Two-level K-nearest neighbors approach for invasive plants detection and classification

Guo, Yanhui; Du, Chunlai; Yun, Zhao; Ting, Tih-Fen; Rothfus, Thomas A.

doi:10.1016/j.asoc.2021.107523

Cited by 11 publications

(5 citation statements)

References 15 publications

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“…Some studies use Unmanned Aerial Systems (UAS) equipped with monitoring systems to detect invasive plants (IPs) that pose a threat to the local ecosystem. With KNN training model to create an automatic and intelligent plant detection method (Guo et al 2021). In industry, there are many studies to use KNN in training models and to assist the operation of the cooling system in order to reduce carbon emissions (Ho and Yu 2021).…”

Section: K-nearest Neighbor Algorithmmentioning

confidence: 99%

AI-Enhanced Audio-Based Predictive Control for Injection Molding Machines in the Era of IoT

Huang,

Chen,

Kanga

et al. 2024

Preprint

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In the past, most of traditional master craftsmen always adopted the acoustic actions to recognition the situation of machine. Along with the development of time and technology, the mode of industry has changed with the Fourth Industrial Revolution (Industry 4.0). The long been known for the mother of industry, mold industry, has been inevitably impacted by Industry 4.0. This research stems from the structure of the six-level IoT model, through Internet connecting sensors, data collection, and the appropriate implementation of human and machine interface to intellectualize the injection molding machine. This research has collected 130 times of audio frequency, and there were 53 effective data sets, in sum there were 34,030,640 datasets. There were 5 manufacturing actions of petroleum molding machines that were successfully identified. Due to the low accuracy of one of the manufacturing actions, the training of audio frequency is based on the other four. In the end, there are 93.64% of accurate AI intelligent identifying models. Concurrently, through labeling the audio characteristics of different manufacturing parameters, the model recognizing audio characteristics from injection molding machines under different injection speed and rotation speed parameters is successfully trained. It is expected that in the future, other researchers can use this research as a reference to further strengthen the correlation between audio characteristics and injection molding machines to engage a more in-depth and diverse application of this topic. JEL Classification: C80, C88, C90.

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Section: K-nearest Neighbor Algorithmmentioning

confidence: 99%

AI-Enhanced Audio-Based Predictive Control for Injection Molding Machines in the Era of IoT

Huang,

Chen,

Kanga

et al. 2024

Preprint

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“…The algorithm calculates the distances between the new example and the known examples in the training set, selects the "k" closest neighbors, and then assigns the most common class among these neighbors to the new example [384]. Some applications related to KNN can be seen in Table 22, such as: Vegetation Classification [377], [385], Plant Count [386], Detection of Disease in Plants [387], Detection of Invasive Plants [388], Coastal Trash Detection [366], Estimation of Chlorophyll Content [389], [390], Plantation Yield Estimate [380], Monitoring of Invasive Plants [391].…”

Section: ) K-nearest Neighbors (Knn)mentioning

confidence: 99%

A Survey on Applications of Unmanned Aerial Vehicles Using Machine Learning

Teixeira,

Miguel,

Silva

et al. 2023

IEEE Access

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Unmanned Aerial Vehicles (UAVs) play an important role in many applications, including health, transport, telecommunications and safe and rescue operations. Their adoption can improve the speed and precision of applications when compared to traditional solutions based on handwork. The use of UAVs brings scientific and technological challenges. In this context, Machine Learning (ML) techniques provide solutions to several problems concerning the use of UAVs in civil and military applications. An increasing number of scientific papers on the use of ML in UAVs context have been published in academic journals. In this work, we present a literature review on the use of ML techniques in UAVs, outlining the most recurrent areas and the most commonly used ML techniques in UAV applications. The results reveal that applications in the areas of environment, communication and security are among the main research topics.INDEX TERMS unmanned aerial vehicle, machine learning, literature review, UAV applications, neural networks.

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“…Wang et al [ 34 ] introduced a combination of localized spatio-temporal association analysis and LR to predict urban growth based on existing land cover configurations. Guo et al [ 35 ] utilized a two-level KNN classifier to identify invasive plants, achieving superior performance compared to CNN models. The outcomes of this study proved effective in safeguarding the ecosystem.…”

Section: Introductionmentioning

confidence: 99%

A reliable jumping-based classification methodology for environment sector

Etemadi,

Khashei,

Hamadani

et al. 2024

Heliyon

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Two-level K-nearest neighbors approach for invasive plants detection and classification

Cited by 11 publications

References 15 publications

AI-Enhanced Audio-Based Predictive Control for Injection Molding Machines in the Era of IoT

AI-Enhanced Audio-Based Predictive Control for Injection Molding Machines in the Era of IoT

A Survey on Applications of Unmanned Aerial Vehicles Using Machine Learning

A reliable jumping-based classification methodology for environment sector

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