Predicting the citation counts of individual papers via a BP neural network

Ruan, Xuanmin; Zhu, Yuanyang; Jiang, Li; Cheng, Ying

doi:10.1016/j.joi.2020.101039

Cited by 106 publications

(70 citation statements)

References 39 publications

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“…The artificial neural network was used to separate and classify the feature dataset 41]. The BP neural network has no strict requirements for the data distribution, it can automatically transform the initial “bottom” feature representation into a “high-level” feature through a multilevel and nonlinear transformation [ 43 ], which ensured that rice and weeds were effectively identified with distinctive parameters. In this study, a three-layer BP neural network (input layer, hidden layer and output layer) was designed to construct a classifier, which can accurately realize any continuous mapping.…”

Section: Methodsmentioning

confidence: 99%

A Tactile Method for Rice Plant Recognition Based on Machine Learning

Chen

Mao

et al. 2020

Sensors

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Accurate and real-time recognition of rice plants is the premise underlying the implementation of precise weed control. However, achieving desired results in paddy fields using the traditional visual method is difficult due to the occlusion of rice leaves and the interference of weeds. The objective of this study was to develop a novel rice plant recognition sensor based on a tactile method which acquires tactile information through physical touch. The tactile sensor would be mounted on the paddy field weeder to provide identification information for the actuator. First, a flexible gasbag filled with air was developed, where vibration features produced by tactile and sliding feedback were acquired when this apparatus touched rice plants or weeds, allowing the subtle vibration data with identification features to be reflected through the voltage value of an air-pressured sensor mounted inside the gasbag. Second, voltage data were preprocessed by three algorithms to optimize recognition features, including dimensional feature, dimensionless feature, and fractal dimension. The three types of features were used to train and test a neural network classifier. To maximize classification accuracy, an optimum set of features (b (variance), f (kurtosis), h (waveform factor), l (box dimension), and m (Hurst exponent)) were selected using a genetic algorithm. Finally, the feature-optimized classifier was trained, and the actual performances of the sensor at different contact positions were tested. Experimental results showed that the recognition rates of the end, middle, and root of the sensor were 90.67%, 98%, and 96% respectively. A tactile-based method with intelligence could produce high accuracy for rice plant recognition, as demonstrated in this study.

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Section: Methodsmentioning

confidence: 99%

A Tactile Method for Rice Plant Recognition Based on Machine Learning

Chen

Mao

et al. 2020

Sensors

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“…By adjusting the relationship between a large number of nodes in the model, the information is processed [17]. [18]. It consists of a large number of nodes and their interconnections, including input layer, hidden layer and output layer.…”

Section: Principle Of Neural Networkmentioning

confidence: 99%

Review of Neural Network Algorithm and Its Application in Reactive Distillation

2021

AJOCS

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Artificial Neural Networks (ANN) can accurately identify and learn the potential relationship between input and output, and have self-learning capabilities and high fault tolerance, which can be used to predict or optimize the performance of complex systems. Reactive distillation integrates reaction and rectification into one device, so that the two processes occur at the same time and at the same place, but at the same time it also produces highly nonlinear robust behavior, making its process control and optimization unable to use conventional methods. Instead, neural network algorithms must be used. This paper briefly describes the research progress of neural network algorithms and reactive distillation technology, and summarizes the application of neural network algorithms in reactive distillation, aiming to provide reference for the development and innovation of industry technology.

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“…BP neural network and support vector machine are very classic classification algorithms, and support vector machine is more used for small sample classification (Ruan et al 2020;Mu et al 2016). In this paper, basing on the seven characteristic parameters of the vessel, the training set data was 70%, and the test set data was 30%.…”

Section: Vessel Pores Classificationmentioning

confidence: 99%

Microstructure identification based on vessel pores feature extraction of high-value hardwood species

Yang

Zhao

Wang

et al. 2021

BioRes

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Because of the diversity of vessel pores in different hardwood species, they are important for wood species identification. In this paper, a Micro CT was used to collect wood images. The experiment was based on six wood types, Pterocarpus macrocarpus, Pterocarpus erinaceus, Dalbergia latifolia, Dalbergia frutescens var. tomentosa, Pterocarpus indicus, and Pterocarpus soyauxii. One-thousand cross-sectional images of 2042 px × 1640 px were collected for each species. One pixel represents 1.95 µm of the real physical dimension. The level set geometric active contour model was used to obtain the contour of the vessel pores. Combined with a variety of morphological processing methods, the binary images of the vessel pores were obtained. The features of the binary images were extracted for classification. Classifiers such as BP neural network and support vector machine were used, the number, roundness, area, perimeter, and other characteristic parameters of the vessel pores were classified, and the accuracy rate was more than 98.9%. The distribution and arrangement of the vessel pores of six kinds of hardwood were obtained through the level set geometric active contour model and image morphology. Then BP neural network and support vector machine were used for realizing the classification of hardwood species.

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Predicting the citation counts of individual papers via a BP neural network

Cited by 106 publications

References 39 publications

A Tactile Method for Rice Plant Recognition Based on Machine Learning

A Tactile Method for Rice Plant Recognition Based on Machine Learning

Review of Neural Network Algorithm and Its Application in Reactive Distillation

Microstructure identification based on vessel pores feature extraction of high-value hardwood species

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