Leaf identification using radial basis function neural networks and SSA based support vector machine

Ahmed, Ali; Hussein, Sherif E.

doi:10.1371/journal.pone.0237645

Cited by 20 publications

(8 citation statements)

References 34 publications

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“…Up to now, researchers have proposed numerous effective methods for species recognition. Commonly used leaf recognition methods include PNN (Wu et al 2007), LDC (Kalyoncu and Toygar 2015), GBDT-PNN (Tang 2020), and SVM (Salman et al 2017;Ahmed and Hussein 2020). As seen in Table 5, the BP-RBF neural network achieved high performance in plant recognition systems using fewer samples and features.…”

Section: Discussionmentioning

confidence: 99%

Leaf recognition using BP-RBF hybrid neural network

Yang

et al. 2021

J. For. Res.

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Plant recognition has great potential in forestry research and management. A new method combined back propagation neural network and radial basis function neural network to identify tree species using a few features and samples. The process was carried out in three steps: image pretreatment, feature extraction, and leaf recognition. In the image pretreatment processing, an image segmentation method based on hue, saturation and value color space and connected component labeling was presented, which can obtain the complete leaf image without veins and background. The BP-RBF hybrid neural network was used to test the influence of shape and texture on species recognition. The recognition accuracy of different classifiers was used to compare classification performance. The accuracy of the BP-RBF hybrid neural network using nine dimensional features was 96.2%, highest among all the classifiers.

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Section: Discussionmentioning

confidence: 99%

Leaf recognition using BP-RBF hybrid neural network

Yang

et al. 2021

J. For. Res.

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“…Through the analysis, it can be seen that the influence of the temperature factor is linear, but that of the porosity is not. Therefore, this study uses MATLAB R2016a to analyze the data and curve fitting [ 29 ]. Taking the temperature and two capacitances as the input variables and the water content as the output variable, support vector machine regression is used to fit the relationship between the temperature and capacitance and the water content at three porosity levels.…”

Section: Materials Analysis Sensor Calibration and Performance Testsmentioning

confidence: 99%

The Design and Experimentation of a Corn Moisture Detection Device Based on Double Capacitors

Han,

Wang,

Shi

et al. 2024

Sensors

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Detecting the moisture content of grain accurately and rapidly has important significance for harvesting, transport, storage, processing, and precision agriculture. There are some problems with the slow detection speeds, unstable detection, and low detection accuracy of moisture contents in corn harvesters. In that case, an online moisture detection device was designed, which is based on double capacitors. A new method of capacitance complementation and integration was proposed to eliminate the limitation of single data. The device is composed of a sampling mechanism and a double-capacitor sensor consisting of a flatbed capacitor and a cylindrical capacitor. The optimum structure size of the capacitor plates was determined by simulation optimization. In addition to this, the detection system with software and hardware was developed to estimate the moisture content. Indoor dynamic measurement tests were carried out to analyze the influence of temperature and porosity. Based on the influencing factors and capacitance, a model was established to estimate the moisture content. Finally, the support vector machine (SVM) regressions between the capacitance and moisture content were built up so that the R2 values were more than 0.91. In the stability test, the standard deviation of the stability test was 1.09%, and the maximum relative error of the measurement accuracy test was 1.22%. In the dynamic verification test, the maximum error of the measurement was 4.62%, less than 5%. It provides a measurement method for the accurate, rapid, and stable detection of the moisture content of corn and other grains.

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“…The dataset points that whenever a point is eliminated, would change the position of the hyperplane. Moreover, they can be seen as the essential parts of the dataset [24].…”

Section: Support Vector Machinementioning

confidence: 99%

Course Evaluation Based on Deep Learning and SSA Hyperparameters Optimization

Pramanik¹,

Sinhababu²,

Nayyar³

et al. 2022

Computers, Materials &Amp; Continua

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Sentiment analysis attracts the attention of Egyptian Decisionmakers in the education sector. It offers a viable method to assess education quality services based on the students' feedback as well as that provides an understanding of their needs. As machine learning techniques offer automated strategies to process big data derived from social media and other digital channels, this research uses a dataset for tweets' sentiments to assess a few machine learning techniques. After dataset preprocessing to remove symbols, necessary stemming and lemmatization is performed for features extraction. This is followed by several machine learning techniques and a proposed Long Short-Term Memory (LSTM) classifier optimized by the Salp Swarm Algorithm (SSA) and measured the corresponding performance. Then, the validity and accuracy of commonly used classifiers, such as Support Vector Machine, Logistic Regression Classifier, and Naive Bayes classifier, were reviewed. Moreover, LSTM based on the SSA classification model was compared with Support Vector Machine (SVM), Logistic Regression (LR), and Naive Bayes (NB). Finally, as LSTM based SSA achieved the highest accuracy, it was applied to predict the sentiments of students' feedback and evaluate their association with the course outcome evaluations for education quality purposes.

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Leaf identification using radial basis function neural networks and SSA based support vector machine

Cited by 20 publications

References 34 publications

Leaf recognition using BP-RBF hybrid neural network

Leaf recognition using BP-RBF hybrid neural network

The Design and Experimentation of a Corn Moisture Detection Device Based on Double Capacitors

Course Evaluation Based on Deep Learning and SSA Hyperparameters Optimization

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