of dollars of damage by spoiling crops, damaging infrastructure and spreading disease. Pest control companies try to mitigate this damage by implementing pest management approaches to respond to and prevent infestations. However, these approaches are labour intensive, as pest control technicians must regularly visit the affected areas for monitoring and evaluation. Current remote sensing technologies can allow for decision making based on real-time data remotely uploaded from pest traps. This reduces the frequency of field visits and improves the pest management process. In this paper, we survey a variety of modern data-driven pest management approaches. We also evaluate wireless communication infrastructures which can be used to facilitate data transfer between pest traps and cloud servers.

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A Distributed K-Means Segmentation Algorithm Applied to Lobesia botrana Recognition

Cited by 55 publications

References 46 publications

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem

A survey on wireless sensor network technologies in pest management applications

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