Leaf Plant Identification System Based on Hidden Naïve Bays Classifier

Eid, Heba F.; Hassanien, Aboul Ella; Kim, Tai-hoon

doi:10.1109/aits.2015.28

Cited by 15 publications

(8 citation statements)

References 9 publications

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“…Second Phase: Training set mode is a typical technique for model evaluation, but can lack the appropriate accuracy and performance when applied to the PID datasets with an HNB classifier. It can quickly determine the HNB classifier reliability and compare models that depend on the same structure by reviewing the returned statistics [ 28 ].…”

Section: Resultsmentioning

confidence: 99%

Diabetes disease prediction system using HNB classifier based on discretization method

Alsewari

Basurra

Bhogal

et al. 2023

Journal of Integrative Bioinformatics

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Diagnosing diabetes early is critical as it helps patients live with the disease in a healthy way – through healthy eating, taking appropriate medical doses, and making patients more vigilant in their movements/activities to avoid wounds that are difficult to heal for diabetic patients. Data mining techniques are typically used to detect diabetes with high confidence to avoid misdiagnoses with other chronic diseases whose symptoms are similar to diabetes. Hidden Naïve Bayes is one of the algorithms for classification, which works under a data-mining model based on the assumption of conditional independence of the traditional Naïve Bayes. The results from this research study, which was conducted on the Pima Indian Diabetes (PID) dataset collection, show that the prediction accuracy of the HNB classifier achieved 82%. As a result, the discretization method increases the performance and accuracy of the HNB classifier.

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Section: Resultsmentioning

confidence: 99%

Diabetes disease prediction system using HNB classifier based on discretization method

Alsewari

Basurra

Bhogal

et al. 2023

Journal of Integrative Bioinformatics

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“…The accuracy was slightly lower when using the individual classifier, namely 91.8% for SVM, 88.3% for Naïve Bayes, and 85.7% for decision tree. The high performance of Naïve Bayes classifier can also be seen from the findings of Eid et al [53]. The computational model was proposed using digital plant images based on biometric features such as shape and vein patterns.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 86%

Review on Techniques for Plant Leaf Classification and Recognition

et al. 2019

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Plant systematics can be classified and recognized based on their reproductive system (flowers) and leaf morphology. Neural networks is one of the most popular machine learning algorithms for plant leaf classification. The commonly used neutral networks are artificial neural network (ANN), probabilistic neural network (PNN), convolutional neural network (CNN), k-nearest neighbor (KNN) and support vector machine (SVM), even some studies used combined techniques for accuracy improvement. The utilization of several varying preprocessing techniques, and characteristic parameters in feature extraction appeared to improve the performance of plant leaf classification. The findings of previous studies are critically compared in terms of their accuracy based on the applied neural network techniques. This paper aims to review and analyze the implementation and performance of various methodologies on plant classification. Each technique has its advantages and limitations in leaf pattern recognition. The quality of leaf images plays an important role, and therefore, a reliable source of leaf database must be used to establish the machine learning algorithm prior to leaf recognition and validation.

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“…Further, there are others works in which five standard modalities are taken into account. For example, all three models, such as SVM, Naïve Bayes, and decision trees, were applied by Eid et al [6] in 2015. Consequently, the corresponding experimental results were 91.8%, 88.3%, and 85.7%, respectively.…”

Section: Fusion Based Methodsmentioning

confidence: 99%

Leaf Recognition Using Convolutional Neural Networks Based Features

Quach¹,

Cuong²,

Pham³

et al. 2021

Preprint

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There is a warning light for the loss of plant habitats worldwide that entails concerted efforts to conserve plant biodiversity. Thus, plant species classification is of crucial importance to address this environmental challenge. In recent years, there is a considerable increase in the number of studies related to plant taxonomy. While some researchers try to improve their recognition performance using novel approaches, others concentrate on computational optimization for their framework. In addition, a few studies are diving into feature extraction to gain significantly in terms of accuracy. In this paper, we propose an effective method for the leaf recognition problem. In our proposed approach, a leaf goes through some pre-processing to extract its refined color image, vein image, xy-projection histogram, handcrafted shape, texture features, and Fourier descriptors. These attributes are then transformed into a better representation by neural network-based encoders before a support vector machine (SVM) model is utilized to classify different leaves. Overall, our approach performs a state-of-the-art result on the Flavia leaf dataset, achieving the accuracy of 99.58% on test sets under random 10-fold

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Leaf Plant Identification System Based on Hidden Naïve Bays Classifier

Cited by 15 publications

References 9 publications

Diabetes disease prediction system using HNB classifier based on discretization method

Diabetes disease prediction system using HNB classifier based on discretization method

Review on Techniques for Plant Leaf Classification and Recognition

Leaf Recognition Using Convolutional Neural Networks Based Features

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